WorldWideScience

Sample records for ground cover classification

  1. Citizen science land cover classification based on ground and satellite imagery: Case study Day River in Vietnam

    Science.gov (United States)

    Nguyen, Son Tung; Minkman, Ellen; Rutten, Martine

    2016-04-01

    Citizen science is being increasingly used in the context of environmental research, thus there are needs to evaluate cognitive ability of humans in classifying environmental features. With the focus on land cover, this study explores the extent to which citizen science can be applied in sensing and measuring the environment that contribute to the creation and validation of land cover data. The Day Basin in Vietnam was selected to be the study area. Different methods to examine humans' ability to classify land cover were implemented using different information sources: ground based photos - satellite images - field observation and investigation. Most of the participants were solicited from local people and/or volunteers. Results show that across methods and sources of information, there are similar patterns of agreement and disagreement on land cover classes among participants. Understanding these patterns is critical to create a solid basis for implementing human sensors in earth observation. Keywords: Land cover, classification, citizen science, Landsat 8

  2. Land Cover - Minnesota Land Cover Classification System

    Data.gov (United States)

    Minnesota Department of Natural Resources — Land cover data set based on the Minnesota Land Cover Classification System (MLCCS) coding scheme. This data was produced using a combination of aerial photograph...

  3. Land Cover Classification Using ALOS Imagery For Penang, Malaysia

    Science.gov (United States)

    Sim, C. K.; Abdullah, K.; MatJafri, M. Z.; Lim, H. S.

    2014-02-01

    This paper presents the potential of integrating optical and radar remote sensing data to improve automatic land cover mapping. The analysis involved standard image processing, and consists of spectral signature extraction and application of a statistical decision rule to identify land cover categories. A maximum likelihood classifier is utilized to determine different land cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The land cover information was extracted from the digital data using PCI Geomatica 10.3.2 software package. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. The classification accuracies from the optical and radar feature combinations are studied. Our research finds that fusion of radar and optical significantly improved classification accuracies. This study indicates that the land cover/use can be mapped accurately by using this approach.

  4. Extreme Learning Machine for land cover classification

    OpenAIRE

    Pal, Mahesh

    2008-01-01

    This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification. In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require setting of one parameter and produce a unique solution. ETM+ multispectral data set (England) was used to judge the suitability of extreme learning machine for remote sensing classifications...

  5. BOREAS AFM-12 1-km AVHRR Seasonal Land Cover Classification

    Science.gov (United States)

    Steyaert, Lou; Hall, Forrest G.; Newcomer, Jeffrey A. (Editor); Knapp, David E. (Editor); Loveland, Thomas R.; Smith, David E. (Technical Monitor)

    2000-01-01

    The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-12 team's efforts focused on regional scale Surface Vegetation and Atmosphere (SVAT) modeling to improve parameterization of the heterogeneous BOREAS landscape for use in larger scale Global Circulation Models (GCMs). This regional land cover data set was developed as part of a multitemporal one-kilometer Advanced Very High Resolution Radiometer (AVHRR) land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. This land cover classification was derived by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly Normalized Difference Vegetation Index (NDVI) image composites (April-September 1992). This regional data set was developed for use by BOREAS investigators, especially those involved in simulation modeling, remote sensing algorithm development, and aircraft flux studies. Based on regional field data verification, this multitemporal one-kilometer AVHRR land cover mapping approach was effective in characterizing the biome-level land cover structure, embedded spatially heterogeneous landscape patterns, and other types of key land cover information of interest to BOREAS modelers.The land cover mosaics in this classification include: (1) wet conifer mosaic (low, medium, and high tree stand density), (2) mixed coniferous-deciduous forest (80% coniferous, codominant, and 80% deciduous), (3) recent visible bum, vegetation regeneration, or rock outcrops-bare ground-sparsely vegetated slow regeneration bum (four classes), (4) open water and grassland marshes, and (5) general agricultural land use/ grasslands (three classes). This land cover mapping approach did not detect small subpixel-scale landscape

  6. EASE-Grid Land Cover Classifications Derived from Boston University MODIS/Terra Land Cover Data

    Data.gov (United States)

    National Aeronautics and Space Administration — These data provide land cover classifications derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The data are...

  7. A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

    Directory of Open Access Journals (Sweden)

    Dong Jiang

    Full Text Available Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1 images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization with convenience.

  8. A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery

    Science.gov (United States)

    Jiang, Dong; Huang, Yaohuan; Zhuang, Dafang; Zhu, Yunqiang; Xu, Xinliang; Ren, Hongyan

    2012-01-01

    Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience. PMID:23049886

  9. Land Cover Classification from Full-Waveform LIDAR Data Based on Support Vector Machines

    Science.gov (United States)

    Zhou, M.; Li, C. R.; Ma, L.; Guan, H. C.

    2016-06-01

    In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

  10. LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    M. Zhou

    2016-06-01

    Full Text Available In this study, a land cover classification method based on multi-class Support Vector Machines (SVM is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs method and it showed that SVM method could achieve better classification results.

  11. D Land Cover Classification Based on Multispectral LIDAR Point Clouds

    Science.gov (United States)

    Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

    2016-06-01

    Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

  12. Texture classification of vegetation cover in high altitude wetlands zone

    Science.gov (United States)

    Wentao, Zou; Bingfang, Wu; Hongbo, Ju; Hua, Liu

    2014-03-01

    The aim of this study was to investigate the utility of datasets composed of texture measures and other features for the classification of vegetation cover, specifically wetlands. QUEST decision tree classifier was applied to a SPOT-5 image sub-scene covering the typical wetlands area in Three River Sources region in Qinghai province, China. The dataset used for the classification comprised of: (1) spectral data and the components of principal component analysis; (2) texture measures derived from pixel basis; (3) DEM and other ancillary data covering the research area. Image textures is an important characteristic of remote sensing images; it can represent spatial variations with spectral brightness in digital numbers. When the spectral information is not enough to separate the different land covers, the texture information can be used to increase the classification accuracy. The texture measures used in this study were calculated from GLCM (Gray level Co-occurrence Matrix); eight frequently used measures were chosen to conduct the classification procedure. The results showed that variance, mean and entropy calculated by GLCM with a 9*9 size window were effective in distinguishing different vegetation types in wetlands zone. The overall accuracy of this method was 84.19% and the Kappa coefficient was 0.8261. The result indicated that the introduction of texture measures can improve the overall accuracy by 12.05% and the overall kappa coefficient by 0.1407 compared with the result using spectral and ancillary data.

  13. Transferability of decision trees for land cover classification in a ...

    African Journals Online (AJOL)

    GChandler

    1Department of Geography and Environmental Studies, Stellenbosch ... 2School of Plant Biology, University of Western Australia, Perth, Australia ... results, a normalised difference vegetation index (NDVI) threshold was applied to each scene. This ... The value of multi-temporal imagery for land cover classification was also.

  14. Hyperspectral Image Land Cover Classification Algorithm Based on Spatial-spectral Coordination Embedding

    Directory of Open Access Journals (Sweden)

    HUANG Hong

    2016-08-01

    Full Text Available Aiming at the problem that in hyperspectral image land cover classification, the traditional classification methods just apply the spectral information while they ignore the relationship between the spatial neighbors, a new dimensionality algorithm called spatial-spectral coordination embedding (SSCE and a new classifier called spatial-spectral coordination nearest neighbor (SSCNN were proposed in this paper. Firstly, the proposed method defines a spatial-spectral coordination distance and the distance is applied to the neighbor selection and low-dimensional embedding. Then, it constructs a spatial-spectral neighborhood graph to maintain the manifold structure of the data set, and enhances the aggregation of data through raising weight of the spatial neighbor points to extract the discriminant features. Finally, it uses the SSCNN to classify the reduced dimensional data. Experimental results using PaviaU and Salinas data set show that the proposed method can effectively improve ground objects classification accuracy comparing with traditional spectral classification methods.

  15. On the Implementation of a Land Cover Classification System for SAR Images Using Khoros

    Science.gov (United States)

    Medina Revera, Edwin J.; Espinosa, Ramon Vasquez

    1997-01-01

    The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.

  16. Land cover classification comparisons among dual polarimetric, pseudo-fully polarimetric, and fully polarimetric SAR imagery

    Science.gov (United States)

    Mishra, Bhogendra; Susaki, Junichi

    2012-10-01

    In this paper, an approach is proposed that predicts fully polarimetric data from dual polarimetric data, and then applies selected supervised algorithm for dual polarimetric, pseudo-fully polarimetric and fully polarimetric dataset for the land cover classification comparison. A regression model has been developed to predict the complex variables of VV polarimetric component and amplitude independently using corresponding complex variables and amplitude in HH and HV bands. Support vector machine (SVM)is implemented for the land cover classification. Coherency matrix and amplitude were used for all dataset for the land cover classification independently.They are used to compare the data from different perspective. Finally, a post processing technique is implemented to remove the isolated pixels appeared as a noise. AVNIR-2 optical data over the same area is used as ground truth data to access the classification accuracy.The result from SVM indicates that the fully polarimetric mode gives the maximum classification accuracy followed by pseudo-fully polarimetric and dual polarimetric datasets using coherency matrix input for fully polarimetric image and pseudo-fully polarimetric image and covariance matrix input for dual polarimetric image. Additionally, it is observed that pseudo-fully polarimetric image with amplitude input does not show the significant improvement over dual polarimetric image with same input.

  17. Land Use and Land Cover, WI Agricultural Statistics Service (WASS) WI Cropland Data Layer. Agriculture and non-ag land cover categories based on survey data (ground truth), satellite imagery classification, FSA common land unit, and 2001 National Land Cover dataset., Published in 2008, 1:100000 (1in=8333ft) scale, Wisconsin Department of Agriculture, Trade & Consumer Protection.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Land Use and Land Cover dataset, published at 1:100000 (1in=8333ft) scale, was produced all or in part from Field Observation information as of 2008. It is...

  18. Classification of artificial (man-made) ground

    OpenAIRE

    Rosenbaum, M.S.; McMillan, A.A.; Powell, J H; Cooper, A.H.; Culshaw, M.G.; Northmore, K.J.

    2003-01-01

    The legacy inherited from anthropogenic processes needs to be addressed in order to provide reliable and up-to-date ground information relevant to development and regeneration in the urban environment. The legacy includes voids as well as anthropogenic deposits (artificial ground). Their characteristics derive from former quarrying and mining activities, industrial processes creating derelict ground, variably consolidated made ground, and contaminated groundwater and soils. All need to be sys...

  19. EASE-Grid 2.0 Land Cover Classifications Derived from Boston University MODIS/Terra Land Cover Data

    Data.gov (United States)

    National Aeronautics and Space Administration — These data provide land cover classifications derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The data are...

  20. Integrating TM and Ancillary Geographical Data with Classification Trees for Land Cover Classification of Marsh Area

    Institute of Scientific and Technical Information of China (English)

    NA Xiaodong; ZHANG Shuqing; ZHANG Huaiqing; LI Xiaofeng; YU Huan; LIU Chunyue

    2009-01-01

    The main objective of this research is to determine the capacity of land cover classification combining spectral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM image texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS information (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to implement and should be applicable in other settings and over larger extents.

  1. Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

    Science.gov (United States)

    Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

    2017-04-26

    Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

  2. Multispectral LiDAR Data for Land Cover Classification of Urban Areas

    Directory of Open Access Journals (Sweden)

    Salem Morsy

    2017-04-01

    Full Text Available Airborne Light Detection And Ranging (LiDAR systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

  3. Indiana forest cover mapping based on multi-stage integrated classification using satellite and in situ forest inventory data

    Science.gov (United States)

    Shao, Gang

    Forest species classification through remote sensing data is a complex process, which usually is done either at a coarse level or with low accuracy. This study examines a multi-stage classification algorithm combining supervised and unsupervised classifications to classify forest areas in Indiana. Integrated classification makes the procedures automatic and reduces human errors. Splitting the classification into two steps increases the accuracy with limited ground data. In the first step, in which the Indiana state forest area is classified, the point plug-in classification algorithm is employed, because plenty of ground data are available. In the second step the classifying of the state forest including a surrounding 8km buffer, the ground data are insufficient to process the point plug-in classification approach. In this case, the polygon plug-in classification algorithm is used to realize the extended area classification at the second stage. The resultant land cover map has six tree species (conifer, mixed forest, oak and hickory, mixed oak and hickory/ hardwood, maple and other hardwood). The overall accuracy is 81.93%.

  4. Land-cover classification with an expert classification algorithm using digital aerial photographs

    Directory of Open Access Journals (Sweden)

    José L. de la Cruz

    2010-05-01

    Full Text Available The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1 bare soil, (2 cereals, including maize (Zea mays L., oats (Avena sativa L., rye (Secale cereale L., wheat (Triticum aestivum L. and barley (Hordeun vulgare L., (3 high protein crops, such as peas (Pisum sativum L. and beans (Vicia faba L., (4 alfalfa (Medicago sativa L., (5 woodlands and scrublands, including holly oak (Quercus ilex L. and common retama (Retama sphaerocarpa L., (6 urban soil, (7 olive groves (Olea europaea L. and (8 burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.

  5. Estimating ground water recharge from topography, hydrogeology, and land cover.

    Science.gov (United States)

    Cherkauer, Douglas S; Ansari, Sajjad A

    2005-01-01

    Proper management of ground water resources requires knowledge of the rates and spatial distribution of recharge to aquifers. This information is needed at scales ranging from that of individual communities to regional. This paper presents a methodology to calculate recharge from readily available ground surface information without long-term monitoring. The method is viewed as providing a reasonable, but conservative, first approximation of recharge, which can then be fine-tuned with other methods as time permits. Stream baseflow was measured as a surrogate for recharge in small watersheds in southeastern Wisconsin. It is equated to recharge (R) and then normalized to observed annual precipitation (P). Regression analysis was constrained by requiring that the independent and dependent variables be dimensionally consistent. It shows that R/P is controlled by three dimensionless ratios: (1) infiltrating to overland water flux, (2) vertical to lateral distance water must travel, and (3) percentage of land cover in the natural state. The individual watershed properties that comprise these ratios are now commonly available in GIS data bases. The empirical relationship for predicting R/P developed for the study watersheds is shown to be statistically viable and is then tested outside the study area and against other methods of calculating recharge. The method produces values that agree with baseflow separation from streamflow hydrographs (to within 15% to 20%), ground water budget analysis (4%), well hydrograph analysis (12%), and a distributed-parameter watershed model calibrated to total streamflow (18%). It has also reproduced the temporal variation over 5 yr observed at a well site with an average error < 12%.

  6. Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

    OpenAIRE

    Li, Guiying; Lu, Dengsheng; MORAN, EMILIO; Hetrick, Scott

    2011-01-01

    This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based clas...

  7. Land cover for Ukraine: the harmonization of remote sensing and ground-based data

    Science.gov (United States)

    Lesiv, M.; Shchepashchenko, D.; Shvidenko, A.; See, L. M.; Bun, R.

    2012-12-01

    This study focuses on the development of a land cover map of the Ukraine through harmonization of remote sensing and ground-based data. At present there is no land cover map of the Ukraine available that is of sufficient accuracy for use in environmental modeling. The existing remote sensing data are not enough accurate. In this study we compare the territory of the Ukraine from three global remote sensing products (GlobCover 2009, MODIS Land Cover and GLC-2000) using a fuzzy logic methodology in order to capture the uncertainty in the classification of land cover. The results for the Ukraine show that GlobCover 2009, MODIS Land Cover and GLC-2000 have a fuzzy agreement of 65%. We developed a weighted algorithm for the creation of a land cover map based on an integration of a number of global land cover and remote sensing products including the GLC-2000, GlobCover 2009, MODIS Land Cover, the Vegetation Continuous Fields product, digital map of administrative units and forest account data at the local level. This weighted algorithm is based on the results of comparing these products and an analysis of a dataset of validation points for different land cover types in the Ukraine. We applied this algorithm to generate a forest land cover type map. This raster map contains a forest expectation index that was calculated for each pixel. Forest land was then allocated based on forest statistics at the local level. Areas with a higher forest expectation index were allocated with forest first until the results matched the forest statistics. The result is the first digital map of forest (with a spatial resolution of 300m) for the Ukraine, which consistent with forest and land accounts, remote sensing datasets and GIS products. The forest land was well defined in forest rich areas (i.e. in the northern part of the Ukraine, the Carpathians and the Crimea); well less accurate areas were identified in the steppe due to heterogeneous land cover. Acknowledgements. This research was

  8. Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia

    Science.gov (United States)

    Wijaya, A.; Sugardiman Budiharto, R. A.; Tosiani, A.; Murdiyarso, D.; Verchot, L. V.

    2015-04-01

    Indonesia possesses the third largest tropical forests coverage following Brazilian Amazon and Congo Basin regions. This country, however, suffered from the highest deforestation rate surpassing deforestation in the Brazilian Amazon in 2012. National capacity for forest change assessment and monitoring has been well-established in Indonesia and the availability of national forest inventory data could largely assist the country to report their forest carbon stocks and change over more than two decades. This work focuses for refining forest cover change mapping and deforestation estimate at national scale applying over 10,000 scenes of Landsat scenes, acquired in 1990, 1996, 2000, 2003, 2006, 2009, 2011 and 2012. Pre-processing of the data includes, geometric corrections and image mosaicking. The classification of mosaic Landsat data used multi-stage visual observation approaches, verified using ground observations and comparison with other published materials. There are 23 land cover classes identified from land cover data, presenting spatial information of forests, agriculture, plantations, non-vegetated lands and other land use categories. We estimated the magnitude of forest cover change and assessed drivers of forest cover change over time. Forest change trajectories analysis was also conducted to observe dynamics of forest cover across time. This study found that careful interpretations of satellite data can provide reliable information on forest cover and change. Deforestation trend in Indonesia was lower in 2000-2012 compared to 1990-2000 periods. We also found that over 50% of forests loss in 1990 remains unproductive in 2012. Major drivers of forest conversion in Indonesia range from shrubs/open land, subsistence agriculture, oil palm expansion, plantation forest and mining. The results were compared with other available datasets and we obtained that the MOF data yields reliable estimate of deforestation.

  9. Database for estimating tree responses of walnut and other hardwoods to ground cover management practices

    Science.gov (United States)

    J.W. Van Sambeek

    2010-01-01

    The ground cover in plantings of walnut and other hardwoods can substantially affect tree growth and seed production. The number of alternative ground covers that have been suggested for establishment in tree plantings far exceeds the number that have already been tested with walnut and other temperate hardwoods. Knowing how other hardwood species respond to ground...

  10. OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    R. T. Alberto

    2016-06-01

    Full Text Available Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.

  11. Multi-source remotely sensed data fusion for improving land cover classification

    Science.gov (United States)

    Chen, Bin; Huang, Bo; Xu, Bing

    2017-02-01

    Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

  12. Land cover classification using random forest with genetic algorithm-based parameter optimization

    Science.gov (United States)

    Ming, Dongping; Zhou, Tianning; Wang, Min; Tan, Tian

    2016-07-01

    Land cover classification based on remote sensing imagery is an important means to monitor, evaluate, and manage land resources. However, it requires robust classification methods that allow accurate mapping of complex land cover categories. Random forest (RF) is a powerful machine-learning classifier that can be used in land remote sensing. However, two important parameters of RF classification, namely, the number of trees and the number of variables tried at each split, affect classification accuracy. Thus, optimal parameter selection is an inevitable problem in RF-based image classification. This study uses the genetic algorithm (GA) to optimize the two parameters of RF to produce optimal land cover classification accuracy. HJ-1B CCD2 image data are used to classify six different land cover categories in Changping, Beijing, China. Experimental results show that GA-RF can avoid arbitrariness in the selection of parameters. The experiments also compare land cover classification results by using GA-RF method, traditional RF method (with default parameters), and support vector machine method. When the GA-RF method is used, classification accuracies, respectively, improved by 1.02% and 6.64%. The comparison results show that GA-RF is a feasible solution for land cover classification without compromising accuracy or incurring excessive time.

  13. Per pixel uncertainty modelling and its spatial representation on land cover maps obtained by hybrid classification.

    Science.gov (United States)

    Pons, Xavier; Sevillano, Eva; Moré, Gerard; Serra, Pere; Cornford, Dan; Ninyerola, Miquel

    2013-04-01

    The usage of remote sensing imagery combined with statistical classifiers to obtain categorical cartography is now common practice. As in many other areas of geographic information quality assessment, knowing the accuracy of these maps is crucial, and the spatialization of quality information is becoming ever more important for a large range of applications. Whereas some classifiers (e.g., maximum likelihood, linear discriminant analysis, naive Bayes, etc) permit the estimation and spatial representation of the uncertainty through a pixel level probabilistic estimator (and, from that, to compute a global accuracy estimator for the whole map), for other methods such a direct estimator does not exist. Regardless of the classification method applied, ground truth data is almost always available (to train the classifier and/or to compute the global accuracy and, usually, a confusion matrix). Our research is devoted to the development of a protocol to spatialize the error on a general framework based on the classifier parameters, and some ground truth reference data. In the methodological experiment presented here we provide an insight into uncertainty modelling for a hybrid classifier that combines unsupervised and supervised stages (implemented in the MiraMon GIS). In this work we describe what we believe is the first attempt to characterise pixel level uncertainty in a two stage classification process. We describe the model setup, show the preliminary results and identify future work that will be undertaken. The study area is a Landsat full frame located at the North-eastern region of the Iberian Peninsula. The six non-thermal bands + NDVI of a multi-temporal set of six geometrically and radiometrically corrected Landsat-5 images (between 2005 and 2007) were submitted to a hybrid classification process, together with some ancillary data (climate, slopes, etc). Training areas were extracted from the Land Cover Map of Catalonia (MCSC), a 0.5 m resolution map created by

  14. Is our Ground-Truth for Traffic Classification Reliable?

    DEFF Research Database (Denmark)

    Carela-Español, Valentín; Bujlow, Tomasz; Barlet-Ros, Pere

    2014-01-01

    The validation of the different proposals in the traffic classification literature is a controversial issue. Usually, these works base their results on a ground-truth built from private datasets and labeled by techniques of unknown reliability. This makes the validation and comparison with other...... solutions an extremely difficult task. This paper aims to be a first step towards addressing the validation and trustworthiness problem of network traffic classifiers. We perform a comparison between 6 well-known DPI-based techniques, which are frequently used in the literature for ground-truth generation....... In order to evaluate these tools we have carefully built a labeled dataset of more than 500 000 flows, which contains traffic from popular applications. Our results present PACE, a commercial tool, as the most reliable solution for ground-truth generation. However, among the open-source tools available...

  15. A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE

    Directory of Open Access Journals (Sweden)

    H. Y. Gu

    2017-09-01

    Full Text Available Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and has a poor versatility. This study has put forward a rule set building method for Land cover classification based on human knowledge and machine learning. The use of machine learning is to build rule sets effectively which will overcome the iterative trial-and-error approach. The use of human knowledge is to solve the shortcomings of existing machine learning method on insufficient usage of prior knowledge, and improve the versatility of rule sets. A two-step workflow has been introduced, firstly, an initial rule is built based on Random Forest and CART decision tree. Secondly, the initial rule is analyzed and validated based on human knowledge, where we use statistical confidence interval to determine its threshold. The test site is located in Potsdam City. We utilised the TOP, DSM and ground truth data. The results show that the method could determine rule set for Land Cover classification semi-automatically, and there are static features for different land cover classes.

  16. The Analysis of Spot-5 Characteristics on land cover classification

    Institute of Scientific and Technical Information of China (English)

    徐开明

    2004-01-01

    Knowledge about land cover and land use has become increasingly important as the Nation plans to overcome the problems of uncontrolled development, deteriorating environmental quality, loss of prime agricultural lands etc. Land use and land cover data are needed in the analysis of environmental processes and problems to know if living conditions and standards are to be improved or maintained at current levels.

  17. The analysis of SPOT-5 characteristics on land cover classification

    Institute of Scientific and Technical Information of China (English)

    XUKai-ming

    2004-01-01

    Knowledge about land cover and land use has become increasingly important as the Nation plans to overeome the problems of uncontrolled development, deteriorating environmental quality, loss of prime agricultural lands etc. Land use and land cover data are needed in the analysis of environmental processes and problems to know if riving conditions and standards are to be improved or maintained at current levels.

  18. Land cover classification of remotely sensed image with hierarchical iterative method

    Institute of Scientific and Technical Information of China (English)

    LI Peijun; HUANG Yingduan

    2005-01-01

    Based on the analysis of the single-stage classification results obtained by the multitemporal SPOT 5 and Landsat 7 ETM + multispectral images separately and the derived variogram texture, the best data combinations for each land cover class are selected, and the hierarchical iterative classification is then applied for land cover mapping. The proposed classification method combines the multitemporal images of different resolutions with the image texture, which can greatly improve the classification accuracy. The method and strategies proposed in the study can be easily transferred to other similar applications.

  19. [Diversity and stability of arthropod community in peach orchard under effects of ground cover vegetation].

    Science.gov (United States)

    Jiang, Jie-xian; Wan, Nian-feng; Ji, Xiang-yun; Dan, Jia-gui

    2011-09-01

    A comparative study was conducted on the arthropod community in peach orchards with and without ground cover vegetation. In the orchard with ground cover vegetation, the individuals of beneficial, neutral, and phytophagous arthropods were 1.48, 1.84 and 0.64 times of those in the orchard without ground cover vegetation, respectively, but the total number of arthropods had no significant difference with that in the orchard without ground cover vegetation. The species richness, Shannon's diversity, and Pielou's evenness index of the arthropods in the orchard with ground cover vegetation were 83.733 +/- 4.932, 4.966 +/- 0.110, and 0.795 +/- 0.014, respectively, being significantly higher than those in the orchard without ground cover vegetation, whereas the Berger-Parker's dominance index was 0.135 +/- 0.012, being significantly lower than that (0.184 +/- 0.018) in the orchard without ground cover vegetation. There were no significant differences in the stability indices S/N and Sd/Sp between the two orchards, but the Nn/Np, Nd/Np, and Sn/Sp in the orchard with ground cover vegetation were 0.883 +/- 0.123. 1714 +/- 0.683, and 0.781 +/- 0.040, respectively, being significantly higher than those in the orchard without ground cover vegetation. Pearson's correlation analysis indicated that in the orchard with ground cover vegetation, the Shannon's diversity index was significantly negatively correlated with Nd/Np, Sd/Sp, and S/N but had no significant correlations with Nn/Np and Sn/Sp, whereas in the orchard without ground cover vegetation, the diversity index was significantly positively correlated with Nn/Np and Nd/Np and had no significant correlations with Sd/Sp, Sn/Sp, and S/N.

  20. Tetlin NWR /Scottie Creek Earth Cover Classification User's Guide

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — In 2005, the U.S. Fish and Wildlife Service and Ducks Unlimited, Inc. began a mapping effort to produce earth cover data for three National Wildlife Refuges (NWRs)...

  1. A Novel Method for Detection and Classification of Covered Conductor Faults

    Directory of Open Access Journals (Sweden)

    Stanislav Misak

    2016-01-01

    Full Text Available Medium-Voltage (MV overhead lines with Covered Conductors (CCs are increasingly being used around the world primarily in forested or dissected terrain areas or in urban areas where it is not possible to utilize MV cable lines. The CC is specific in high operational reliability provided by the conductor core insulation compared to Aluminium-Conductor Steel-Reinforced (ACSR overhead lines. The only disadvantage of the CC is rather the problematic detection of faults compared to the ACSR. In this work, we consider the following faults: the contact of a tree branch with a CC and the fall of a conductor on the ground. The standard protection relays are unable to detect the faults and so the faults pose a risk for individuals in the vicinity of the conductor as well as it compromises the overall safety and reliability of the MV distribution system. In this article, we continue with our previous work aimed at the method enabling detection of the faults and we introduce a method enabling a classification of the fault type. Such a classification is especially important for an operator of an MV distribution system to plan the optimal maintenance or repair the faulty conductors since the fall of a tree branch can be solved later whereas the breakdown of a conductor means an immediate action of the operator.

  2. Land Cover Heterogeneity Effects on Sub-Pixel and Per-Pixel Classifications

    Directory of Open Access Journals (Sweden)

    Trung V. Tran

    2014-04-01

    Full Text Available Per-pixel and sub-pixel are two common classification methods in land cover studies. The characteristics of a landscape, particularly the land cover itself, can affect the accuracies of both methods. The objectives of this study were to: (1 compare the performance of sub-pixel vs. per-pixel classification methods for a broad heterogeneous region; and (2 analyze the impact of land cover heterogeneity (i.e., the number of land cover classes per pixel on both classification methods. The results demonstrated that the accuracy of both per-pixel and sub-pixel classification methods were generally reduced by increasing land cover heterogeneity. Urban areas, for example, were found to have the lowest accuracy for the per-pixel method, because they had the highest heterogeneity. Conversely, rural areas dominated by cropland and grassland had low heterogeneity and high accuracy. When a sub-pixel method was used, the producer’s accuracy for artificial surfaces was increased by more than 20%. For all other land cover classes, sub-pixel and per-pixel classification methods performed similarly. Thus, the sub-pixel classification was only advantageous for heterogeneous urban landscapes. Both creators and users of land cover datasets should be aware of the inherent landscape heterogeneity and its potential effect on map accuracy.

  3. Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

    Science.gov (United States)

    Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

    2017-06-01

    During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are

  4. Legume ground covers alter defoliation response of black walnut saplings to drought and anthracnose

    Science.gov (United States)

    J. W. Van Sambeek

    2003-01-01

    Growth and premature defoliation of black walnut saplings underplanted 5 or 6 years earlier with six different ground covers were quantified in response to a summer drought or anthracnose. Walnut saplings growing with ground covers of hairy vetch, crownvetch, and to a lesser extent sericea lespedeza continued to have more rapid height and diameter growth than saplings...

  5. Experimental evaluation of ALS point cloud ground extraction over different land cover in the Malopolska Province

    Science.gov (United States)

    Korzeniowska, Karolina; Mandlburger, Gottfried; Klimczyk, Agata

    2013-04-01

    The paper presents an evaluation of different terrain point extraction algorithms for Airborne Laser Scanning (ALS) point clouds. The research area covers eight test sites in the Małopolska Province (Poland) with varying point density between 3-15points/m² and surface as well as land cover characteristics. In this paper the existing implementations of algorithms were considered. Approaches based on mathematical morphology, progressive densification, robust surface interpolation and segmentation were compared. From the group of morphological filters, the Progressive Morphological Filter (PMF) proposed by Zhang K. et al. (2003) in LIS software was evaluated. From the progressive densification filter methods developed by Axelsson P. (2000) the Martin Isenburg's implementation in LAStools software (LAStools, 2012) was chosen. The third group of methods are surface-based filters. In this study, we used the hierarchic robust interpolation approach by Kraus K., Pfeifer N. (1998) as implemented in SCOP++ (Trimble, 2012). The fourth group of methods works on segmentation. From this filtering concept the segmentation algorithm available in LIS was tested (Wichmann V., 2012). The main aim in executing the automatic classification for ground extraction was operating in default mode or with default parameters which were selected by the developers of the algorithms. It was assumed that the default settings were equivalent to the parameters on which the best results can be achieved. In case it was not possible to apply an algorithm in default mode, a combination of the available and most crucial parameters for ground extraction were selected. As a result of these analyses, several output LAS files with different ground classification were achieved. The results were described on the basis of qualitative and quantitative analyses, both being in a formal description. The classification differences were verified on point cloud data. Qualitative verification of ground extraction was

  6. Influence of Lossy Compressed DEM on Radiometric Correction for Land Cover Classification of Remote Sensing Images

    Science.gov (United States)

    Moré, G.; Pesquer, L.; Blanes, I.; Serra-Sagristà, J.; Pons, X.

    2012-12-01

    delimiting coastline, avoiding the confusion between elevation and no-data values. Six (from March 2005 to May 2007) geometrically corrected Landsat-5 images on the path-row 197-031 have been used. The six optical bands and the NDVI for each date have been introduced in a powerful hybrid classification process. The training areas and the ground truth have been obtained from the Mapa de Cobertes del Sòl de Catalunya (v. 3), a land cover map created by photointerpretation of 0.5 m orthophotomaps acquired between 2005 and 2007 and covering all the extension of Catalonia. The legend has been reduced from 233 categories to 21. Preliminary results have shown that the effect on land cover classification of applying lossy compression to the DEM used in the radiometric correction is small (lower than 1%) even for compression ratios up to 200:1. Comparing classification performance after a compression of 5:1 and and a compression of 200:1 with both coding standards showed that: a) the percentage of correctly classified image was 73%; b) 20% was wrongly classified; c) 3.5% was wrongly classified at compression ratio 5:1; and d) also 3.5% was wrongly classified at compression ratio 200:1. These results are the first in the literature to analyze the effect of DEM lossy compressing when DEM are employed for radiometric correction.

  7. Land Cover Classification in Complex and Fragmented Agricultural Landscapes of the Ethiopian Highlands

    Directory of Open Access Journals (Sweden)

    Michael Eggen

    2016-12-01

    Full Text Available Ethiopia is a largely agrarian country with nearly 85% of its employment coming from agriculture. Nevertheless, it is not known how much land is under cultivation. Mapping land cover at finer resolution and global scales has been particularly difficult in Ethiopia. The study area falls in a region of high mapping complexity with environmental challenges which require higher quality maps. Here, remote sensing is used to classify a large area of the central and northwestern highlands into eight broad land cover classes that comprise agriculture, grassland, woodland/shrub, forest, bare ground, urban/impervious surfaces, water, and seasonal water/marsh areas. We use data from Landsat spectral bands from 2000 to 2011, the Normalized Difference Vegetation Index (NDVI and its temporal mean and variance, together with a digital elevation model, all at 30-m spatial resolution, as inputs to a supervised classifier. A Support Vector Machines algorithm (SVM was chosen to deal with the size, variability and non-parametric nature of these data stacks. In post-processing, an image segmentation algorithm with a minimum mapping unit of about 0.5 hectares was used to convert per pixel classification results into an object based final map. Although the reliability of the map is modest, its overall accuracy is 55%—encouraging results for the accuracy of agricultural uses at 85% suggest that these methods do offer great utility. Confusion among grassland, woodland and barren categories reflects the difficulty of classifying savannah landscapes, especially in east central Africa with monsoonal-driven rainfall patterns where the ground is obstructed by clouds for significant periods of time. Our analysis also points out the need for high quality reference data. Further, topographic analysis of the agriculture class suggests there is a significant amount of sloping land under cultivation. These results are important for future research and environmental monitoring in

  8. Land-Use and Land-Cover Mapping Using a Gradable Classification Method

    Directory of Open Access Journals (Sweden)

    Keigo Kitada

    2012-05-01

    Full Text Available Conventional spectral-based classification methods have significant limitations in the digital classification of urban land-use and land-cover classes from high-resolution remotely sensed data because of the lack of consideration given to the spatial properties of images. To recognize the complex distribution of urban features in high-resolution image data, texture information consisting of a group of pixels should be considered. Lacunarity is an index used to characterize different texture appearances. It is often reported that the land-use and land-cover in urban areas can be effectively classified using the lacunarity index with high-resolution images. However, the applicability of the maximum-likelihood approach for hybrid analysis has not been reported. A more effective approach that employs the original spectral data and lacunarity index can be expected to improve the accuracy of the classification. A new classification procedure referred to as “gradable classification method” is proposed in this study. This method improves the classification accuracy in incremental steps. The proposed classification approach integrates several classification maps created from original images and lacunarity maps, which consist of lacnarity values, to create a new classification map. The results of this study confirm the suitability of the gradable classification approach, which produced a higher overall accuracy (68% and kappa coefficient (0.64 than those (65% and 0.60, respectively obtained with the maximum-likelihood approach.

  9. Law school design blends functionalism, energy conservation. [Earth-covered with ground-cover growing on roof

    Energy Technology Data Exchange (ETDEWEB)

    1976-08-01

    Construction is under way on a new University of Minnesota Law School Building, whose distinctive features include a stepped design on its southern elevation and an earth-covered roof to promote energy conservation. The design is described with emphasis on the library facilities. Energy conservation was a major design factor. The portion of the earth-covered roof will be 15 inches thick planted with low ground-cover vegetation. Overall ..mu.. value of the building envelope will be 0.11. (MCW)

  10. Standard land-cover classification scheme for remote-sensing applications in South Africa

    CSIR Research Space (South Africa)

    Thompson, M

    1996-01-01

    Full Text Available For large areas, satellite remote-sensing techniques have now become the single most effective method for land-cover and land-use data acquisition. However, the majority of land-cover (and land-use) classification schemes used have been developed...

  11. Finite mixture models for sub-pixel coastal land cover classification

    CSIR Research Space (South Africa)

    Ritchie, Michaela C

    2017-05-01

    Full Text Available mixture models have been used to generate sub-pixel land cover classifications, however, traditionally this makes use of mixtures of normal distributions. However, these models fail to represent many land cover classes accurately, as these are usually...

  12. Mapping land cover in urban residential landscapes using fine resolution imagery and object-oriented classification

    Science.gov (United States)

    A knowledge of different types of land cover in urban residential landscapes is important for building social and economic city-wide policies including landscape ordinances and water conservation programs. Urban landscapes are typically heterogeneous, so classification of land cover in these areas ...

  13. Classification methodology and operational implementation of the land cover database of the Netherlands

    NARCIS (Netherlands)

    Thunnissen, H.A.M.; Noordman, E.J.M.

    1996-01-01

    Timely and accurate information on land cover on regional and national scales is required to support environmental policy and for physical planning purposes. In 1987 the LGN1 land cover database was produced with satellite images. An improved classification method has been developed, consisting of t

  14. TEXTURE BASED LAND COVER CLASSIFICATION ALGORITHM USING GABOR WAVELET AND ANFIS CLASSIFIER

    Directory of Open Access Journals (Sweden)

    S. Jenicka

    2016-05-01

    Full Text Available Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely sensed image, Adaptive Neuro Fuzzy Inference System (ANFIS has been used. The strength of ANFIS classifier is that it combines the merits of fuzzy logic and neural network. Hence in this article, land cover classification of remotely sensed image has been performed using Gabor wavelet and ANFIS classifier. The classification accuracy of the classified image obtained is found to be 92.8%.

  15. Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

    Directory of Open Access Journals (Sweden)

    Stefan Dech

    2012-09-01

    Full Text Available We present a novel and innovative automated processing environment for the derivation of land cover (LC and land use (LU information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS, as introduced by the Open Geospatial Consortium (OGC, are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

  16. A Grid Service for Automatic Land Cover Classification Using Hyperspectral Images

    Science.gov (United States)

    Jasso, H.; Shin, P.; Fountain, T.; Pennington, D.; Ding, L.; Cotofana, N.

    2004-12-01

    Hyperspectral images are collected using Airborne Visible/Infrared Imaging Spectrometer (Aviris) optical sensors [1]. 224 contiguous channels are measured across the spectral range, from 400 to 2500 nanometers. We present a system for the automatic classification of land cover using hyperspectral images, and propose an architecture for deploying the system in a grid environment that harnesses distributed file storage and CPU resources for the task. Originally, we ran the following data mining algorithms on a 300x300 image of a section of the Sevilleta National Wildlife Refuge in New Mexico [2]: Maximum Likelihood, Naive Bayes Classifier, Minimum Distance, and Support Vector Machine (SVM). For this, ground truth for 673 pixels was manually collected according to eight possible land covers: river, riparian, agriculture, arid upland, semi-arid upland, barren, pavement, or clouds. The classification accuracies for these algorithms were of 96.4%, 90.9%, 88.4%, and 77.6%, respectively [3]. In this study, we noticed that the slope between adjacent frequencies produces specific patterns across the whole spectrum, giving a good indication of the pixel's land cover type. Wavelet analysis makes these global patterns explicit, by breaking down the signal into variable-sized windows, where long time windows capture low-frequency information and short time windows capture high-frequency information. High frequency information translates to information among close neighbors while low frequency information displays the overall trend of the features. We pre-processed the data using different families of wavelets, resulting in an increase in the performance of the Naive Bayesian Classifier and SVM to 94.2% and 90.1%, respectively. Classification accuracy with SVM was further increased to 97.1 % by modifying the mechanism by which multi-class is achieved using basic two-class SVMs. The original winner-take-all SVM scheme was replaced with a one-against-one scheme, in which k(k-1

  17. Interannual changes in snow cover and its impact on ground surface temperatures in Livingston Island (Antarctica)

    Science.gov (United States)

    Nieuwendam, Alexandre; Ramos, Miguel; Vieira, Gonçalo

    2015-04-01

    In permafrost areas the seasonal snow cover is an important factor on the ground thermal regime. Snow depth and timing are important in ground insulation from the atmosphere, creating different snow patterns and resulting in spatially variable ground temperatures. The aim of this work is to characterize the interactions between ground thermal regimes and snow cover and the influence on permafrost spatial distribution. The study area is the ice-free terrains of northwestern Hurd Peninsula in the vicinity of the Spanish Antarctic Station "Juan Carlos I" and Bulgarian Antarctic Station "St. Kliment Ohridski". Air and ground temperatures and snow thickness data where analysed from 4 sites along an altitudinal transect in Hurd Peninsula from 2007 to 2012: Nuevo Incinerador (25 m asl), Collado Ramos (110 m), Ohridski (140 m) and Reina Sofia Peak (275 m). The data covers 6 cold seasons showing different conditions: i) very cold with thin snow cover; ii) cold with a gradual increase of snow cover; iii) warm with thick snow cover. The data shows three types of periods regarding the ground surface thermal regime and the thickness of snow cover: a) thin snow cover and short-term fluctuation of ground temperatures; b) thick snow cover and stable ground temperatures; c) very thick snow cover and ground temperatures nearly constant at 0°C. a) Thin snow cover periods: Collado Ramos and Ohridski sites show frequent temperature variations, alternating between short-term fluctuations and stable ground temperatures. Nuevo Incinerador displays during most of the winter stable ground temperatures; b) Cold winters with a gradual increase of the snow cover: Nuevo Incinerador, Collado Ramos and Ohridski sites show similar behavior, with a long period of stable ground temperatures; c) Thick snow cover periods: Collado Ramos and Ohridski show long periods of stable ground, while Nuevo Incinerador shows temperatures close to 0°C since the beginning of the winter, due to early snow cover

  18. Determination of Land Cover/land Use Using SPOT 7 Data with Supervised Classification Methods

    Science.gov (United States)

    Bektas Balcik, F.; Karakacan Kuzucu, A.

    2016-10-01

    Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management in the rural part of the cities. There is a strong need to produce accurate, reliable, and up-to-date land use/land cover maps for sustainable monitoring and management. In this study, SPOT 7 imagery was used to test the potential of the data for land cover/land use mapping. Catalca is selected region located in the north west of the Istanbul in Turkey, which is mostly covered with agricultural fields and forest lands. The potentials of two classification algorithms maximum likelihood, and support vector machine, were tested, and accuracy assessment of the land cover maps was performed through error matrix and Kappa statistics. The results indicated that both of the selected classifiers were highly useful (over 83% accuracy) in the mapping of land use/cover in the study region. The support vector machine classification approach slightly outperformed the maximum likelihood classification in both overall accuracy and Kappa statistics.

  19. 3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS

    Directory of Open Access Journals (Sweden)

    X. Zou

    2016-06-01

    Full Text Available Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green, at 1064 nm near infrared (NIR and at 1550nm intermediate infrared (IR. It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

  20. Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

    Science.gov (United States)

    Dong, Weihua; Lan, Jianhang; Liang, Shunlin; Yao, Wei; Zhan, Zhicheng

    2017-08-01

    LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5-1.5 m), medium (1.5-6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification.

  1. Assessment of Classification Accuracies of SENTINEL-2 and LANDSAT-8 Data for Land Cover / Use Mapping

    Science.gov (United States)

    Hale Topaloğlu, Raziye; Sertel, Elif; Musaoğlu, Nebiye

    2016-06-01

    This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

  2. Estonian soil classification as a tool for recording information on soil cover and its matching with local site types, plant covers and humus forms classifications

    Science.gov (United States)

    Kõlli, Raimo; Tõnutare, Tõnu; Rannik, Kaire; Krebstein, Kadri

    2015-04-01

    Estonian soil classification (ESC) has been used successfully during more than half of century in soil survey, teaching of soil science, generalization of soil databases, arrangement of soils sustainable management and others. The Estonian normally developed (postlithogenic) mineral soils (form 72.4% from total area) are characterized by mean of genetic-functional schema, where the pedo-ecological position of soils (ie. location among other soils) is given by means of three scalars: (i) 8 stage lithic-genetic scalar (from rendzina to podzols) separates soils each from other by parent material, lithic properties, calcareousness, character of soil processes and others, (ii) 6 stage moisture and aeration conditions scalar (from aridic or well aerated to permanently wet or reductic conditions), and (iii) 2-3 stage soil development scalar, which characterizes the intensity of soil forming processes (accumulation of humus, podzolization). The organic soils pedo-ecological schema, which links with histic postlithogenic soils, is elaborated for characterizing of peatlands superficial mantle (form 23.7% from whole soil cover). The position each peat soil species among others on this organic (peat) soil matrix schema is determined by mean of 3 scalars: (i) peat thickness, (ii) type of paludification or peat forming peculiarities, and (iii) stage of peat decomposition or peat type. On the matrix of abnormally developed (synlithogenic) soils (all together 3.9%) the soil species are positioned (i) by proceeding in actual time geological processes as erosion, fluvial processes (at vicinity of rivers, lakes or sea) or transforming by anthropogenic and technological processes, and (ii) by 7 stage moisture conditions (from aridic to subaqual) of soils. The most important functions of soil cover are: (i) being a suitable environment for plant productivity; (ii) forming adequate conditions for decomposition, transformation and conversion of falling litter (characterized by humus

  3. Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification

    Indian Academy of Sciences (India)

    B T Abe; O O Olugbara; T Marwala

    2014-06-01

    The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.

  4. Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

    Science.gov (United States)

    Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam

    2016-11-01

    This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel s for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

  5. Diseases of Ornamental and Shade Trees, Shrubs, Vines, and Ground Covers.

    Science.gov (United States)

    Nichols, Lester P.

    This agriculture extension service publication from Pennsylvania State University covers the identification and control of common ornamental trees, shrubs, and ground cover diseases. The publication is divided into sections. The first section discusses the diseases of ornamental and shade trees, including general diseases and diseases of specific…

  6. A higher order conditional random field model for simultaneous classification of land cover and land use

    Science.gov (United States)

    Albert, Lena; Rottensteiner, Franz; Heipke, Christian

    2017-08-01

    We propose a new approach for the simultaneous classification of land cover and land use considering spatial as well as semantic context. We apply a Conditional Random Fields (CRF) consisting of a land cover and a land use layer. In the land cover layer of the CRF, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Intra-layer edges of the CRF model spatial dependencies between neighbouring image sites. All spatially overlapping sites in both layers are connected by inter-layer edges, which leads to higher order cliques modelling the semantic relation between all land cover and land use sites in the clique. A generic formulation of the higher order potential is proposed. In order to enable efficient inference in the two-layer higher order CRF, we propose an iterative inference procedure in which the two classification tasks mutually influence each other. We integrate contextual relations between land cover and land use in the classification process by using contextual features describing the complex dependencies of all nodes in a higher order clique. These features are incorporated in a discriminative classifier, which approximates the higher order potentials during the inference procedure. The approach is designed for input data based on aerial images. Experiments are carried out on two test sites to evaluate the performance of the proposed method. The experiments show that the classification results are improved compared to the results of a non-contextual classifier. For land cover classification, the result is much more homogeneous and the delineation of land cover segments is improved. For the land use classification, an improvement is mainly achieved for land use objects showing non-typical characteristics or similarities to other land use classes. Furthermore, we have shown that the size of the super-pixels has an influence on the level of detail of the classification result, but also on the

  7. EVALUATION OF DECISION TREE CLASSIFICATION ACCURACY TO MAP LAND COVER IN CAPIXABA, ACRE

    Directory of Open Access Journals (Sweden)

    Symone Maria de Melo Figueiredo

    2006-03-01

    Full Text Available This study evaluated the accuracy of mapping land cover in Capixaba, state of Acre, Brazil, using decision trees. Elevenattributes were used to build the decision trees: TM Landsat datafrom bands 1, 2, 3, 4, 5, and 7; fraction images derived from linearspectral unmixing; and the normalized difference vegetation index (NDVI. The Kappa values were greater than 0,83, producingexcellent classification results and demonstrating that the technique is promising for mapping land cover in the study area.

  8. Testing of Land Cover Classification from Multispectral Airborne Laser Scanning Data

    Science.gov (United States)

    Bakuła, K.; Kupidura, P.; Jełowicki, Ł.

    2016-06-01

    Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return

  9. Land cover classification accuracy from electro-optical, X, C, and L-band Synthetic Aperture Radar data fusion

    Science.gov (United States)

    Hammann, Mark Gregory

    The fusion of electro-optical (EO) multi-spectral satellite imagery with Synthetic Aperture Radar (SAR) data was explored with the working hypothesis that the addition of multi-band SAR will increase the land-cover (LC) classification accuracy compared to EO alone. Three satellite sources for SAR imagery were used: X-band from TerraSAR-X, C-band from RADARSAT-2, and L-band from PALSAR. Images from the RapidEye satellites were the source of the EO imagery. Imagery from the GeoEye-1 and WorldView-2 satellites aided the selection of ground truth. Three study areas were chosen: Wad Medani, Sudan; Campinas, Brazil; and Fresno- Kings Counties, USA. EO imagery were radiometrically calibrated, atmospherically compensated, orthorectifed, co-registered, and clipped to a common area of interest (AOI). SAR imagery were radiometrically calibrated, and geometrically corrected for terrain and incidence angle by converting to ground range and Sigma Naught (?0). The original SAR HH data were included in the fused image stack after despeckling with a 3x3 Enhanced Lee filter. The variance and Gray-Level-Co-occurrence Matrix (GLCM) texture measures of contrast, entropy, and correlation were derived from the non-despeckled SAR HH bands. Data fusion was done with layer stacking and all data were resampled to a common spatial resolution. The Support Vector Machine (SVM) decision rule was used for the supervised classifications. Similar LC classes were identified and tested for each study area. For Wad Medani, nine classes were tested: low and medium intensity urban, sparse forest, water, barren ground, and four agriculture classes (fallow, bare agricultural ground, green crops, and orchards). For Campinas, Brazil, five generic classes were tested: urban, agriculture, forest, water, and barren ground. For the Fresno-Kings Counties location 11 classes were studied: three generic classes (urban, water, barren land), and eight specific crops. In all cases the addition of SAR to EO resulted

  10. Pixel-Based Land Cover Classification by Fusing Hyperspectral and LIDAR Data

    Science.gov (United States)

    Jahan, F.; Awrangjeb, M.

    2017-09-01

    Land cover classification has many applications like forest management, urban planning, land use change identification and environment change analysis. The passive sensing of hyperspectral systems can be effective in describing the phenomenology of the observed area over hundreds of (narrow) spectral bands. On the other hand, the active sensing of LiDAR (Light Detection and Ranging) systems can be exploited for characterising topographical information of the area. As a result, the joint use of hyperspectral and LiDAR data provides a source of complementary information, which can greatly assist in the classification of complex classes. In this study, we fuse hyperspectral and LiDAR data for land cover classification. We do a pixel-wise classification on a disjoint set of training and testing samples for five different classes. We propose a new feature combination by fusing features from both hyperspectral and LiDAR, which achieves competent classification accuracy with low feature dimension, while the existing method requires high dimensional feature vector to achieve similar classification result. Also, for the reduction of the dimension of the feature vector, Principal Component Analysis (PCA) is used as it captures the variance of the samples with a limited number of Principal Components (PCs). We tested our classification method using PCA applied on hyperspectral bands only and combined hyperspectral and LiDAR features. Classification with support vector machine (SVM) and decision tree shows that our feature combination achieves better classification accuracy compared to the existing feature combination, while keeping the similar number of PCs. The experimental results also show that decision tree performs better than SVM and requires less execution time.

  11. Land cover mapping based on random forest classification of multitemporal spectral and thermal images.

    Science.gov (United States)

    Eisavi, Vahid; Homayouni, Saeid; Yazdi, Ahmad Maleknezhad; Alimohammadi, Abbas

    2015-05-01

    Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8's spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82%, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.

  12. GENERATION OF 2D LAND COVER MAPS FOR URBAN AREAS USING DECISION TREE CLASSIFICATION

    DEFF Research Database (Denmark)

    Höhle, Joachim

    2014-01-01

    A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objec...

  13. Continuous Change Detection and Classification (CCDC) of Land Cover Using All Available Landsat Data

    Science.gov (United States)

    Zhu, Z.; Woodcock, C. E.

    2012-12-01

    A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. This new algorithm is capable of detecting many kinds of land cover change as new images are collected and at the same time provide land cover maps for any given time. To better identify land cover change, a two step cloud, cloud shadow, and snow masking algorithm is used for eliminating "noisy" observations. Next, a time series model that has components of seasonality, trend, and break estimates the surface reflectance and temperature. The time series model is updated continuously with newly acquired observations. Due to the high variability in spectral response for different kinds of land cover change, the CCDC algorithm uses a data-driven threshold derived from all seven Landsat bands. When the difference between observed and predicted exceeds the thresholds three consecutive times, a pixel is identified as land cover change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model fitting are used as classification inputs for the Random Forest Classifier (RFC). We applied this new algorithm for one Landsat scene (Path 12 Row 31) that includes all of Rhode Island as well as much of Eastern Massachusetts and parts of Connecticut. A total of 532 Landsat images acquired between 1982 and 2011 were processed. During this period, 619,924 pixels were detected to change once (91% of total changed pixels) and 60,199 pixels were detected to change twice (8% of total changed pixels). The most frequent land cover change category is from mixed forest to low density residential which occupies more than 8% of total land cover change pixels.

  14. Improving land cover classification using input variables derived from a geographically weighted principal components analysis

    Science.gov (United States)

    Comber, Alexis J.; Harris, Paul; Tsutsumida, Narumasa

    2016-09-01

    This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested.

  15. MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    M. Rußwurm

    2017-05-01

    Full Text Available Land cover classification (LCC is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN, with a classical non-temporal convolutional neural network (CNN model and an additional support vector machine (SVM baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  16. Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

    Science.gov (United States)

    Rußwurm, M.; Körner, M.

    2017-05-01

    Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  17. Wavelet-SVM classifier based on texture features for land cover classification

    Science.gov (United States)

    Zhang, Ning; Wu, Bingfang; Zhu, Jianjun; Zhou, Yuemin; Zhu, Liang

    2008-12-01

    Texture features are recognized to be a special hint in images, which represent the spatial relations of the gray pixels. Nowadays, the applications of the texture analysis in image classification spread abroad. Combined with wavelet multi-resolution analysis or support vector machine statistical learning theory, texture analysis could improve the quality of classification increasingly. In this paper, we focus on the land cover for the Three Gorges reservoir using remote sensing data SPOT-5, a new classification method, wavelet-SVM classifier based on texture features, is employed for this study. Compare to the traditional maximum likelihood classifier and SVM classifier only use spectrum feature, this method produces more accurate classification results. According to the real environment of the Three Gorges reservoir land cover, a best texture group is selected from several texture features. Decompose the image at different levels, which is one of the main advantage of wavelet, and then compute the texture features in every sub-image, and the next step is eliminating the redundant, every texture features are centralized on the first principal components using principal component analysis. Finally, with the first principal components inputted, we can get the classification result using SVM in every decomposition scale, but what the problem we couldn't overlook is how to select the best SVM parameters. So an iterative rule based on the classification accuracy is induced, the more accuracy, the proper parameters.

  18. Effects of ground cover from branches of arboreal species on weed growth and maize yield

    Directory of Open Access Journals (Sweden)

    Paulo Sérgio Lima e Silva

    Full Text Available ABSTRACTCultivating maize under systems of alley cropping results in improvements to the soil, a reduction in weeds and an increase in yield. Studies using ground cover from tree shoots produce similar results. The aim of this study was to evaluate the effects on weed growth and maize yield of ground cover made up of 30 t ha-1 (fresh matter of branches from the tree species: neem (Azadirachta indica A. Juss, gliricidia [Gliricidia sepium(Jacq. Kunth ex Walp.], leucaena [Leucaena leucocephala (Lam. de Wit.] and sabiá (Mimosa caesalpiniifolia Benth.. Two treatment groups (cultivars and weed control were evaluated. The cultivars AG 1041 and AL Bandeirantes were subjected to the following treatments: no hoeing, double hoeing, and ground a cover of branches of the above species when sowing the maize. A randomised block design was used with split lots (cultivars in the lots and ten replications. The cultivars did not differ for green ear or grain yield. Double hoeing was more effective than ground cover at reducing the growth of weeds. However, both weeding and ground cover resulted in similar yields for green ears and grain, which were greater than those obtained with the unweeded maize.

  19. A comparative study on manifold learning of hyperspectral data for land cover classification

    Science.gov (United States)

    Ozturk, Ceyda Nur; Bilgin, Gokhan

    2015-03-01

    This paper focuses on the land cover classification problem by employing a number of manifold learning algorithms in the feature extraction phase, then by running single and ensemble of classifiers in the modeling phase. Manifolds are learned on training samples selected randomly within available data, while the transformation of the remaining test samples is realized for linear and nonlinear methods via the learnt mappings and a radial-basis function neural network based interpolation method, respectively. The classification accuracies of the original data and the embedded manifolds are investigated with several classifiers. Experimental results on a 200-band hyperspectral image indicated that support vector machine was the best classifier for most of the methods, being nearly as accurate as the best classification rate of the original data. Furthermore, our modified version of random subspace classifier could even outperform the classification accuracy of the original data for local Fisher's discriminant analysis method despite of a considerable decrease in the extrinsic dimension.

  20. Land cover data from Landsat single-date archive imagery: an integrated classification approach

    Science.gov (United States)

    Bajocco, Sofia; Ceccarelli, Tomaso; Rinaldo, Simone; De Angelis, Antonella; Salvati, Luca; Perini, Luigi

    2012-10-01

    The analysis of land cover dynamics provides insight into many environmental problems. However, there are few data sources which can be used to derive consistent time series, remote sensing being one of the most valuable ones. Due to their multi-temporal and spatial coverage needs, such analysis is usually based on large land cover datasets, which requires automated, objective and repeatable procedures. The USGS Landsat archives provide free access to multispectral, high-resolution remotely sensed data starting from the mid-eighties; in many cases, however, only single date images are available. This paper suggests an objective approach for generating land cover information from 30m resolution and single date Landsat archive satellite imagery. A procedure was developed integrating pixel-based and object-oriented classifiers, which consists of the following basic steps: i) pre-processing of the satellite image, including radiance and reflectance calibration, texture analysis and derivation of vegetation indices, ii) segmentation of the pre-processed image, iii) its classification integrating both radiometric and textural properties. The integrated procedure was tested for an area in Sardinia Region, Italy, and compared with a purely pixel-based one. Results demonstrated that a better overall accuracy, evaluated against the available land cover cartography, was obtained with the integrated (86%) compared to the pixel-based classification (68%) at the first CORINE Land Cover level. The proposed methodology needs to be further tested for evaluating its trasferability in time (constructing comparable land cover time series) and space (for covering larger areas).

  1. Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

    Science.gov (United States)

    Elhag, Mohamed; Boteva, Silvena

    2016-10-01

    Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

  2. The effect of atmospheric and topographic correction methods on land cover classification accuracy

    Science.gov (United States)

    Vanonckelen, Steven; Lhermitte, Stefaan; Van Rompaey, Anton

    2013-10-01

    Mapping of vegetation in mountain areas based on remote sensing is obstructed by atmospheric and topographic distortions. A variety of atmospheric and topographic correction methods has been proposed to minimize atmospheric and topographic effects and should in principle lead to a better land cover classification. Only a limited number of atmospheric and topographic combinations has been tested and the effect on class accuracy and on different illumination conditions is not yet researched extensively. The purpose of this study was to evaluate the effect of coupled correction methods on land cover classification accuracy. Therefore, all combinations of three atmospheric (no atmospheric correction, dark object subtraction and correction based on transmittance functions) and five topographic corrections (no topographic correction, band ratioing, cosine correction, pixel-based Minnaert and pixel-based C-correction) were applied on two acquisitions (2009 and 2010) of a Landsat image in the Romanian Carpathian mountains. The accuracies of the fifteen resulting land cover maps were evaluated statistically based on two validation sets: a random validation set and a validation subset containing pixels present in the difference area between the uncorrected classification and one of the fourteen corrected classifications. New insights into the differences in classification accuracy were obtained. First, results showed that all corrected images resulted in higher overall classification accuracies than the uncorrected images. The highest accuracy for the full validation set was achieved after combination of an atmospheric correction based on transmittance functions and a pixel-based Minnaert topographic correction. Secondly, class accuracies of especially the coniferous and mixed forest classes were enhanced after correction. There was only a minor improvement for the other land cover classes (broadleaved forest, bare soil, grass and water). This was explained by the position

  3. Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

    Directory of Open Access Journals (Sweden)

    Shezhou Luo

    2015-12-01

    Full Text Available Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging and CASI (Compact Airborne Spectrographic Imager hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity and CASI data (48 bands with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM and maximum likelihood (MLC classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions: (1 the layer stacking fusion data; (2 the PCA fusion data; (3 the LiDAR data alone; and (4 the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964 using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the

  4. A review of supervised object-based land-cover image classification

    Science.gov (United States)

    Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

    2017-08-01

    Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial

  5. Classification based on pruning and double covered rule sets for the internet of things applications.

    Science.gov (United States)

    Li, Shasha; Zhou, Zhongmei; Wang, Weiping

    2014-01-01

    The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy.

  6. Analysis and Evaluation of IKONOS Image Fusion Algorithm Based on Land Cover Classification

    Institute of Scientific and Technical Information of China (English)

    Xia; JING; Yan; BAO

    2015-01-01

    Different fusion algorithm has its own advantages and limitations,so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object images was also depended upon the sensor types and special research purposes. Firstly,five fusion methods,i. e. IHS,Brovey,PCA,SFIM and Gram-Schmidt,were briefly described in the paper. And then visual judgment and quantitative statistical parameters were used to assess the five algorithms. Finally,in order to determine which one is the best suitable fusion method for land cover classification of IKONOS image,the maximum likelihood classification( MLC) was applied using the above five fusion images. The results showed that the fusion effect of SFIM transform and Gram-Schmidt transform were better than the other three image fusion methods in spatial details improvement and spectral information fidelity,and Gram-Schmidt technique was superior to SFIM transform in the aspect of expressing image details. The classification accuracy of the fused image using Gram-Schmidt and SFIM algorithms was higher than that of the other three image fusion methods,and the overall accuracy was greater than 98%. The IHS-fused image classification accuracy was the lowest,the overall accuracy and kappa coefficient were 83. 14% and 0. 76,respectively. Thus the IKONOS fusion images obtained by the Gram-Schmidt and SFIM were better for improving the land cover classification accuracy.

  7. Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

    Directory of Open Access Journals (Sweden)

    Fanjie Kong

    2016-09-01

    Full Text Available Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase.

  8. Forested land cover classification on the Cumberland Plateau, Jackson County, Alabama: a comparison of Landsat ETM+ and SPOT5 images

    Science.gov (United States)

    Yong Wang; Shanta Parajuli; Callie Schweitzer; Glendon Smalley; Dawn Lemke; Wubishet Tadesse; Xiongwen Chen

    2010-01-01

    Forest cover classifications focus on the overall growth form (physiognomy) of the community, dominant vegetation, and species composition of the existing forest. Accurately classifying the forest cover type is important for forest inventory and silviculture. We compared classification accuracy based on Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and Satellite...

  9. Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

    Science.gov (United States)

    Zhu, Zhe; Gallant, Alisa L.; Woodcock, Curtis E.; Pengra, Bruce; Olofsson, Pontus; Loveland, Thomas R.; Jin, Suming; Dahal, Devendra; Yang, Limin; Auch, Roger F.

    2016-12-01

    The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. Training data were selected from map products of the U.S. Geological Survey's Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening "Function of mask" (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow

  10. GENERATION OF 2D LAND COVER MAPS FOR URBAN AREAS USING DECISION TREE CLASSIFICATION

    OpenAIRE

    J. Höhle

    2014-01-01

    A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a ...

  11. Environmental impact classification with fuzzy sets for urban land cover from satellite remote sensing data

    Science.gov (United States)

    Zoran, Maria A.; Nicolae, Doina N.; Talianu, Camelia

    2004-10-01

    Urban area is a mosaic of complex, interacting ecosystems, rich natural resources and socio-economic activity. Dramatic changes in urban's land cover are due to natural and anthropogenic causes. A scientific management system for protection, conservation and restoration must be based on reliable information on bio-geophysical and geomorphologic, dynamics processes, and climatic change effects. Synergetic use of quasi-simultaneously acquired multi-sensor data may therefore allow for a better approach of change detection and environmental impact classification and assessment in urban area. It is difficult to quantify the environmental impacts of human and industrial activities in urban areas. There are often many different indicators than can conflict with each other, frequently important observations are lacking, and potentially valuable information may non-quantitative in nature. Fuzzy set theory offers a modern methodology for dealing with these problems and provides useful approach to difficult classification problems for satellite remote sensing data. This paper describes how fuzzy logic can be applied to analysis of environmental impacts for urban land cover. Based on classified Landsat TM, SPOT images and SAR ERS-1 for Bucharest area, Romania, it was performed a land cover classification and subsequent environmental impact analysis.

  12. Inter-annual stability of land cover classification: Explorations and improvements

    Science.gov (United States)

    Abercrombie, Stewart Parker

    Land cover information is a key input to many earth system models, and thus accurate and consistent land cover maps are critically important to global change science. However, existing global land cover products show unrealistically high levels of year- to-year change. This thesis explores methods to improve accuracies for global land cover classifications, with a focus on reducing spurious year-to-year variation in results derived from MODIS data. In the first part of this thesis I use clustering to identify spectrally distinct sub-groupings within defined land cover classes, and assess the spectral separability of the resulting sub-classes. Many of the sub-classes are difficult to separate due to a high degree of overlap in spectral space. In the second part of this thesis, I examine two methods to reduce year-to-year variation in classification labels. First, I evaluate a technique to construct training data for a per-pixel supervised classification algorithm by combining multiple years of spectral measurements. The resulting classifier achieves higher accuracy and lower levels of year-to-year change than a reference classifier trained using a single year of data. Second, I use a spatio-temporal Markov Random Field (MRF) model to post-process the predictions of a per-pixel classifier. The MRF framework reduces spurious label change to a level comparable to that achieved by a post-hoc heuristic stabilization technique. The timing of label change in the MRF processed maps better matched disturbance events in a reference data, whereas the heuristic stabilization results in label changes that lag several years behind disturbance events.

  13. Estimation of snow cover distribution in Beas basin, Indian Himalaya using satellite data and ground measurements

    Indian Academy of Sciences (India)

    H S Negi; A V Kulkarni; B S Semwal

    2009-10-01

    In the present paper,a methodology has been developed for the mapping of snow cover in Beas basin,Indian Himalaya using AWiFS (IRS-P6)satellite data.The complexities in the mapping of snow cover in the study area are snow under vegetation,contaminated snow and patchy snow. To overcome these problems,field measurements using spectroradiometer were carried out and reflectance/snow indices trend were studied.By evaluation and validation of different topographic correction models,it was observed that,the normalized difference snow index (NDSI)values remain constant with the variations in slope and aspect and thus NDSI can take care of topography effects.Different snow cover mapping methods using snow indices are compared to find the suitable mapping technique.The proposed methodology for snow cover mapping uses the NDSI (estimated using planetary re flectance),NIR band reflectance and forest/vegetation cover information.The satellite estimated snow or non-snow pixel information using proposed methodology was validated with the snow cover information collected at three observatory locations and it was found that the algorithm classify all the sample points correctly,once that pixel is cloud free.The snow cover distribution was estimated using one year (2004 –05)cloud free satellite data and good correlation was observed between increase/decrease areal extent of seasonal snow cover and ground observed fresh snowfall and standing snow data.

  14. Computer implemented land cover classification using LANDSAT MSS digital data: A cooperative research project between the National Park Service and NASA. 3: Vegetation and other land cover analysis of Shenandoah National Park

    Science.gov (United States)

    Cibula, W. G.

    1981-01-01

    Four LANDSAT frames, each corresponding to one of the four seasons were spectrally classified and processed using NASA-developed computer programs. One data set was selected or two or more data sets were marged to improve surface cover classifications. Selected areas representing each spectral class were chosen and transferred to USGS 1:62,500 topographic maps for field use. Ground truth data were gathered to verify the accuracy of the classifications. Acreages were computed for each of the land cover types. The application of elevational data to seasonal LANDSAT frames resulted in the separation of high elevation meadows (both with and without recently emergent perennial vegetation) as well as areas in oak forests which have an evergreen understory as opposed to other areas which do not.

  15. Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

    Science.gov (United States)

    Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

    2016-06-01

    The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

  16. PERFORMANCE EVALUATION OF DISTANCE MEASURES IN PROPOSED FUZZY TEXTURE MODEL FOR LAND COVER CLASSIFICATION OF REMOTELY SENSED IMAGE

    Directory of Open Access Journals (Sweden)

    S. Jenicka

    2014-04-01

    Full Text Available Land cover classification is a vital application area in satellite image processing domain. Texture is a useful feature in land cover classification. The classification accuracy obtained always depends on the effectiveness of the texture model, distance measure and classification algorithm used. In this work, texture features are extracted using the proposed multivariate descriptor, MFTM/MVAR that uses Multivariate Fuzzy Texture Model (MFTM supplemented with Multivariate Variance (MVAR. The K_Nearest Neighbour (KNN algorithm is used for classification due to its simplicity coupled with efficiency. The distance measures such as Log likelihood, Manhattan, Chi squared, Kullback Leibler and Bhattacharyya were used and the experiments were conducted on IRS P6 LISS-IV data. The classified images were evaluated based on error matrix, classification accuracy and Kappa statistics. From the experiments, it is found that log likelihood distance with MFTM/MVAR descriptor and KNN classifier gives 95.29% classification accuracy.

  17. Fractional Vegetation Cover of East African Wetlands Observed on Ground and from Space

    Science.gov (United States)

    Schmidt, M.; Amler, E.; Guerschmann, J. P.; Scarth, P.; Behn, K.; Thonfeld, F.

    2016-08-01

    Wetlands are important ecosystems providing numerous ecosystem services. They are of particular importance to communities in East Africa where agriculture is the most important economic sector and where food availability to households critical. During an intensive field campaign in the dry season of 2013 were Fractional Vegetation Cover (FVC) measurements, botanical vegetation cover and vegetation structure estimates acquired in three wetland test sites within the East African region. FVC cover data were collated in three strata: ground layer, midstorey and overstorey (woody vegetation greater than 2 m). Fractional cover estimates for the green and no-green vegetative fraction were calculated for Landsat MODIS imagery. These FVC data products were evaluated a) with FVC field data and b) relative to each other for their usability in the East African region. First results show some promise for further studies.

  18. Ground cover influence on evaporation and stable water isotopes in soil water

    Science.gov (United States)

    Magdalena Warter, Maria; Jiménez-Rodríguez, Cesar D.; Coenders-Gerrits, Miriam; Teuling, Adriaan J. Ryan

    2017-04-01

    Forest ecosystems are characterized by complex structures which influence hydrological processes such as evaporation. The vertical stratification of the forest modifies the effect of the evaporation process due to the composition and local distribution of species within the forest. The evaluation of it will improve the understanding of evaporation in forest ecosystems. To determine the influence of forest understory on the fractionation front, four ground cover types were selected from the Speulderbos forest in the Netherlands. The native species of Thamariskmoss (Thuidium thamariscinum), Rough Stalked Feathermoss (Brachythecium rutabulum), and Haircapmoss (Polytrichum commune) as well as one type of litter made up of Douglas-Fir needles (Pseudotsuga menziesii) were used to analyse the rate of evaporation and changes on the isotopic concentration of the soil water on an in-situ basis in a controlled environment. Over a period of 4 weeks soil water content and atmospheric conditions were continuously measured, while the rainfall simulations were performed with different amounts and timings. The reference water added to the boxes keeps a stable composition along the trial period with a δ ^2H value of -42.59±1.15 \\permil} and δ 18O of -6.01±0.21 \\permil}. The evaporation front in the four ground covers is located between 5 and 10 cm depth and deuterium excess values are bigger than 5 \\permil. The litter layer of Douglas-Fir needles is the cover with higher fractionation in respect to the added water at 10 cm depth (δ ^2H: -29.79 \\permil), while the Haircapmoss keeps the lower fractionation rate at 5 cm and 10 cm (δ ^2H: -33.62 and δ ^2H: -35.34 \\permil). The differences showed by the soil water beneath the different ground covers depict the influence of ground cover on fractionation rates of the soil water, underlining the importance of the spatial heterogeneity of the evaporation front in the first 15 cm of soil.

  19. An ensemble classification approach for improved Land use/cover change detection

    Science.gov (United States)

    Chellasamy, M.; Ferré, T. P. A.; Humlekrog Greve, M.; Larsen, R.; Chinnasamy, U.

    2014-11-01

    Change Detection (CD) methods based on post-classification comparison approaches are claimed to provide potentially reliable results. They are considered to be most obvious quantitative method in the analysis of Land Use Land Cover (LULC) changes which provides from - to change information. But, the performance of post-classification comparison approaches highly depends on the accuracy of classification of individual images used for comparison. Hence, we present a classification approach that produce accurate classified results which aids to obtain improved change detection results. Machine learning is a part of broader framework in change detection, where neural networks have drawn much attention. Neural network algorithms adaptively estimate continuous functions from input data without mathematical representation of output dependence on input. A common practice for classification is to use Multi-Layer-Perceptron (MLP) neural network with backpropogation learning algorithm for prediction. To increase the ability of learning and prediction, multiple inputs (spectral, texture, topography, and multi-temporal information) are generally stacked to incorporate diversity of information. On the other hand literatures claims backpropagation algorithm to exhibit weak and unstable learning in use of multiple inputs, while dealing with complex datasets characterized by mixed uncertainty levels. To address the problem of learning complex information, we propose an ensemble classification technique that incorporates multiple inputs for classification unlike traditional stacking of multiple input data. In this paper, we present an Endorsement Theory based ensemble classification that integrates multiple information, in terms of prediction probabilities, to produce final classification results. Three different input datasets are used in this study: spectral, texture and indices, from SPOT-4 multispectral imagery captured on 1998 and 2003. Each SPOT image is classified

  20. Exploring diversity in ensemble classification: Applications in large area land cover mapping

    Science.gov (United States)

    Mellor, Andrew; Boukir, Samia

    2017-07-01

    Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area

  1. Classification of debris-covered glaciers and rock glaciers in the Andes of central Chile

    Science.gov (United States)

    Janke, Jason R.; Bellisario, Antonio C.; Ferrando, Francisco A.

    2015-07-01

    In the Dry Andes of Chile (17 to 35° S), debris-covered glaciers and rock glaciers are differentiated from true glaciers based on the percentage of surface debris cover, thickness of surface debris, and ice content. Internal ice is preserved by an insulating cover of thick debris, which acts as a storage reservoir to release water during the summer and early fall. These landforms are more numerous than glaciers in the central Andes; however, the existing legislation only recognizes uncovered or semicovered glaciers as a water resource. Glaciers, debris-covered glaciers, and rock glaciers are being altered or removed by mining operations to extract valuable minerals from the mountains. In addition, agricultural expansion and population growth in this region have placed additional demands on water resources. In a warmer climate, as glaciers recede and seasonal water availability becomes condensed over the course of a snowmelt season, rock glaciers and debris-covered glaciers contribute a larger component of base flow to rivers and streams. As a result, identifying and locating these features to implement sustainable regional planning for water resources is important. The objective of this study is to develop a classification system to identify debris-covered glaciers and rock glaciers based on the interpretation of satellite imagery and aerial photographs. The classification system is linked to field observations and measurements of ice content. Debris-covered glaciers have three subclasses: surface coverage of semi (class 1) and fully covered (class 2) glaciers differentiates the first two forms, whereas debris thickness is critical for class 3 when glaciers become buried with more than 3 m of surface debris. Based on field observations, the amount of ice decreases from more than 85%, to 65-85%, to 45-65% for semi, fully, and buried debris-covered glaciers, respectively. Rock glaciers are characterized by three stages. Class 4 rock glaciers have pronounced

  2. Land cover classification based on object-oriented with airborne lidar and high spectral resolution remote sensing image

    Science.gov (United States)

    Li, Fangfang; Liu, Zhengjun; Xu, Qiangqiang; Ren, Haicheng; Zhou, Xingyu; Yuan, Yonghua

    2016-10-01

    In order to improve land cover classification accuracy of the coastal tidal wetland area in Dafeng, this paper take advantage of hyper-spectral remote sensing image with high spatial resolution airborne Lidar data. The introduction of feature extraction, band selection and nDSM models to reduce the dimension of the original image. After segmentation process that combining FNEA segmentation with spectral differences segmentation method, the paper finalize the study area through the establishment of the rule set classification of land cover classification. The results show that the proposed classification for land cover classification accuracy has improved significantly, including housing, shadow, water, vegetation classification of high precision. That is to say that the method can meet the needs of land cover classification of the coastal tidal wetland area in Dafeng. This innovation is the introduction of principal component analysis, and the use of characteristic index, shape and characteristics of various types of data extraction nDSM feature to improve the accuracy and speed of land cover classification.

  3. Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks

    Directory of Open Access Journals (Sweden)

    Qi Lv

    2015-01-01

    Full Text Available Land use and land cover (LULC mapping in urban areas is one of the core applications in remote sensing, and it plays an important role in modern urban planning and management. Deep learning is springing up in the field of machine learning recently. By mimicking the hierarchical structure of the human brain, deep learning can gradually extract features from lower level to higher level. The Deep Belief Networks (DBN model is a widely investigated and deployed deep learning architecture. It combines the advantages of unsupervised and supervised learning and can archive good classification performance. This study proposes a classification approach based on the DBN model for detailed urban mapping using polarimetric synthetic aperture radar (PolSAR data. Through the DBN model, effective contextual mapping features can be automatically extracted from the PolSAR data to improve the classification performance. Two-date high-resolution RADARSAT-2 PolSAR data over the Great Toronto Area were used for evaluation. Comparisons with the support vector machine (SVM, conventional neural networks (NN, and stochastic Expectation-Maximization (SEM were conducted to assess the potential of the DBN-based classification approach. Experimental results show that the DBN-based method outperforms three other approaches and produces homogenous mapping results with preserved shape details.

  4. A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

    Directory of Open Access Journals (Sweden)

    Salman Qadri

    2016-01-01

    Full Text Available The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI. Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class. By implementing a cross validation method (80-20, we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively.

  5. Assessing post-fire ground cover in Mediterranean shrublands with field spectrometry and digital photography

    Science.gov (United States)

    Montorio Llovería, Raquel; Pérez-Cabello, Fernando; García-Martín, Alberto

    2016-09-01

    Fire severity can be assessed by identifying and quantifying the fractional abundance of post-fire ground cover types, an approach with great capacity to predict ecosystem response. Focused on shrubland formations of Mediterranean-type ecosystems, three burned areas (Ibieca and Zuera wildfires and Peñaflor experimental fire) were sampled in the summers of 2006 and 2007. Two different ground measurements were made for each of the 356 plots: (i) 3-band high spatial resolution photography (HSRP) and (ii) the hemispherical-conical reflectance factor (HCRF) in the visible to near-infrared spectral range (VNIR, 400-900 nm). Stepwise multiple lineal regression (SMLR) models were fitted to spectral variables (HCRF, first derivative spectra or FDS, and four absorption indices) to estimate the fractional cover of seven post-fire ground cover types (vegetation and soil - unburned and charred components - and ash - char and ash, individually and as a combined category). Models were developed and validated at the Peñaflor site (training, n = 217; validation, n = 88) and applied to the samples from the Ibieca and Zuera sites (n = 51). The best results were observed for the abundance estimations of green vegetation (Radj.20.70-0.90), unburned soil (Radj.20.40-0.75), and the combination of ashes (Radj.20.65-0.80). In comparison of spectral data, FDS outperforms reflectance or absorption data because of its higher accuracy levels and, importantly, its greater capacity to yield generalizable models. Future efforts should be made to improve the estimation of intermediate severity levels and upscaling the developed models. In the context of fire severity assessment, our study demonstrates the potential of hyperspectral data to estimate in a quick and objective manner post-fire ground cover fractions and thus provide valuable information to guide management responses.

  6. Is ground cover vegetation an effective biological control enhancement strategy against olive pests?

    Directory of Open Access Journals (Sweden)

    Daniel Paredes

    Full Text Available Ground cover vegetation is often added or allowed to generate to promote conservation biological control, especially in perennial crops. Nevertheless, there is inconsistent evidence of its effectiveness, with studies reporting positive, nil or negative effects on pest control. This might arise from differences between studies at the local scale (e.g. orchard management and land use history, the landscape context (e.g. presence of patches of natural or semi-natural vegetation near the focal orchard, or regional factors, particularly climate in the year of the study. Here we present the findings from a long-term regional monitoring program conducted on four pest species (Bactrocera oleae, Prays oleae, Euphyllura olivina, Saissetia oleae in 2,528 olive groves in Andalusia (Spain from 2006 to 2012. Generalized linear mixed effect models were used to analyze the effect of ground cover on different response variables related to pest abundance, while accounting for variability at the local, landscape and regional scales. There were small and inconsistent effects of ground cover on the abundance of pests whilst local, landscape and regional variability explained a large proportion of the variability in pest response variables. This highlights the importance of local and landscape-related variables in biological control and the potential effects that might emerge from their interaction with practices, such as groundcover vegetation, implemented to promote natural enemy activity. The study points to perennial vegetation close to the focal crop as a promising alternative strategy for conservation biological control that should receive more attention.

  7. Is ground cover vegetation an effective biological control enhancement strategy against olive pests?

    Science.gov (United States)

    Paredes, Daniel; Cayuela, Luis; Gurr, Geoff M; Campos, Mercedes

    2015-01-01

    Ground cover vegetation is often added or allowed to generate to promote conservation biological control, especially in perennial crops. Nevertheless, there is inconsistent evidence of its effectiveness, with studies reporting positive, nil or negative effects on pest control. This might arise from differences between studies at the local scale (e.g. orchard management and land use history), the landscape context (e.g. presence of patches of natural or semi-natural vegetation near the focal orchard), or regional factors, particularly climate in the year of the study. Here we present the findings from a long-term regional monitoring program conducted on four pest species (Bactrocera oleae, Prays oleae, Euphyllura olivina, Saissetia oleae) in 2,528 olive groves in Andalusia (Spain) from 2006 to 2012. Generalized linear mixed effect models were used to analyze the effect of ground cover on different response variables related to pest abundance, while accounting for variability at the local, landscape and regional scales. There were small and inconsistent effects of ground cover on the abundance of pests whilst local, landscape and regional variability explained a large proportion of the variability in pest response variables. This highlights the importance of local and landscape-related variables in biological control and the potential effects that might emerge from their interaction with practices, such as groundcover vegetation, implemented to promote natural enemy activity. The study points to perennial vegetation close to the focal crop as a promising alternative strategy for conservation biological control that should receive more attention.

  8. Covering of heating load of object by using ground heat as a renewable energy source

    Directory of Open Access Journals (Sweden)

    Čenejac Aleksandra R.

    2012-01-01

    Full Text Available Rational use of energy, improving energy performance of buildings and use of renewable energy sources are the most important measures for reducing consumption of non-renewable primary energy (solid, liquid, and gaseous fuels, environmental protection and for the future sustainable development of mankind. In the total primary energy consumption great part is related to building industry, for heating spaces in which people stay and live. Renewable energy sources (RES present natural resources and they are one of the alternatives that allow obtaining heat for heating buildings, and by that they provide a significant contribution to the energy balance of a country. This paper analyzes the participation of ground source as RES, when the vertical (the probe in the ground and horizontal (registry in the ground heat exchangers are used for covering heating load of the building.

  9. Application of the probability-based covering algorithm model in text classification

    Institute of Scientific and Technical Information of China (English)

    ZHOU; Ying

    2009-01-01

    The probability-based covering algorithm(PBCA)is a new algorithm based on probability distribution.It decides,by voting,the class of the tested samples on the border of the coverage area,based on the probability of training samples.When using the original covering algorithm(CA),many tested samples that are located on the border of the coverage cannot be classified by the spherical neighborhood gained.The network structure of PBCA is a mixed structure composed of both a feed-forward network and a feedback network.By using this method of adding some heterogeneous samples and enlarging the coverage radius,it is possible to decrease the number of rejected samples and improve the rate of recognition accuracy.Relevant computer experiments indicate that the algorithm improves the study precision and achieves reasonably good results in text classification.

  10. Phytomass and soil organic carbon inventories related to land cover classification and periglacial features in Taimyr Peninsula,Siberia

    Science.gov (United States)

    Ramage, J. L.; Hugelius, G.; Kuhry, P.; Palmtag, J.; Lashchinskiy, N.

    2012-12-01

    The predicted increase in atmospheric temperatures is expected to affect the turnover of soil organic carbon in permafrost soils through modifications of the soil thermal regime. However, the tundra biome is formed of a mosaic of diverse landscape types with differing patterns of soil organic carbon storage and partitioning. Among these, differences in e.g. vegetation diversity and soil movements due to freeze-thaw processes are of main importance for assessing potential C remobilization under a changing climate. In this study, we described the storage of soil organic carbon (SOC) and the aboveground phytomass carbon in relation to geomorphology and periglacial features for two areas on Taymir Peninsula (Arctic Russia). An average of 29.5 kg C m2, calculated by upscaling with a land cover classification, is stored in the upper soil meter at these two study sites. The mean C phytomass storage amounts to ca.0.406 Kg C m2, or only 1.38 % of the total SOC storage. The topography, at different scales, plays an important role in the carbon partitioning. High amounts of soil organic carbon are found in highland areas and within the patterned ground features found in peatlands. The highest amounts of aboveground phytomass carbon are found in deciduous shrubs and moss layers. The large variability in carbon distribution within land cover types among the sites reveals the challenge of upscaling the carbon storage values over the Arctic and thus highlights the necessity to carry out detailed field inventories in this region.

  11. An Integrated Spatial and Spectral Approach to the Classification of Mediterranean Land Cover Types: the SSC Method.

    NARCIS (Netherlands)

    Jong, de S.M.; Hornstra, T.; Maas, H.G.

    2001-01-01

    Classification of remotely sensed images is often based on assigning classes on a pixel by pixel basis. Such a classification ignores often useful reflectance information in neighbouring pixels. Open types of natural land cover such as maquis and garrigue ecosystems as found in the Mediterranean reg

  12. Polarimetric SAR Data for Urban Land Cover Classification Using Finite Mixture Model

    Science.gov (United States)

    Mahdianpari, Masoud; Akbari, Vahid; Mohammadimanesh, Fariba; Alioghli Fazel, Mohammad

    2013-04-01

    Image classification techniques play an important role in automatic analysis of remote sensing data. This paper demonstrates the potential of polarimetric synthetic aperture radar (PolSAR) for urban land cover mapping using an unsupervised classification approach. Analysis of PolSAR images often shows that non-Gaussian models give better representation of the scattering vector statistics. Hence, processing algorithms based on non-Gaussian statistics should improve performance, compared to complex Gaussian distributions. Several distributions could be used to model SAR image texture with different spatial correlation properties and various degrees of inhomogeneity [1-3]. Statistical properties are widely used for image segmentation and land cover classification of PolSAR data. The pixel-based approaches cluster individual pixels through analysis of their statistical properties. Those methods work well on the relatively coarse spatial resolution images. But classification results based on pixelwise analysis demonstrate the pepper-salt effect of speckle in medium and high resolution applications such as urban area monitoring [4]. Therefore, the expected improvement of the classification results is hindered by the increase of textural differences within a class. In such situation, enhancement could be made through exploring the contextual correlation among pixels by Markov random field (MRF) models [4, 5]. The potential of MRF models to retrieve spatial contextual information is desired to improve the accuracy and reliability of image classification. Unsupervised contextual polarimetric SAR image segmentation is addressed by combining statistical modeling and spatial context within an MRF framework. We employ the stochastic expectation maximization (SEM) algorithm [6] to jointly perform clustering of the data and parameter estimation of the statistical distribution conditioned to each image cluster and the MRF model. This classification method is applied on medium

  13. Land Use / Land Cover Classification of kanniykumari Coast, Tamilnadu, India. Using Remote Sensing and Gis Techniques

    Directory of Open Access Journals (Sweden)

    Hajeeran Beevi.N,

    2015-07-01

    Full Text Available The land use/ land cover details of Kanniyakuamri coast which is Located in the southern part of Tamil Nadu (India is studied. Satellite imagery is used to identify the Land use/ Land cover status of the study area. The software like ERDAS and Arc GIS are used to demarcate the land use / Land cover features of Kanniyakuamari coast. Remote sensing and GIS provided consistent and accurate base line information than many of the conventional surveys employed for such a task. The total area of Kanniyakumari coast is 715 sq.km. The land use / land cover classes of the study area has been categorized into thirteen such as Plantation, Sandy area, Water logged area, Scrub forest, Crop Land, Water bodies, Land with scrub, Reserve forest, Land without Scrub, Salt area, Beach Ridge, Settlement and Fallow land on the basis NRSA Classifications. Among these categories, land with scrub land is predominantly found all over the study area, It is occupied about 336.36 sq.km (44.61 percent, Crop Land 273.82 sq.km(38.29 percent, water bodies lands sharing about 20.44 sq.km (2.85 percent , settlement occupied with 6.96 sq.km (0.97 percent, and Fallow land was occupied 13.98 sq.km ( 1.95 percent .

  14. Vegetation type classification and vegetation cover percentage estimation in urban green zone using pleiades imagery

    Science.gov (United States)

    Trisakti, Bambang

    2017-01-01

    Open green space in the urban area has aims to maintain the availability of land as a water catchment area, creating aspects of urban planning through a balance between the natural environment and the built environment that are useful for the public needs. Local governments have to make the green zone plan map and monitor the green space changes in their territory. Medium and high resolution satellite imageries have been widely utilized to map and monitor the changes of vegetation cover as an indicator of green space area. This paper describes the use of pleaides imagery to classify vegetation types and estimate vegetation cover percentage in the green zone. Vegetation cover was mapped using a combination of NDVI and blue band. Furthermore, vegetation types in the green space were classified using unsupervised and supervised (ISODATA and MLEN) methods. Vegetation types in the study area were divided into sparse vegetation, low-medium vegetation and medium-high vegetation. The classification accuracies were 97.9% and 98.9% for unsupervised and supervised method respectively. The vegetation cover percentage was determined by calculating the ratio between the vegetation type area and the green zone area. These information are useful to support green zone management activities.

  15. IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA

    Directory of Open Access Journals (Sweden)

    Jati Pratomo

    2016-10-01

    Full Text Available The usage of Unmanned Aerial Vehicle (UAV has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urban features are not normally distributed. There are advantages in using the Markov Random Field (MRF for urban land cover classification as it assumes that neighboring pixels have a higher probability to be classified in the same class rather than a different class. This research aimed to determine the impact of the smoothness (λ and the updating temperature (Tupd on the accuracy result (κ in MRF. We used a UAV VHIR sized 587 square meters, with six-centimetre resolution, taken in Bogor Regency, Indonesia. The result showed that the kappa value (κ increases proportionally with the smoothness (λ until it reaches the maximum (κ, then the value drops. The usage of higher (Tupd has resulted in better (κ although it also led to a higher Standard Deviations (SD. Using the most optimal parameter, MRF resulted in slightly higher (κ compared with MLC.

  16. Land Cover Classification for Polarimetric SAR Images Based on Mixture Models

    Directory of Open Access Journals (Sweden)

    Wei Gao

    2014-04-01

    Full Text Available In this paper, two mixture models are proposed for modeling heterogeneous regions in single-look and multi-look polarimetric SAR images, along with their corresponding maximum likelihood classifiers for land cover classification. The classical Gaussian and Wishart models are suitable for modeling scattering vectors and covariance matrices from homogeneous regions, while their performance deteriorates for regions that are heterogeneous. By comparison, the proposed mixture models reduce the modeling error by expressing the data distribution as a weighted sum of multiple component distributions. For single-look and multi-look polarimetric SAR data, complex Gaussian and complex Wishart components are adopted, respectively. Model parameters are determined by employing the expectation-maximization (EM algorithm. Two maximum likelihood classifiers are then constructed based on the proposed mixture models. These classifiers are assessed using polarimetric SAR images from the RADARSAT-2 sensor of the Canadian Space Agency (CSA, the AIRSAR sensor of the Jet Propulsion Laboratory (JPL and the EMISAR sensor of the Technical University of Denmark (DTU. Experiment results demonstrate that the new models fit heterogeneous regions preferably to the classical models and are especially appropriate for extremely heterogeneous regions, such as urban areas. The overall accuracy of land cover classification is also improved due to the more refined modeling.

  17. Attribution of local climate zones using a multitemporal land use/land cover classification scheme

    Science.gov (United States)

    Wicki, Andreas; Parlow, Eberhard

    2017-04-01

    Worldwide, the number of people living in an urban environment exceeds the rural population with increasing tendency. Especially in relation to global climate change, cities play a major role considering the impacts of extreme heat waves on the population. For urban planners, it is important to know which types of urban structures are beneficial for a comfortable urban climate and which actions can be taken to improve urban climate conditions. Therefore, it is essential to differ between not only urban and rural environments, but also between different levels of urban densification. To compare these built-up types within different cities worldwide, Stewart and Oke developed the concept of local climate zones (LCZ) defined by morphological characteristics. The original LCZ scheme often has considerable problems when adapted to European cities with historical city centers, including narrow streets and irregular patterns. In this study, a method to bridge the gap between a classical land use/land cover (LULC) classification and the LCZ scheme is presented. Multitemporal Landsat 8 data are used to create a high accuracy LULC map, which is linked to the LCZ by morphological parameters derived from a high-resolution digital surface model and cadastral data. A bijective combination of the different classification schemes could not be achieved completely due to overlapping threshold values and the spatially homogeneous distribution of morphological parameters, but the attribution of LCZ to the LULC classification was successful.

  18. DTM generation using land cover classification based on low density lidar data

    Science.gov (United States)

    Koma, Zsófia; Zlinszky, András

    2014-05-01

    While the point density of local LIDAR surveys continues to increase, most regional or national LIDAR campaigns are carried out with medium or low density, and have the main purpose of DTM generation. Many different point selection and filtering algorithms are already established. Depending on land cover and vegetation, some perform better than others, but no algorithm exists that works perfectly for all types of land cover. Therefore, our method applies several different DTM generation and filtering algorithms for different spatial units depending on their land cover and vegetation. Land cover and vegetation are mapped based on the original raw LIDAR dataset. Two discrete echo airborne LIDAR measurements were used, one with 1 point/m2 and a larger area with 0.4 point/m2 density. The datasets were used together for DTM generation after relative georeferencing by strip adjustment. We defined several land cover categories depending on how they influence vertical distribution of LIDAR points: buildings, waterways, grasslands, crop fields, wetlands, and forests. The study area was classified to these categories based on a decision tree algorithm using parameters calculated from the original LIDAR dataset (sigmaZ, reflectance, aspect, slope, echoratio, roughness), at resolution identical to the output DTM. For the points within spatial units belonging to each of these categories, we implemented different filtering and interpolation methods to select ground points. For buildings, roof and wall points were removed and the resulting gap filled by interpolated based on the neighbouring data. In forests we calculated a first smooth approximate surface based on minimum points every 10 meter cells. We calculated a residual value for every point of this surface in this class. Then we analysed the point cloud based on residuals value and made an optimum threshold which classified the datasets for non-ground and ground points. In wetlands and croplands, the point height range

  19. Applying object-based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification

    Science.gov (United States)

    Hsieh, Yi-Ta; Chen, Chaur-Tzuhn; Chen, Jan-Chang

    2017-01-01

    In general, considerable human and material resources are required for performing a forest inventory survey. Using remote sensing technologies to save forest inventory costs has thus become an important topic in forest inventory-related studies. Leica ADS-40 digital aerial photographs feature advantages such as high spatial resolution, high radiometric resolution, and a wealth of spectral information. As a result, they have been widely used to perform forest inventories. We classified ADS-40 digital aerial photographs according to the complex forest land cover types listed in the Fourth Forest Resource Survey in an effort to establish a classification method for categorizing ADS-40 digital aerial photographs. Subsequently, we classified the images using the knowledge-based classification method in combination with object-based analysis techniques, decision tree classification techniques, classification parameters such as object texture, shape, and spectral characteristics, a class-based classification method, and geographic information system mapping information. Finally, the results were compared with manually interpreted aerial photographs. Images were classified using a hierarchical classification method comprised of four classification levels (levels 1 to 4). The classification overall accuracy (OA) of levels 1 to 4 is within a range of 64.29% to 98.50%. The final result comparisons showed that the proposed classification method achieved an OA of 78.20% and a kappa coefficient of 0.7597. On the basis of the image classification results, classification errors occurred mostly in images of sunlit crowns because the image values for individual trees varied. Such a variance was caused by the crown structure and the incident angle of the sun. These errors lowered image classification accuracy and warrant further studies. This study corroborates the high feasibility for mapping complex forest land cover types using ADS-40 digital aerial photographs.

  20. Impact of the variability of the seasonal snow cover on the ground surface regimes in Hurd Peninsula (Livingston Island, Antarctic)

    Science.gov (United States)

    Nieuwendam, Alexandre; Ramos, Miguel; Vieira, Gonçalo

    2014-05-01

    Seasonally snow cover has a great impact on the thermal regime of the active layer and permafrost. Ground temperatures over a year are strongly affected by the timing, duration, thickness, structure and physical and thermal properties of snow cover. The purpose of this communication is to characterize the shallow ground thermal regimes, with special reference to the understanding of the influence snow cover in permafrost spatial distribution, in the ice-free areas of the north western part of Hurd Peninsula in the vicinity of the Spanish Antarctic Station "Juan Carlos I" and Bulgarian Antarctic Station "St. Kliment Ohridski". We have analyzed and ground temperatures as well as snow thickness data in four sites distributed along an altitudinal transect in Hurd Peninsula from 2007 to 2013: Nuevo Incinerador (25 m asl), Collado Ramos (110 m), Ohridski (140 m) and Reina Sofia Peak (275 m). At each study site, data loggers were installed for the monitoring of air temperatures (at 1.5 m high), ground temperatures (5, 20 and 40 cm depth) and for snow depth (2, 5, 10, 20, 40, 80 and 160 cm) at 4-hour intervals. The winter data suggests the existence of three types of seasonal stages regarding the ground surface thermal regime and the thickness of snow cover: (a) shallow snow cover with intense ground temperatures oscillations; (b) thick snow cover and low variations of soil temperatures; and (c) stability of ground temperatures. Ground thermal conditions are also conditioned by a strong variability. Winter data indicates that Nuevo Incinerador site experiences more often thicker snow cover with higher ground temperatures and absence of ground temperatures oscillations. Collado Ramos and Ohridski show frequent variations of snow cover thickness, alternating between shallow snow cover with high ground temperature fluctuation and thick snow cover and low ground temperature fluctuation. Reina Sofia in all the years has thick snow cover with little variations in soil

  1. An initial analysis of LANDSAT 4 Thematic Mapper data for the classification of agricultural, forested wetland, and urban land covers

    Science.gov (United States)

    Quattrochi, D. A.; Anderson, J. E.; Brannon, D. P.; Hill, C. L.

    1982-01-01

    An initial analysis of LANDSAT 4 thematic mapper (TM) data for the delineation and classification of agricultural, forested wetland, and urban land covers was conducted. A study area in Poinsett County, Arkansas was used to evaluate a classification of agricultural lands derived from multitemporal LANDSAT multispectral scanner (MSS) data in comparison with a classification of TM data for the same area. Data over Reelfoot Lake in northwestern Tennessee were utilized to evaluate the TM for delineating forested wetland species. A classification of the study area was assessed for accuracy in discriminating five forested wetland categories. Finally, the TM data were used to identify urban features within a small city. A computer generated classification of Union City, Tennessee was analyzed for accuracy in delineating urban land covers. An evaluation of digitally enhanced TM data using principal components analysis to facilitate photointerpretation of urban features was also performed.

  2. Mechanizing Weakly Ground Termination Proving of Term Rewriting Systems by Structural and Cover-Set Inductions

    Institute of Scientific and Technical Information of China (English)

    Su Feng

    2005-01-01

    The paper presents three formal proving methods for generalized weakly ground terminating property, i.e.,weakly terminating property in a restricted domain of a term rewriting system, one with structural induction, one with cover-set induction, and the third without induction, and describes their mechanization based on a meta-computation model for term rewriting systems-dynamic term rewriting calculus. The methods can be applied to non-terminating, nonconfluent and/or non-left-linear term rewriting systems. They can do "forward proving" by applying propositions in the proof, as well as "backward proving" by discovering lemmas during the proof.

  3. UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

    Science.gov (United States)

    Sturdivant, Emily; Lentz, Erika; Thieler, E. Robert; Farris, Amy; Weber, Kathryn; Remsen, David P.; Miner, Simon; Henderson, Rachel

    2017-01-01

    The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

  4. Improvement of Ground Truth Classification of Soviet Peaceful Nuclear Explosions

    Science.gov (United States)

    Mackey, K. G.; Fujita, K.; Bergman, E.

    2016-12-01

    From the 1960's through the late 1980's, the Soviet Union conducted 122 Peaceful Nuclear Explosions across its territory. These PNEs are now very important to the seismological community as so-called Ground Truth (GT) events. The PNE locations are widely distributed, thus GT0-1 locations, meaning that true location is known to within 1 km or better, are used as calibration events for developing seismic velocity models, model validation, seismic discrimination, etc. The nuclear monitoring/verification community generally utilizes published lists of PNE locations as known or verified GT events, though in reality there are errors and some PNEs are poorly located. We have determined or validated GT0-1 locations for 85 of the Soviet PNEs. Some PNEs published as GT1 or better also have larger errors. Our locations were determined using an integrated approach encompassing published open literature, analysis of satellite imagery and regional seismic data. We have visited and verified 10 PNE sites across Kazakhstan and Ukraine, allowing GPS coordinates to be obtained in the field.

  5. Enhanced figure-ground classification with background prior propagation.

    Science.gov (United States)

    Chen, Yisong; Chan, Antoni B

    2015-03-01

    We present an adaptive figure-ground segmentation algorithm that is capable of extracting foreground objects in a generic environment. Starting from an interactively assigned background mask, an initial background prior is defined and multiple soft-label partitions are generated from different foreground priors by progressive patch merging. These partitions are fused to produce a foreground probability map. The probability map is then binarized via threshold sweeping to create multiple hard-label candidates. A set of segmentation hypotheses is formed using different evaluation scores. From this set, the hypothesis with maximal local stability is propagated as the new background prior, and the segmentation process is repeated until convergence. Similarity voting is used to select a winner set, and the corresponding hypotheses are fused to yield the final segmentation result. Experiments indicate that our method performs at or above the current state-of-the-art on several data sets, with particular success on challenging scenes that contain irregular or multiple-connected foregrounds.

  6. Object-oriented land cover classification using HJ-1 remote sensing imagery

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The object-oriented information extraction technique was used to improve classification accuracy,and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolution.We used two key techniques:the selection of optimum image segmentation scale and the development of an appropriate object-oriented information extraction strategy.With the principle of minimizing merge cost of merging neighboring pixels/objects,we used spatial autocorrelation index Moran’s I and the variance index to select the optimum segmentation scale.The Nearest Neighborhood(NN) classifier based on sampling and a knowledge-based fuzzy classifier were used in the object-oriented information extraction strategy.In this classification step,feature optimization was used to improve information extraction accuracy using reduced data dimension.These two techniques were applied to land cover information extraction for Shanghai city using a HJ-1 CCD image.Results indicate that the information extraction accuracy of the object-oriented method was much higher than that of the pixel-based method.

  7. Testing Texture of VHR Panchromatic Data as a Feature of Land Cover Classification

    Science.gov (United States)

    Lewiński, Stanisław; Aleksandrowicz, Sebastian; Banaszkiewicz, Marek

    2015-04-01

    While it is well-known that texture can be used to classify very high resolution (VHR) data, the limits of its applicability have not been unequivocally specified. This study examines whether it is possible to divide satellite images into two classes associated with "low" and "high" texture values in the initial stage of processing VHR images. This approach can be effectively used in object-oriented classification. Based on the panchromatic channel of KOMPSAT-2 images from five areas of Europe, datasets with down-sampled pixel resolutions of 1, 2, 4, 8, and 16 m were prepared. These images were processed using different texture analysis techniques in order to discriminate between basic land cover classes. Results were assessed using the normalized feature space distance expressed by the Jeffries-Matusita distance. The best results were observed for images with the highest resolution processed by the Laplacian filter. Our research shows that a classification approach based on the idea of "low" and "high" textures can be effectively applied to panchromatic data with a resolution of 8 m or higher.

  8. Evidence of wintertime CO2 emission from snow-covered grounds in high latitudes

    Institute of Scientific and Technical Information of China (English)

    方精云; 唐艳鸿KOIZUMI; Hiroshi(Division; of; Plant; Ecology; National; Institute; of; Agro-Environmental; Sciences; Tsukuba; 305; Japan)BEKKU; Yukiko(National; Polar; Institute; Tokyo; 192; Japan)

    1999-01-01

    In order to measure CO2 flux in wintertime arctic ecosystems, CO2 gas was sampled from various snow-covered grounds by using a closed chamber method during the First China Arctic Scientific Expedition from March to May in 1995. The CO2 gas samples were measured by using an infra-red analyzer (IRGA). The results showed that (ⅰ) CO2 emission was detected from all kinds of the snow-covered grounds, which provides direct evidence that the arctic tundra is functioning as a source of atmospheric CO2; (ⅱ) CO2 release was also detected from the permanent ice profile and icecap, and (ⅲ) CO2 evolution from terrestrial ecosystems in higher latitudes increased with an increase of surface temperature in accordance with the exponential function. This indicates a close coincidence with that under normal temperature conditions, and provides a useful method for predicting change in CO2 flux in the arctic ecosystems with the global climate change.

  9. An improved MTI filter for ground clutter reduction in UAV classification

    Science.gov (United States)

    Wan, Fangyuan; Liu, Qinglai; Wang, Chen; Guo, Xin; Lin, Zhiping

    2017-06-01

    In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly been used in many civil applications. However, they also pose a significant threat in restricted zones. Radar can be used to detect and discriminate UAVs. Due to the low flying altitude of the UAVs, it is found that the radar signals also include some unwanted echoes, reflected by building, ground, trees and grasses etc. Consequently, it has not been possible to get the clean UAVs characteristics for further classification. In this paper, the MTI filter is applied to cancel the ground clutter and based this, an improved MTI filter is further proposed. Compared with the traditional MTI filter, the improved one significantly enhances ground clutter rejection capability while maintaining most of the target power. As the result, the cleaner UAVs classification characteristics can be obtained. The effectiveness of the proposed method has been verified by an experimental CW radar dataset, collected from a helicopter UAV.

  10. Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon.

    Science.gov (United States)

    Kahya, Oguzhan; Bayram, Bulent; Reis, Selcuk

    2010-01-01

    The main objective of this study is to generate a knowledge base which is composed of user-defined variables and included raster imagery, vector coverage, spatial models, external programs, and simple scalars and to develop an expert classification using Landsat 7 (ETM+) imagery for land cover classification in a part of Trabzon city. Expert systems allow for the integration of remote-sensed data with other sources of geo-referenced information such as land use data, spatial texture, and digital elevation model to obtain greater classification accuracy. Logical decision rules are used with the various datasets to assign class values for each pixel. Expert system is very suitable for the work of image interpretation as a powerful means of information integration. Landsat ETM data acquired in the year 2000 were initially classified into seven classes for land cover using a maximum likelihood decision rule. An expert system was constructed to perform post-classification sorting of the initial land cover classification using additional spatial datasets such as land use data. The overall accuracy of expert classification was 95.80%. Individual class accuracy ranged from 75% to 100% for each class.

  11. Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project

    Directory of Open Access Journals (Sweden)

    Recep Gundogan

    2008-02-01

    Full Text Available The satellite imagery has been effectively utilized for classifying land covertypes and detecting land cover conditions. The Advanced Spaceborne Thermal Emissionand Reflection Radiometer (ASTER sensor imagery has been widely used in classificationprocess of land cover. However, atmospheric corrections have to be made by preprocessingsatellite sensor imagery since the electromagnetic radiation signals received by the satellitesensors can be scattered and absorbed by the atmospheric gases and aerosols. In this study,an ASTER sensor imagery, which was converted into top-of-atmosphere reflectance(TOA, was used to classify the land use/cover types, according to COoRdination ofINformation on the Environment (CORINE land cover nomenclature, for an arearepresenting the heterogonous characteristics of eastern Mediterranean regions inKahramanmaras, Turkey. The results indicated that using the surface reflectance data ofASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of83.2% and 0.79, respectively and low-cost cover mapping as a part of inventory forCORINE Land Cover Project.

  12. Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

    Science.gov (United States)

    Paneque-Gálvez, Jaime; Mas, Jean-François; Moré, Gerard; Cristóbal, Jordi; Orta-Martínez, Martí; Luz, Ana Catarina; Guèze, Maximilien; Macía, Manuel J.; Reyes-García, Victoria

    2013-08-01

    Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines - SVM), and hybrid (unsupervised-supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different

  13. Soft supervised self-organizing mapping (3SOM) for improving land cover classification with MODIS time-series

    Science.gov (United States)

    Lawawirojwong, Siam

    Classification of remote sensing data has long been a fundamental technique for studying vegetation and land cover. Furthermore, land use and land cover maps are a basic need for environmental science. These maps are important for crop system monitoring and are also valuable resources for decision makers. Therefore, an up-to-date and highly accurate land cover map with detailed and timely information is required for the global environmental change research community to support natural resource management, environmental protection, and policy making. However, there appears to be a number of limitations associated with data utilization such as weather conditions, data availability, cost, and the time needed for acquiring and processing large numbers of images. Additionally, improving the classification accuracy and reducing the classification time have long been the goals of remote sensing research and they still require the further study. To manage these challenges, the primary goal of this research is to improve classification algorithms that utilize MODIS-EVI time-series images. A supervised self-organizing map (SSOM) and a soft supervised self-organizing map (3SOM) are modified and improved to increase classification efficiency and accuracy. To accomplish the main goal, the performance of the proposed methods is investigated using synthetic and real landscape data derived from MODIS-EVI time-series images. Two study areas are selected based on a difference of land cover characteristics: one in Thailand and one in the Midwestern U.S. The results indicate that time-series imagery is a potentially useful input dataset for land cover classification. Moreover, the SSOM with time-series data significantly outperforms the conventional classification techniques of the Gaussian maximum likelihood classifier (GMLC) and backpropagation neural network (BPNN). In addition, the 3SOM employed as a soft classifier delivers a more accurate classification than the SSOM applied as

  14. A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

    Science.gov (United States)

    Jung, Jinha; Pasolli, Edoardo; Prasad, Saurabh; Tilton, James C.; Crawford, Melba M.

    2014-01-01

    Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

  15. AN ASSESSMENT OF CITIZEN CONTRIBUTED GROUND REFERENCE DATA FOR LAND COVER MAP ACCURACY ASSESSMENT

    Directory of Open Access Journals (Sweden)

    G. M. Foody

    2015-08-01

    Full Text Available It is now widely accepted that an accuracy assessment should be part of a thematic mapping programme. Authoritative good or best practices for accuracy assessment have been defined but are often impractical to implement. Key reasons for this situation are linked to the ground reference data used in the accuracy assessment. Typically, it is a challenge to acquire a large sample of high quality reference cases in accordance to desired sampling designs specified as conforming to good practice and the data collected are normally to some degree imperfect limiting their value to an accuracy assessment which implicitly assumes the use of a gold standard reference. Citizen sensors have great potential to aid aspects of accuracy assessment. In particular, they may be able to act as a source of ground reference data that may, for example, reduce sample size problems but concerns with data quality remain. The relative strengths and limitations of citizen contributed data for accuracy assessment are reviewed in the context of the authoritative good practices defined for studies of land cover by remote sensing. The article will highlight some of the ways that citizen contributed data have been used in accuracy assessment as well as some of the problems that require further attention, and indicate some of the potential ways forward in the future.

  16. Ground penetrating radar detection of subsnow liquid overflow on ice-covered lakes in interior Alaska

    Directory of Open Access Journals (Sweden)

    A. Gusmeroli

    2012-07-01

    Full Text Available Lakes are abundant throughout the pan-Arctic region. For many of these lakes ice cover lasts for up to two thirds of the year. This frozen cover allows human access to these lakes, which are therefore used for many subsistence and recreational activities, including water harvesting, fishing, and skiing. Safe access to these lakes may be compromised, however, when, after significant snowfall, the weight of the snow acts on the ice and causes liquid water to spill through weak spots and overflow at the snow-ice interface. Since visual detection of subsnow liquid overflow (SLO is almost impossible our understanding on SLO processes is still very limited and geophysical methods that allow SLO detection are desirable. In this study we demonstrate that a commercially available, lightweight 1GHz, ground penetrating radar system can detect and map extent and intensity of SLO. Radar returns from wet snow-ice interfaces are at least twice as much in strength than returns from dry snow-ice interface. The presence of SLO also affects the quality of radar returns from the base of the lake ice. During dry conditions we were able to profile ice thickness of up to 1 m, conversely, we did not retrieve any ice-water returns in areas affected by SLO.

  17. A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region

    Science.gov (United States)

    Li, Guiying; Lu, Dengsheng; Moran, Emilio; Dutra, Luciano; Batistella, Mateus

    2012-06-01

    This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms - maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better land-cover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the

  18. Land Cover Classification of the Jornada Experimental Range with Simulated HyspIRI Data

    Science.gov (United States)

    Thorp, K. R.; French, A. N.

    2011-12-01

    The proposed NASA mission, HyspIRI, would facilitate the use of hyperspectral satellite remote sensing images for monitoring a variety of Earth system processes. We utilized four years of AVIRIS data of the USDA Jornada Experimental Range in southern New Mexico to simulate the visible and near-infrared bands of the planned HyspIRI satellite. Vegetation dynamics at Jornada has been the subject of several recent studies due to concerns of invasive plant species encroaching on native rangeland grasses. Our objective was to assess the added value of simulated HyspIRI images to appropriately classify rangeland vegetation. The AVIRIS images were georeferenced to an orthophoto of the region and 's6' was implemented for atmospheric correction. Images were resampled to simulate HyspIRI wavebands in the visible and near-infrared. Supervised image classification based on observed spectra of rangeland vegetation species was used to map spatial vegetation cover class and temporal dynamics over four years. Forthcoming results will identify the added value of hyperspectral images, as compared to broadband images, for monitoring vegetation dynamics at Jornada.

  19. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

    Directory of Open Access Journals (Sweden)

    Qingqing Lu

    2014-01-01

    Full Text Available Ground penetrating radar (GPR is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

  20. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

    OpenAIRE

    Qingqing Lu; Jiexin Pu; Zhonghua Liu

    2014-01-01

    Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficient...

  1. Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

    Institute of Scientific and Technical Information of China (English)

    ZHOU Hui-lin; TIAN Mao; CHEN Xiao-li

    2005-01-01

    Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed-forward multi-layer perceptron classifier. Experimental results based on the measured GPR echo signals obtained from the Mei-shan railway are presented.

  2. Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover

    Science.gov (United States)

    Li, Mengmeng; Bijker, Wietske; Stein, Alfred

    2015-04-01

    Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a Binary Partition Tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pléiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction.

  3. Ground cover rice production system facilitates soil carbon and nitrogen stocks at regional scale

    Directory of Open Access Journals (Sweden)

    M. Liu

    2015-02-01

    Full Text Available Rice production is increasingly challenged by irrigation water scarcity, however covering paddy rice soils with films (ground cover rice production system: GCRPS can significantly reduce water demand as well as overcome temperature limitations at the beginning of the vegetation period resulting in increased grain yields in colder regions of rice production with seasonal water shortages. It has been speculated that the increased soil aeration and temperature under GCRPS may result in losses of soil organic carbon and nitrogen stocks. Here we report on a regional scale experiment, conducted by sampling paired adjacent Paddy and GCRPS fields at 49 representative sites in the Shiyan region, which is typical for many mountainous areas across China. Parameters evaluated included soil C and N stocks, soil physical and chemical properties, potential carbon mineralization rates, fractions of soil organic carbon and stable carbon isotopic composition of plant leaves. Furthermore, root biomass was quantified at maximum tillering stage at one of our paired sites. Against expectations the study showed that: (1 GCRPS significantly increased soil organic C and N stocks 5–20 years following conversion of production systems, (2 there were no differences between GCRPS and Paddy in soil physical and chemical properties for the various soil depths with the exception of soil bulk density, (3 GCRPS had lower mineralization potential for soil organic C compared with Paddy over the incubation period, (4 GCRPS showed lower δ15N in the soils and plant leafs indicating less NH3 volatilization in GCRPS than in Paddy; and (5 GCRPS increased yields and root biomass in all soil layers down to 40 cm depth. Our results suggest that GCRPS is an innovative rice production technique that not only increases yields using less irrigation water, but that it also is environmentally beneficial due to increased soil C and N stocks at regional scale.

  4. An evidence gathering and assessment technique designed for a forest cover classification algorithm based on the Dempster-Shafer theory of evidence

    Science.gov (United States)

    Szymanski, David Lawrence

    This thesis presents a new approach for classifying Landsat 5 Thematic Mapper (TM) imagery that utilizes digitally represented, non-spectral data in the classification step. A classification algorithm that is based on the Dempster-Shafer theory of evidence is developed and tested for its ability to provide an accurate representation of forest cover on the ground at the Anderson et al (1976) level II. The research focuses on defining an objective, systematic method of gathering and assessing the evidence from digital sources including TM data, the normalized difference vegetation index, soils, slope, aspect, and elevation. The algorithm is implemented using the ESRI ArcView Spatial Analyst software package and the Grid spatial data structure with software coded in both ArcView Avenue and also C. The methodology uses frequency of occurrence information to gather evidence and also introduces measures of evidence quality that quantify the ability of the evidence source to differentiate the Anderson forest cover classes. The measures are derived objectively and empirically and are based on common principles of legal argument. The evidence assessment measures augment the Dempster-Shafer theory and the research will determine if they provide an argument that is mentally sound, credible, and consistent. This research produces a method for identifying, assessing, and combining evidence sources using the Dempster-Shafer theory that results in a classified image containing the Anderson forest cover class. Test results indicate that the new classifier performs with accuracy that is similar to the traditional maximum likelihood approach. However, confusion among the deciduous and mixed classes remains. The utility of the evidence gathering method and also the evidence assessment method is demonstrated and confirmed. The algorithm presents an operational method of using the Dempster-Shafer theory of evidence for forest classification.

  5. The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands

    Directory of Open Access Journals (Sweden)

    Yuanyuan Chen

    2014-12-01

    Full Text Available The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of the proposed method, five other classifications (the Wishart supervised classification, the proposed method without polarimetric parameters, the proposed method without an object-based analysis, the proposed method without textural and geometric information and the proposed method using the nearest-neighbor classifier were applied for comparison. The results indicated that some polarimetric parameters, such as Shannon entropy, Krogager_Kd, Alpha, HAAlpha_T11, VanZyl3_Vol, Derd, Barnes2_T33, polarization fraction, Barnes1_T33, Neuman_delta_mod and entropy, greatly improved the classification results. The shape index was a useful feature in distinguishing fish ponds and rivers. The distance to the sea can be regarded as an important factor in reducing the confusion between herbaceous wetland vegetation and grasslands. Furthermore, the decision tree algorithm increased the overall accuracy by 6.8% compared with the nearest neighbor classifier. This research demonstrated that different polarimetric parameters and the object-based approach significantly improved the performance of land cover classification in coastal wetlands using ALOS PALSAR data.

  6. Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery

    Science.gov (United States)

    Zillmann, E.; Schonert, M.; Lilienthal, H.; Siegmann, B.; Jarmer, T.; Rosso, P.; Weichelt, T.

    2015-04-01

    Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations. This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R2 value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.

  7. Machine Learning Approaches for High-resolution Urban Land Cover Classification: A Comparative Study

    Energy Technology Data Exchange (ETDEWEB)

    Vatsavai, Raju [ORNL; Chandola, Varun [ORNL; Cheriyadat, Anil M [ORNL; Bright, Eddie A [ORNL; Bhaduri, Budhendra L [ORNL; Graesser, Jordan B [ORNL

    2011-01-01

    The proliferation of several machine learning approaches makes it difficult to identify a suitable classification technique for analyzing high-resolution remote sensing images. In this study, ten classification techniques were compared from five broad machine learning categories. Surprisingly, the performance of simple statistical classification schemes like maximum likelihood and Logistic regression over complex and recent techniques is very close. Given that these two classifiers require little input from the user, they should still be considered for most classification tasks. Multiple classifier systems is a good choice if the resources permit.

  8. Comparison of pixel -based and artificial neural networks classification methods for detecting forest cover changes in Malaysia

    Science.gov (United States)

    Deilmai, B. R.; Kanniah, K. D.; Rasib, A. W.; Ariffin, A.

    2014-02-01

    According to the FAO (Food and Agriculture Organization), Malaysia lost 8.6% of its forest cover between 1990 and 2005. In forest cover change detection, remote sensing plays an important role. A lot of change detection methods have been developed, and most of them are semi-automated. These methods are time consuming and difficult to apply. One of the new and robust methods for change detection is artificial neural network (ANN). In this study, (ANN) classification scheme is used to detect the forest cover changes in the Johor state in Malaysia. Landsat Thematic Mapper images covering a period of 9 years (2000 and 2009) are used. Results obtained with ANN technique was compared with Maximum likelihood classification (MLC) to investigate whether ANN can perform better in the tropical environment. Overall accuracy of the ANN and MLC techniques are 75%, 68% (2000) and 80%, 75% (2009) respectively. Using the ANN method, it was found that forest area in Johor decreased as much as 1298 km2 between 2000 and 2009. The results also showed the potential and advantages of neural network in classification and change detection analysis.

  9. Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms.

    Science.gov (United States)

    Alaqtash, Murad; Sarkodie-Gyan, Thompson; Yu, Huiying; Fuentes, Olac; Brower, Richard; Abdelgawad, Amr

    2011-01-01

    An automated gait classification method is developed in this study, which can be applied to analysis and to classify pathological gait patterns using 3D ground reaction force (GRFs) data. The study involved the discrimination of gait patterns of healthy, cerebral palsy (CP) and multiple sclerosis subjects. The acquired 3D GRFs data were categorized into three groups. Two different algorithms were used to extract the gait features; the GRFs parameters and the discrete wavelet transform (DWT), respectively. Nearest neighbor classifier (NNC) and artificial neural networks (ANN) were also investigated for the classification of gait features in this study. Furthermore, different feature sets were formed using a combination of the 3D GRFs components (mediolateral, anterioposterior, and vertical) and their various impacts on the acquired results were evaluated. The best leave-one-out (LOO) classification accuracy 85% was achieved. The results showed some improvement through the application of a features selection algorithm based on M-shaped value of vertical force and the statistical test ANOVA of mediolateral and anterioposterior forces. The optimal feature set of six features enhanced the accuracy to 95%. This work can provide an automated gait classification tool that may be useful to the clinician in the diagnosis and identification of pathological gait impairments.

  10. Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

    Directory of Open Access Journals (Sweden)

    Enliang Song

    2013-07-01

    Full Text Available One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM, by addressing the neighborhood preserving embedding (NPE algorithm of manifold learning on the wavelet packet node energy (WPNE of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity.

  11. Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

    OpenAIRE

    A. M. Dieye; Roy, D.P.; N. P. Hanan; Liu, S.(State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China); Hansen, M.; Touré, A.

    2011-01-01

    Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SO...

  12. Analysis of the Effects of Different Land Use and Land Cover Classification on Surface Meteorological Variables using WRF Model

    Science.gov (United States)

    Sati, A. P.

    2015-12-01

    The continuous population growth and the subsequent economic expansion over centuries have been the primary drivers of land use /land cover (LULC) changes resulting in the environmental changes across the globe. Most of the urban areas being developed today are on the expense of agricultural or barren lands and the changes result from various practices such as deforestation, changing agriculture practices, rapid expansion of urban centers etc.For modeling applications, classification of land use is important and periodic updates of land cover are necessary to capture change due to LULC changes.Updated land cover and land use data derived from satellites offer the possibility of consistent and regularly collected information on LULC. In this study we explore the application of Landsat based LULC classification inWeather Research and Forecasting (WRF) model in predicting the meteorology over Delhi, India. The supervised classification of Landsat 8 imagery over Delhi region is performed which update the urban extent as well as other Land use for the region. WRF model simulations are performed using LULC classification from Landsat data, United States Geological Survey (USGS) and Moderate Resolution Imaging Spectroradiometer (MODIS) for various meteorological parameters. Modifications in LULC showed a significant effect on various surface meteorological parameters such as temperature, humidity, wind circulations and other underlying surface parameters. There is a considerable improvement in the spatial distribution of the surface meteorological parameters with correction in input LULC. The study demonstrates the improved LULC classification from Landsat data than currently in vogue and their potential to improve numerical weather simulations especially for expanding urban areas.The continuous population growth and the subsequent economic expansion over centuries have been the primary drivers of land use /land cover (LULC) changes resulting in the environmental changes

  13. Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods

    Science.gov (United States)

    Wang, X. Y.; Guo, Y. G.; He, J.; Du, L. T.

    2016-10-01

    Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier.

  14. An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery

    Directory of Open Access Journals (Sweden)

    Siamak Khorram

    2009-07-01

    Full Text Available This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM neural network module, and a supervised Multilayer Perceptron (MLP neural network module using the Backpropagation (BP training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA. The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC classifications of a Landsat Thematic Mapper (TM image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR Digital Orthophoto Quarter Quad (DOQQ data. The supervised MLP network obtained the most accurate classification accuracy as compared to the two unsupervised SOM networks. Additionally, the classification performance of the refined SOM network was found to be significantly better than that of the standard SOM network essentially due to the incorporation of SA. This is mainly due to the SA-assisted classification utilizing the scheduling cooling scheme. It is concluded that our automated ANN classification system can be utilized for LU/LC applications and will be particularly useful when traditional statistical classification methods are not suitable due to a statistically abnormal distribution of the input data.

  15. An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica

    Directory of Open Access Journals (Sweden)

    Alfredo Fernández-Landa

    2016-07-01

    Full Text Available REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.

  16. Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information

    Directory of Open Access Journals (Sweden)

    Farrah Melissa Muharam

    2015-05-01

    Full Text Available Assessing nitrogen (N status is important from economic and environmental standpoints. To date, many spectral indices to estimate cotton chlorophyll or N content have been purely developed using statistical analysis approach where they are often subject to site-specific problems. This study describes and tests a novel method of utilizing physical characteristics of N-fertilized cotton and combining field spectral measurements made at different spatial scales as an approach to estimate in-season chlorophyll or leaf N content of field-grown cotton. In this study, leaf greenness estimated from spectral measurements made at the individual leaf, canopy and scene levels was combined with percent ground cover to produce three different indices, named TCCLeaf, TCCCanopy, and TCCScene. These indices worked best for estimating leaf N at early flowering, but not for chlorophyll content. Of the three indices, TCCLeaf showed the best ability to estimate leaf N (R2 = 0.89. These results suggest that the use of green and red-edge wavelengths derived at the leaf scale is best for estimating leaf greenness. TCCCanopy had a slightly lower R2 value than TCCLeaf (0.76, suggesting that the utilization of yellow and red-edge wavelengths obtained at the canopy level could be used as an alternative to estimate leaf N in the absence of leaf spectral information. The relationship between TCCScene and leaf N was the lowest (R2 = 0.50, indicating that the estimation of canopy greenness from scene measurements needs improvement. Results from this study confirmed the potential of these indices as efficient methods for estimating in-season leaf N status of cotton.

  17. Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

    Science.gov (United States)

    Poulter, B.; MacBean, N.; Hartley, A.; Khlystova, I.; Arino, O.; Betts, R.; Bontemps, S.; Boettcher, M.; Brockmann, C.; Defourny, P.; Hagemann, S.; Herold, M.; Kirches, G.; Lamarche, C.; Lederer, D.; Ottlé, C.; Peters, M.; Peylin, P.

    2015-07-01

    Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land-atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

  18. A study on monitoring land use/cover change of mining area based on ticket-voting SVM classification

    Science.gov (United States)

    Lin, Yi; Yu, Jie; Ying, Min; Shen, Mingge

    2015-08-01

    Based on the development of classification algorithm applied in monitoring spatio-temporal dynamic changes of coal-- mining areas, several improvements were made on feature space and classification model in this paper. There were two innovations in our study: 1) During building the feature spaces, a new index for extracting information about mining area was created, which can classify mining area and settlements efficiently; 2) a special ticket-voting SVM algorithm with wavelet kernel function was proposed, which provides higher classification accuracy than other traditional classifiers via the secondary classification. Here we took the northeast plain of Pei county in Xuzhou city as a studying region, applying the proposed method to implement the classification by using the image of multi-temporal TM/ETM from the year of 1987 to 2013. How to carry on deep analysis combined with various non-spatial data is much more significant. Then we studied the rules of dynamic changes of land use/cover and further analyzed their driving factors by combining RS interpretation with GIS spatial analysis techniques. In this study, image recognition technology was applied to the problems of environmental change in coal mining area. These explanations provide some valuable supports for human to recognize and deal with the conflicts between economic development and environmental protection in coal mining areas.

  19. PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain

    Directory of Open Access Journals (Sweden)

    Chensong Tao

    2017-07-01

    Full Text Available Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR. Target polarimetric response is strongly dependent on its orientation. Backscattering responses of the same target with different orientations to the SAR flight path may be quite different. This target orientation diversity effect hinders PolSAR image understanding and interpretation. Roll-invariant polarimetric features such as entropy, anisotropy, mean alpha angle, and total scattering power are independent of the target orientation and are commonly adopted for PolSAR image classification. On the other aspect, target orientation diversity also contains rich information which may not be sensed by roll-invariant polarimetric features. In this vein, only using the roll-invariant polarimetric features may limit the final classification accuracy. To address this problem, this work uses the recently reported uniform polarimetric matrix rotation theory and a visualization and characterization tool of polarimetric coherence pattern to investigate hidden polarimetric features in the rotation domain along the radar line of sight. Then, a feature selection scheme is established and a set of hidden polarimetric features are selected in the rotation domain. Finally, a classification method is developed using the complementary information between roll-invariant and selected hidden polarimetric features with a support vector machine (SVM/decision tree (DT classifier. Comparison experiments are carried out with NASA/JPL AIRSAR and multi-temporal UAVSAR data. For AIRSAR data, the overall classification accuracy of the proposed classification method is 95.37% (with SVM/96.38% (with DT, while that of the conventional classification method is 93.87% (with SVM/94.12% (with DT, respectively. Meanwhile, for multi-temporal UAVSAR data, the mean overall classification accuracy of the proposed method is up to 97.47% (with SVM/99.39% (with DT, which is also higher

  20. Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series

    Directory of Open Access Journals (Sweden)

    Charlotte Pelletier

    2017-02-01

    Full Text Available Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM and Random Forests (RF. A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise.

  1. Water consumption and water-saving characteristics of a ground cover rice production system

    Science.gov (United States)

    Jin, Xinxin; Zuo, Qiang; Ma, Wenwen; Li, Sen; Shi, Jianchu; Tao, Yueyue; Zhang, Yanan; Liu, Yang; Liu, Xiaofei; Lin, Shan; Ben-Gal, Alon

    2016-09-01

    The ground cover rice production system (GCRPS) offers a potentially water-saving alternative to the traditional paddy rice production system (TPRPS) by furrow irrigating mulched soil beds and maintaining soils under predominately unsaturated conditions. The guiding hypothesis of this study was that a GCRPS would decrease both physiological and non-physiological water consumption of rice compared to a TPRPS while either maintaining or enhancing production. This was tested in a two-year field experiment with three treatments (TPRPS, GCRPSsat keeping root zone average soil water content near saturated, and GCRPS80% keeping root zone average soil water content as 80-100% of field water capacity) and a greenhouse experiment with four treatments (TPRPS, GCRPSsat, GCRPSfwc keeping root zone average soil water content close to field water capacity, and GCRPS80%). The water-saving characteristics of GCRPS were analyzed as a function of the measured soil water conditions, plant parameters regarding growth and production, and water input and consumption. In the field experiment, significant reduction in both physiological and non-physiological water consumption under GCRPS lead to savings in irrigation water of ∼61-84% and reduction in total input water of ∼35-47%. Compared to TPRPS, deep drainage was reduced ∼72-88%, evaporation was lessened ∼83-89% and transpiration was limited ∼6-10% under GCRPS. In addition to saving water, plant growth and grain yield were enhanced under GCRPS due to increased soil temperature in the root zone. Therefore, water use efficiencies (WUEs), based on transpiration, irrigation and total input water, were respectively improved as much as 27%, 609% and 110% under GCRPS. Increased yield attributed to up to ∼19%, decreased deep drainage accounted for ∼75%, decreased evaporation accounted for ∼14% and reduced transpiration for ∼5% of the enhancement in WUE of input water under GCRPS, while increased runoff and water storage had

  2. VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA USING REMOTE SENSIGN AND GIS TECHNIQUES

    Directory of Open Access Journals (Sweden)

    A. G. Koppad

    2017-10-01

    Full Text Available The study was conducted in Uttara Kannada districts during the year 2012–2014. The study area lies between 13.92° N to 15.52° N latitude and 74.08° E to 75.09° E longitude with an area of 10,215 km2. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32 % (6468.70 sq km followed by agriculture 12.88 % (1315.31 sq. km, sparse forest 10.59 % (1081.37 sq. km, open land 6.09 % (622.37 sq. km, horticulture plantation and least was forest plantation (1.07 %. Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non- vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

  3. The role of snow cover in ground thermal conditions in three sites with contrasted topography in Sierra Nevada (Spain)

    Science.gov (United States)

    Oliva, Marc; Salvador, Ferran; Gómez Ortiz, Antonio; Salvà, Montserrat

    2014-05-01

    Snow cover has a high capacity to insulate the soil from the external thermal influences. In regions of high snowfall, such as the summit areas of the highest Iberian mountain ranges, the presence of a thick snow cover may condition the existence or inexistence of permafrost conditions. In order to analyze the impact of the thickness, duration and interannual variability of snow cover on the ground thermal regime in the massif of Sierra Nevada, we have analyzed soil temperatures at a depth of 2 cm for the period 2006-2012 in three sites of contrasting topography as well as air temperatures for the same period: (a) Corral del Veleta (3100 m) in a rock glacier located in the northern Veleta cirque, with high and persistent snow cover. (b) Collado de los Machos (3300 m), in a summit area with relict stone circles, with little snow accumulation due to wind effect. (c) Río Seco (3000 m), in a solifluction lobe located in this southern glacial cirque with moderate snowfall. Considering the air and 2 cm depth soil temperature records, the freezing degree-days were calculated for each year from November to May in order to characterize the role of snow as a thermal insulator of the ground during the cold season (Frauenfeld et al., 2007). In all cases, the highest values of freezing degree-days correspond to years with little snowfall (2006-2007, 2007-2008, 2011-2012), while in years with a thicker snow cover (2008-2009, 2009-2010, 2010-2011) the total freezing degree-days were significantly lower. The accumulation of freezing degree-days is maximum at the wind-exposed site of Collado de los Machos, where the wind redistributes snow and favours the penetration of cold into the ground. The opposite pattern occurs in the Veleta cirque, where most persistent snow cover conditions determine lower accumulated freezing degree-days than in Collado de los Machos and Rio Seco.

  4. Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution

    Directory of Open Access Journals (Sweden)

    Zhiyong Lv

    2016-12-01

    Full Text Available Land cover classification using very high spatial resolution (VHSR imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO, object correlative index (OCI spatial feature based method, a recursive filter (RF, and a rolling guided filter (RGF, and has shown a 6%–18% improvement in overall accuracy.

  5. Classification accuracy analysis of selected land use and land cover products in a portion of West-Central Lower Michigan

    Science.gov (United States)

    Ma, Kin Man

    2007-12-01

    Remote sensing satellites have been utilized to characterize and map land cover and its changes since the 1970s. However, uncertainties exist in almost all land use and land cover maps classified from remotely sensed images. In particular, it has been recognized that the spatial mis-registration of land cover maps can affect the true estimates of land use/land cover (LULC) changes. This dissertation addressed the following questions: what are the spatial patterns, magnitudes, and cover-dependencies of classification uncertainty associated with West-Central Lower Michigan's LULC products and how can the adverse effects of spatial misregistration on accuracy assessment be reduced? Two Michigan LULC products were chosen for comparison: 1998 Muskegon River Watershed (MRW) Michigan Resource Information Systems LULC map and a 2001 Integrated Forest Monitoring and Assessment Prescription Project (IFMAP). The 1m resolution 1998 MRW LULC map was derived from U.S. Geological Survey Digital Orthophoto Quarter Quadrangle (USGS DOQQs) color infrared imagery and was used as the reference map, since it has a thematic accuracy of 95%. The IFMAP LULC map was co-registered to a series of selected 1998 USGS DOQQs. The total combined root mean square error (rmse) distance of the georectified 2001 IFMAP was +/-12.20m. A spatial uncertainty buffer of at least 1.5 times the rmse was set at 20m so that polygon core areas would be unaffected by spatial misregistration noise. A new spatial misregistration buffer protocol (SPATIALM_ BUFFER) was developed to limit the effect of spatial misregistration on classification accuracy assessment. Spatial uncertainty buffer zones of 20m were generated around LULC polygons of both datasets. Eight-hundred seventeen (817) stratified random accuracy assessment points (AAPs) were generated across the 1998 MRW map. Classification accuracy and kappa statistics were generated for both the 817 AAPs and 604 AAPs comparisons. For the 817 AAPs comparison, the

  6. Assessing alternative measures of tree canopy cover: Photo-interpreted NAIP and ground-based estimates

    Science.gov (United States)

    Chris Toney; Greg Liknes; Andy Lister; Dacia Meneguzzo

    2012-01-01

    In preparation for the development of the National Land Cover Database (NLCD) 2011 tree canopy cover layer, a pilot project for research and method development was completed in 2010 by the USDA Forest Service Forest Inventory and Analysis (FIA) program and Remote Sensing Applications Center (RSAC).This paper explores one of several topics investigated during the NLCD...

  7. Comparing three spaceborne optical sensors via fine scale pixel-based urban land cover classification products

    CSIR Research Space (South Africa)

    Breytenbach, Andre

    2013-08-01

    Full Text Available of Remote Sensing, vol. 45, pp. 1-18. Fitzgerald, RW & Lees, BG 1994, 'Assessing the Classification Accuracy of Multisource Remote Sensing Data', Remote Sensing of Environment, vol. 47, pp. 362-368. Fröhlich, B, Bach, E, Walde, I, Hese, S, Schmullius, C...

  8. A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

    Science.gov (United States)

    Pahlavani, Parham; Bigdeli, Behnaz

    2016-12-01

    Hyperspectral images contain extremely rich spectral information that offer great potential to discriminate between various land cover classes. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral classification. Furthermore, in the presence of mixed coverage pixels, crisp classifiers produced errors, omission and commission. This paper presents a mutual information-Dempster-Shafer system through an ensemble classification approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to overcome high dimensionality. Then, a fuzzy maximum likelihood classifies each band subset. Finally, Dempster-Shafer is applied to fuse the results of the fuzzy classifiers. In order to assess the proposed method, a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral data. Furthermore, a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed method. The proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual information-Dempster-Shafer based classification of hyperspectral data.

  9. Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

    Directory of Open Access Journals (Sweden)

    Xueke Li

    2016-05-01

    Full Text Available The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1 opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m to EO-1 Hyperion (with a spatial resolution of 30 m. The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC, pixel-based support vector machine (PSVM, hybrid maximum likelihood classifier (HMLC, and hybrid support vector machine (HSVM were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the

  10. Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: the case of Kahramanmaraş, Turkey.

    Science.gov (United States)

    Alphan, Hakan; Doygun, Hakan; Unlukaplan, Yüksel I

    2009-04-01

    This study assessed land cover (LC) changes in Kahramanmaraş (K.Maraş) and its environs by using multitemporal Landsat and ASTER imagery, respectively belong to 1989, 2000 and 2004. A priori defined nine land cover classes in the classification scheme were urban and built-up, forest, sparsely vegetated areas, grassland, vegetated stream beds, unvegetated stream beds, bare areas, crop fields, and water bodies. Individual classifications were employed using the combination of both unsupervised and supervised classification methods. Iterative Self Organizing Data Analysis (ISODATA) was used to reduce spectral variation in the scenes arising from complex pattern of crop fields. Maximum Likelihood classifier was used in the LC classification of the individual images. Image pairs of consecutive dates were compared by overlaying the thematic LC maps and cross-tabulating the LC statistics. Urbanization and expansion of agriculture were the major reasons for the dramatic LC conversions. The amount of conversion from crop fields to water occurred as large as 927.67 ha, accounting for 73% of the total land-to-water conversion. Conversions to agriculture have mainly been occurred from grasslands and sparsely vegetated areas as large as 1,314.95 and 1,325.84 ha, respectively. Urban coverage doubled in this period as a result of 1,443.45 ha of increase. Urban area increased in the second period from 2,920 to 3,526 ha. Conversions to agriculture occurred at high amounts. A total of 1,075.79 ha area changed from sparsely vegetated areas to crop fields. A landscape-level environmental monitoring scheme based on satellite remote sensing was proposed for effective environmental resource management.

  11. Plant functional type classification for Earth System Models: results from the European Space Agency's Land Cover Climate Change Initiative

    Directory of Open Access Journals (Sweden)

    B. Poulter

    2015-01-01

    Full Text Available Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land-cover datasets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI, with land cover (LC_CCI as one of thirteen Essential Climate Variables targeted for research development. The LC_CCI was implemented in three phases, first responding to a survey of user needs, then developing a global, moderate resolution, land-cover dataset for three time periods, or epochs, 2000, 2005, and 2010, and the last phase resulting in a user-tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFT. The translation was performed as part of consultative process among map producers and users and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three-earth system modeling teams shows significant differences between the LC_CCI PFT dataset and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as Phase 2 of the European Space Agency CCI program continues.

  12. Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data

    Directory of Open Access Journals (Sweden)

    Johanna E. Ayala-Izurieta

    2017-05-01

    Full Text Available We presented a methodology to accurately classify mountainous regions in the tropics. These landscapes are complex in terms of their geology, ecosystems, climate and land use. Obtaining accurate maps to assess land cover change is essential. The objectives of this study were to (1 map vegetation using the Random Forest Classifier (RFC, spectral vegetation index (SVI, and ancillar geographic data (2 identify important variables that help differentiate vegetation cover, and (3 assess the accuracy of the vegetation cover classification in hard-to-reach Ecuadorian mountain region. We used Landsat 7 ETM+ satellite images of the entire scene, a RFC algorithm, and stratified random sampling. The altitude and the two band enhanced vegetation index (EVI2 provide more information on vegetation cover than the traditional and often use normalized difference vegetation index (NDVI in other settings. We classified the vegetation cover of mountainous areas within the 1016 km2 area of study, at 30 m spatial resolution, using RFC that yielded a land cover map with an overall accuracy of 95%. The user´s accuracy and the half-width of the confidence interval for 95% of the basic map units, forest (FOR, páramo (PAR, crop (CRO and pasture (PAS were 95.85% ± 2.86%, 97.64% ± 1.24%, 91.53% ± 3.35% and 82.82% ± 7.74%, respectively. The overall disagreement was 4.47%, which results from adding 0.43% of quantity disagreement and 4.04% of allocation disagreement. The methodological framework presented in this paper and the combined use of SVIs, ancillary geographic data, and the RFC allowed the accurate mapping of hard-to-reach mountain landscapes as well as uncovering the underlying factors that help differentiate vegetation cover in the Ecuadorian mountain geosystem.

  13. Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

    Science.gov (United States)

    Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

  14. Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

    Science.gov (United States)

    Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

  15. Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in senegal

    Science.gov (United States)

    Dieye, A. M.; Roy, D. P.; Hanan, N. P.; Liu, S.; Hansen, M.; Touré, A.

    2012-02-01

    Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

  16. Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

    Directory of Open Access Journals (Sweden)

    A. M. Dieye

    2011-07-01

    Full Text Available Spatially explicit land cover land use (LCLU change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS. The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

  17. Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in senegal

    Directory of Open Access Journals (Sweden)

    A. M. Dieye

    2012-02-01

    Full Text Available Spatially explicit land cover land use (LCLU change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS. The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

  18. Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

    Science.gov (United States)

    Dieye, A.M.; Roy, D.P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

    2011-01-01

    Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs. ?? 2011 Author(s).

  19. Object-Based Method Outperforms Per-Pixel Method for Land Cover Classification in a Protected Area of the Brazilian Atlantic Rainforest Region

    Institute of Scientific and Technical Information of China (English)

    T.RITTL; M.COOPER; R.J.HECK; M.V.R.BALLESTER

    2013-01-01

    Conventional image classification based on pixels hinders the possibilities to obtain information contained in images,while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image.The objective of this study was to investigate the performance of different classification methods for land cover mapping in the vicinity of the Alto Ribeira Tourist State Park,a Brazilian Atlantic rainforest area.Two classification methods were tested,including i) a hybrid per-pixel classification using the image processing software ERDAS Imagine version 9.1 and ii) an object-based classification using the software eCognition version 5.In the first method,six different classes were established,while in the second method,another two classes were established in addition to the six classes in the first method.Accuracy assessment of the classification results presented showed that the object-based classification with a Kappa index value of 0.8687 outperformed the per-pixel classification with a Kappa index value of 0.2224.Application of the user's knowledge during the object-based classification process achieved the desired quality;therefore,the use of inter-relationships between objects,superclasses,subclasses,and neighboring classes were critical to improving the efficiency of land cover classification.

  20. Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

    Science.gov (United States)

    Weiqi Zhou; Austin Troy; Morgan Grove

    2008-01-01

    Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

  1. Classification of ground moving targets using bicepstrum-based features extracted from Micro-Doppler radar signatures

    Science.gov (United States)

    Molchanov, Pavlo O.; Astola, Jaakko T.; Egiazarian, Karen O.; Totsky, Alexander V.

    2013-12-01

    In this article, a novel bicepstrum-based approach is suggested for ground moving radar target classification. Distinctive classification features were extracted from short-time backscattering bispectrum estimates of the micro-Doppler signature. Real radar data were obtained using surveillance Doppler microwave radar operating at 34 GHz. Classifier performance was studied in detail using the Gaussian Mixture Mode and Maximum Likelihood decision making rule, and the results were verified on a multilayer perceptron and Support Vector Machine. Experimental real radar measurements demonstrated that it is quite feasible to discern three classes of humans (single, two and three persons) walking in a vegetation cluttered environment using proposed bicepstrum-based classification features. Sophisticated bispectrum-based signal processing provides the extraction of new classification features in the form of phase relationships in the radar data. It provides a novel insight into moving radar target classification compared to the commonly used energy-based strategy.

  2. Land cover classification and economic assessment of citrus groves using remote sensing

    Science.gov (United States)

    Shrivastava, Rahul J.; Gebelein, Jennifer L.

    The citrus industry has the second largest impact on Florida's economy, following tourism. Estimation of citrus area coverage and annual forecasts of Florida's citrus production are currently dependent on labor-intensive interpretation of aerial photographs. Remotely sensed data from satellites has been widely applied in agricultural yield estimation and cropland management. Satellite data can potentially be obtained throughout the year, making it especially suitable for the detection of land cover change in agriculture and horticulture, plant health status, soil and moisture conditions, and effects of crop management practices. In this study, we analyzed land cover of citrus crops in Florida using Landsat Enhanced Thematic Mapper Plus (ETM+) imagery from the University of Maryland Global Land Cover Facility (GLCF). We hypothesized that an interdisciplinary approach combining citrus production (economic) data with citrus land cover area per county would yield a correlation between observable spectral reflectance throughout the year, and the fiscal impact of citrus on local economies. While the data from official sources based on aerial photography were positively correlated, there were serious discrepancies between agriculture census data and satellite-derived cropland area using medium-resolution satellite imagery. If these discrepancies can be resolved by using imagery of higher spatial resolution, a stronger correlation would be observed for citrus production based on satellite data. This would allow us to predict the economic impact of citrus from satellite-derived spectral data analysis to determine final crop harvests.

  3. Great Lakes Ice Cover Classification and Mapping Using Satellite Synthetic Aperture Radar (SAR) Data

    Science.gov (United States)

    Nghiem, S.; Leshkevich, G.; Kwok, R.

    1998-01-01

    Owing to the size and extent of the Great Lakes and the variety of ice types features found there, the timely and objective qualities inherent in computer processing of satellite data make it well suited for monitoring and mapping ice cover.

  4. Quantifying the impact of cloud cover on ground radiation flux measurements using hemispherical images

    NARCIS (Netherlands)

    Roupioz, L.; Colin, J.; Jia, L.; Nerry, F.; Menenti, M.

    2015-01-01

    Linking observed or estimated ground incoming solar radiation with cloud coverage is difficult since the latter is usually poorly described in standard meteorological observation protocols. To investigate the benefits of detailed observation and characterization of cloud coverage and distribution

  5. Land Cover

    Data.gov (United States)

    Kansas Data Access and Support Center — The Land Cover database depicts 10 general land cover classes for the State of Kansas. The database was compiled from a digital classification of Landsat Thematic...

  6. Development of an object-based classification model for mapping mountainous forest cover at high elevation using aerial photography

    Science.gov (United States)

    Lateb, Mustapha; Kalaitzidis, Chariton; Tompoulidou, Maria; Gitas, Ioannis

    2016-08-01

    Climate change and overall temperature increase results in changes in forest cover in high elevations. Due to the long life cycle of trees, these changes are very gradual and can be observed over long periods of time. In order to use remote sensing imagery for this purpose it needs to have very high spatial resolution and to have been acquired at least 50 years ago. At the moment, the only type of remote sensing imagery with these characteristics is historical black and white aerial photographs. This study used an aerial photograph from 1945 in order to map the forest cover at the Olympus National Park, at that date. An object-based classification (OBC) model was developed in order to classify forest and discriminate it from other types of vegetation. Due to the lack of near-infrared information, the model had to rely solely on the tone of the objects, as well as their geometric characteristics. The model functioned on three segmentation levels, using sub-/super-objects relationships and utilising vegetation density to discriminate forest and non-forest vegetation. The accuracy of the classification was assessed using 503 visually interpreted and randomly distributed points, resulting in a 92% overall accuracy. The model is using unbiased parameters that are important for differentiating between forest and non-forest vegetation and should be transferrable to other study areas of mountainous forests at high elevations.

  7. Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth.

    Science.gov (United States)

    Valindria, Vanya V; Lavdas, Ioannis; Bai, Wenjia; Kamnitsas, Konstantinos; Aboagye, Eric O; Rockall, Andrea G; Rueckert, Daniel; Glocker, Ben

    2017-08-01

    When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.

  8. The In-Transit Vigilant Covering Tour Problem of Routing Unmanned Ground Vehicles

    Science.gov (United States)

    2012-08-01

    15 Figure 2. A classic VRP ...17 Figure 3. Solution for a VRP ........................................................................................18 Figure 4. Solution...of NP-hard problems, such as the Traveling Salesman Problem (TSP), Vehicle Routing Problem ( VRP ), and Covering Salesman Problem (CSP) etc. We will

  9. Filling of Cloud-Induced Gaps for Land Use and Land Cover Classifications Around Refugee Camps

    Science.gov (United States)

    Braun, Andreas; Hagensieker, Ron; Hochschild, Volker

    2016-08-01

    Clouds cover is one of the main constraints in the field of optical remote sensing. Especially the use of multispectral imagery is affected by either fully obscured data or parts of the image which remain unusable. This study compares four algorithms for the filling of cloud induced gaps in classified land cover products based on Markov Random Fields (MRF), Random Forest (RF), Closest Spectral Fit (CSF) operators. They are tested on a classified image of Sentinel-2 where artificial clouds are filled by information derived from a scene of Sentinel-1. The approaches rely on different mathematical principles and therefore produced results varying in both pattern and quality. Overall accuracies for the filled areas range from 57 to 64 %. Best results are achieved by CSF, however some classes (e.g. sands and grassland) remain critical through all approaches.

  10. Mapping wind erosion hazard in Australia using MODIS-derived ground cover, soil moisture and climate data

    Science.gov (United States)

    Yang, X.; Leys, J.

    2014-03-01

    This paper describes spatial modeling methods to identify wind erosion hazard (WEH) areas across Australia using the recently available time-series products of satellite-derived ground cover, soil moisture and wind speed. We implemented the approach and data sets in a geographic information system to produce WEH maps for Australia at 500 m ground resolution on a monthly basis for the recent thirteen year period (2000-2012). These maps reveal the significant wind erosion hazard areas and their dynamic tendencies at paddock and regional scales. Dust measurements from the DustWatch network were used to validate the model and interpret the dust source areas. The modeled hazard areas and changes were compared with results from a rule-set approach and the Computational Environmental Management System (CEMSYS) model. The study demonstrates that the time series products of ground cover, soil moisture and wind speed can be jointly used to identify landscape erodibility and to map seasonal changes of wind erosion hazard across Australia. The time series wind erosion hazard maps provide detailed and useful information to assist in better targeting areas for investments and continuous monitoring, evaluation and reporting that will lead to reduced wind erosion and improved soil condition.

  11. Optimizing land cover change detection using combined pixel-based and object-based image classification in a mountainous area in Mexico

    NARCIS (Netherlands)

    Aguirre Gutiérrez, J.; Seijmonsbergen, A.C.; Duivenvoorden, J.; Epiphanio, J.C.N.; Galvão, L.S.; dos Campos, S.J.

    2011-01-01

    Inventories of past and present land cover changes form the basis for future conservation strategies and landscape management. In this study Landsat images of a mountainous area in Mexico are used in an object-based and pixel-based image classification. The land cover categories with the highest

  12. Permafrost, Seasonally Frozen Ground, Snow Cover and Vegetation in the USSR

    Science.gov (United States)

    1984-12-01

    Late Quaternary History and the Formation of Sedi- ments in the Marginal and Inland Seas (Pozdnechetvertichnaia Istorlia i Sedimentogenez...rasprostraneniia snezhnogo pokrova na poverkhnosti sushi zemnogo shara). In Geography of Snow Cover (Geo- grafiya Snezhnogo Prokrova). Moscow: Izdat...Papers, 18(3): 198-202. (36-1668) Vigdorchik, M.E. (1980) Arctic Pleistocene History and the Development of Submarine Permafrost. Boulder

  13. [Effects of ground cover and water-retaining agent on winter wheat growth and precipitation utilization].

    Science.gov (United States)

    Wu, Ji-Cheng; Guan, Xiu-Juan; Yang, Yong-Hui

    2011-01-01

    An investigation was made at a hilly upland in western Henan Province to understand the effects of water-retaining agent (0, 45, and 60 kg x hm(-2)), straw mulching (3000 and 6000 kg x hm(-2)), and plastic mulching (thickness straw- or plastic mulching was combined with the use of water-retaining agent. Comparing with the control, all the measures increased the soil moisture content at different growth stages by 0.1%-6.5%. Plastic film mulching had the best water-retention effect before jointing stage, whereas water-retaining agent showed its best effect after jointing stage. Soil moisture content was the lowest at flowering and grain-filling stages. Land cover increased the grain yield by 2.6%-20.1%. The yield increment was the greatest (14.2%-20.1%) by the combined use of straw mulching and water-retaining agent, followed by plastic mulching combined with water-retaining agent (11.9% on average). Land cover also improved the precipitation use efficiency (0.4-3.2 kg x mm(-1) x hm(-2)) in a similar trend as the grain yield. This study showed that land cover and water-retaining agent improved soil moisture and nutrition conditions and precipitation utilization, which in turn, promoted the tillering of winter wheat, and increased the grain number per ear and the 1000-grain mass.

  14. The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics

    Directory of Open Access Journals (Sweden)

    Jędrzej S. Bojanowski

    2014-12-01

    Full Text Available Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP are often used to characterize the accuracy of satellite-based cloud cover. Infrequent overpasses of polar orbiting satellites combined with the 3- or 6-h SYNOP frequency lead to collocation time differences of up to 3 h. The associated collocation error degrades the cloud cover performance statistics such as the Hanssen-Kuiper’s discriminant (HK by up to 45%. Limiting the time difference to 10 min, on the other hand, introduces a sampling error due to a lower number of corresponding satellite and SYNOP observations. This error depends on both the length of the validated time series and the SYNOP frequency. The trade-off between collocation and sampling error call for an optimum collocation time difference. It however depends on cloud cover characteristics and SYNOP frequency, and cannot be generalized. Instead, a method is presented to reconstruct the unbiased (true HK from HK affected by the collocation differences, which significantly (t-test p < 0.01 improves the validation results.

  15. Effect of heavy metals on seed germination and seedling growth of common ragweed and roadside ground cover legumes.

    Science.gov (United States)

    Bae, Jichul; Benoit, Diane L; Watson, Alan K

    2016-06-01

    In southern Québec, supplement roadside ground covers (i.e. Trifolium spp.) struggle to establish near edges of major roads and thus fail to assist turf recruitment. It creates empty niches vulnerable to weed establishment such as common ragweed (Ambrosia artemisiifolia). We hypothesized that heavy metal stresses may drive such species shifts along roadside edges. A growth chamber experiment was conducted to assess effects of metals (Zn, Pb, Ni, Cu, and Cd) on germination and seedling behaviors of roadside weed (A. artemisiifolia) and ground cover legumes (Coronilla varia, Lotus corniculatus, and Trifolium arvense). All metals inhibited T. arvense germination, but the effect was least on A. artemisiifolia. Low levels of Pb and Ni promoted germination initiation of A. artemisiifolia. Germination of L. corniculatus was not affected by Zn, Pb, and Ni, but inhibited by Cu and Cd. Germination of C. varia was decreased by Ni, Cu, and Cd and delayed by Zn and Pb. Metal additions hindered seedling growth of all test species, and the inhibitory effect on the belowground growth was greater than on the aboveground growth. Seedling mortality was lowest in A. artemisiifolia but highest in T. arvense when exposed to the metal treatments. L. corniculatus and C. varia seedlings survived when subjected to high levels of Zn, Pb, and Cd. In conclusion, the successful establishment of A. artemisiifolia along roadside edges can be associated with its greater tolerance of heavy metals. The findings also revealed that L. corniculatus is a potential candidate for supplement ground cover in metal-contaminated roadside edges in southern Québec, especially sites contaminated with Zn and Pb.

  16. Low-cost computer classification of land cover in the Portland area, Oregon, by signature extension techniques

    Science.gov (United States)

    Gaydos, Leonard

    1978-01-01

    Computer-aided techniques for interpreting multispectral data acquired by Landsat offer economies in the mapping of land cover. Even so, the actual establishment of the statistical classes, or "signatures," is one of the relatively more costly operations involved. Analysts have therefore been seeking cost-saving signature extension techniques that would accept training data acquired for one time or place and apply them to another. Opportunities to extend signatures occur in preprocessing steps and in the classification steps that follow. In the present example, land cover classes were derived by the simplest and most direct form of signature extension: Classes statistically derived from a Landsat scene for the Puget Sound area, Wash., were applied to the Portland area, Oreg., using data for the next Landsat scene acquired less than 25 seconds down orbit. Many features can be recognized on the reduced-scale version of the Portland land cover map shown in this report, although no statistical assessment of its accuracy is available.

  17. EXPLORATION ON METHOD OF AUTO-CLASSIFICATION FOR MAIN GROUND OBJECTS OF THREE GORGES RESERVOIR AREA

    Institute of Scientific and Technical Information of China (English)

    ZHANG Bao-lei; SONG Meng-qiang; ZHOU Wan-cun

    2005-01-01

    Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting the main types of ground objects in the Three Gorges Reservoir area under relatively high accuracy, after finishing such preprocessing tasks as correcting the topographical spectrum and synthesizing the data. Taking the specialized image analysis software-eCognition as the platform, the research achieved the goal of classifying through choosing samples, picking out the best wave bands, and producing the identifying functions. At the same time the extraction process partly dispelled the influence of such phenomena as the same thing with different spectrums, different things with the same spectrum, border transitions, etc. The research did certain exploration in the aspect of technological route and method of using automatic extraction of the remote sensing image to obtain the information of land cover for the regions whose ground objects have complicated spectrums.

  18. Comparing distinct ground-based lightning location networks covering the Netherlands

    Science.gov (United States)

    de Vos, Lotte; Leijnse, Hidde; Schmeits, Maurice; Beekhuis, Hans; Poelman, Dieter; Evers, Läslo; Smets, Pieter

    2015-04-01

    Lightning can be detected using a ground-based sensor network. The Royal Netherlands Meteorological Institute (KNMI) monitors lightning activity in the Netherlands with the so-called FLITS-system; a network combining SAFIR-type sensors. This makes use of Very High Frequency (VHF) as well as Low Frequency (LF) sensors. KNMI has recently decided to replace FLITS by data from a sub-continental network operated by Météorage which makes use of LF sensors only (KNMI Lightning Detection Network, or KLDN). KLDN is compared to the FLITS system, as well as Met Office's long-range Arrival Time Difference (ATDnet), which measures Very Low Frequency (VLF). Special focus lies on the ability to detect Cloud to Ground (CG) and Cloud to Cloud (CC) lightning in the Netherlands. Relative detection efficiency of individual flashes and lightning activity in a more general sense are calculated over a period of almost 5 years. Additionally, the detection efficiency of each system is compared to a ground-truth that is constructed from flashes that are detected by both of the other datasets. Finally, infrasound data is used as a fourth lightning data source for several case studies. Relative performance is found to vary strongly with location and time. As expected, it is found that FLITS detects significantly more CC lightning (because of the strong aptitude of VHF antennas to detect CC), though KLDN and ATDnet detect more CG lightning. We analyze statistics computed over the entire 5-year period, where we look at CG as well as total lightning (CC and CG combined). Statistics that are considered are the Probability of Detection (POD) and the so-called Lightning Activity Detection (LAD). POD is defined as the percentage of reference flashes the system detects compared to the total detections in the reference. LAD is defined as the fraction of system recordings of one or more flashes in predefined area boxes over a certain time period given the fact that the reference detects at least one

  19. Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN and Landsat Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Karsten Schulz

    2009-11-01

    Full Text Available Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML or k-Nearest Neighbor (k-NN indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.

  20. Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques.

    Science.gov (United States)

    Koch, David E; Mohler, Rhett L; Goodin, Douglas G

    2007-11-01

    Landscape epidemiology has made significant strides recently, driven in part by increasing availability of land cover data derived from remotely-sensed imagery. Using an example from a study of land cover effects on hantavirus dynamics at an Atlantic Forest site in eastern Paraguay, we demonstrate how automated classification methods can be used to stratify remotely-sensed land cover for studies of infectious disease dynamics. For this application, it was necessary to develop a scheme that could yield both land cover and land use data from the same classification. Hypothesizing that automated discrimination between classes would be more accurate using an object-based method compared to a per-pixel method, we used a single Landsat Enhanced Thematic Mapper+ (ETM+) image to classify land cover into eight classes using both per-pixel and object-based classification algorithms. Our results show that the object-based method achieves 84% overall accuracy, compared to only 43% using the per-pixel method. Producer's and user's accuracies for the object-based map were higher for every class compared to the per-pixel classification. The Kappa statistic was also significantly higher for the object-based classification. These results show the importance of using image information from domains beyond the spectral domain, and also illustrate the importance of object-based techniques for remote sensing applications in epidemiological studies.

  1. Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques

    Directory of Open Access Journals (Sweden)

    David E. Koch

    2007-11-01

    Full Text Available Landscape epidemiology has made significant strides recently, driven in part by increasing availability of land cover data derived from remotely-sensed imagery. Using an example from a study of land cover effects on hantavirus dynamics at an Atlantic Forest site in eastern Paraguay, we demonstrate how automated classification methods can be used to stratify remotely-sensed land cover for studies of infectious disease dynamics. For this application, it was necessary to develop a scheme that could yield both land cover and land use data from the same classification. Hypothesizing that automated discrimination between classes would be more accurate using an object-based method compared to a per-pixel method, we used a single Landsat Enhanced Thematic Mapper+ (ETM+ image to classify land cover into eight classes using both per-pixel and object-based classification algorithms. Our results show that the objectbased method achieves 84% overall accuracy, compared to only 43% using the per-pixel method. Producer’s and user’s accuracies for the object-based map were higher for every class compared to the per-pixel classification. The Kappa statistic was also significantly higher for the object-based classification. These results show the importance of using image information from domains beyond the spectral domain, and also illustrate the importance of object-based techniques for remote sensing applications in epidemiological studies.

  2. After the fire: benefits of reduced ground cover for vervet monkeys (Cercopithecus aethiops).

    Science.gov (United States)

    Jaffe, Karin Enstam; Isbell, Lynne A

    2009-03-01

    Here we describe changes in ranging behavior and other activities of vervet monkeys (Cercopithecus aethiops) after a wildfire eliminated grass cover in a large area near the study group's home range. Soon after the fire, the vervets ranged farther away from tall trees that provide refuge from mammalian predators, and moved into the burned area where they had never been observed to go before the fire occurred. Visibility at vervet eye-level was 10 times farther in the burned area than in unburned areas. They traveled faster, and adult females spent more time feeding and less time scanning bipedally in the burned area than in the unburned area. The burned area's greater visibility may have lowered the animals' perceived risk of predation there, and may have provided them with an unusual opportunity to eat acacia ants.

  3. Understanding the Effect of Land Cover Classification on Model Estimates of Regional Carbon Cycling in the Boreal Forest Biome

    Science.gov (United States)

    Kimball, John; Kang, Sinkyu

    2003-01-01

    The original objectives of this proposed 3-year project were to: 1) quantify the respective contributions of land cover and disturbance (i.e., wild fire) to uncertainty associated with regional carbon source/sink estimates produced by a variety of boreal ecosystem models; 2) identify the model processes responsible for differences in simulated carbon source/sink patterns for the boreal forest; 3) validate model outputs using tower and field- based estimates of NEP and NPP; and 4) recommend/prioritize improvements to boreal ecosystem carbon models, which will better constrain regional source/sink estimates for atmospheric C02. These original objectives were subsequently distilled to fit within the constraints of a 1 -year study. This revised study involved a regional model intercomparison over the BOREAS study region involving Biome-BGC, and TEM (A.D. McGuire, UAF) ecosystem models. The major focus of these revised activities involved quantifying the sensitivity of regional model predictions associated with land cover classification uncertainties. We also evaluated the individual and combined effects of historical fire activity, historical atmospheric CO2 concentrations, and climate change on carbon and water flux simulations within the BOREAS study region.

  4. Temporal monitoring of the soil freeze-thaw cycles over snow-cover land by using off-ground GPR

    KAUST Repository

    Jadoon, Khan

    2013-07-01

    We performed off-ground ground-penetrating radar (GPR) measurements over a bare agricultural field to monitor the freeze-thaw cycles over snow-cover. The GPR system consisted of a vector network analyzer combined with an off-ground monostatic horn antenna, thereby setting up an ultra-wideband stepped-frequency continuous-wave radar. Measurements were performed during nine days and the surface of the bare soil was exposed to snow fall, evaporation and precipitation as the GPR antenna was mounted 110 cm above the ground. Soil surface dielectric permittivity was retrieved using an inversion of time-domain GPR data focused on the surface reflection. The GPR forward model used combines a full-waveform solution of Maxwell\\'s equations for three-dimensional wave propagation in planar layered media together with global reflection and transmission functions to account for the antenna and its interactions with the medium. Temperature and permittivity sensors were installed at six depths to monitor the soil dynamics in the top 8 cm depth. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and permittivity data and in particular freeze and thaw events were clearly visible. A good agreement of the trend was observed between the temperature, permittivity and GPR time-lapse data with respect to five freeze-thaw cycles. The GPR-derived permittivity was in good agreement with sensor observations. The proposed method appears to be promising for the real-time mapping and monitoring of the frozen layer at the field scale. © 2013 IEEE.

  5. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  6. Investigating Hydrogeologic Controls on Sandhill Wetlands in Covered Karst with 2D Resistivity and Ground Penetrating Radar

    Science.gov (United States)

    Downs, C. M.; Nowicki, R. S.; Rains, M. C.; Kruse, S.

    2015-12-01

    In west-central Florida, wetland and lake distribution is strongly controlled by karst landforms. Sandhill wetlands and lakes are sand-filled upland basins whose water levels are groundwater driven. Lake dimensions only reach wetland edges during extreme precipitation events. Current wetland classification schemes are inappropriate for identifying sandhill wetlands due to their unique hydrologic regime and ecologic expression. As a result, it is difficult to determine whether or not a wetland is impacted by groundwater pumping, development, and climate change. A better understanding of subsurface structures and how they control the hydrologic regime is necessary for development of an identification and monitoring protocol. Long-term studies record vegetation diversity and distribution, shallow ground water levels and surface water levels. The overall goals are to determine the hydrologic controls (groundwater, seepage, surface water inputs). Most recently a series of geophysical surveys was conducted at select sites in Hernando and Pasco County, Florida. Electrical resistivity and ground penetrating radar were employed to image sand-filled basins and the top of the limestone bedrock and stratigraphy of wetland slopes, respectively. The deepest extent of these sand-filled basins is generally reflected in topography as shallow depressions. Resistivity along inundated wetlands suggests the pools are surface expressions of the surficial aquifer. However, possible breaches in confining clay layers beneath topographic highs between depressions are seen in resistivity profiles as conductive anomalies and in GPR as interruptions in otherwise continuous horizons. These data occur at sites where unconfined and confined water levels are in agreement, suggesting communication between shallow and deep groundwater. Wetland plants are observed outside the historic wetland boundary at many sites, GPR profiles show near-surface layers dipping towards the wetlands at a shallower

  7. Accounting for Hydrologic State in Ground-Penetrating Radar Classification Systems

    Science.gov (United States)

    2014-04-22

    on ground - penetrating radar (GPR) signals, particularly those associated with landmines , and (2) investigate the potential for developing contextual... ground - penetrating radar (GPR) signals, particularly those associated with landmines , and (2) investigate the potential for developing contextual GPR...on ground - penetrating radar (GPR) signals, particularly those associated with landmines , and (2) investigate the potential for developing contextual

  8. Urban land use and land cover classification using the neural-fuzzy inference approach with Formosat-2 data

    Science.gov (United States)

    Chen, Ho-Wen; Chang, Ni-Bin; Yu, Ruey-Fang; Huang, Yi-Wen

    2009-10-01

    This paper presents a neural-fuzzy inference approach to identify the land use and land cover (LULC) patterns in large urban areas with the 8-meter resolution of multi-spectral images collected by Formosat-2 satellite. Texture and feature analyses support the retrieval of fuzzy rules in the context of data mining to discern the embedded LULC patterns via a neural-fuzzy inference approach. The case study for Taichung City in central Taiwan shows the application potential based on five LULC classes. With the aid of integrated fuzzy rules and a neural network model, the optimal weights associated with these achievable rules can be determined with phenomenological and theoretical implications. Through appropriate model training and validation stages with respect to a groundtruth data set, research findings clearly indicate that the proposed remote sensing technique can structure an improved screening and sequencing procedure when selecting rules for LULC classification. There is no limitation of using broad spectral bands for category separation by this method, such as the ability to reliably separate only a few (4-5) classes. This normalized difference vegetation index (NDVI)-based data mining technique has shown potential for LULC pattern recognition in different regions, and is not restricted to this sensor, location or date.

  9. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

  10. Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

    Directory of Open Access Journals (Sweden)

    Yanfei Zhong

    2017-08-01

    Full Text Available Hyperspectral images and light detection and ranging (LiDAR data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI, gray-level co-occurrence matrix (GLCM and digital surface model (DSM are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC, support vector machine (SVM and multinomial logistic regression (MLR are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF. The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.

  11. Study of seasonal snow cover influencing the ground thermal regime on western flank of Da Xing'anling Mountains, northeastern China

    Institute of Scientific and Technical Information of China (English)

    XiaoLi Chang; HuiJun Jin; YanLin Zhang; HaiBin Sun

    2015-01-01

    Although many studies relevant to snow cover and permafrost have focused on alpine, arctic, and subarctic areas, there is still a lack of understanding of the influences of seasonal snow cover on the thermal regime of the soils in permafrost regions in the mid-latitudes and boreal regions, such as that on the western flank of the Da Xing'anling (Hinggan) Mountains, northeastern China. This paper gives a detailed analysis on meteorological data series from 2001 to 2010 provided by the Gen'he Weather Station, which is located in a talik of discontinuous permafrost zone and with sparse meadow on the observation field. It is inferred that snow cover is important for the ground thermal regime in the middle Da Xing'anling Mountains. Snow cover of 10-cm in thickness and five to six months in duration (generally November to next March) can reduce the heat loss from the ground to the atmosphere by 28%, and by 71% if the snow depth increases to 36 cm. Moreover, the occurrence of snow cover resulted in mean annual ground surface temperatures 4.7–8.2°C higher than the mean annual air temperatures recorded at the Gen'he Weather Station. The beginning date for stable snow cover establishment (SE date) and the initial snow depth (SDi) also had a great influences on the ground freezing process. Heavy snowfall before ground surface freeze-up could postpone and retard the freezing process in Gen'he. As a result, the duration of ground freezing was shortened by at least 20 days and the maximum depth of frost penetration was as much as 90 cm shallower.

  12. Unmasking the soil cover's disruption by use of a dynamic model of measurement aerospace parameters of ground vegetation

    Directory of Open Access Journals (Sweden)

    E. V. Vysotskaya

    2016-03-01

    Full Text Available The "Introduction" describes topicality and importance of revealing the soil cover's disruption for a wide range of fields. It was shown that spectral brightness and colorimetric parameters of ground vegetation can be used for this task. However, a traditional scheme of data processing for remote sensing requires a long-term observations and can not always be applied, if quick decision-making is necessary or there is lack of information. Such cases require the use of special methods, one of which is a dynamic model developed with authors' participation based on the following basic relationships: (+,- (-, - (+, 0, (-, 0 (0,0. The section "Brief description of a dynamic model" describes the basic principles of dynamic systems used to solve the problem. Using above-mentioned relationships, the dynamics of a system consisting of several components is constructed and its main properties are listed. The main feature of this model is that the identification of structure and parameters of the dynamic system does not required sequential order of observations (as for models based on time series. This feature of the model enables for identifying the system's parameters of dynamics of the natural system to use information from a single picture taken from the spacecraft rather than long-term observations. The section "Materials and Methods" describes specific colorimetric parameters used to analyze the vegetation cover. The section "Obtained results" contains an example of the model's application to a satellite image for detecting the differences in two sites of a field with vegetation. One site is a recultivated area near the liquidated gas-oil well, another site is non-recultivated area at a considerable distance from the well (500-1000 m. The simulation results are described by eight signed graphs (4 graphs for each sites, whose structure allows to identify the system differences between the two cases. The section "Conclusions" summarizes the results of

  13. A comparison of ground and satellite observations of cloud cover to saturation pressure differences during a cold air outbreak

    Energy Technology Data Exchange (ETDEWEB)

    Alliss, R.J.; Raman, S. [North Carolina State Univ., Raleigh, NC (United States)

    1996-04-01

    The role of clouds in the atmospheric general circulation and the global climate is twofold. First, clouds owe their origin to large-scale dynamical forcing, radiative cooling in the atmosphere, and turbulent transfer at the surface. In addition, they provide one of the most important mechanisms for the vertical redistribution of momentum and sensible and latent heat for the large scale, and they influence the coupling between the atmosphere and the surface as well as the radiative and dynamical-hydrological balance. In existing diagnostic cloudiness parameterization schemes, relative humidity is the most frequently used variable for estimating total cloud amount or stratiform cloud amount. However, the prediction of relative humidity in general circulation models (GCMs) is usually poor. Even for the most comprehensive GCMs, the predicted relative humidity may deviate greatly from that observed, as far as the frequency distribution of relative humidity is concerned. Recently, there has been an increased effort to improve the representation of clouds and cloud-radiation feedback in GCMs, but the verification of cloudiness parameterization schemes remains a severe problem because of the lack of observational data sets. In this study, saturation pressure differences (as opposed to relative humidity) and satellite-derived cloud heights and amounts are compared with ground determinations of cloud cover over the Gulf Stream Locale (GSL) during a cold air outbreak.

  14. An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms

    Directory of Open Access Journals (Sweden)

    René Roland Colditz

    2015-07-01

    Full Text Available Land cover mapping for large regions often employs satellite images of medium to coarse spatial resolution, which complicates mapping of discrete classes. Class memberships, which estimate the proportion of each class for every pixel, have been suggested as an alternative. This paper compares different strategies of training data allocation for discrete and continuous land cover mapping using classification and regression tree algorithms. In addition to measures of discrete and continuous map accuracy the correct estimation of the area is another important criteria. A subset of the 30 m national land cover dataset of 2006 (NLCD2006 of the United States was used as reference set to classify NADIR BRDF-adjusted surface reflectance time series of MODIS at 900 m spatial resolution. Results show that sampling of heterogeneous pixels and sample allocation according to the expected area of each class is best for classification trees. Regression trees for continuous land cover mapping should be trained with random allocation, and predictions should be normalized with a linear scaling function to correctly estimate the total area. From the tested algorithms random forest classification yields lower errors than boosted trees of C5.0, and Cubist shows higher accuracies than random forest regression.

  15. 基于TM影像的面向对象地表覆被信息提取%Land Cover Information Extraction Based on Object-oriented Classification

    Institute of Scientific and Technical Information of China (English)

    翁中银; 何政伟; 范娟; 叶娇珑

    2013-01-01

    Taking Yubei District which located in the southwest of China as object, based on TM remote sensing images, we obtained the cognition ability for each band of land cover through the information quantity statistics and spectral characteristic analysis. Using object-oriented classification of multi-resolution segmentation extracted the information of land cover. The total classification accuracy was 88.42% and the Kappa coefficient was 0.854 7. Then the result was compared with the results of supervised and unsupervised classification, the accuracy of object-oriented classification was higher than supervised and unsupervised classification. It approves that the technology of object-oriented classification of multi-resolution segmentation is applicable to the classification of remote sensing image of rolling country.%为验证基于TM影像的面向对象分类方法对复杂地区地表覆被信息提取的可行性,以地处西南地区的渝北为例进行实验.利用样本数据对各个波段的光谱特征进行分析,取得对各波段覆被探测能力的初步认识;基于光谱特征的多尺度分割,运用面向对象分类方法对其分类.面向对象的分类方法总精度和Kappa系数分别为88.42%和0.854 7,将其与监督、非监督分类结果对比分析.结果表明,该方法有效抑制了“椒盐”现象,取得较好的分类结果.

  16. Ground truth? Concept-based communities versus the external classification of physics manuscripts

    CERN Document Server

    Palchykov, Vasyl; Boyarsky, Alexey; Garlaschelli, Diego

    2016-01-01

    Community detection techniques are widely used to infer hidden structures within interconnected systems. Despite demonstrating high accuracy on benchmarks, they reproduce the external classification for many real-world systems with a significant level of discrepancy. A widely accepted reason behind such outcome is the unavoidable loss of non-topological information (such as node attributes) encountered when the original complex system is represented as a network. In this article we emphasize that the observed discrepancies may also be caused by a different reason: the external classification itself. For this end we use scientific publication data which i) exhibit a well defined modular structure and ii) hold an expert-made classification of research articles. Having represented the articles and the extracted scientific concepts both as a bipartite network and as its unipartite projection, we applied modularity optimization to uncover the inner thematic structure. The resulting clusters are shown to partly ref...

  17. Estimation of source location and ground impedance using a hybrid multiple signal classification and Levenberg-Marquardt approach

    Science.gov (United States)

    Tam, Kai-Chung; Lau, Siu-Kit; Tang, Shiu-Keung

    2016-07-01

    A microphone array signal processing method for locating a stationary point source over a locally reactive ground and for estimating ground impedance is examined in detail in the present study. A non-linear least square approach using the Levenberg-Marquardt method is proposed to overcome the problem of unknown ground impedance. The multiple signal classification method (MUSIC) is used to give the initial estimation of the source location, while the technique of forward backward spatial smoothing is adopted as a pre-processer of the source localization to minimize the effects of source coherence. The accuracy and robustness of the proposed signal processing method are examined. Results show that source localization in the horizontal direction by MUSIC is satisfactory. However, source coherence reduces drastically the accuracy in estimating the source height. The further application of Levenberg-Marquardt method with the results from MUSIC as the initial inputs improves significantly the accuracy of source height estimation. The present proposed method provides effective and robust estimation of the ground surface impedance.

  18. Effects of post-fire salvage logging and a skid trail treatment on ground cover, soils, and sediment production in the interior western United States

    Science.gov (United States)

    Joseph W. Wagenbrenner; Lee H. MacDonald; Robert N. Coats; Peter R. Robichaud; Robert E. Brown

    2015-01-01

    Post-fire salvage logging adds another set of environmental effects to recently burned areas, and previous studies have reported varying impacts on vegetation, soil disturbance, and sediment production with limited data on the underlying processes. Our objectives were to determine how: (1) ground-based post-fire logging affects surface cover, soil water repellency,...

  19. A method for cloud detection and opacity classification based on ground based sky imagery

    Directory of Open Access Journals (Sweden)

    M. S. Ghonima

    2012-07-01

    Full Text Available Digital images of the sky obtained using a total sky imager (TSI are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR to the RBR of a clear sky library (CSL generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

  20. A method for cloud detection and opacity classification based on ground based sky imagery

    Directory of Open Access Journals (Sweden)

    M. S. Ghonima

    2012-11-01

    Full Text Available Digital images of the sky obtained using a total sky imager (TSI are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR to the RBR of a clear sky library (CSL generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

  1. A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data

    Science.gov (United States)

    Wu, M. F.; Sun, Z. C.; Yang, B.; Yu, S. S.

    2016-11-01

    In order to reduce the “salt and pepper” in pixel-based urban land cover classification and expand the application of fusion of multi-source data in the field of urban remote sensing, WorldView-2 imagery and airborne Light Detection and Ranging (LiDAR) data were used to improve the classification of urban land cover. An approach of object- oriented hierarchical classification was proposed in our study. The processing of proposed method consisted of two hierarchies. (1) In the first hierarchy, LiDAR Normalized Digital Surface Model (nDSM) image was segmented to objects. The NDVI, Costal Blue and nDSM thresholds were set for extracting building objects. (2) In the second hierarchy, after removing building objects, WorldView-2 fused imagery was obtained by Haze-ratio-based (HR) fusion, and was segmented. A SVM classifier was applied to generate road/parking lot, vegetation and bare soil objects. (3) Trees and grasslands were split based on an nDSM threshold (2.4 meter). The results showed that compared with pixel-based and non-hierarchical object-oriented approach, proposed method provided a better performance of urban land cover classification, the overall accuracy (OA) and overall kappa (OK) improved up to 92.75% and 0.90. Furthermore, proposed method reduced “salt and pepper” in pixel-based classification, improved the extraction accuracy of buildings based on LiDAR nDSM image segmentation, and reduced the confusion between trees and grasslands through setting nDSM threshold.

  2. Some Analyses on Effects of Site Classification on Ground Motion Characteristics in the Chi-Chi,Taiwan Earthquake

    Institute of Scientific and Technical Information of China (English)

    Dong Di; Yang Jian; Liu Rui

    2006-01-01

    According to the epicenter distance and the site classification, the 404 groups of earthquake recordings of the main shock of the Chi-Chi, Taiwan China earthquake in 1999 are catalogued.Based on these data, we analyze the statistical features of duration, PGA, envelopes and the response spectra ratio of the horizontal and vertical components of the acceleration recordings. The results of these analyses show that the effect of site classification on the acceleration of various components is obvious; furthermore, fault direction also has certain effects on the characteristics of the horizontal components of ground motion. The detailed research results are as follows: ( 1 ) the duration of the horizontal components of acceleration records increases with the softening of the site; (2) the direction of fault slip has some effects on PGA's attenuation features; (3) the average envelopes of acceleration records at different distances and site classes are basically single peak functions of time and the envelopes of horizontal and vertical components of ground motion are obviously different; (4) with the same epicenter distance, EW/NS response spectrum ratios tend to approximate 1.0 as the site becomes softer and the period shorter. V/H response spectrum ratios in short periods (< 0. 1s) increase with the softening of site, however, V/H ratios within the long-period range ( > characteristic period) decrease with the softening of the site, and the decrease of V/EW ratio speeds up relatively.

  3. Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data

    Directory of Open Access Journals (Sweden)

    Sylvia Herrmann

    2011-04-01

    Full Text Available Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber. Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland. They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90% when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems.

  4. Ground truth? Concept-based communities versus the external classification of physics manuscripts

    OpenAIRE

    Palchykov, V.; Gemmetto, V.; Boiarsky, O.; Garlaschelli, D.

    2016-01-01

    Community detection techniques are widely used to infer hidden structures within interconnected systems. Despite demonstrating high accuracy on benchmarks, they reproduce the external classification for many real-world systems with a significant level of discrepancy. A widely accepted reason behind such outcome is the unavoidable loss of non-topological information (such as node attributes) encountered when the original complex system is represented as a network. In this article we emphasize ...

  5. Intensity of Ground Cover Crop Arachis pintoi, Rhizobium Inoculation and Phosphorus Application and Their Effects on Field Growth and Nutrient Status of Cocoa Plants

    Directory of Open Access Journals (Sweden)

    John Bako Baon

    2006-08-01

    Full Text Available Arachis pintoiis potentially as a cover crop for cocoa (Theobroma cacaoL. farm, however information regarding its effect on the growth of cocoa plants in the field is very limited. The objective of this experiment is to investigate the combined influence of ground cover crop A. pintoi, rhizobial bacterial inoculation and phosphorus (P fertilizer on the growth of cocoa in the field and nutrient status. This experiment laid out in split-split plot design consisted of three levels of cover crop (without, A. pintoiand Calopogonium caeruleum, two levels of rhizobium inoculation (not inoculated and inoculated and two levels of phosphorus application (no P added and P added. The results showed that in field condition the presence of A. pintoias cover crop did not affect the growth of cocoa. On the other hand, C. caeruleumas cover crop tended to restrict cocoa growth compared to A. pintoi. Application of P increased leaf number of cocoa plant. Biomass production of A. pintoiwas 40% higher than C. caeruleum. Soil organic carbon and nitrogen contents were not affected by ground cover crops, though higher value (0.235% N and 1.63% organic C was obtained from combined treatments of inoculation and P addition or neither inoculation nor P addition. In the case of no rhizobium inoculation, soil N content in cocoa farm with A. pintoicover crop was lower than that of without cover crop or with C. caeruleum. Cover crop increased plant N content when there was no inoculation, on the other hand rhizobium inoculation decreased N content of cocoa tissue. Tissue P content of cocoa plant was not influenced by A. Pintoicover crop or by rhizobium inoculation, except that the P tissue content of cocoa was 28% higher when the cover crop was C. caeruleumand inoculated. Key words : Arachis pintoi, Theobroma cacao, Calopogonium caeruleum, rhizobium, nitrogen, phosphorus.

  6. Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

    Directory of Open Access Journals (Sweden)

    François Waldner

    2015-08-01

    Full Text Available With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0.83, 0.85 and 0.79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables’ time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI. Six random forest classifiers were trained and applied along the season with different inputs: spectral bands, NDVI, as well as FAPAR, LAI and FCOVER, separately and jointly. Classifications with structural biophysical variables reached end-of-season overall accuracies ranging from 73%–76% when used alone and 77% when used jointly. This corresponds to 90% and 95% of the accuracy level achieved with the spectral bands and NDVI. FCOVER appears to be the most promising biophysical variable for classification. When assuming that the cropland extent is known, crop type classification reaches 89% with spectral information, 87% with the NDVI and 81%–84% with biophysical variables.

  7. Epiphyte-cover on seagrass (Zostera marina L. leaves impedes plant performance and radial O2 loss from the below-ground tissue

    Directory of Open Access Journals (Sweden)

    Kasper Elgetti Brodersen

    2015-08-01

    Full Text Available The O2 budget of seagrasses is a complex interaction between several sources and sinks, which is strongly regulated by light availability and mass transfer over the diffusive boundary layer (DBL surrounding the plant. Epiphyte growth on leaves may thus strongly affect the O2 availability of the seagrass plant and its capability to aerate its rhizosphere as a defence against plant toxins.We used electrochemical and fiber-optic microsensors to quantify the O2 flux, DBL and light microclimate around leaves with and without filamentous algal epiphytes. We also quantified the below-ground radial O2 loss from roots (~1 mm from the root-apex to elucidate how this below-ground oxic microzone was affected by the presence of epiphytes.Epiphyte-cover on seagrass leaves (~21% areal cover resulted in reduced light quality and quantity for photosynthesis, thus leading to reduced plant fitness. A ~4 times thicker diffusive boundary layer around leaves with epiphyte-cover impeded gas (and nutrient exchange with the surrounding water-column and thus the amount of O2 passively diffusing into the leaves in darkness. During light exposure of the leaves, radial oxygen loss from the below-ground tissue was ~2 times higher from plants without epiphyte-cover. In contrast, no O2 was detectable at the surface of the root-cap tissue of plants with epiphyte-cover during darkness, leaving the plants more susceptible to sulphide intrusion.Epiphyte growth on seagrass leaves thus negatively affects the light climate and O2 uptake in darkness, hampering the plants performance and thereby reducing the oxidation capability of its below-ground tissue.

  8. Testing the enemies hypothesis in peach orchards in two different geographic areas in eastern China: the role of ground cover vegetation.

    Directory of Open Access Journals (Sweden)

    Nian-Feng Wan

    Full Text Available Many studies have supported the enemies hypothesis, which suggests that natural enemies are more efficient at controlling arthropod pests in polyculture than in monoculture agro-ecosystems. However, we do not yet have evidence as to whether this hypothesis holds true in peach orchards over several geographic locations. In the two different geographic areas in eastern China (Xinchang a town in the Shanghai municipality, and Hudai, a town in Jiangsu Province during a continuous three-year (2010-2012 investigation, we sampled arthropod pests and predators in Trifolium repens L. and in tree canopies of peach orchards with and without the ground cover plant T. repens. No significant differences were found in the abundances of the main groups of arthropod pests and predators in T. repens between Hudai and Xinchang. The abundance, richness, Simpson's index, Shannon-Wiener index, and Pielou evenness index of canopy predators in ground cover areas increased by 85.5, 27.5, 3.5, 16.7, and 7.9% in Xinchang, and by 87.0, 27.6, 3.5, 17.0 and 8.0% in Hudai compared to those in the controls, respectively. The average abundance of Lepidoptera, Coleoptera, Homoptera, true bugs and Acarina canopy pests in ground cover areas decreased by 9.2, 10.2, 17.2, 19.5 and 14.1% in Xinchang, and decreased by 9.5, 8.2, 16.8, 20.1 and 16.6% in Hudai compared to that in control areas, respectively. Our study also found a higher density of arthropod species resources in T. repens, as some omnivorous pests and predators residing in T. repens could move between the ground cover and the orchard canopy. In conclusion, ground cover in peach orchards supported the enemies hypothesis, as indicated by the fact that ground cover T. repens promoted the abundance and diversity of predators and reduced the number of arthropod pests in tree canopies in both geographical areas.

  9. Phytoseiidae (Acari: Mesostigmata) within citrus orchards in Florida: species distribution, relative and seasonal abundance within trees, associated vines and ground cover plants.

    Science.gov (United States)

    Childers, Carl C; Denmark, Harold A

    2011-08-01

    Seven citrus orchards on reduced- to no-pesticide spray programs were sampled for predacious mites in the family Phytoseiidae (Acari: Mesostigmata) in central and south central Florida. Inner and outer canopy leaves, open flowers, fruit, twigs, and trunk scrapings were sampled monthly between September 1994 and January 1996. Vines and ground cover plants were sampled monthly between September 1994 and January 1996 in five of these orchards. The two remaining orchards were on full herbicide programs and ground cover plants were absent. Thirty-three species of phytoseiid mites were identified from 35,405 specimens collected within citrus tree canopies within the seven citrus orchards, and 8,779 specimens from vines and ground cover plants within five of the seven orchards. The six most abundant phytoseiid species found within citrus tree canopies were: Euseius mesembrinus (Dean) (20,948), Typhlodromalus peregrinus (Muma) (8,628), Iphiseiodes quadripilis (Banks) (2,632), Typhlodromips dentilis (De Leon) (592), Typhlodromina subtropica Muma and Denmark (519), and Galendromus helveolus (Chant) (315). The six most abundant species found on vines or ground cover plants were: T. peregrinus (6,608), E. mesembrinus (788), T. dentilis (451), I. quadripilis (203), T. subtropica (90), and Proprioseiopsis asetus (Chant) (48). The remaining phytoseiids included: Amblyseius aerialis (Muma), A. herbicolus (Chant), A. largoensis (Chant), A. multidentatus (Chant), A. sp. near multidentatus, A. obtusus (Koch), Chelaseius vicinus (Muma), Euseius hibisci Chant, Galendromus gratus (Chant), Metaseiulus mcgregori (Chant), Neoseiulus mumai (Denmark), N. vagus (Denmark), Phytoscutus sexpilis (Muma), Phytoseiulus macropilis (Banks), Proprioseiopsis detritus (Muma), P. dorsatus (Muma), P. macrosetae (Banks), P. rotundus (Muma), P. solens (De Leon), Typhlodromips deleoni (Muma), T. dillus (De Leon), T. dimidiatus (De Leon), T. mastus Denmark and Muma, T. simplicissimus (De Leon), and T. sp

  10. Testing the enemies hypothesis in peach orchards in two different geographic areas in eastern China: the role of ground cover vegetation.

    Science.gov (United States)

    Wan, Nian-Feng; Ji, Xiang-Yun; Jiang, Jie-Xian

    2014-01-01

    Many studies have supported the enemies hypothesis, which suggests that natural enemies are more efficient at controlling arthropod pests in polyculture than in monoculture agro-ecosystems. However, we do not yet have evidence as to whether this hypothesis holds true in peach orchards over several geographic locations. In the two different geographic areas in eastern China (Xinchang a town in the Shanghai municipality, and Hudai, a town in Jiangsu Province) during a continuous three-year (2010-2012) investigation, we sampled arthropod pests and predators in Trifolium repens L. and in tree canopies of peach orchards with and without the ground cover plant T. repens. No significant differences were found in the abundances of the main groups of arthropod pests and predators in T. repens between Hudai and Xinchang. The abundance, richness, Simpson's index, Shannon-Wiener index, and Pielou evenness index of canopy predators in ground cover areas increased by 85.5, 27.5, 3.5, 16.7, and 7.9% in Xinchang, and by 87.0, 27.6, 3.5, 17.0 and 8.0% in Hudai compared to those in the controls, respectively. The average abundance of Lepidoptera, Coleoptera, Homoptera, true bugs and Acarina canopy pests in ground cover areas decreased by 9.2, 10.2, 17.2, 19.5 and 14.1% in Xinchang, and decreased by 9.5, 8.2, 16.8, 20.1 and 16.6% in Hudai compared to that in control areas, respectively. Our study also found a higher density of arthropod species resources in T. repens, as some omnivorous pests and predators residing in T. repens could move between the ground cover and the orchard canopy. In conclusion, ground cover in peach orchards supported the enemies hypothesis, as indicated by the fact that ground cover T. repens promoted the abundance and diversity of predators and reduced the number of arthropod pests in tree canopies in both geographical areas.

  11. Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

    NARCIS (Netherlands)

    Poulter, B.; MacBean, N.; Hartley, A.; Khlystova, I.; Arino, O.; Betts, R.; Bontemps, S.; Boettcher, M.; Brockmann, C.; Defourny, P.; Hagemann, S.; Herold, M.; Kirches, C.; Lamarche, C.; Lederer, D.; Ottlé, C.; Peters, M.; Peylin, P.

    2015-01-01

    Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily trans

  12. a Comparative Study Between Pair-Point Clique and Multi-Point Clique Markov Random Field Models for Land Cover Classification

    Science.gov (United States)

    Hu, B.; Li, P.

    2013-07-01

    Markov random field (MRF) is an effective method for description of local spatial-temporal dependence of image and has been widely used in land cover classification and change detection. However, existing studies only use pair-point clique (PPC) to describe spatial dependence of neighbouring pixels, which may not fully quantify complex spatial relations, particularly in high spatial resolution images. In this study, multi-point clique (MPC) is adopted in MRF model to quantitatively express spatial dependence among pixels. A modified least squares fit (LSF) method based on robust estimation is proposed to calculate potential parameters for MRF models with different types. The proposed MPC-MRF method is evaluated and quantitatively compared with traditional PPCMRF in urban land cover classification using high resolution hyperspectral HYDICE data of Washington DC. The experimental results revealed that the proposed MPC-MRF method outperformed the traditional PPC-MRF method in terms of classification details. The MPC-MRF provides a sophisticated way of describing complex spatial dependence for relevant applications.

  13. Automated Classification of Land Cover Using Landsat 8 Oli Surface Reflectance Product and Spectral Pattern Analysis Concept - Case Study in Hanoi, Vietnam

    Science.gov (United States)

    Nguyen Dinh, Duong

    2016-06-01

    Recently USGS released provisional Landsat 8 Surface Reflectance product, which allows conducting land cover mapping over large composed of number of image scenes without necessity of atmospheric correction. In this study, the authors present a new concept for automated classification of land cover. This concept is based on spectral patterns analysis of reflected bands and can be automated using predefined classification rule set constituted of spectral pattern shape, total reflected radiance index (TRRI) and ratios of spectral bands. Given a pixel vector B6 = {b1,b2,b3,b4,b5,b6} where b1, b2,...,b6 denote bands 2, 3, ...,7 of OLI sensor respectively. By using the pixel vector B6 we can construct spectral reflectance curve. Each spectral curve is featured by a shape, which can be described in simplified form of an analogue pattern, which is consisted of 15 digits of 0, 1 and 2 showing mutual relative position of spectral vertices. Value of comparison between band i and j is 2 if bj > bi, 1 if bj = bi and 0 if bj < bi. Simplified spectral pattern is defined by 15 digits as m1,2m1,3m1,4m1,5m1,6m2,3m2,4m2,5m2,6m3,4m3,5m3,6m4,5m4,6m5,6 where mi,j is result of comparison of reflectance between bi and bj and has values of 0, 1 and 2. After construction of SSP for each pixel in the input image, the original image will be decomposed to component images, which contain pixels with the same SRCS pattern. The decomposition can be written analytically by equation A = Σnk=1Ck where A stands for original image with 6 spectral bands, n is number of component images decomposed from A and Ck is component image. For this study, we use Landsat 8 OLI reflectance image LC81270452013352LGN00 and LC81270452015182LGN00. For the decomposition, we use only six reflective bands. Each land cover class is defined by SSP code, threshold values for TRRI and band ratios. Automated classification of land cover was realized with 8 classes: forest, shrub, grass, water, wetland, develop land, barren

  14. Real-time classification of ground from lidar data for helicopter navigation

    Science.gov (United States)

    Eisenkeil, Ferdinand; Schafhitzel, Tobias; Kühne, Uwe; Deussen, Oliver

    2013-05-01

    Helicopter pilots often have to deal with bad weather conditions and degraded views. Such situations may decrease the pilots' situational awareness significantly. The worst-case scenario would be a complete loss of visual reference during an off-field landing due to brownout or white out. In order to increase the pilots' situational awareness, helicopters nowadays are equipped with different sensors that are used to gather information about the terrain ahead of the helicopter. Synthetic vision systems are used to capture and classify sensor data and to visualize them on multifunctional displays or pilot's head up displays. This requires the input data to be a reliably classified into obstacles and ground. In this paper, we present a regularization-based terrain classifier. Regularization is a popular segmentation method in computer vision and used in active contours. For a real-time application scenario with LIDAR data, we developed an optimization that uses different levels of detail depending on the accuracy of the sensor. After a preprocessing step where points are removed that cannot be ground, the method fits a shape underneath the recorded point cloud. Once this shape is calculated, the points below this shape can be distinguished from elevated objects and are classified as ground. Finally, we demonstrate the quality of our segmentation approach by its application on operational flight recordings. This method builds a part of an entire synthetic vision processing chain, where the classified points are used to support the generation of a real-time synthetic view of the terrain as an assistance tool for the helicopter pilot.

  15. Land Cover Classification for the Louisiana GAP Analysis, UTM Zone 15 NAD83, USGS [landcover_la_gap_usgs_1998

    Data.gov (United States)

    Louisiana Geographic Information Center — This data set consists of digital data describing the land use/land cover (mainly vegetation, but including water and urban environments) for the State of Louisiana...

  16. STRUCTURE AND CHARACTERISTICS OF TI-AL-NI SYSTEM COVERING, APPLIED ON THE STEEL GROUND USING ELECTRON-BEAM HEATING

    Directory of Open Access Journals (Sweden)

    I. V. Murashova

    2011-01-01

    Full Text Available The morphology of the system Ti-Al-Ni covering, received by means of self-distributing high-temperature synthesis, initiated by electron-beam heating, on the basis of steel St3 is investigated.

  17. Comparative techniques used to evaluate Thematic Mapper data for land cover classification in Logan County, West Virginia

    Science.gov (United States)

    Brumfield, J. O.; Witt, R. G.; Blodget, H. W.; Marcell, R. F.

    1985-01-01

    Several digital data processing techniques were evaluated in an effort to identify and map active/abandoned, partially reclaimed, and fully revegetated surface mine areas in the central portion of Logan County. The TM data were first subjected to various enhancement procedures, including a linear contrast stretch, principal components and canonical analysis transformations. At the same time, four general procedures were followed to produce six classifications as a means of comparing the techniques involved. Preliminary results show that various feature extraction/data reduction techniques provide classification results equal or superior to the more straightforward unsupervised clustering technique. Analyst interaction time for labelling clusters is reduced using the canonical analysis and principal components procedures, though the canonical technique has clearly produced better results to date.

  18. COMPARATIVE ASSESSMENT OF FOREST COVER IN THE REPUBLICS OF MORDOVIA AND MARI EL ACCORDING TO THE RESULTS OF THE LANDSAT SATELITE IMAGES CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    E. S. Vdovin

    2015-01-01

    Full Text Available Thestudy presents the results of an assessment of forest cover of the territories of the republics of Mordovia and Mari El on the color classification results of multispectral Landsat 8 in comparison with the data of the state register of forests. The study highlights the problem of transformation of the structure of land due to natural afforestation of agricultural land. Emphasized the importance of managing the recovery process "wildlife" in the regions of compact residence of the Finno-Ugric peoples using the methods of ecological planning of land for the purpose of solving the reconstruction of the ethnic environment of the Finno-Ugric peoples.

  19. Trends of six month nighttime ground-based cloud cover values over Manila Observatory (14.64N, 121.07E)

    Science.gov (United States)

    Gacal, G. F. B.; Lagrosas, N.

    2016-12-01

    The ground reflects thermal radiation during nighttime. Clouds reflect this radiation to the ground and cause increase in ambient temperature. In this study, trends of nighttime cloud cover are analyzed using a commercial camera (Canon Powershot A2300) that is operated continuously to capture images of clouds at 5 minute interval. The camera is situated inside a rain-proof box with a glass oculus and is placed on the rooftop of the Manila Observatory building. To detect pixels with clouds, the pictures are converted from its native JPEG format to grayscale format. The pixels are then screened for clouds by looking at the values of pixels with and without clouds. In grayscale format, pixels with clouds have greater pixel values than pixels without clouds. Based on the observations, a threshold pixel value of 17 is employed to discern pixels with clouds from pixels without clouds. When moon is present in the image, the grayscale image, which is in 8-bit unsigned integer format, is converted into double format. The moon signals are modelled using a two dimensional Gaussian function and is subtracted from the converted image (Gacal et al, 2016). This effectively removes the moon signals but preserves the cloud signals. This method is applied to the data collected from the months of January, February, March, October, November and December 2015. In Manila, dry months are from November to April. Wet months are from May to October. The trends of nighttime cloud cover values over Manila Observatory are shown in the figure below. Frequency distribution of cloud cover values of the first and last three months of the year show that dry and wet months have higher and lower frequency of low cloud cover values, respectively. The trend also exhibits a decrease of cloud cover from October to December but increases back from January until March. This is exhibited in the decrease in the frequency of cloud cover values in the 20%-100% range from October to December. This can be

  20. Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

    Directory of Open Access Journals (Sweden)

    Tessio Novack

    2011-10-01

    Full Text Available The objective of this study is to compare WorldView-2 (WV-2 and QuickBird-2-simulated (QB-2 imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. Four different feature selection algorithms were used in order to verify to which data set the most relevant object-based features belong to. Object-based classifications were performed with four different supervised algorithms applied to each data set and the obtained accuracies and model performances indexes were compared. Segmentation experiments carried out involving bands exclusively available in the WV-2 sensor generated segments slightly more similar to our reference segments (only about 0.23 discrepancy. Fifty seven percent of the different selected features and 53% of all the 80 selections refer to features that can only be calculated with the additional bands of the WV-2 sensor. On the other hand, 57% of the most relevant features and 63% of the second most relevant features can also be calculated considering only the QB-2 bands. In 10 out of 16 classifications, higher Kappa values were achieved when features related to the additional bands of the WV-2 sensor were also considered. In most cases, classifications carried out with the 8-band-related features generated less complex and more efficient models than those generated only with QB-2 band-related features. Our results lead to the conclusion that spectrally similar classes like ceramic tile roofs and bare soil, as well as asphalt and dark asbestos roofs can be better distinguished when the additional bands of the WV-2 sensor are used throughout the object-based classification process.

  1. Eupalopsellidae and Stigmaeidae (Acari: Prostigmata) within citrus orchards in Florida: species distribution, relative and seasonal abundance within trees, associated vines, and ground cover plants.

    Science.gov (United States)

    Childers, Carl C; Ueckermann, Eduard A

    2014-10-01

    Seven citrus orchards on reduced- to no-pesticide spray programs were sampled for predacious mites in the families Eupalopsellidae and Stigmaeidae (Acari: Prostigmata) in central and south central Florida. Inner and outer canopy leaves, fruit, twigs, and trunk scrapings were sampled monthly between August 1994 and January 1996. Open flowers were sampled in March from five of the sites. Two species of eupalopsellid mites (Exothorhis caudata Summers and Saniosulus harteni (van-Dis and Ueckermann)) were identified from 252 specimens collected within citrus tree canopies within the seven citrus orchards of which 249 were E. caudata. Only two E. caudata were collected from ground cover plants within five of the seven orchards. Eight species of Stigmaeidae were identified from 5,637 specimens: Agistemus floridanus Gonzalez, A. terminalis Gonzalez, Eustigmaeus arcuata (Chandhri), E. sp. near arcuata, E. segnis (Koch), Mediostigmaeus citri (Rakha and McCoy), Stigmaeus seminudus Wood, and Zetzellia languida Gonzalez were collected from within citrus tree canopies from seven orchard sites. Agistemus floridanus was the only species in either family that was abundant with 5,483 collected from within citrus tree canopies compared with only 39 from vine or ground cover plants. A total of 431 samples from one or more of 82 vines and ground cover plants were sampled monthly between September 1994 and January 1996 in five of these orchards and one or more eupalopsellids or stigmaeids were collected from 19 of these plants. Richardia brasiliensis (Meg.) Gomez had nine A. floridanus from 5 of 25 samples collected from this plant. Solanum sp. had five A. floridanus from three samples taken. Both eupalopsellid and stigmaeid species numbers represented orchards were on full herbicide programs and ground cover plants were absent. Agistemus floridanus was more abundant in the citrus orchards with on-going or recent herbicide programs compared with orchards having well-developed ground

  2. Land cover change assessment using object-oriented classification based on image segmentation in the Binah river watershed (Togo and Benin)

    Science.gov (United States)

    Badjana, M. H.; Helmschrot, J.; Wala, K.; Flugel, W. A.; Afouda, A.; Akpagana, K.

    2014-12-01

    Assessing and monitoring land cover changes over time, especially in Sub-Saharan Africa characterized by both a high population growth and the highest rate of land degradation in the world is of high relevance for sustainable land management, water security and food production. In this study, land cover changes between 1972 and 2013 were investigated in the Binah river watershed (North of Togo and Benin) using advanced remote sensing and GIS technologies to support sustainable land and water resources management efforts. To this end, multi-temporal satellite images - Landsat MSS (1972), TM (1987) and ETM+ (2013) were processed using object-oriented classification based on image segmentation and post-classification comparison methods. Five main land cover classes namely agricultural land, forest land, savannah, settlements and water bodies have been identified with overall accuracies of 75.11% (1972), 81.82% (1987), and 86.1% (2013) and respective Kappa statistics of 0.67, 0.76 and 0.83. These classification results helped to explicitly assess the spatio-temporal pattern of land cover within the basin. The results indicate that savannah as the main vegetation type in the basin has decreased from 63.3% of the basin area in 1972 to 60.4% in 1987 and 35.6% in 2013. Also the forest land which covered 20.7% in 1972 has decreased to 12.7% in 1987 and 11.7% in 2013. This severe decrease in vegetation mainly resulted from the extension of agricultural areas and settlements, which is, thus, considered as the main driving force. In fact, agricultural land increased of 61.4% from 1972 to 1987, 81.4% from 1987 to 2013 and almost twice from 1972 to 2013 while human settlements increased from 0.8% of the basin area in 1972 to 2.5% in 1987 and 7.7% in 2013. The transition maps illustrate the conversion of savannah to agricultural land at each time step relating to slash and burn agriculture, but also demonstrate the threat of environmental degradation of the savannah biome

  3. 基于模糊集合理论的中国区域土地覆盖数据集融合及精度分析%CHINA LAND COVER CLASSIFICATION FUSION BASED ON EXPERT DECISION AND ACCURACY ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    崔林丽; 陈昭; 尹球; 唐世浩; 刘荣高

    2014-01-01

    theory,is usually performed by experts according to semantic rules.Scoring is followed by the voting and decision-making procedure.Among the affinity scores of a pixel,the highest one suggests that the pixel falls into the target class linked by itself.In addition,we have also exploited spatial correlation by weighting the affinity scores of the neighboring pixels.When fusion is completed,a synthetic map (SYNMAP) combining the features of all original classification products is created.Overall consistency of class between SYNMAP and each land cover is engaged to evaluate the fusion method.All the datasets,including SYNMAP,are evaluated after being further categorized into a few of simple classes,each of which include several original or target legends.Note that classification accuracy,which offers an absolute index and is commonly seen,is not presented in the paper since we are short of ground truth data.Nevertheless,the goal of the fuzzy-theory-based method is to produce a fused map that accommodates all the advantages of different original land cover data sets and reconcile their discrepancy caused by the disagreement of different classification system.Therefore,the index of consistency between two land covers should suffice.In our experiment,ESA,MODIS/IGBP,MODIS/UMD,and MODIS/PFT are employed as the original land covers to be fused.IGBP legends are set as the target.Meanwhile,nine simple classes are used during evaluation.Overall consistencies indicate improved agreement of SYNMAP with all the other land cover products.It means that the proposed fusion method has successfully combined various features of different land cover products.The conclusions can be used for national and regional numerical model and ecological environment evaluation for further research and applications.

  4. Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task

    Directory of Open Access Journals (Sweden)

    Carl Salk

    2016-09-01

    Full Text Available The idea that closer things are more related than distant things, known as ‘Tobler’s first law of geography’, is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI, it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications.

  5. A New View of Classification in Astronomy with the Archetype Technique: An Astronomical Case of the NP-complete Set Cover Problem

    CERN Document Server

    Zhu, Guangtun

    2016-01-01

    We introduce a new generic Archetype technique for source classification and identification, based on the NP-complete set cover problem (SCP) in computer science and operations research (OR). We have developed a new heuristic SCP solver, by combining the greedy algorithm and the Lagrangian Relaxation (LR) approximation method. We test the performance of our code on the test cases from Beasley's OR Library and show that our SCP solver can efficiently yield solutions that are on average 99% optimal in terms of the cost. We discuss how to adopt SCP for classification purposes and put forward a new Archetype technique. We use an optical spectroscopic dataset of extragalactic sources from the Sloan Digital Sky Survey (SDSS) as an example to illustrate the steps of the technique. We show how the technique naturally selects a basis set of physically-motivated archetypal systems to represent all the extragalactic sources in the sample. We discuss several key aspects in the technique and in any general classification ...

  6. Effect of Polythene-covering on Above-ground tuberization and storage roots yield in Cassava (Manihot esculenta Crantz

    Directory of Open Access Journals (Sweden)

    Abdullahi N

    2014-02-01

    Full Text Available Present study aimed to investigate the effectiveness of polythene-covering on activation of dormant auxiliary buds on the stem for lateral tuber formation and the resultant effect on total storage roots yield. Three time intervals i.e. 1 day after planting, 30 days after planting and 60 days after planting used as treatment, and uncovered stem used as control. Treatments were tested in randomized complete block design with three replications. Regardless of the variety, stem polythene-covering at day 1 after planting showed the highest effect with respect to storage roots production and yield components tested. However, the effect of stem polythene-covering at day 1 after planting in terms of dry mass partitioning to storage roots was the lowest across all the treatments (25.50 to 27.37% of the biomass compared to that of stem covering at day 60 after planting (33.10 to 37.20%. This study opens new perspectives in cassava yield improvement which hitherto has not been exploited.

  7. Temporal Monitoring of the Soil Freeze-Thaw Cycles over a Snow-Covered Surface by Using Air-Launched Ground-Penetrating Radar

    KAUST Repository

    Jadoon, Khan

    2015-09-18

    We tested an off-ground ground-penetrating radar (GPR) system at a fixed location over a bare agricultural field to monitor the soil freeze-thaw cycles over a snow-covered surface. The GPR system consisted of a monostatic horn antenna combined with a vector network analyzer, providing an ultra-wideband stepped-frequency continuous-wave radar. An antenna calibration experiment was performed to filter antenna and back scattered effects from the raw GPR data. Near the GPR setup, sensors were installed in the soil to monitor the dynamics of soil temperature and dielectric permittivity at different depths. The soil permittivity was retrieved via inversion of time domain GPR data focused on the surface reflection. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and dielectric permittivity measurements. In particular, five freeze and thaw events were clearly detectable, indicating that the GPR signals respond to the contrast between the dielectric permittivity of frozen and thawed soil. The GPR-derived permittivity was in good agreement with sensor observations. Overall, the off-ground nature of the GPR system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale.

  8. Temporal Monitoring of the Soil Freeze-Thaw Cycles over a Snow-Covered Surface by Using Air-Launched Ground-Penetrating Radar

    Directory of Open Access Journals (Sweden)

    Khan Zaib Jadoon

    2015-09-01

    Full Text Available We tested an off-ground ground-penetrating radar (GPR system at a fixed location over a bare agricultural field to monitor the soil freeze-thaw cycles over a snow-covered surface. The GPR system consisted of a monostatic horn antenna combined with a vector network analyzer, providing an ultra-wideband stepped-frequency continuous-wave radar. An antenna calibration experiment was performed to filter antenna and back scattered effects from the raw GPR data. Near the GPR setup, sensors were installed in the soil to monitor the dynamics of soil temperature and dielectric permittivity at different depths. The soil permittivity was retrieved via inversion of time domain GPR data focused on the surface reflection. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and dielectric permittivity measurements. In particular, five freeze and thaw events were clearly detectable, indicating that the GPR signals respond to the contrast between the dielectric permittivity of frozen and thawed soil. The GPR-derived permittivity was in good agreement with sensor observations. Overall, the off-ground nature of the GPR system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale.

  9. Classification of Fundamental Groups of Galois Covers of Surfaces of Small Degree Degenerating to Nice Plane Arrangements

    CERN Document Server

    Amram, Meirav; Shwartz, Robert; Teicher, Mina

    2010-01-01

    Let $X$ be a surface of degree $n$, projected onto $\\mathbb{CP}^2$. The surface has a natural Galois cover with Galois group $S_n.$ It is possible to determine the fundamental group of a Galois cover from that of the complement of the branch curve of $X.$ In this paper we survey the fundamental groups of Galois covers of all surfaces of small degree $n \\leq 4$, that degenerate to a nice plane arrangement, namely a union of $n$ planes such that no three planes meet in a line. We include the already classical examples of the quadric, the Hirzebruch and the Veronese surfaces and the degree $4$ embedding of $\\mathbb{CP}^1 \\times \\mathbb{CP}^1,$ and also add new computations for the remaining cases: the cubic embedding of the Hirzebruch surface $F_1$, the Cayley cubic (or a smooth surface in the same family), for a quartic surface that degenerates to the union of a triple point and a plane not through the triple point, and for a quartic $4$-point. In an appendix, we also include the degree $8$ surface $\\mathbb{CP}...

  10. Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics

    Directory of Open Access Journals (Sweden)

    Carmelo Riccardo Fichera

    2012-03-01

    Full Text Available Remote Sensing (RS data and techniques, in combination with GIS and landscape metrics, are fundamental to analyse and characterise Land Cover (LC and its changes. The case study here described, has been conducted in the area of Avellino (Southern Italy. To characterise the dynamics of changes during a fifty year period (1954÷2004, a multi-temporal set of images has been processed: aerial photos (1954, and Landsat scenes (MSS 1975, TM 1985 and 1993, ETM+ 2004. LC pattern and its changes are linked to both natural and social processes whose driving role has been clearly demonstrated in the case study: after the disastrous Irpinia earthquake (1980, specific zoning laws and urban plans have significantly addressed landscape changes.

  11. Study of growth and development features of ten ground cover plants in Kish Island green space in warm season

    Directory of Open Access Journals (Sweden)

    S. Shooshtarian

    2016-05-01

    Full Text Available Having special ecological condition, Kish Island has a restricted range of native species of ornamental plants. Expansion of urban green space in this Island is great of importance due to its outstanding touristy position in the South of Iran. The purpose of this study was to investigate the growth and development of groundcover plants planted in four different regions of Kish Island and to recommend the most suitable and adaptable species for each region. Ten groundcover species included Festuca ovina L., Glaucium flavum Crantz., Frankenia thymifolia Desf., Sedum spurium Bieb., Sedum acre L., .Potentilla verna L., Carpobrotus acinaciformis (L. L. Bolus., Achillea millefolium L., Alternanthera dentata Moench. and Lampranthus spectabilis Haw. Evaluation of growth and development had been made by measurement of morphological characteristics such as height, covering area, leaf number and area, dry and fresh total weights and visual scoring. Physiological traits included proline and chlorophyll contents evaluated. This study was designed in factorial layout based on completely randomized blocks design with six replicates. Results showed that in terms of indices such as covering area, visual quality, height, total weight, and chlorophyll content, Pavioon and Sadaf plants had the most and the worst performances, respectively in comparison to other regions’ plants. Based on evaluated characteristics, C. acinaciformis, L. spectabilis and F. thymifolia had the most expansion and growth in all quadruplet regions and are recommend for planting in Kish Island and similar climates.

  12. PROTOTIPO DE UN SISTEMA INTEGRADO DIGITAL PARA LA CLASIFICACIÓN DE COBERTURAS Y USOS DE LA TIERRA A NIVEL DE FINCA BANANERA DIGITAL INTEGRATED SYSTEM FOR THE CLASSIFICATION OF LAND COVER AND USE TO BANANA FARM LEVEL

    Directory of Open Access Journals (Sweden)

    Darío Antonio Castañeda Sánchez

    2006-06-01

    Full Text Available Se desarrolló un prototipo de un sistema integral para la clasificación de coberturas y usos de la tierra, aplicable a los sistemas bananeros. Este se basó en dos criterios, el de la participación comunitaria y el del sensoramiento remoto. El primero se fundamenta en la incorporación del conocimiento que tiene la comunidad de su entorno mediante talleres y cartografía social, el segundo propone el empleo de herramientas tecnológicas de bajo costo para el levantamiento de las coberturas y uso de la tierra, como la adquisición de fotografías aéreas de baja altitud usando un sistema conformado por una cometa o globo, un equipo para la adquisición de las imágenes y un equipo de control en tierra. La propuesta fue aplicada mediante un estudio de caso en una finca bananera ubicada en la región de Urabá (Colombia. El análisis de imágenes permitió la agrupación de las coberturas en clases o grupos y con el aporte de la participación comunitaria se describieron los usos para cada cobertura. Finalmente se hizo un análisis de las normas ambientales relacionadas con la distribución espacial de las coberturas, hallándose por ejemplo áreas de retiro del cultivo respecto a recursos o zonas vulnerables así como su cumplimiento o no de la normatividad.An integrated system was developed for the classification of land cover and use that is applicable to banana systems. It was based on two criteria; community participation and remote sensing. The former is based upon incorporation of the knowledge that the community has regarding its surroundings through workshops and social cartography; the latter proposes the use of low cost technological tools for establishing land covers and uses, such as acquiring low altitude aerial photographs using a system comprised of a kite or balloon, equipment for image acquisition, and ground based control equipment. The proposal was applied through a case study in a banana plantation located in the Urab

  13. Research on remote sensing image segmentation based on ant colony algorithm: take the land cover classification of middle Qinling Mountains for example

    Science.gov (United States)

    Mei, Xin; Wang, Qian; Wang, Quanfang; Lin, Wenfang

    2009-10-01

    Remote sensing image based on the complexity of the background features, has a wealth of spatial information, how to extract huge amounts of data in the region of interest is a serious problem. Image segmentation refers to certain provisions in accordance with the characteristics of the image into different regions, and it is the key of remote sensing image recognition and information extraction. Reasonably fast image segmentation algorithm is the base of image processing; traditional segmentation methods have a lot of the limitations. Traditional threshold segmentation method in essence is an ergodic process, the low efficiency impacts on its application. The ant colony algorithm is a populationbased evolutionary algorithm heuristic biomimetic, since proposed, it has been successfully applied to the TSP, job-shop scheduling problem, network routing problem, vehicle routing problem, as well as other cluster analysis. Ant colony optimization algorithm is a fast heuristic optimization algorithm, easily integrates with other methods, and it is robust. Improved ant colony algorithm can greatly enhance the speed of image segmentation, while reducing the noise on the image. The research background of this paper is land cover classification experiments according to the SPOT images of Qinling area. The image segmentation based on ant colony algorithm is carried out and compared with traditional methods. Experimental results show that improved the ant colony algorithm can quickly and accurately segment target, and it is an effective method of image segmentation, it also has laid a good foundation of image classification for the follow-up work.

  14. Land Cover/Land Use Classification and Change Detection Analysis with Astronaut Photography and Geographic Object-Based Image Analysis

    Science.gov (United States)

    Hollier, Andi B.; Jagge, Amy M.; Stefanov, William L.; Vanderbloemen, Lisa A.

    2017-01-01

    For over fifty years, NASA astronauts have taken exceptional photographs of the Earth from the unique vantage point of low Earth orbit (as well as from lunar orbit and surface of the Moon). The Crew Earth Observations (CEO) Facility is the NASA ISS payload supporting astronaut photography of the Earth surface and atmosphere. From aurora to mountain ranges, deltas, and cities, there are over two million images of the Earth's surface dating back to the Mercury missions in the early 1960s. The Gateway to Astronaut Photography of Earth website (eol.jsc.nasa.gov) provides a publically accessible platform to query and download these images at a variety of spatial resolutions and perform scientific research at no cost to the end user. As a demonstration to the science, application, and education user communities we examine astronaut photography of the Washington D.C. metropolitan area for three time steps between 1998 and 2016 using Geographic Object-Based Image Analysis (GEOBIA) to classify and quantify land cover/land use and provide a template for future change detection studies with astronaut photography.

  15. Land Use and Land Cover, Existing land use derived from orthoimagery. Ground-truthing from discussion with local plan commission members., Published in 2000, 1:12000 (1in=1000ft) scale, Portage County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Land Use and Land Cover dataset current as of 2000. Existing land use derived from orthoimagery. Ground-truthing from discussion with local plan commission members..

  16. Crop cover the principal influence on non-crop ground beetle (Coleoptera, Carabidae) activity and assemblages at the farm scale in a long-term assessment.

    Science.gov (United States)

    Eyre, M D; Sanderson, R A; McMillan, S D; Critchley, C N R

    2016-04-01

    Ground beetle data were generated using pitfall traps in the 17-year period from 1993 to 2009 and used to investigate the effects of changes in surrounding crop cover on beetle activity and assemblages, together with the effects of weather variability. Beetles were recorded from non-crop field margins (overgrown hedges). Crop cover changes explained far more variation in the beetle assemblages recorded than did temperature and rainfall variation. A reduction in management intensity and disturbance in the crops surrounding the traps, especially the introduction and development of willow coppice, was concomitant with changes in individual species activity and assemblage composition of beetles trapped in non-crop habitat. There were no consistent patterns in either overall beetle activity or in the number of species recorded over the 17-year period, but there was a clear change from assemblages dominated by smaller species with higher dispersal capability to ones with larger beetles with less dispersal potential and a preference for less disturbed agroecosystems. The influence of surrounding crops on ground beetle activity in non-crop habitat has implications for ecosystem service provision by ground beetles as pest predators. These results are contrary to conventional assumptions and interpretations, which suggest activity of pest predators in crops is influenced primarily by adjacent non-crop habitat. The long-term nature of the assessment was important in elucidation of patterns and trends, and indicated that policies such as agri-environment schemes should take cropping patterns into account when promoting management options that are intended to enhance natural pest control.

  17. [Pediatric cases in preclinical emergency medicine: critical aspects in the range of missions covered by ground ambulance and air rescue services].

    Science.gov (United States)

    Schlechtriemen, T; Masson, R; Burghofer, K; Lackner, C K; Altemeyer, K H

    2006-03-01

    The aim of this study was to demonstrate differences in structure and severity of pediatric emergencies treated by aeromedical (air rescue) or ground ambulances services. Conclusions for the training of emergency physicians are discussed. In a 3-year study period, a total of 9,274 pediatric emergencies covered by the ADAC air rescue service are compared to 4,344 pediatric patients of ground ambulance services in Saarland. In aeromedical services pediatric emergencies are more frequent (12.9% vs. 6.4%), trauma predominates (59.9% vs. 35.6%) and severe injuries or diseases occur more frequently (30.5% vs. 15.0%). In both groups pediatric emergency cases are concentrated into very few diagnostic groups: more than one third of the cases involving pre-school children is due to convulsions. Respiratory diseases and intoxication are the next most frequent causes and are more common in ground ambulance patients. Head trauma is the most common diagnosis in cases of pediatric trauma, followed by musculoskeletal and thoracoabdominal trauma. All types of severe trauma are more frequent in pediatric patients of the aeromedical services. Training of emergency physicians should include pediatric life support and specific information about frequent pediatric emergency situations. For emergency physicians in aeromedical services, an intensive training in pediatric trauma life support is also necessary.

  18. Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation for the ICESat-2 Mission

    Science.gov (United States)

    Herzfeld, Ute Christina; McDonald, Brian W.; Neumann, Thomas Allen; Wallin, Bruce F.; Neumann, Thomas A.; Markus, Thorsten; Brenner, Anita; Field, Christopher

    2014-01-01

    NASA's Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission is a decadal survey mission (2016 launch). The mission objectives are to measure land ice elevation, sea ice freeboard, and changes in these variables, as well as to collect measurements over vegetation to facilitate canopy height determination. Two innovative components will characterize the ICESat-2 lidar: 1) collection of elevation data by a multibeam system and 2) application of micropulse lidar (photon-counting) technology. A photon-counting altimeter yields clouds of discrete points, resulting from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of the returned points to reflectors of interest. The objective of this paper is to derive an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2 data, based on airborne observations with a Sigma Space micropulse lidar. The mathematical algorithm uses spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors, and geostatistical classification parameters and hyperparameters. Validation shows that ground and canopy elevation, and hence canopy height, can be expected to be observable with high accuracy by ICESat-2 for all expected beam energies considered for instrument design (93.01%-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp), and 72.85%-98.68% for 0.48 msp). The algorithm derived here is generally applicable for elevation determination from photoncounting lidar altimeter data collected over forested areas, land ice, sea ice, and land surfaces, as well as for cloud detection.

  19. An Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation of the ICESat-2 Mission

    Science.gov (United States)

    Herzfeld, Ute C.; McDonald, Brian W.; Wallins, Bruce F.; Markus, Thorsten; Neumann, Thomas A.; Brenner, Anita

    2012-01-01

    The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat

  20. Effects of spatially variable snow cover on thermal regime and hydrology of an Arctic ice wedge polygon landscape identified using ground penetrating radar and LIDAR datasets

    Science.gov (United States)

    Gusmeroli, A.; Liljedahl, A. K.; Peterson, J. E.; Hubbard, S. S.; Hinzman, L. D.

    2012-12-01

    Ice wedge polygons are common in Arctic terrains underlain by permafrost. Permafrost degradation could transform low- into high centered polygons, causing profound changes in the hydrologic regime of Arctic lands, which in turn, could affect the energy balance and subsurface biodegradation of organic carbon responsible for greenhouse gas production. Understanding the linkages between microtopography, snow cover, thermal properties, and thaw depth is critical for developing a predictive understanding of terrestrial ecosystems and their feedbacks to climate. In this study, we use high frequency (500-1000 MHz) ground penetrating radar (GPR) data acquired in spring 2012 within the Next Generation Ecosystem Experiment (NGEE) study site in Barrow, AK to characterize the spatial variability of snow distribution. We compare it's distribution to microtopography, estimated using LIDAR data, and thaw depth, also estimated using ground penetrating radar collected at different times during the year and simulated over time using mechanistic thermal-hydrologic modeling. The high spatial resolution offered by LIDAR and ground penetrating radar permit detailed investigations of the control of microtopography on snow and thaw layer depth. Results suggest that microtopographical variations are responsible for substantial differences in snow accumulation. In low centered polygons, snow depth can be up to four times greater in the troughs than on the rims. Both modeling and observations suggest that the microtopography-governed snow thickness affects the thermal properties of the subsurface and thus the thaw layer thickness; regions with thicker snowpack generally correspond to regions of greater thaw depth. We conclude that a transition from low- to high centered polygons will not only impact watershed runoff but, since snow accumulation is sensitive to the microtopography, it will also impact snow distribution. In turn, snow distribution affects thaw depth thickness, and the

  1. Ground measurements of the hemispherical-directional reflectance of Arctic snow covered tundra for the validation of satellite remote sensing products

    Science.gov (United States)

    Ball, C. P.; Marks, A. A.; Green, P.; Mac Arthur, A.; Fox, N.; King, M. D.

    2013-12-01

    Surface albedo is the hemispherical and wavelength integrated reflectance over the visible, near infrared and shortwave infrared regions of the solar spectrum. The albedo of Arctic snow can be in excess of 0.8 and it is a critical component in the global radiation budget because it determines the proportion of solar radiation absorbed, and reflected, over a large part of the Earth's surface. We present here our first results of the angularly resolved surface reflectance of Arctic snow at high solar zenith angles (~80°) suitable for the validation of satellite remote sensing products. The hemispherical directional reflectance factor (HDRF) of Arctic snow covered tundra was measured using the GonioRAdiometric Spectrometer System (GRASS) during a three-week field campaign in Ny-Ålesund, Svalbard, in March/April 2013. The measurements provide one of few existing HDRF datasets at high solar zenith angles for wind-blown Arctic snow covered tundra (conditions typical of the Arctic region), and the first ground-based measure of HDRF at Ny-Ålesund. The HDRF was recorded under clear sky conditions with 10° intervals in view zenith, and 30° intervals in view azimuth, for several typical sites over a wavelength range of 400-1500 nm at 1 nm resolution. Satellite sensors such as MODIS, AVHRR and VIIRS offer a method to monitor the surface albedo with high spatial and temporal resolution. However, snow reflectance is anisotropic and is dependent on view and illumination angle and the wavelength of the incident light. Spaceborne sensors subtend a discrete angle to the target surface and measure radiance over a limited number of narrow spectral bands. Therefore, the derivation of the surface albedo requires accurate knowledge of the surfaces bidirectional reflectance as a function of wavelength. The ultimate accuracy to which satellite sensors are able to measure snow surface properties such as albedo is dependant on the accuracy of the BRDF model, which can only be assessed

  2. Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR and Landsat 8 Multispectral Imagery — Richards Island, Canada

    Directory of Open Access Journals (Sweden)

    Tobias Ullmann

    2014-09-01

    Full Text Available In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR data of dual-polarized TerraSAR-X (HH/VV and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X and up to 87% for supervised classification (Landsat 8 and Radarsat-2 for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering and wetland vegetation (dominant double bounce and volume scattering. These classes had high potential to be automatically detected with unsupervised classification techniques.

  3. Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

    Science.gov (United States)

    Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

    2016-06-01

    The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

  4. Ecosystem Service Valuation Assessments for Protected Area Management: A Case Study Comparing Methods Using Different Land Cover Classification and Valuation Approaches.

    Directory of Open Access Journals (Sweden)

    Charlotte E L Whitham

    Full Text Available Accurate and spatially-appropriate ecosystem service valuations are vital for decision-makers and land managers. Many approaches for estimating ecosystem service value (ESV exist, but their appropriateness under specific conditions or logistical limitations is not uniform. The most accurate techniques are therefore not always adopted. Six different assessment approaches were used to estimate ESV for a National Nature Reserve in southwest China, across different management zones. These approaches incorporated two different land-use land cover (LULC maps and development of three economic valuation techniques, using globally or locally-derived data. The differences in ESV across management zones for the six approaches were largely influenced by the classifications of forest and farmland and how they corresponded with valuation coefficients. With realistic limits on access to time, data, skills and resources, and using acquired estimates from globally-relevant sources, the Buffer zone was estimated as the most valuable (2.494 million ± 1.371 million CNY yr(-1 km(-2 and the Non-protected zone as the least valuable (770,000 ± 4,600 CNY yr(-1 km(-2. However, for both LULC maps, when using the locally-based and more time and skill-intensive valuation approaches, this pattern was generally reversed. This paper provides a detailed practical example of how ESV can differ widely depending on the availability and appropriateness of LULC maps and valuation approaches used, highlighting pitfalls for the managers of protected areas.

  5. Ecosystem Service Valuation Assessments for Protected Area Management: A Case Study Comparing Methods Using Different Land Cover Classification and Valuation Approaches.

    Science.gov (United States)

    Whitham, Charlotte E L; Shi, Kun; Riordan, Philip

    2015-01-01

    Accurate and spatially-appropriate ecosystem service valuations are vital for decision-makers and land managers. Many approaches for estimating ecosystem service value (ESV) exist, but their appropriateness under specific conditions or logistical limitations is not uniform. The most accurate techniques are therefore not always adopted. Six different assessment approaches were used to estimate ESV for a National Nature Reserve in southwest China, across different management zones. These approaches incorporated two different land-use land cover (LULC) maps and development of three economic valuation techniques, using globally or locally-derived data. The differences in ESV across management zones for the six approaches were largely influenced by the classifications of forest and farmland and how they corresponded with valuation coefficients. With realistic limits on access to time, data, skills and resources, and using acquired estimates from globally-relevant sources, the Buffer zone was estimated as the most valuable (2.494 million ± 1.371 million CNY yr(-1) km(-2)) and the Non-protected zone as the least valuable (770,000 ± 4,600 CNY yr(-1) km(-2)). However, for both LULC maps, when using the locally-based and more time and skill-intensive valuation approaches, this pattern was generally reversed. This paper provides a detailed practical example of how ESV can differ widely depending on the availability and appropriateness of LULC maps and valuation approaches used, highlighting pitfalls for the managers of protected areas.

  6. Computerized identification and classification of stance phases as made by front or hind feet of walking cows based on 3-dimensional ground reaction forces

    DEFF Research Database (Denmark)

    Skjøth, Flemming; Thorup, V. M.; do Nascimento, Omar Feix

    2013-01-01

    Lameness is a frequent disorder in dairy cows and in large dairy herds manual lameness detection is a time-consuming task. This study describes a method for automatic identification of stance phases in walking cows, and their classification as made by a front or a hind foot based on ground reaction...... force information. Features were derived from measurements made using two parallel 3-dimensional force plates. The approach presented is based on clustering of Centre of Pressure (COP) trace points over space and time, combined with logical sequencing of stance phases based on the dynamics...

  7. Classification of Debris-Covered Glaciers and Rock Glaciers in the Andes of Central Chile - An Approach Integrating Field Measurements, High-Resolution Satellite Imagery, and Coring Data to Estimate Water Resources

    Science.gov (United States)

    Janke, J. R.; Bellisario, A. C.; Ferrando, F. A.

    2014-12-01

    In the Dry Andes of Chile (17 to 35° S), debris-covered glaciers and rock glaciers are differentiated from "true" glaciers based on the percentage of surface debris cover, thickness of surface debris, and ice content. These landforms are more numerous than glaciers in the Central Andes; however, there are often omitted from inventories. Glaciers, debris covered glaciers, and rock glaciers are being removed by mining, while agricultural expansion and population growth have placed an additional demand on water resources. As a result, it is important to identify and locate these features to implement sustainable solutions. The objective of this study is to develop a classification system to identify debris-covered glaciers and rock glaciers based on satellite imagery interpretation. The classification system is linked to field observations and measurements of ice content. Debris covered glaciers have three subclasses: surface coverage of semi (Class 1) and fully covered (Class 2) glaciers differentiates the first two forms, whereas debris thickness is critical for Class 3 when glaciers become buried with more than 3 m of surface debris. The amount of ice decreases from more than 85%, to 65-85%, to 45-65% for semi, fully, and buried debris-covered glaciers, respectively. Rock glaciers are characterized by three stages. Class 4 rock glaciers have pronounced transverse ridges and furrows that arch across the surface, which indicate flow produce via ice. Class 5 rock glaciers have ridges and furrows that appear linear in the direction of flow, and Class 6 rock glaciers have subdued surface topography that has been denudated as the rock glacier ceases movement. Ice content decreases from 25-45% ice, to 10-25% ice, to less than 10% ice from Class 4 to 6, respectively. The classification scheme can be used to identify and map debris covered glaciers and rock glaciers to create an inventory to better estimate available water resources at the basin-wide scale.

  8. MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS

    Directory of Open Access Journals (Sweden)

    A. M. Bernales

    2016-06-01

    Full Text Available The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC and land surface temperature (LST. Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas and looking for common predictors between LSTs of these two different farming periods.

  9. GAP Land Cover - Image

    Data.gov (United States)

    Minnesota Department of Natural Resources — This raster dataset is a simple image of the original detailed (1-acre minimum), hierarchically organized vegetation cover map produced by computer classification of...

  10. GAP Land Cover - Vector

    Data.gov (United States)

    Minnesota Department of Natural Resources — This vector dataset is a detailed (1-acre minimum), hierarchically organized vegetation cover map produced by computer classification of combined two-season pairs of...

  11. Expression profile analysis of genes involved in horizontal gravitropism bending growth in the creeping shoots of ground-cover chrysanthemum by suppression subtractive hybridization.

    Science.gov (United States)

    Xia, Shengjun; Chen, Yu; Jiang, Jiafu; Chen, Sumei; Guan, Zhiyong; Fang, Weimin; Chen, Fadi

    2013-01-01

    The molecular mechanisms underlying gravitropic bending of shoots are poorly understood and how genes related with this growing progress is still unclear. To identify genes related to asymmetric growth in the creeping shoots of chrysanthemum, suppression subtractive hybridization was used to visualize differential gene expression in the upper and lower halves of creeping shoots of ground-cover chrysanthemum under gravistimulation. Sequencing of 43 selected clones produced 41 unigenes (40 singletons and 1 unigenes), which were classifiable into 9 functional categories. A notable frequency of genes involve in cell wall biosynthesis up-regulated during gravistimulation in the upper side or lower side were found, such as beta tubulin (TUB), subtilisin-like protease (SBT), Glutathione S-transferase (GST), and expensing-like protein (EXP), lipid transfer proteins (LTPs), glycine-rich protein (GRP) and membrane proteins. Our findings also highlighted the function of some metal transporter during asymmetric growth, including the boron transporter (BT) and ZIP transporter (ZT), which were thought primarily for maintaining the integrity of cell walls and played important roles in cellulose biosynthesis. CmTUB (beta tubulin) was cloned, and the expression profile and phylogeny was examined, because the cytoskeleton of plant cells involved in the plant gravitropic bending growth is well known.

  12. Quality Control Methodologies for Advanced EMI Sensor Data Acquisition and Anomaly Classification - Former Southwestern Proving Ground, Arkansas

    Science.gov (United States)

    2015-07-01

    DEMONSTRATION REPORT Quality Control Methodologies for Advanced EMI Sensor Data Acquisition and Anomaly Classification – Former Southwestern...concentrations. A total of 11.23 acres of dynamic surveys were conducted using MetalMapper advanced electromagnetic induction ( EMI ) sensor. A total of...Order Navigation Points ................................................................................13 5.2.3 Initial EMI Survey

  13. Diversity and stability of arthropod community in peach orchard under effects of ground cover vegetation%桃园生草对桃树节肢动物群落多样性与稳定性的影响

    Institute of Scientific and Technical Information of China (English)

    蒋杰贤; 万年峰; 季香云; 淡家贵

    2011-01-01

    A comparative study was conducted on the arthropod community in peach orchards with and without ground cover vegetation. In the orchard with ground cover vegetation, the individuals of beneficial, neutral, and phytophagous arthropods were 1. 48, 1. 84 and 0. 64 times of those in the orchard without ground cover vegetation, respectively, but the total number of arthropods had no significant difference with that in the orchard without ground cover vegetation. The species richness, Shannon' s diversity, and Pielou' s evenness index of the arthropods in the orchard with ground cov-er vegetation were 83. 733±4. 932, 4. 966±0. 110, and 0. 795±0. 014, respectively, being signifi-cantly higher than those in the orchard without ground cover vegetation, whereas the Berger-Parker' s dominance index was 0. 135±0. 012, being significantly lower than that (0. 184±0. 018) in the orchard without ground cover vegetation. There were no significant differences in the stability indices S/N and Sd/Sp between the two orchards, but the Nn/Np, Nd/Np, and Sn/Sp in the orchard with ground cover vegetation were 0. 883±0. 123. 1714±0. 683, and 0. 781 ±0. 040, respectively, being significantly higher than those in the orchard without ground cover vegetation. Pearson' s cor-relation analysis indicated that in the orchard with ground cover vegetation, the Shannon' s diversity index was significantly negatively correlated with Nd/Np, Sd/Sp, and S/N but had no significant correlations with Nn/Np and Sn/Sp, whereas in the orchard without ground cover vegetation, the di-versity index was significantly positively correlated with Nn/Np and Nd/Np and had no significant correlations with Sd/Sp ,Sn/Sp, and S/N.%对种植白三叶草的桃园(生草桃园)和非生草桃园的桃树节肢动物群落进行分析比较.结果表明:生草桃园桃树天敌、中性类群和植食类群数量分别是非生草桃园的1.48、1.84和0.64倍,而节肢动物群落个体总数无显著差异;与非

  14. Object-based method outperforms per-pixel method for land cover classification in a protected area of the Brazilian Atlantic rainforest region

    NARCIS (Netherlands)

    Francischinelli Rittl, T.; Cooper, M.; Heck, R.J.; Ballester, V.R.

    2013-01-01

    Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image. The objective of this

  15. Monitoring of a debris-covered and avalanche-fed glacier in the Eastern Italian Alps using ground-based SfM-MVS

    Science.gov (United States)

    Piermattei, Livia; Carturan, Luca; Cazorzi, Federico; Colucci, Renato R.; Dalla Fontana, Giancarlo; Forte, Emanuele

    2015-04-01

    The Montasio Occidentale glacier is a 0.07 km2 wide, avalanche-fed glacier located at very low-altitude (1860-2050 m a.s.l.) in the Eastern Italian Alps. The glacier is still active and shows a detectable mass transfer from the accumulation area to the lower ablation area, which is covered by a thick debris mantle. Geometric changes and mass balance have been monitored starting in 2010, combining glaciological methods and high-resolution geodetic surveying with a terrestrial laser scanner (TLS). The TLS technique has proved to be very effective in determining the volume change of this glacier, but presents several limitations as high costs, high level of specialized training and low portability. On the other hand, the recent improvements in close-range photogrammetric techniques like the Structure from Motion (SfM), combined with dense image matching algorithms as Multi View Stereo (MVS), make them competitive for high quality 3D models production. The purpose of this work was to apply ground-based photogrammetric surveys for the monitoring of the annual mass balance and surface processes of Montasio Occidentale glacier. A consumer-grade SLR camera and the SfM-MVS software PhotoScan were used to detect the changes in the surface topography of the glacier from 2012 to 2014. Different data acquisition settings were tested, in order to optimize the quality and the spatial coverage of the 3D glacier model. The accuracy of the image-based 3D models was estimated in stable areas outside the glacier, using the TLS 3D model as reference. A ground penetrating radar (GPR) survey was carried out in 2014, simultaneously to the photogrammetric survey, that was used to compare the snow height estimations obtained by photogrammetry with those obtained by geophysics. The achieved results indicate that the resolution and accuracy of the 3D models generated by the SfM-MVS technique are comparable with those obtained from TLS surveys. Consequently, almost identical volumetric changes

  16. Geospatial Method for Computing Supplemental Multi-Decadal U.S. Coastal Land-Use and Land-Cover Classification Products, Using Landsat Data and C-CAP Products

    Science.gov (United States)

    Spruce, J. P.; Smoot, James; Ellis, Jean; Hilbert, Kent; Swann, Roberta

    2012-01-01

    This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, non-woody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP value-added products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.

  17. Non-phytoseiid Mesostigmata within citrus orchards in Florida: species distribution, relative and seasonal abundance within trees, associated vines and ground cover plants and additional collection records of mites in citrus orchards.

    Science.gov (United States)

    Childers, Carl C; Ueckermann, Eduard A

    2015-03-01

    Seven citrus orchards on reduced- to no-pesticide spray programs in central and south central Florida were sampled for non-phytoseiid mesostigmatid mites. Inner and outer canopy leaves, fruits, twigs and trunk scrapings were sampled monthly between August 1994 and January 1996. Open flowers were sampled in March from five of the sites. A total of 431 samples from one or more of 82 vine or ground cover plants were sampled monthly in five of the seven orchards. Two of the seven orchards (Mixon I and II) were on full herbicide programs and vines and ground cover plants were absent. A total of 2,655 mites (26 species) within the families: Ascidae, Blattisociidae, Laelapidae, Macrochelidae, Melicharidae, Pachylaelapidae and Parasitidae were identified. A total of 685 mites in the genus Asca (nine species: family Ascidae) were collected from within tree samples, 79 from vine or ground cover plants. Six species of Blattisociidae were collected: Aceodromus convolvuli, Blattisocius dentriticus, B. keegani, Cheiroseius sp. near jamaicensis, Lasioseius athiashenriotae and L. dentatus. A total of 485 Blattisociidae were collected from within tree samples compared with 167 from vine or ground cover plants. Low numbers of Laelapidae and Macrochelidae were collected from within tree samples. One Zygoseius furciger (Pachylaelapidae) was collected from Eleusine indica. Four species of Melicharidae were identified from 34 mites collected from within tree samples and 1,190 from vine or ground cover plants: Proctolaelaps lobatus was the most abundant species with 1,177 specimens collected from seven ground cover plants. One Phorytocarpais fimetorum (Parasitidae) was collected from inner leaves and four from twigs. Species of Ascidae, Blattisociidae, Melicharidae, Laelapidae and Pachylaelapidae were collected from 31 of the 82 vine or ground cover plants sampled, representing only a small fraction of the total number of Phytoseiidae collected from the same plants. Including the

  18. The Investigation of Species and Application of Ground Cover Plants in Jiaozuo%焦作市地被植物种类及应用调查

    Institute of Scientific and Technical Information of China (English)

    韩红军; 张桂芝; 马君丽; 孔德政

    2012-01-01

    根据对焦作市建成区地被植物进行实地调查,统计得出焦作市作为地被植物应用的灌木,藤本,一、二年生花卉,宿根、球根花卉、草类共有192种65科151属.灌木应用较多,宿根、球根花卉,一、二年生花卉应用较少;提出了应用频率最高的地被植物有:马棘、月季、剑麻、铺地柏、迎春等;焦作地被植物应用形式主要有以下几种;模纹花坛和绿篱,旷地造景,路缘造景等.最后提出优化灌草比例,引进新优品种的建议.%Based on the investigation of ground cover plants, which be divided into Bush, Fujimoto, one or two annual flower, Perennial and bulbs flowers and grasses, which we proposes 192 species in the Building area in the city of Jiaozuo belong to 65 families and 151 genera. Bush is widely used, on the contrary, one or two annual flowers and Perennial and bulbs flowers used very seldom. And we discover these plants as indigofera and rose and jasmine and sisal and winter juniper etc are used the most frequently. There are these kinds of application forms as follows: mode pattern flower and hedgerow, open areas landscaping, road edge landscaping. At the last, we proposed that Optimization the Proportion of bush and grass and introduction new and excellent variety.

  19. Optimizing placements of ground-based snow sensors for areal snow cover estimation using a machine-learning algorithm and melt-season snow-LiDAR data

    Science.gov (United States)

    Oroza, C.; Zheng, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

    2016-12-01

    We present a structured, analytical approach to optimize ground-sensor placements based on time-series remotely sensed (LiDAR) data and machine-learning algorithms. We focused on catchments within the Merced and Tuolumne river basins, covered by the JPL Airborne Snow Observatory LiDAR program. First, we used a Gaussian mixture model to identify representative sensor locations in the space of independent variables for each catchment. Multiple independent variables that govern the distribution of snow depth were used, including elevation, slope, and aspect. Second, we used a Gaussian process to estimate the areal distribution of snow depth from the initial set of measurements. This is a covariance-based model that also estimates the areal distribution of model uncertainty based on the independent variable weights and autocorrelation. The uncertainty raster was used to strategically add sensors to minimize model uncertainty. We assessed the temporal accuracy of the method using LiDAR-derived snow-depth rasters collected in water-year 2014. In each area, optimal sensor placements were determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys was compared to 100 configurations of sensors selected at random. We found the accuracy of the model from the proposed placements to be higher and more consistent in each remaining survey than the average random configuration. We found that a relatively small number of sensors can be used to accurately reproduce the spatial patterns of snow depth across the basins, when placed using spatial snow data. Our approach also simplifies sensor placement. At present, field surveys are required to identify representative locations for such networks, a process that is labor intensive and provides limited guarantees on the networks' representation of catchment independent variables.

  20. A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery

    CSIR Research Space (South Africa)

    Salmon, BP

    2015-07-01

    Full Text Available accuracy while keeping the computational costs tractable. We also expand the typical 1-tier protograph used in conventional CRFs to a 2-tier graph to encapsulate the temporal dimension. This further improves the classification accuracy by modeling...

  1. A Single Classifier Using Principal Components Vs Multi-Classifier System: In Landuse-LandCover Classification of WorldView-2 Sensor Data

    OpenAIRE

    L .N. Eeti; K. M. Buddhiraju; Bhattacharya, A.

    2014-01-01

    In remote sensing community, Principal Component Analysis (PCA) is widely utilized for dimensionality reduction in order to deal with high spectral-dimension data. However, dimensionality reduction through PCA results in loss of some spectral information. Analysis of an Earth-scene, based on first few principal component bands/channels, introduces error in classification, particularly since the dimensionality reduction in PCA does not consider accuracy of classification as a requirem...

  2. Adding structure to land cover - using fractional cover to study animal habitat use.

    Science.gov (United States)

    Bevanda, Mirjana; Horning, Ned; Reineking, Bjoern; Heurich, Marco; Wegmann, Martin; Mueller, Joerg

    2014-01-01

    Linking animal movements to landscape features is critical to identify factors that shape the spatial behaviour of animals. Habitat selection is led by behavioural decisions and is shaped by the environment, therefore the landscape is crucial for the analysis. Land cover classification based on ground survey and remote sensing data sets are an established approach to define landscapes for habitat selection analysis. We investigate an approach for analysing habitat use using continuous land cover information and spatial metrics. This approach uses a continuous representation of the landscape using percentage cover of a chosen land cover type instead of discrete classes. This approach, fractional cover, captures spatial heterogeneity within classes and is therefore capable to provide a more distinct representation of the landscape. The variation in home range sizes is analysed using fractional cover and spatial metrics in conjunction with mixed effect models on red deer position data in the Bohemian Forest, compared over multiple spatio-temporal scales. We analysed forest fractional cover and a texture metric within each home range showing that variance of fractional cover values and texture explain much of variation in home range sizes. The results show a hump-shaped relationship, leading to smaller home ranges when forest fractional cover is very homogeneous or highly heterogeneous, while intermediate stages lead to larger home ranges. The application of continuous land cover information in conjunction with spatial metrics proved to be valuable for the explanation of home-range sizes of red deer.

  3. An improved DS acoustic-seismic modality fusion algorithm based on a new cascaded fuzzy classifier for ground-moving targets classification in wireless sensor networks

    Science.gov (United States)

    Pan, Qiang; Wei, Jianming; Cao, Hongbing; Li, Na; Liu, Haitao

    2007-04-01

    A new cascaded fuzzy classifier (CFC) is proposed to implement ground-moving targets classification tasks locally at sensor nodes in wireless sensor networks (WSN). The CFC is composed of three and two binary fuzzy classifiers (BFC) respectively in seismic and acoustic signal channel in order to classify person, Light-wheeled (LW) Vehicle, and Heavywheeled (HW) Vehicle in presence of environmental background noise. Base on the CFC, a new basic belief assignment (bba) function is defined for each component BFC to give out a piece of evidence instead of a hard decision label. An evidence generator is used to synthesize available evidences from BFCs into channel evidences and channel evidences are further temporal-fused. Finally, acoustic-seismic modality fusion using Dempster-Shafer method is performed. Our implementation gives significantly better performance than the implementation with majority-voting fusion method through leave-one-out experiments.

  4. Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

    Science.gov (United States)

    Kallepalli, Akhil; Kumar, Anil; Khoshelham, Kourosh; James, David B.

    2016-10-01

    Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c-Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and

  5. Classification of the ground states and topological defects in a rotating two-component Bose-Einstein condensate

    Energy Technology Data Exchange (ETDEWEB)

    Mason, Peter [Laboratoire de Physique Statistique, Ecole Normale Superieure, UPMC Paris 06, Universite Paris Diderot, CNRS, 24 rue Lhomond, F-75005 Paris (France); Institut Jean Le Rond D' Alembert, UMR 7190 CNRS-UPMC, 4 place Jussieu, F-75005 Paris (France); Aftalion, Amandine [CNRS and Universite Versailles-Saint-Quentin-en-Yvelines, Laboratoire de Mathematiques de Versailles, CNRS UMR 8100, 45 avenue des Etats-Unis, F-78035 Versailles Cedex (France)

    2011-09-15

    We classify the ground states and topological defects of a rotating two-component condensate when varying several parameters: the intracomponent coupling strengths, the intercomponent coupling strength, and the particle numbers. No restriction is placed on the masses or trapping frequencies of the individual components. We present numerical phase diagrams which show the boundaries between the regions of coexistence, spatial separation, and symmetry breaking. Defects such as triangular coreless vortex lattices, square coreless vortex lattices, and giant skyrmions are classified. Various aspects of the phase diagrams are analytically justified thanks to a nonlinear {sigma} model that describes the condensate in terms of the total density and a pseudo-spin representation.

  6. CLASSIFICATION OF GROUND BEETLES (COLEOPTERA, CARABIDAE IN SPECIES AND GENERA USING ASC-ANALYSIS OF THEIR IMAGES

    Directory of Open Access Journals (Sweden)

    Lutsenko Y. V.

    2016-09-01

    Full Text Available From a huge number of the organisms inhabiting our planet, insects make 70%, being the most numerous of the invertebrate animal classes numbering more than 2 million types. It is difficult to find such place where it would be impossible to meet representatives of this huge class. They completely took over the entire environment - water, the land, air. For them, it is the common characteristic: complex instincts, omnivorous, high fecundity, and for some of them – a public way of life. Insects can be found at tremendous heights, reaching the level of 5000 meters, and they inhabit the desert where it practically never rains, not to mention the absence of any vegetation. Deep caves where no sunlight, nor the conditions for food and existence of living organisms — it is also the habitat of insects, they can be found far beyond the Arctic circle, and even on many Islands of Antarctica, where in addition to lifeless rock, it would seem that there is nothing else. Among insects, one of the largest and most numerous families are the ground beetles (Carabidae. They subtly respond to changes in soil and vegetation, hydrothermal and micro-climatic conditions of the environment, which makes them a convenient model subject to various environmental and Zoological researches. Ground beetles belong to a large number of genera and species, often difficult to see, in this regard, we use many different signs to diagnose. We have taken into consideration the coloration, body shape, external structure, surface structure, size, and arrangement of the genitals and chaetotaxy. Due to the fact, that the number of ground beetles is enormous, and, using their appearance, it is very difficult to determine their generic identity, there is a need of automation of the identification process, due to which we require a special mechanism that would increase the accuracy of these insects. In the previous work of the authors (http://ej.kubagro.ru/2016/05/pdf/01.pdf we

  7. Water-saving ground cover rice production system reduces net greenhouse gas fluxes in an annual rice-based cropping system

    Directory of Open Access Journals (Sweden)

    Z. Yao

    2014-06-01

    Full Text Available To safeguard food security and preserve precious water resources, the technology of water-saving ground cover rice production system (GCRPS is being increasingly adopted for the rice cultivation. However, changes in soil water status and temperature under GCRPS may affect soil biogeochemical processes that control the biosphere–atmosphere exchanges of methane (CH4, nitrous oxide (N2O and carbon dioxide (CO2. The overall goal of this study is to better understand how net ecosystem greenhouse gas exchanges (NEGE and grain yields are affected by GCRPS in an annual rice-based cropping system. Our evaluation was based on measurements of the CH4 and N2O fluxes and soil heterotrophic respiration (CO2 emission over a complete year, as well as the estimated soil carbon sequestration intensity for six different fertilizer treatments for conventional paddy and GCRPS. The fertilizer treatments included urea application and no N fertilization for both conventional paddy (CUN and CNN and GCRPS (GUN and GNN, solely chicken manure (GCM and combined urea and chicken manure applications (GUM for GCRPS. Averaging across all the fertilizer treatments, GCRPS increased annual N2O emission and grain yield by 40% and 9%, respectively, and decreased annual CH4 emission by 69%, while GCRPS did not affect soil CO2 emissions relative to the conventional paddy. The annual direct emission factors of N2O were 4.01, 0.087 and 0.50% for GUN, GCM and GUM, respectively, and 1.52% for the conventional paddy (CUN. The annual soil carbon sequestration intensity under GCRPS was estimated to be an average of −1.33 Mg C ha−1 yr−1, which is approximately 44% higher than the conventional paddy. The annual NEGE were 10.80–11.02 Mg CO2-eq ha−1 yr−1 for the conventional paddy and 3.05–9.37 Mg CO2-eq ha−1 yr−1 for the GCRPS, suggesting the potential feasibility of GCRPS in reducing net greenhouse effect from rice cultivation. Using organic fertilizers for GCRPS

  8. 基于LiDAR高度纹理和神经网络的地物分类%Land cover classification using LiDAR height texture and ANNs

    Institute of Scientific and Technical Information of China (English)

    乔纪纲; 刘小平; 张亦汉

    2011-01-01

    使用LiDAR单一数据进行点云分割工作时,基于斜率的严格分割LiDAR点云的方法不能很好的适应复杂地物的分类工作.本文将LiDAR粗分割后的点云转换为高度图像和反射强度图像,并求取高度图像GLCM高度纹理.将4种GLCM高度纹理、地面粗糙系数、平均高度和平均反射强度共7种纹理作为识别地面覆盖物的特征,并利用后向传播神经网络(BP-ANN)方法对LiDAR数据进行地物识别.实验表明,这种方法能够从LiDAR独立数据源中有效的实现地物分类,实验获得的精度大于90%.与传统的最大似然法进行对比,BP-ANN的分类精度高于最大似然法.当预设地面类型能同时满足被光学影像和LiDAR数据识别的条件时,LiDAR高度纹理分类与光学影像分类结果的一致性达到76.5%.%The method of strict slope threshold algorithm is not sufficient to achieve complex object identification or ground features classification from LiDAR data. In this research, artificial intelligence is used to classify the ground features based on the LiDAR height texture. Average elevation image, average intensity image and ground roughness index image are derived from LiDAR points. Then, 4 GLCM texture features including entropy, various, second moment and homogeneity texture are measured. Finally, BP-ANNs are used to classify the texture measure into five ground feature types. A coastal area of Zhujiang Delta,South of China. is taken as the study area. The method employed in this research can efficiently work with single LiDAR data source and the accuracy of classification result is > 90%, and the classification accuracy of Maximal Likelihood method (ML) is 86.8% for comparison. When the result of ANNs classification is compared with the result of optical image classification, it can be found that 76.5% sample points are in accord.

  9. Classification of freshwater ice conditions on the Alaskan Arctic Coastal Plain using ground penetrating radar and TerraSAR-X satellite data

    Science.gov (United States)

    Jones, Benjamin M.; Gusmeroli, Alessio; Arp, Christopher D.; Strozzi, Tazio; Grosse, Guido; Gaglioti, Benjamin V.; Whitman, Matthew S.

    2013-01-01

    Arctic freshwater ecosystems have responded rapidly to climatic changes over the last half century. Lakes and rivers are experiencing a thinning of the seasonal ice cover, which may increase potential over-wintering freshwater habitat, winter water supply for industrial withdrawal, and permafrost degradation. Here, we combined the use of ground penetrating radar (GPR) and high-resolution (HR) spotlight TerraSAR-X (TSX) satellite data (1.25 m resolution) to identify and characterize floating ice and grounded ice conditions in lakes, ponds, beaded stream pools, and an alluvial river channel. Classified ice conditions from the GPR and the TSX data showed excellent agreement: 90.6% for a predominantly floating ice lake, 99.7% for a grounded ice lake, 79.0% for a beaded stream course, and 92.1% for the alluvial river channel. A GIS-based analysis of 890 surface water features larger than 0.01 ha showed that 42% of the total surface water area potentially provided over-wintering habitat during the 2012/2013 winter. Lakes accounted for 89% of this area, whereas the alluvial river channel accounted for 10% and ponds and beaded stream pools each accounted for Arctic with increasing stressors related to climate and land use change.

  10. The power of low-resolution spectroscopy: On the spectral classification of planet candidates in the ground-based CoRoT follow-up

    CERN Document Server

    Eiff, M Ammler-von; Guenther, E W; Stecklum, B; Cabrera, J

    2015-01-01

    Planetary transits detected by the CoRoT mission can be mimicked by a low-mass star in orbit around a giant star. Spectral classification helps to identify the giant stars and also early-type stars which are often excluded from further follow-up. We study the potential and the limitations of low-resolution spectroscopy to improve the photometric spectral types of CoRoT candidates. In particular, we want to study the influence of the signal-to-noise ratio (SNR) of the target spectrum in a quantitative way. We built an own template library and investigate whether a template library from the literature is able to reproduce the classifications. Including previous photometric estimates, we show how the additional spectroscopic information improves the constraints on spectral type. Low-resolution spectroscopy ($R\\approx$1000) of 42 CoRoT targets covering a wide range in SNR (1-437) and of 149 templates was obtained in 2012-2013 with the Nasmyth spectrograph at the Tautenburg 2m telescope. Spectral types have been d...

  11. Progress in the study of vegetation cover classification of multispectral remote sensing imagery%多光谱遥感影像植被覆盖分类研究进展

    Institute of Scientific and Technical Information of China (English)

    闫利; 江维薇

    2016-01-01

    利用多光谱遥感影像进行植被覆盖分类是目前遥感技术应用的热点研究领域之一。在广泛调研文献的基础上,综述了近年来多光谱遥感影像植被分类研究现状和进展,较全面深入地分析了各种植被分类特征、分类算法的优缺点、适应性和应用情况,指出了当前面临的难点和挑战,并对未来发展趋势进行了展望。未来多光谱遥感影像的植被分类不仅要从分类算法上进行创新,提高分类器的自动化程度、分类效率和学习速度,扩大适用范围,增强鲁棒性,而且同样不能忽视对植被分类新特征的挖掘,提高特征的可分性,融合多源数据、利用多时相影像、挖掘更多新特征参与植被分类是未来的发展趋势。%Vegetation cover classification using multispectral remote sensing imagery is a hot research area, in which various new methods emerge endlessly. On the basis of reading a large number of references, the authors summarized in this paper the status and progress of vegetation cover classification with multispectral remote sensing imagery , analyzed advantages and disadvantages, adaptation and application of each vegetation classification feature and method, pointed out current difficulties and challenge, and predicted future development trend. The analysis suggests that future vegetation cover classification of multispectral remote sensing imagery needs not only innovation of classifier in the aspects of improvement of automation, efficiency, learning rate, adaptation and robustness, but also feature mining of vegetation classification. For the purpose of enhancing such aspects as using feature reparability and fusing multisource data, the adoption of multi -temporal images and the tapping of more new features in vegetation classification seem to be future trends.

  12. The short term influence of aboveground biomass cover crops on C sequestration and β–glucosidase in a vineyard ground under semiarid conditions

    Directory of Open Access Journals (Sweden)

    Fernando Peregrina

    2014-10-01

    Full Text Available Tillage and semiarid Mediterranean climatic conditions accelerate soil organic matter losses in Spanish vineyards. Previous studies showed that cover crops can increase soil organic carbon (SOC in Mediterranean vineyards. The objectives of this study were to evaluate the influence of two different cover crops in the short term on soil C sequestration in a semiarid vineyard and to study the potential use of both β–glucosidase enzimatic activity (GLU and the GLU/SOC ratio in order to assess the SOC increase. The experiment was carried out in a cv. Tempranillo (Vitis vinifera L. vineyard on a Oxyaquic Xerorthent soil in Rioja winegrowing region (NE, Spain. The experimental design was established in 2009 with three treatments: conventional tillage; sown barley cover crop (Hordeum vulgare, L.; sown Persian clover cover crop (Trifolium resupinatum L.. Carbon in the aboveground biomass with each cover crop was monitored. Soil was sampled in June 2011 and June 2012, and SOC, GLU and the GLU/SOC ratio were determined. After 3 years both cover crops increased SOC at soil surface with C sequestration rates of 0.47 and 1.19 t C ha-1 yr-1 for BV and CV respectively. GLU and GLU/SOC ratio increased in both cover crops at 0-5 cm soil depth. The C sequestration rates and GLU were related to the cover crops aboveground biomass. In consequence, in semiarid vineyards under cover crops GLU could be an appropriate indicator to asses the increase of SOC and the soil quality improvement in the short-term (2-3 years.

  13. Land cover/use classification of Cairns, Queensland, Australia: A remote sensing study involving the conjunctive use of the airborne imaging spectrometer, the large format camera and the thematic mapper simulator

    Science.gov (United States)

    Heric, Matthew; Cox, William; Gordon, Daniel K.

    1987-01-01

    In an attempt to improve the land cover/use classification accuracy obtainable from remotely sensed multispectral imagery, Airborne Imaging Spectrometer-1 (AIS-1) images were analyzed in conjunction with Thematic Mapper Simulator (NS001) Large Format Camera color infrared photography and black and white aerial photography. Specific portions of the combined data set were registered and used for classification. Following this procedure, the resulting derived data was tested using an overall accuracy assessment method. Precise photogrammetric 2D-3D-2D geometric modeling techniques is not the basis for this study. Instead, the discussion exposes resultant spectral findings from the image-to-image registrations. Problems associated with the AIS-1 TMS integration are considered, and useful applications of the imagery combination are presented. More advanced methodologies for imagery integration are needed if multisystem data sets are to be utilized fully. Nevertheless, research, described herein, provides a formulation for future Earth Observation Station related multisensor studies.

  14. The comparison analysis of land cover change based on vegetation index and multispectral classification (Case study Leihitu Peninsula Ambon City District

    Directory of Open Access Journals (Sweden)

    W.A. Siahaya

    2015-07-01

    Full Text Available The study utilizes Landsat-7 ETM+ 2001and Landsat TM5 2009 based on Normalized Differences Vegetation Index (NDVI and 457 colour composite at the study area located in Leihitu Peninsula, Ambon City District, Ambon Island, Moluccas Province. The classified satellite data under NDVI and 457 colour composite of 2001 and 2009 of 2001 and 2009 were used to determine land cover change that have occurred in the study areas. This study attempts to use a comparative change detection analysis in land cover that has occurred in the study area with NDVI and 457 colour composite over 9 year period (2001 to 2009. The results of the present study disclose that total area increased their land cover were bare land and impermeable surface, herbaceous and shrubs, low density vegetation, and medium density vegetation, while high density vegetation is decreasing in both NDVI and 457 colour composite analysis. Overall accuracy was estimated to be around 94.3 % for NDVI and for 457 Colour composites was 84.7%. The study area has experienced a change in its land cover between 2001 and 2009 in both NDVI and 457 false colour composite analyses. The whole land cover types have experienced increased in both methods, except high density vegetation. The transformations of spectral vegetation (NDVI product more closely with actual land cover compared with 457 colour composite product.

  15. 煤田火区特征的土地覆盖分类方法——以乌达煤田火区为例%The approaches of land cover classification of the Wuda coal fire area

    Institute of Scientific and Technical Information of China (English)

    张春燕; 马建伟; 赵铁雄; 康利花; 郭杉; 关燕宁; 武建军; 李加洪; 蔡丹路; 孔冰; 贾跃荣; 安旭东

    2011-01-01

    Land cover change is an important scientific issue for the land evaluation and eco-environmental change forecasting. It is the necessary means to study the eco-environmental changes in coal fire zone by acquiring high precise land cover map through accurate classification approaches. In this paper, Maximum likelihood classification (MLC), Spectral Angle Mapping (SAM), object-oriented classification (OOC) and the multi-level classification based on compound subregion (MCBCS) approaches are used to classify land cover in the Wuda coal fire area. The results show that the multi-level classification based on compound subregion method leads to the highest accuracy up to 92.97% and the Kappa Coefficient is 0.9155. This method segments the study area based on the thermal characteristic, thermal anomalies, landscapc and the disturbing to ecosystems. It reduces the confusion among different landcover types. emphasizes the zonal and regularity of land cover of the coal fire area in Gobi. and increases the separability of land cover. The multi-level classification based on compound subregion increases the whole accuracy by the accuracy improvement of single land cover.%土地覆盖变化是土地分析与评价和生态环境变化预测的重要科学基础,通过精确的土地覆盖分类方法获取高精度的土地覆盖图是研究煤田火区生态环境变化的必要手段.本文以最大似然法、光谱角度法、面向对象分类法和基于复合分区的分层分类法进行乌达煤田火区土地覆盖分类的方法研究.研究结果表明,基于复合分区的分层分类方法分类精度较高,总体分类精度为92.97%,kappa系数为0.9155.该方法通过基于地表热辐射特征、热异常状况、地貌类型,以及对生态系统扰动状况等的划分,减少了地物信息的混淆度,即通过提高单一地物的分类精度来提高总体分类精度,突出位于戈壁区的煤田火区土地覆盖的地带性和规律性特征,增加

  16. Impact of no-till cover cropping of Italian ryegrass on above and below ground faunal communities inhabiting a soybean field with special emphasis on soybean cyst nematodes

    Science.gov (United States)

    Two field trials were conducted in Maryland to evaluate the ability of an Italian ryegrass (IR) (Lolium multiflorum) cover crop in a no-till soybean (Glycine max) planting to 1) reduce populations of plant-parasitic nematodes (i.e., the soybean cyst nematode, Heterodera glycines and lesion nematodes...

  17. One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

    Directory of Open Access Journals (Sweden)

    Daniel Guidici

    2017-06-01

    Full Text Available In this study, a 1-D Convolutional Neural Network (CNN architecture was developed, trained and utilized to classify single (summer and three seasons (spring, summer, fall of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF and Support Vector Machine (SVM classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM, 1.9% (CNN to 3.5% (RF over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products.

  18. Effects of environmental chemicals on useful insects and pests. Studies on the aluminium tolerance of some forest ground cover species. Nutz- und Schadinsekten in Abhaengigkeit von Umweltchemikalien. Aluminiumtoleranz von Waldbodenpflanzen

    Energy Technology Data Exchange (ETDEWEB)

    Albert, A.; Bogenschuetz, H.; Buecking, W.; Hradetzky, J.; Koenig, E.; Kublin, E.

    1986-01-01

    In the present issue one of four contributions deals with the aluminium tolerance of some forest ground cover species. Growth results are indicated for the forest ground cover species Poa nemoralis, Luzula luzuloides, Deschampsia flexuosa, Nardus stricta, Milium effusum and Melica uniflora as potted cultures on sand receiving nitrogen in different ratios of form and in different concentrations, the aluminium concentration being variable in the culture broths with a pH-value of 4.0. Low aluminium concentrations (10.8 mg/l Al) in the culture broths enhanced the growth of all species, some species were adversely affected and showed impaired growth (Poa nemoralis, Milium effusum, Melica uniflora) from high aluminium-ion concentrations (108 mg/l Al), but others had their best growth results - varying according to the form of nitrogen offered - only if aluminium concentrations in the culture broth were high. The species examined accumulate aluminium in their above-ground biomass to varying extents. With 21 figs., 12 tabs.

  19. Research on Remote Sensing Fusion Method of Land Cover Classification Based on DEM and MODIS%DEM和MODIS数据融合的土地覆盖分类遥感方法研究

    Institute of Scientific and Technical Information of China (English)

    张思琪

    2016-01-01

    随着搭载在TERRA卫星上的中分辨率成像光谱仪(MODIS)的出现,它以数据丰富、时间分辨率高和覆盖范围广等特点,为植被遥感估产提供了较好的数据源。本文利用植被受坡度影响的特性,从数字高程模型(digital elevation models,简称DEM)中提取坡度信息,考虑到MODIS能提供多时相及丰富的数据,采用DEM产生的坡度和两个时相MODIS影像数据及植被指数复合提取植被面积,经过比较试验证明,在南方丘陵山区的复杂地形区域,多源信息复合相对于单纯利用单景影像数据可以明显提高土地分类估算的精度。%Because of the hilly region, more cloud and variety of plants, it is very difficult to perform the land cover classification. With the launch of TERRA, Moderate resolution Imaging Spectroradiometer (MODIS), with abundant information, quickly acquiring data and wide range of coverage, is a new data for estimation of rice yield. This study considered the characteristics of hilly region, and the digital slope image derived from the DEM map and multitemporal MODIS were used for the purpose of improving the classification accuracy of MODIS in large hilly region. The results showed that the slope and mutitemporal MODIS image can improve the accuracy of land cover classification in hilly region than only one MODIS image.

  20. SAR数据与光学数据融合在土地覆盖分类中的应用研究%Use Fusion of SAR and Optical images for Land Cover Classification

    Institute of Scientific and Technical Information of China (English)

    姬永杰; 岳彩荣; 张王菲

    2016-01-01

    采用ALOS-1-P ALSAR数据的强度信息、 HV/HH极化比值信息和HV&HH相干系数与TM影像融合,以支持向量机( SVM)的方法对土地覆盖进行分类,对比了TM影像、 TM+SAR强度影像、 TM+HV/HH比值影像、 TM+相干影像的分类结果。结果表明:分类精度由高到依次为TM+相干影像>TM+HV/HH比值影像>TM+SAR强度影像>TM影像;采用SAR数据与光学数据融合,可以在不同程度上提高土地利用覆盖分类的精度。%In this paper, we fused TM image with intensity information of ALOS-1-PALSAR data, the informa-tion of HV/HH polarization ratio and coherence coefficient. The support vector machine ( SVM) method was used to classify the land cover. The classification results of TM image, TM+SAR intensity image, TM+HV/HH ratio image and TM+ coherence image were compared. The results showed that the highest classification accuracy was the fusion of TM and coherence image, the followed one was TM and HV/HH, then was TM and intensity image and the low-est one is TM image. To varying degrees, the classification accuracy of land utilizes and cover could be improved with the fusion of optical images and SAR images.

  1. Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations

    Directory of Open Access Journals (Sweden)

    C. L. Huang

    2015-09-01

    Full Text Available Accurately measuring the spatial distribution of the snow depth is difficult because stations are sparse, particularly in western China. In this study, we develop a novel scheme that produces a reasonable spatial distribution of the daily snow depth using kriging interpolation methods. These methods combine the effects of elevation with information from Moderate Resolution Imaging Spectroradiometer (MODIS snow cover area (SCA products. The scheme uses snow-free pixels in MODIS SCA images with clouds removed to identify virtual stations, or areas with zero snow depth, to compensate for the scarcity and uneven distribution of stations. Four types of kriging methods are tested: ordinary kriging (OK, universal kriging (UK, ordinary co-kriging (OCK, and universal co-kriging (UCK. These methods are applied to daily snow depth observations at 50 meteorological stations in northern Xinjiang Province, China. The results show that the spatial distribution of snow depth can be accurately reconstructed using these kriging methods. The added virtual stations improve the distribution of the snow depth and reduce the smoothing effects of the kriging process. The best performance is achieved by the OK method in cases with shallow snow cover and by the UCK method when snow cover is widespread.

  2. A review of the developments of self-etching primers and adhesives -Effects of acidic adhesive monomers and polymerization initiators on bonding to ground, smear layer-covered teeth.

    Science.gov (United States)

    Ikemura, Kunio; Kadoma, Yoshinori; Endo, Takeshi

    2011-01-01

    This paper reviews the developments of self-etching primers and adhesives, with a special focus on the effect of acidic adhesive monomers and polymerization initiators on bonding to ground, smear layer-covered teeth. Ionized acidic adhesive monomers chemically interact with tooth substrates and facilitate good bonding to ground dentin. Polymerization initiators in self-etching primers further promote effective bonding to ground dentin. To promote bonding to both dentin and enamel, phosphonic acid monomers such as 6-methacryloyloxyhexyl phosphonoacetate (6-MHPA) were developed. These novel adhesive monomers also have a water-soluble nature and are hence endowed with sufficient demineralization capability. A new single-bottle, self-etching, 2-hydroxyethyl methacrylate (HEMA)-free adhesive comprising 6-MHPA and 4-acryloyloxyethoxycarbonylphthalic acid (4-AET) was developed. This novel adhesive enabled strong adhesion to both ground enamel and dentin, but its formulation stability was influenced by pH value of the adhesive. To develop hydrolytically stable, single-bottle, self-etching adhesives, hydrolytically stable, radical-polymerizable acidic monomers with amide or ether linkages have been developed.

  3. GAP Land Cover - Tiled Raster

    Data.gov (United States)

    Minnesota Department of Natural Resources — This raster dataset is a detailed (1-acre minimum), hierarchically organized vegetation cover map produced by computer classification of combined two-season pairs of...

  4. Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

    Science.gov (United States)

    Zobler, L.; Lewis, R.

    1988-01-01

    The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

  5. Locally optimized separability enhancement indices for urban land cover mapping

    DEFF Research Database (Denmark)

    Feyisa, Gudina L.; Meilby, Henrik; Darrel Jenerette, G.

    2016-01-01

    Landsat data were used to assess urbanization-induced dynamics in Land use/cover (LULC), surface thermal intensity, and its relationships with urban biophysical composition. The study was undertaken in Addis Ababa city, Ethiopia. Ground-based data and high resolution images were used as reference...... data in LULC classification. To more accurately quantify landscape patterns and their changes, we applied new locally optimized separability enhancement indices and decision rules (SEI–DR approach) to address commonly observed classification accuracy problems in urban environments. We tested the SEI...... classification method, use of hotspot analysis, and the investigations of the UHI for an African city fill important research gaps for studies of urban thermal variation....

  6. National Land Cover Database: 1986-1993

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — NLCD 92 (National Land Cover Dataset 1992) is a 21-category land cover classification scheme that has been applied consistently over the conterminous U.S. It is...

  7. National Land Cover Database: 1986-1993

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — NLCD 92 (National Land Cover Dataset 1992) is a 21-category land cover classification scheme that has been applied consistently over the conterminous U.S. It is...

  8. 9种多年生地被植物在华北高寒区的抗寒性%Study on Cold -resistance of Several Ground Cover Plants in the Cold Plateau of North China

    Institute of Scientific and Technical Information of China (English)

    张晓磊; 马建平; 宋国亮; 李欣儒; 张立峰

    2012-01-01

    The study with introduced ground cover plants as meterials. Through observations on their natural growth conditions and growth morphology and demonstration tests of artificial low-temperature stress root cold physiological changes and physical growth of strain recovery validation studies, the results showed that 0 - -18℃ low temperature processing, the relative conductivity, soluble sugar and praline contents of nine perennial ground cover plants were all on the rise, while in the - 18- - 36℃ processing, soluble sugar and free proline content of Platycodon grandiforus. Hemerocallis stella remained rise after fall. The Hosta plantaginea, Aster novibelgii, Lilium brownii var. viridulum showed continuous downward trend. Combination of winter cold stress, sexual and physical strain to restore growth status showed that nine perennial ground cover plants could be successful overwintering in north China. Basis of resistance to the cold, the orders were Platycodon grandiforus 〉 HemerocaUis stella 〉 Hemerocallis middenclorffii 〉 Paeonia lactiflora 〉 P. lactiflora 〉 Sedum spectabile 〉 Hosta plantaginea 〉 Lilium brownii var. viridulum.%以引种的9种多年生地被植物为材料,通过对其在华北高寒区自然生长条件下的越冬性与生长形态观测,以及人工低温胁迫下根系抗寒生理指标变化与株体生长恢复的实证研究表明,0-18℃处理温段,9种地被植物的相对电导率、可溶性糖和游离脯氨酸含量均呈上升趋势;在-18--6℃处理温段,桔梗、金娃娃萱草可溶性搪和游离脯氨酸含量仍保持上升而后再下降,而玉椿、荷兰菊、百合则呈持续下降趋势。结合越冬性与低温胁迫下株体恢复生长状况认为,9种地被植物在华北高寒区常年环境下均可越冬,其抗寒能力依次为:桔梗〉金娃娃萱草〉大花萱草〉单瓣芍药〉重瓣芍药〉八宝景天〉玉簪〉荷兰菊〉百合。

  9. 氯盐融雪剂对4种地被植物种子萌发的影响%Effect of chloride deicing salts on seed germination of four ground covers species

    Institute of Scientific and Technical Information of China (English)

    杨冬; 周广柱

    2015-01-01

    The effects of deicing salts on seed germination and growth of shoots and roots of four kinds of ground covers (Poa pratensts, Bromus inermis Layss,Coreopsis basalis,Cosmos bipinnatus Cav. ) were studied. The length of roots and shoots of ground covers were also determined in this paper. The results showed that the inhibition effect on seed germination and growth response in the four kinds of ground covers was increased with increasing concentration of deicing salts. Cosmos bipinnatus Cav.showed the highest tolerance to deicing salts, followed by Bromus inermis Layss, Coreopsis basalis and Poa pratensis. The critical value of tolerance to deicing salts were 14.69 g/L, 10.04 g/L, 7.38 g/L and 7.31 g/L forCosmos bipinnatus Cav, Bromus inermis Layss, Coreopsis basalis and Poa pratensis,respectively and the maximum value were 21.08 g/L, 16.51 g/L, 14.67 g/L and 13.50 g/L, respectively.%以草地早熟禾、无芒雀麦、金鸡菊、波斯菊4种地被植物种子为研究材料,探讨不同浓度氯盐类融雪剂对其发芽的影响。结果表明:随着融雪剂浓度的增加,4种地被植物种子萌发、幼芽、幼根生长受到的抑制作用增强。4种地被植物对融雪剂胁迫的耐受能力大小依次为波斯菊>无芒雀麦>金鸡菊>草地早熟禾,耐受临界值分别为14.69 g/L、10.04 g/L、7.38 g/L和7.31 g/L,极限值分别为21.08 g/L、16.51 g/L、14.67 g/L和13.50 g/L。

  10. Classification of very high resolution satellite remote sensing data in a pilot phase of the forest cover classification of the Democratic Republic of Congo, Forêts d'Afrique Central Evaluées par Télédetection (FACET) product

    Science.gov (United States)

    Singa Monga Lowengo, C.

    2012-12-01

    The Observatoire Satellital des Forêts d'Afrique Centrale (OSFAC) based in Kinshasa, serves as the focal point of the GOFC-GOLD network for Central Africa. OSFAC's long term objective is building regional capacity to use remotely sensed data to map forest cover and forest cover change across Central Africa. OSFAC archives and disseminates satellite data, offers training in geospatial data applications in coordination with the University of Kinshasa, and provides technical support to CARPE partners. Forêts d'Afrique Centrale Évaluées par Télédétection (FACET) is an OSFAC initiative that implements the UMD/SDSU methodology at the national level and quantitatively evaluates the spatiotemporal dynamics of forest cover in Central Africa. The multi-temporal series of FACET data is a useful contribution to many projects, such as biodiversity monitoring, climate modeling, conservation, natural resource management, land use planning, agriculture and REDD+. I am working as Remote Sensing and GIS Officer in various projects of OSFAC. My activities include forest cover and lands dynamics monitoring in Congo Basin. I am familiar with the use of digital mapping software, GIS and RS (Arc GIS, ENVI and PCI Geomatica etc.), classification and spatial Analysis of satellite images, 3D modeling, etc. I started as an intern at OSFAC, Assistant Trainer (Professional Training) and Consultant than permanent employee since October 2009. To assist in the OSFAC activities regarding the monitoring of forest cover and the CARPE program in the context of natural resources management, I participated in the development of the FACET Atlas (Republic of Congo). I received data from Matt Hansen (map.img), WRI and Brazzaville (shapefiles). With all these data I draw maps of the ROC Atlas and statistics of forest cover and forest loss. We organize field work on land to collect data to validate the FACET product. Therefore, to assess forest cover in the region of Kwamouth and Kahuzi-Maiko Biega

  11. Effects of ground cover on the niches of main insect pests and their natural enemies in peach orchard%桃园生草对桃树上主要害虫及天敌生态位的影响

    Institute of Scientific and Technical Information of China (English)

    万年峰; 季香云; 蒋杰贤; 淡家贵

    2011-01-01

    调查了种植白三叶草桃园(生草区)与不种草桃园(对照区)桃树上主要害虫及天敌数量,并对其生态位进行了测定.结果表明:生草区桃树害虫的水平生态位、垂直生态位和时间生态位宽度最大的分别是桃红颈天牛(0.999)、茶翅蝽(0.964)和茶翅蝽(0.795),而对照区其值分别是0.918、0.792和0.632;生草区桃树天敌的水平生态位、垂直生态位和时间生态位宽度最大的都是蜘蛛,分别为0.996、0.983和0.932,而在对照区其值分别是0.900、0.800和0.818;生草区桃树害虫的三维生态位宽度依次为茶翅蝽>叶蝉>蜡蝉>桃蚜>桃潜叶蛾>桃蛀螟>桃红颈天牛>梨小食心虫>桑白蚧,而对照区为桃蚜>叶蝉>茶翅蝽>桃红颈天牛>梨小食心虫>蜡蝉>桑白蚧>桃潜叶蛾>桃蛀螟;生草区桃树天敌的三维生态位宽度为蜘蛛>小花蝽>草蛉>寄生蜂>瓢虫>食蚜蝇,而对照区为蜘蛛>草蛉>瓢虫>寄生蜂>小花蝽>食蚜蝇;对照区小花蝽、瓢虫、食蚜蝇、寄生蜂均有推迟活动迹象;生草区小花蝽与害虫的三维生态位重叠值都比相应的对照区大,天敌与害虫在时间上的同步性和空间上的同域性总体优于对照区.%Taking the peach orchards with and without ground cover Trifolium repens as test ob jects, an investigation was made on the quantities of main insect pests and their natural enemies on peach trees, with the niches of the insect pests and natural enemies measured. Among the main msect pests, Aromia bungii in the peach orchard with ground cover T. repens had the widest horizontal niche breadth ( 0. 999) , Halyomorpha halys had the widest vertical niche breadth ( 0. 964 ) and widest time niche breadth (0. 795 ) , while the corresponding values in the orchard without T. repens were 0. 918, 0. 792, and 0. 632, respectively. Among the natural enemies, spiders in the peach orchard with ground cover T. repens had the widest

  12. Benthic Cover

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Benthic cover (habitat) maps are derived from aerial imagery, underwater photos, acoustic surveys, and data gathered from sediment samples. Shallow to moderate-depth...

  13. Land Cover Classification for Fanno Creek, Oregon

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Fanno Creek is a tributary to the Tualatin River and flows though parts of the southwest Portland metropolitan area. The stream is heavily influenced by urban runoff...

  14. Acoustic Characterization of Grass-cover Ground

    Science.gov (United States)

    2014-11-20

    for noise and rever- beration control. Examples of porous media are cements, ceramics, rocks, building insulation , foams and soil. Characterizing the... air is consid- ered as lossless fluid and Eqs. 2.1 and 2.2 can be applied without approximations. However, when acoustic waves travel in narrow...3.1) In Eq. 3.1, P1 and P2 are the acoustic pressures measured at each respective microphone, Pi is the incident pressure, k0 is wave number in air , ω

  15. Effects of Different Concentrations of Gibberellin on the Flowering of Ground-cover Chrysanthemum 'Zichonglou'%不同浓度赤霉素对地被菊‘紫重楼’开花特性的影响

    Institute of Scientific and Technical Information of China (English)

    王媛; 崔雁汇; 孔一昌; 张强; 吕晋慧

    2012-01-01

    The effects of different concentrations of gibberellin on plant height, crown breadth, flowering characteristic (flowering season, flower number, petals number, flower diameter) and the pollen germination viability of ground-cover Chrysanthemum ' Zichonglou' were studied, which could provided substantial base for the hybrid breeding and regulating flower season of ground-cover Chrysanthemum. The results showed that 100-500 mg/L of gibberellin might cause the dewing color season 6-10 days ahead of time, the starting flower season 7-12 days ahead of time, and the abundant flowering season 2-7 days ahead of time. With the increase of gibberellin concentration ranging from 0 to 500 mg/L, internode and plant height were increased, but the flower number, petals number, crown breadth, and flower diameter were inhibited. The longest internode and plant height occurred with 500 mg/L gibberellin treatment. The pollen germination viability were improved by 100-300 mg/L of gibberellin, and impressed by 500 mg/L gibberellin.%笔者探讨不同浓度赤霉素(GA3)对地被菊‘紫重楼’株高、冠幅、开花特性(花期、开花量、花朵重瓣性、花径)和花粉生活力的影响,旨在为地被菊杂交育种、花期调控提供参考依据.试验结果表明,喷施100~500 mg/L GA3后,‘紫重楼’露色期、始花期及盛花期分别提前6~10天、7~12天和2~7天;GA3有利于节间伸长和株高增加,但植株开花量和花瓣重瓣性降低,冠幅、花径减小.其中,500 mg/L GA3处理下的地被菊节间长度与株高显著高于其他水平;100~300 mg/L GA3处理可促进花粉生活力,500 mg/L对花粉生活力有抑制作用.

  16. Effect of intercropping wheat with forage legumes on wheat production and ground cover Efeito do consórcio entre trigo e leguminosas forrageiras na produção de trigo e na cobertura de solo

    Directory of Open Access Journals (Sweden)

    Gilberto Omar Tomm

    2001-03-01

    Full Text Available The use of winter legumes in southern Brazil is hindered by the slow growth of these species during establishment exposing soil surface to erosion. Introduction of these species along with spring wheat (Triticum aestivum L. was studied as a means of increasing ground cover during their initial establishment period, without reducing wheat grain yield. Two experiments were conducted in nearby areas, one in each year. Birdsfoot trefoil (Lotus corniculatus L., red clover (Trifolium pratense L. cultivar Quiñequelli, white clover (T. repens L., and arrowleaf clover (T. vesiculosum Savi did not reduce cereal yield in either year. Wheat yield was reduced by intercropped red clover cultivar Kenland and by subclover (T. subterraneum L. in the first year. No grain yield differences due to intercropping with any legume were detected in the second year, when rainfall was below normal. Intercropping with wheat showed to be a practical alternative to enhance ground cover at establishing forage legumes.O uso de leguminosas forrageiras no sul do Brasil é dificultado pelo lento crescimento dessas espécies no ano de estabelecimento, o que expõe o solo à erosão. Estudou-se a introdução dessas leguminosas concomitantemente ao trigo (Triticum aestivum L. com o objetivo de aumentar a cobertura de solo durante o seu desenvolvimento inicial, sem reduzir o rendimento de grãos de trigo. Foram realizados dois experimentos em áreas próximas, um em cada ano. O cornichão (Lotus corniculatus L., o trevo-vermelho (Trifolium pratense L., cultivar Quiñequelli, o trevo-branco (T. repens L. e o trevo-vesiculoso (T. vesiculosum Savi não reduziram o rendimento de trigo em nenhum dos anos. O rendimento de grãos de trigo foi reduzido pelo trevo-vermelho, cultivar Kenland, e pelo trevo subterrâneo (T. subterraneum L., no primeiro ano. No segundo ano, em que, durante o período de desenvolvimento de trigo, a precipitação pluvial foi inferior à normal, não se

  17. Study on Water Adaptability of Seven Common Species of Ground Cover Plants in South China%华南地区7种常见园林地被植物水分适应性研究

    Institute of Scientific and Technical Information of China (English)

    钱瑭璜; 雷江丽; 庄雪影

    2012-01-01

    Water adaptability of seven common ground cover plants in South China were studied by pot experiment. The effect of biomass increment, root-crown ratio, florescence, diurnal variations of net photosynthetic rate and diurnal variations of net transpiration rate were determined in different soil water content. The experimental results showed that 7 ground cover plants could grow strongly in the soil w ith the water holding rate above 70% to 75%. Schefflera arboricola, Rhoeo discolor (L'He'rit.) Hance and Syngonium podophyllum Schott 'White Butterfly' could grow well and possess good ornamental value in the soil with minimum water holding rates of 30% to 35%; lxora coccinea L., Excoecaria cochinchinensis, Hymenocallis littoralis and Nephrolepis auriculata could grow well in the soil with minimum water holding rates of 50% to 55%.%以华南地区7种常见园林地被植物为研究对象,通过盆栽控水试验研究,综合比较了不同水分条件下植株的生长量、根冠比、花期、花量、净光合速率日变化、净蒸腾速率日变化等生长及光合指标的变化趋势.结果表明:1)在水分条件下限为土壤持水率的70%~75%时,7种参试植物均有较旺盛的生长势;2)在满足各参试植物园林观赏性的前提下,鹅掌藤(Schefflera arboricola)、蚌花[Rhoeo discolor (L’Hérit.)Hance]和[白蝶合果芋(Syngonium podophyllum Schott ‘White Butterfly’)在水分条件下限为土壤持水率的30%~35%时可以正常生长;而红花龙船化(Ixora coceinea L.)、红背桂(Excoecaria cochinchinensis)、水鬼蕉(Hymenocallis littoralis)和肾蕨(Nephrolepis auriculata)在水分条件下限为土壤持水率的50%~55%时可以正常生长.

  18. Managed Clearings: an Unaccounted Land-cover in Urbanizing Regions

    Science.gov (United States)

    Singh, K. K.; Madden, M.; Meentemeyer, R. K.

    2016-12-01

    Managed clearings (MC), such as lawns, public parks and grassy transportation medians, are a common and ecologically important land cover type in urbanizing regions, especially those characterized by sprawl. We hypothesize that MC is underrepresented in land cover classification schemes and data products such as NLCD (National Land Cover Database) data, which may impact environmental assessments and models of urban ecosystems. We visually interpreted and mapped fine scale land cover with special attention to MC using 2012 NAIP (National Agriculture Imagery Program) images and compared the output with NLCD data. Areas sampled were 50 randomly distributed 1*1km blocks of land in three cities of the Char-lanta mega-region (Atlanta, Charlotte, and Raleigh). We estimated the abundance of MC relative to other land cover types, and the proportion of land-cover types in NLCD data that are similar to MC. We also assessed if the designations of recreation, transportation, and utility in MC inform the problem differently than simply tallying MC as a whole. 610 ground points, collected using the Google Earth, were used to evaluate accuracy of NLCD data and visual interpretation for consistency. Overall accuracy of visual interpretation and NLCD data was 78% and 58%, respectively. NLCD data underestimated forest and MC by 14.4km2 and 6.4km2, respectively, while overestimated impervious surfaces by 10.2km2 compared to visual interpretation. MC was the second most dominant land cover after forest (40.5%) as it covered about 28% of the total area and about 13% higher than impervious surfaces. Results also suggested that recreation in MC constitutes up to 90% of area followed by transportation and utility. Due to the prevalence of MC in urbanizing regions, the addition of MC to the synthesis of land-cover data can help delineate realistic cover types and area proportions that could inform ecologic/hydrologic models, and allow for accurate prediction of ecological phenomena.

  19. Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran.

    Science.gov (United States)

    Sabr, Abutaleb; Moeinaddini, Mazaher; Azarnivand, Hossein; Guinot, Benjamin

    2016-12-01

    In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.

  20. Study on Introduction and Cultivation Techniques of Four Color--leafed Plants of Ground Cover%四种地被类彩叶植物引种栽培技术研究

    Institute of Scientific and Technical Information of China (English)

    王太平

    2011-01-01

    对引进的4个地被类植物优良品种进行了引种试验,结果表明:红叶石楠和金森女贞在高度生长和冠幅增长方面优势明显,其次为红花檀木、洒金珊瑚;栽培试验表明:4个地被类植物经过几个生长期的栽培试验,形成了一套较完整的栽培技术措施,为4个优良品种的推广应用提供了技术保障。%In this paper, four species of ground cover were studied through introduction experiment and cul- tivation experiment. The results of introduction experiment show that Photinia serru alta and Ligustrum japonicum 'Howardii" have clear advantages on high-growth and crown-growth, followed by Lorpetalum Chinese Oliv. var. rubrum Yieh and Var. variegata D'ombr. The results of cultivation experiment show that after the experiment of several growth periods, a complete cultivation technique of these four species has been formed,which provides technical support for applying these four fine varieties.

  1. Twitter content classification

    OpenAIRE

    2010-01-01

    This paper delivers a new Twitter content classification framework based sixteen existing Twitter studies and a grounded theory analysis of a personal Twitter history. It expands the existing understanding of Twitter as a multifunction tool for personal, profession, commercial and phatic communications with a split level classification scheme that offers broad categorization and specific sub categories for deeper insight into the real world application of the service.

  2. The contribution of vegetation cover and bare soil to pixel reflectance in an arid ecosystem

    Science.gov (United States)

    Steele, C. M.; Smith, A.; Campanella, A.; Rango, A.

    2008-12-01

    The heterogeneity of vegetation and soils in arid and semi-arid environments complicates the analysis of medium spatial resolution remotely sensed imagery. A single pixel may contain several different types of vegetation, as well as a sizeable proportion of bare soil. We have used linear mixture modeling to explore the contribution of vegetation cover and bare soil to pixel reflectance. In October, 2006, aerial imagery (0.25 m spatial resolution) was acquired for our study sites in the Jornada Experimental Range, southern New Mexico. Imagery was also acquired from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for June and November, 2006. These data corresponded with pre- and post monsoon conditions. Object-based feature extraction was used to classify the aerial imagery to shrub, grass and bare ground cover classes. Percent cover was then calculated for each cover class. Visible-near-infrared and shortwave infrared ASTER reflectance data from both dates were combined into a single 18-band dataset (30 m spatial resolution). A vector overlay from the classification results of the aerial imagery was used to define pure endmember pixels in the ASTER imagery. Estimates of the proportions of shrub, grass and bare ground cover from the linear mixture modeling approach were compared with cover calculated using feature extraction from the aerial imagery. The results indicate that reflectance in ASTER pixels is likely to be a linear combination of the cover proportions of the three main cover types (shrubs, grass, bare ground). However, noticeable outliers in the relationship between cover calculated from each method, indicate there may be other variables that affect the accuracy with which we can estimate cover using linear mixture modeling.

  3. CLASIFICACIÓN NO SUPERVISADA DE COBERTURAS VEGETALES SOBRE IMÁGENES DIGITALES DE SENSORES REMOTOS: “LANDSAT - ETM+” NONSUPERVISED CLASSIFICATION OF VEGETABLE COVERS ON DIGITAL IMAGES OF REMOTE SENSORS: "LANDSAT - ETM+"

    Directory of Open Access Journals (Sweden)

    Mauricio Arango Gutiérrez

    2005-06-01

    .The plant species diversity in Colombia and the lack of inventories of them suggests the need for a process that facilitates the work of investigators in these disciplines. Remote satellite sensors such as LANDSAT ETM+ and non-supervised artificial intelligence techniques, such as self-organizing maps - SOM, could provide viable alternatives for advancing in the rapid obtaining of information related to zones with different vegetative covers in the national geography. The zone proposed for the study case was classified in a supervised form by the method of maximum likelihood by another investigation in forest sciences and eight types of vegetative covers were discriminated. This information served as a base line to evaluate the performance of the non-supervised sort keys ISODATA and SOM. However, the information that the images provided had to first be purified according to the criteria of use and data quality, so that adequate information for these non-supervised methods were used. For this, several concepts were used; such as, image statistics, spectral behavior of the vegetative communities, sensor characteristics and the average divergence that allowed to define the best bands and their combinations. Principal component analysis was applied to these to reduce to the number of data while conserving a large percentage of the information. The non-supervised techniques were applied to these purified data, modifying some parameters that could yield a better convergence of the methods. The results obtained were compared with the supervised classification via confusion matrices and it was concluded that there was not a good convergence of non-supervised classification methods with this process for the case of vegetative covers.

  4. Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

    Science.gov (United States)

    Selkowitz, D.J.; Stehman, S.V.

    2011-01-01

    The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft, as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches. ?? 2011.

  5. Ground-water discharge and base-flow nitrate loads of nontidal streams, and their relation to a hydrogeomorphic classification of the Chesapeake Bay Watershed, middle Atlantic Coast

    Science.gov (United States)

    Bachman, L. Joseph; Lindsey, Bruce D.; Brakebill, John W.; Powars, David S.

    1998-01-01

    Existing data on base-flow and groundwater nitrate loads were compiled and analyzed to assess the significance of groundwater discharge as a source of the nitrate load to nontidal streams of the Chesapeake Bay watershed. These estimates were then related to hydrogeomorphic settings based on lithology and physiographic province to provide insight on the areal distribution of ground-water discharge. Base-flow nitrate load accounted for 26 to about 100 percent of total-flow nitrate load, with a median value of 56 percent, and it accounted for 17 to 80 percent of total-flow total-nitrogen load, with a median value of 48 percent. Hydrograph separations were conducted on continuous streamflow records from 276 gaging stations within the watershed. The values for base flow thus calculated were considered an estimate of ground-water discharge. The ratio of base flow to total flow provided an estimate of the relative importance of ground-water discharge within a basin. Base-flow nitrate loads, total-flow nitrate loads, and total-flow total-nitrogen loads were previously computed from water-quality and discharge measurements by use of a regression model. Base-flow nitrate loads were available from 78 stations, total-flow nitrate loads were available from 86 stations, and total-flow total-nitrogen loads were available for 48 stations. The percentage of base-flow nitrate load to total-flow nitrate load could be computed for 57 stations, whereas the percentage of base-flow nitrate load to totalflow total-nitrogen load could be computed for 36 stations. These loads were divided by the basin area to obtain yields, which were used to compare the nitrate discharge from basins of different sizes. The results indicate that ground-water discharge is a significant source of water and nitrate to the total streamflow and nitrate load. Base flow accounted for 16 to 92 percent of total streamflow at the 276 sampling sites, with a median value of 54 percent. It is estimated that of the 50

  6. Ground Vehicle Robotics

    Science.gov (United States)

    2013-08-20

    Ground Vehicle Robotics Jim Parker Associate Director, Ground Vehicle Robotics UNCLASSIFIED: Distribution Statement A. Approved for public...DATE 20 AUG 2013 2. REPORT TYPE Briefing Charts 3. DATES COVERED 09-05-2013 to 15-08-2013 4. TITLE AND SUBTITLE Ground Vehicle Robotics 5a...Willing to take Risk on technology -User Evaluated -Contested Environments -Operational Data Applied Robotics for Installation & Base Ops -Low Risk

  7. Use of various remote sensing land cover products for PFT mapping over Siberia

    Directory of Open Access Journals (Sweden)

    C. Ottlé

    2013-06-01

    Full Text Available High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover datasets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing earth system models. Earth system models also require specific land cover classification systems based on plant functional types, rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover datasets against one another and with auxiliary data to identify key uncertainties that contribute to variability in Plant Functional Type (PFT classifications that would introduce errors in earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, continuous vegetation fields (MODIS-VCF and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation. The GlobCover dataset, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFTs maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT dataset, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground-truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to better represent the water and carbon cycles in northern

  8. Use of various remote sensing land cover products for PFT mapping over Siberia

    Science.gov (United States)

    Ottlé, C.; Lescure, J.; Maignan, F.; Poulter, B.; Wang, T.; Delbart, N.

    2013-06-01

    High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover datasets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing earth system models. Earth system models also require specific land cover classification systems based on plant functional types, rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover datasets against one another and with auxiliary data to identify key uncertainties that contribute to variability in Plant Functional Type (PFT) classifications that would introduce errors in earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, continuous vegetation fields (MODIS-VCF) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover dataset, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFTs maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT dataset, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground-truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to better represent the water and carbon cycles in northern latitudes. Updated

  9. Use of various remote sensing land cover products for plant functional type mapping over Siberia

    Science.gov (United States)

    Ottlé, C.; Lescure, J.; Maignan, F.; Poulter, B.; Wang, T.; Delbart, N.

    2013-11-01

    High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover

  10. Vegetation Cover Mapping Based on Remote Sensing and Digital Elevation Model Data

    Science.gov (United States)

    Korets, M. A.; Ryzhkova, V. A.; Danilova, I. V.; Prokushkin, A. S.

    2016-06-01

    An algorithm of forest cover mapping based on combined GIS-based analysis of multi-band satellite imagery, digital elevation model, and ground truth data was developed. Using the classification principles and an approach of Russian forest scientist Kolesnikov, maps of forest types and forest growing conditions (FGC) were build. The first map is based on RS-composite classification, while the second map is constructed on the basis of DEM-composite classification. The spatial combination of this two layers were also used for extrapolation and mapping of ecosystem carbon stock values (kgC/m2). The proposed approach was applied for the test site area (~3600 km2), located in the Northern Siberia boreal forests of Evenkia near Tura settlement.

  11. The Land Surface Temperature Impact to Land Cover Types

    Science.gov (United States)

    Ibrahim, I.; Abu Samah, A.; Fauzi, R.; Noor, N. M.

    2016-06-01

    Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R2 = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

  12. Sganzerla Cover

    Directory of Open Access Journals (Sweden)

    Victor da Rosa

    2014-06-01

    Full Text Available http://dx.doi.org/10.5007/2175-7917.2014v19n1p158 Neste artigo, realizo uma leitura do cinema de Rogério Sganzerla, desde o clássico O bandido da luz vermelha até os documentários filmados na década de oitenta, a partir de duas noções centrais: cover e over. Para isso, parto de uma controvérsia com o ensaio de Ismail Xavier, Alegorias do subdesenvolvimento, em que o crítico realiza uma leitura do cinema brasileiro da década de sessenta através do conceito de alegoria; depois releio uma série de textos críticos do próprio Sganzerla, publicados em Edifício Sganzerla, procurando repensar as ideias de “herói vazio” ou “cinema impuro” e sugerindo assim uma nova relação do seu cinema com o tempo e a representação; então busco articular tais ideias com certos procedimentos de vanguarda, como a falsificação, a cópia, o clichê e a colagem; e finalmente procuro mostrar que, no cinema de Sganzerla, a partir principalmente de suas reflexões sobre Orson Welles, a voz é usada de maneira a deformar a interpretação naturalista.

  13. Cover Picture.

    Science.gov (United States)

    Breuning; Ruben; Lehn; Renz; Garcia; Ksenofontov; Gütlich; Wegelius; Rissanen

    2000-07-17

    The cover picture shows how both, fine arts and science, avail themselves of a system of intertwined symbolic and iconic languages. They make use of a common set of abstracted signs to report on their results. Thus, already in 1925, Wassily Kandinsky painted a masterpiece (bottom), which now, 75 years later, might be regarded as a blueprint for a scientific project. In his painting, Kandinsky pictured a grid-shaped sign that resembles in effect an actual molecular switch. Apparently following an enigmatic protocol, the groups of Lehn and Gütlich (see p. 2504 ff. for more details) constructed a grid-type inorganic architecture that operates as a three-level magnetic switch (center) triggered by three external perturbations (p, T, hnu). The switching principle is based on the spin-crossover phenomenon of Fe(II) ions and can be monitored by Mössbauer spectroscopy (left) and magnetic measurements (rear). Maybe not by chance, the English translation of the title of the painting "signs" is a homonym of "science", since both presented works are a product of the insatiable curiosity of man and his untiring desire to recognize his existence.

  14. Sampling strategies for estimating forest cover from remote sensing-based two-stage inventories

    Institute of Scientific and Technical Information of China (English)

    Piermaria; Corona; Lorenzo; Fattorini; Maria; Chiara; Pagliarella

    2015-01-01

    Background: Remote sensing-based inventories are essential in estimating forest cover in tropical and subtropical countries, where ground inventories cannot be performed periodically at a large scale owing to high costs and forest inaccessibility(e.g. REDD projects) and are mandatory for constructing historical records that can be used as forest cover baselines. Given the conditions of such inventories, the survey area is partitioned into a grid of imagery segments of pre-fixed size where the proportion of forest cover can be measured within segments using a combination of unsupervised(automated or semi-automated) classification of satellite imagery and manual(i.e. visual on-screen)enhancements. Because visual on-screen operations are time expensive procedures, manual classification can be performed only for a sample of imagery segments selected at a first stage, while forest cover within each selected segment is estimated at a second stage from a sample of pixels selected within the segment. Because forest cover data arising from unsupervised satellite imagery classification may be freely available(e.g. Landsat imagery)over the entire survey area(wall-to-wall data) and are likely to be good proxies of manually classified cover data(sample data), they can be adopted as suitable auxiliary information.Methods: The question is how to choose the sample areas where manual classification is carried out. We have investigated the efficiency of one-per-stratum stratified sampling for selecting segments and pixels, where to carry out manual classification and to determine the efficiency of the difference estimator for exploiting auxiliary information at the estimation level. The performance of this strategy is compared with simple random sampling without replacement.Results: Our results were obtained theoretically from three artificial populations constructed from the Landsat classification(forest/non forest) available at pixel level for a study area located in central Italy

  15. Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

    Science.gov (United States)

    Hansen, M.C.; Egorov, Alexey; Potapov, P.V.; Stehman, S.V.; Tyukavina, A.; Turubanova, S.A.; Roy, David P.; Goetz, S.J.; Loveland, T.R.; Ju, J.; Kommareddy, A.; Kovalskyy, Valeriy; Forsyth, C.; Bents, T.

    2014-01-01

    Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by

  16. 采用随机森林法的天绘数据干旱区城市土地覆盖分类%Random forest classification of land cover information of urban areas in arid regions based on TH-1 data

    Institute of Scientific and Technical Information of China (English)

    田绍鸿; 张显峰

    2016-01-01

    基于天绘一号(TH-1,或称MS-1)卫星多光谱数据,采用随机森林分类方法(random forests classification,RFC)对位于中亚干旱区的我国新疆维吾尔族自治区阿勒泰地区北屯市及周边区域的土地覆盖进行了分类研究.针对北屯市不透水层与裸土混杂的情况,将纹理特征与植被信息构建最优组合,建立有效的RFC分类器,提高对易混淆土地覆盖类型的分类识别精度.结果表明,采用RFC的分类精度高于最大似然法分类结果,总体分类精度提高了近10%.经过优化选择的特征组合在对干旱区中小城市土地覆盖进行分类时表现良好,能得到较高精度的分类结果,可满足新疆中小城市发展规划对土地覆盖信息的需求.%Random-forest classification (RFC) method was used to extract the land cover information from the TH-1 satellite remotely sensed multispectral data in Beitun Town and its adjacent areas within the arid region of Altay,Xinjiang.Owing to the mixture of the impervious covers and the exposed soils inside the city,the textural and vegetation features were derived from the TH-1 panchromatic image and multispectral bands and subsequently applied to creating optimal feature set so as to implement the RFC classification.The optimized classifier can achieve better identification of some confused land cover classes.The results show that the RFC possesses higher accuracy than the conventional maximum likelihood classification (MLC)with the same TH-1 image,with their total accuracy being 82.26% and 72.61%,respectively.In addition,favorable applicability is observed in the land cover classification in the arid urban region using optimized combined multi-feature methods,which can provide land cover information for the urban development and planning in the medium and small cities of Xinjiang.

  17. Performance and Carbon Emission Analysis on Glass-covering Greenhouse Heating with Ground Source Heat Pump Technology%玻璃温室地源热泵供暖性能与碳排放分析

    Institute of Scientific and Technical Information of China (English)

    柴立龙; 马承伟

    2012-01-01

    The heating test was conducted in a glass-covering multi-span greenhouse ( 756 m ) with groundwater-style GSHP technology. The heat quantity estimating models based on air enthalpy difference method ( AEDM) were developed according to the heating characteristics of GSHP. The economical performance and carbon footprint ( greenhouse gas emission level) of the GSHP was analyzed and compared with currently widely used coal fired heating system ( CFHs) and natural gas fired heating system (GFHs) based on investigated various energy sources price during heating tests. According to the compared results, the GSHPs heating cost is higher than CFHs, but lower than GFHs. Meanwhile, GSHPs CO2 emission during heating is higher than GFHs, but lower than CFHs.In view of the strong coupling between temperature and relative humidity in the greenhouse simulation system, an adaptive decoupling method based on dynamic matrix control was proposed. Taking the measure of feedforward compensation to eliminate interaction between channels of temperature and humidity, an adaptive decoupling algorithm by weighting was designed. The proposed method can adjust the decoupling parameters online under different operating modes, effectively overcome the effect of model severe mismatch to control accuracy. Compared with the traditional PID control, simulation and experimental results both indicated the proposed strategy greatly improved the control performance.%在北京地区一栋玻璃连栋温室(756 m2)中采用地下水式地源热泵(ground source heat pump,简称GSHP)技术进行了冬季供暖试验,并结合GSHP技术的供热特点构建了基于供热末端空气焓差法的供热量计算模型以及供热系统性能分析方法.根据供暖期北京地区能源价格水平,对比当前广泛使用的燃煤供暖系统和天然气供暖系统,系统地评价了GSHP技术的碳排放(温室气体排放水平)和供暖经济性.GSHP供暖成本低于同期燃气供暖,但

  18. The Library of Congress, Dewey Decimal, and Universal Decimal Classification Systems are Incomplete and Unsystematic. A Review of: Zins, C., & Santos, P. L. V. A. C. (2011. Mapping the knowledge covered by library classification systems. Journal of the American Society for Information Science and Technology, 62(5, 877-901. doi:10.1002/asi.21481

    Directory of Open Access Journals (Sweden)

    Cari Merkley

    2011-01-01

    . This means that there was at least one class or subclass in each of the three systems that corresponded to the subclasses in these pillars. The remaining seven pillars were only partially covered by the three systems to varying degrees. For example, the coverage of religion in LCC and DDC show evidence of a bias towards Christianity and incomplete coverage of other faiths. In addition to the lack of completeness in terms of subject coverage, the researchers found inconsistencies and problems with how relationships between subjects were illustrated by the systems. For example, botany should be a subclass of biology, but the subjects occupy the same level in the LCC, DDC, and UDC systems. Researchers also noted cases where subclasses on the same level were not mutually exclusive e.g., the BR (Christianity and BS (The Bible subclasses in LCC. Overall, LLC performed slightly better than DDC or UDC, covering 47 of the 55 unique subject categories in the 10 Pillars. It was followed by UDC with 44 out of 55, and DDC with 43 out of 55. Some of the 55 unique subject categories in the 10 Pillars system were not represented by any of the systems: 3 subclasses under Society (Society at Large – Area Based, Social Groups – Age, and Social Groups – Ethnicity, 2 under Technology (Technologies – Materials and Technologies – Processes, and 1 under Foundations (Methodology.Conclusion – The researchers conclude that none of the three major classification systems analyzed provides complete and systematic coverage of the world of knowledge, and call for the library community to move to new systems, such as the 10 Pillars of Knowledge.

  19. Modeling increasing effect of soil temperature through plastic film mulch in ground cover rice production system using CERES-Rice%基于CERES-Rice模型的覆膜旱作稻田增温效应模拟

    Institute of Scientific and Technical Information of China (English)

    马雯雯; 金欣欣; 石建初; 宁松瑞; 李森; 陶玥玥; 张亚男; 左强

    2015-01-01

    水稻覆膜旱作技术具有显著的节水、增温、防污和减排效应,是节水稻作技术体系的重要措施之一,将CERES-Rice模型用于覆膜旱作条件时,必须首先解决覆膜增温效应的准确模拟问题。该文拟应用热量传输理论及目前旱地作物生产系统中采用的覆膜增温效应模拟方法,来模拟水稻覆膜旱作生产体系中的增温效应,从而为完善 CERES-Rice 模型并使其能用于覆膜旱作水稻的生长模拟奠定基础。参数调校与模型检验验证通过2013、2014年在湖北房县开展的2 a水稻覆膜旱作田间试验来进行,共涉及淹水(对照)、覆膜湿润栽培和覆膜旱作共3个水分处理,分别对2个生长季、2个覆膜处理地表5 cm及地下10、20 cm处温度的变化过程进行了模拟,结果表明:经过参数调校后,所建立的覆膜增温模型可较好地模拟覆膜稻田地表和剖面上土壤温度的变化规律,地表5 cm处土壤温度模拟值与实测值的均方根差、相对均方根差分别低于1.8℃和10%,相关系数在0.89以上(P<0.01);尽管地下10、20 cm处的模拟误差稍大,也基本可满足要求,相应的均方根误差<3.2℃,相对均方根差<15%,相关系数>0.65(P<0.01)。%As one of the most promising water-saving rice production technologies, the ground cover rice production system (GCRPS) has been found to save water application, increase soil temperature, and reduce nitrogen pollution and methane emission. However, the feasibility of CERES-Rice, a software package widely and successfully applied in the traditional paddy rice production system (TPRPS), for simulating the rice growth in the GCRPS still remains unknown and needs further research. Undoubtedly, it should be based on accurately quantifying the effect of soil temperature enhancement caused by the ground cover material (chosen as the plastic film in this study). Therefore, the objective of

  20. Application of ground-truth for classification and quantification of bird movements on migratory bird habitat initiative sites in southwest Louisiana: final report

    Science.gov (United States)

    Barrow, Wylie C.; Baldwin, Michael J.; Randall, Lori A.; Pitre, John; Dudley, Kyle J.

    2013-01-01

    This project was initiated to assess migrating and wintering bird use of lands enrolled in the Natural Resources Conservation Service’s (NRCS) Migratory Bird Habitat Initiative (MBHI). The MBHI program was developed in response to the Deepwater Horizon oil spill in 2010, with the goal of improving/creating habitat for waterbirds affected by the spill. In collaboration with the University of Delaware (UDEL), we used weather surveillance radar data (Sieges 2014), portable marine radar data, thermal infrared images, and visual observations to assess bird use of MBHI easements. Migrating and wintering birds routinely make synchronous flights near dusk (e.g., departure during migration, feeding flights during winter). Weather radars readily detect birds at the onset of these flights and have proven to be useful remote sensing tools for assessing bird-habitat relations during migration and determining the response of wintering waterfowl to wetland restoration (e.g., Wetlands Reserve Program lands). However, ground-truthing is required to identify radar echoes to species or species group. We designed a field study to ground-truth a larger-scale, weather radar assessment of bird use of MBHI sites in southwest Louisiana. We examined seasonal bird use of MBHI fields in fall, winter, and spring of 2011-2012. To assess diurnal use, we conducted total area surveys of MBHI sites in the afternoon, collecting data on bird species composition, abundance, behavior, and habitat use. In the evenings, we quantified bird activity at the MBHI easements and described flight behavior (i.e., birds landing in, departing from, circling, or flying over the MBHI tract). Our field sampling captured the onset of evening flights and spanned the period of collection of the weather radar data analyzed. Pre- and post-dusk surveys were conducted using a portable radar system and a thermal infrared camera. Landbirds, shorebirds, and wading birds were commonly found on MBHI fields during diurnal

  1. Raster Vs. Point Cloud LiDAR Data Classification

    Science.gov (United States)

    El-Ashmawy, N.; Shaker, A.

    2014-09-01

    Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the

  2. Construction of Land Use and Land Cover Classification System Based on Non-point Pollution and Its Application%基于面源污染的土地利用与覆被分类系统的构建及其应用

    Institute of Scientific and Technical Information of China (English)

    练雄; 蔡永立; 李武陵

    2011-01-01

    On the basis of the national land use classification, further refinement and adjustment are carried out, this paper puts forward a land use and land cover two class classification system based on ecological service function and human disturbance degrees of land cover. It is first used to classify land use and land cover types in Dishui lake watershed. In the system the first layer including farmland, woodland, road and building, suggests potential runoff size of land cover type. Land use and land cover types in the second layer are more specialized that grass lands are divided into wild grass land and lawn and vinyl house is divided from farmland and ponds are divided into culture pond and natural pond, rural residential are divided into poultry farms, parks and green space is divided from woodland and lawn, bypass trees are divided into woodlands and so on. This system will help to estimate total productivity of practical non-point pollution of region, which will resolve the LInkage between land use/land cover classification system and non-point estimation.%依据生态服务功能和人类干扰程度,在全国土地利用分类标准的基础上进一步细化和调整,提出基于面源污染问题的土地利用/土地覆被二级分类系统,并应用到上海市滴水湖集水区.一级指标分为林地、耕地、建筑等,反映了地表径流方式;二级进一步细化和调整,将草地分为自然的荒草地和人工草坪,耕地细分出大棚,坑塘细分出养殖池,农村住宅细分出家禽养殖场,公园与绿地归为林地和草地,旁道树归为林地等,这些类型可以与实际面源污染相对应,较好地解决了土地利用/土地覆被分类系统与面源估算之间衔接的问题.

  3. Forests and Forest Cover, Forest areas as captured by orthophotography. Contains some attribution of forest type depending on imagery and ground-truthing if available., Published in 2007, 1:2400 (1in=200ft) scale, Howard County Government.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Forests and Forest Cover dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of 2007. It is described...

  4. Land Use and Land Cover, Existing land use derived from orthoimagery. Ground-truthing from discussion with local plan commission members., Published in 2000, 1:12000 (1in=1000ft) scale, Portage County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Land Use and Land Cover dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as of 2000. It is described...

  5. Classification of Land Utilization and Covering Based on Support Vector Machine---with case of Laoha River catachment%基于 SVM 的土地利用/覆盖分类--以老哈河流域为例

    Institute of Scientific and Technical Information of China (English)

    李硕

    2015-01-01

    选取老哈河流域为研究区域,以2007年的两景Landsat5的TM影像为数据源,对该地区进行土地利用/覆盖分类。由于该区域土地覆盖类型复杂,影像较难区分且容易造成错分类。该研究中采用支持向量机( Support Vector Machine,SVM)分类法,通过引入径向基核函数进行非线性变换映射至高维空间,提取它们的非线性特征,增强不同类型之间的可分性,减少错分现象,提高遥感图像分类的精度。通过试验,提取出了2007年的老哈河流域的土地利用/覆盖现状图,以校验该方法的可行性。%The Laoha River catchment is selected as the study catchment.Based on the data source of TM image of Landsat 5 in 2007, classification of the land utilization and covering in the catchment is studied.As the land covering of this catchment is complicated in classification, the images are difficult to separate and easy to classify.In this study, classification method of support vector machine (SVM) is applied.By utilization of radial basis function, the non-linear conversion is conducted to the high-dimensional space, abstrac-ting their non-linear characteristics, strengthening the separation between different types, reducing mistaken classification and improving accuracy of the remote-sense image classification.Through tests, the land utilization and covering status images of the Laoha River catch-ment in 2007 are abstracted to verify the feasibility of this method.

  6. Ground Vehicle Robotics Presentation

    Science.gov (United States)

    2012-08-14

    Mr. Jim Parker Associate Director Ground Vehicle Robotics Distribution Statement A. Approved for public release Report Documentation Page...Briefing 3. DATES COVERED 01-07-2012 to 01-08-2012 4. TITLE AND SUBTITLE Ground Vehicle Robotics Presentation 5a. CONTRACT NUMBER 5b. GRANT...ABSTRACT Provide Transition-Ready, Cost-Effective, and Innovative Robotics and Control System Solutions for Manned, Optionally-Manned, and Unmanned

  7. ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

    Directory of Open Access Journals (Sweden)

    M. R. M. Salleh

    2015-10-01

    Full Text Available Airborne Light Detection and Ranging (LiDAR technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM. High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN algorithm technique in producing ground points. Next, the ground control points (GCPs used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870 with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924 obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

  8. 地被植物在郑州都市区园林绿化中的组成结构及管理对策研究%The investigation and analysis about common ground cover plants of metropolitan area parks in Zhengzhou City

    Institute of Scientific and Technical Information of China (English)

    汪进; 杨旭; 高闪闪; 何瑞珍

    2014-01-01

    The ground cover plants in the major urban parks and plazas of Zhengzhou City were investigated by using on‐the‐spot statistical investigation in this paper ,and the vegeta‐tion characteristics , selection criteria , and maintenance management , etc . w ere discussed and analysed .The results showed that the species of ground cover plants are simplex ,with smaller size plantation and often separatly planted with many bare grounds and extensive management at late stage . T herefore , some effective measurs are proposed by reasonable planting according to the viewing characteristis of different species in different growth seasonsand enhancing their management in time ,which can greatly improve their ornamental value ,and hence increase the level of ground cover plants landscaping .%采用实地调查统计的方法对郑州市各大公园及路边广场的地被植物及其特点、选择标准、养护管理等进行了研究分析。结果表明:当前地被植物品种单一,种植面积较小,且多单独种植,混合应用较少,较多地段没有地被植物覆盖,后期管理粗放。建议充分利用每种地被植物不同时期的观赏特点,种植时合理搭配,及时管理,以大大提高观赏价值,从而提高地被植物的造景水平。

  9. EvaluationofLandCoverChangesRemoteSensingTechnique (Case Study: Hableh Rood Subwatershed of ShahrabadBasin

    Directory of Open Access Journals (Sweden)

    Khadijeh Abolfathi

    2016-03-01

    Full Text Available The growing population and increasing socio-economic necessities creates a pressure on land use/land cover. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This study investigates the land use changes in part of Hableh Rood Watershed of Iran using Landsat 7 and 8 (Sensor ETM+ and OLI images between 2001 and 2013. Supervised classification was used for classification of Landsat images. Four land use classes were delineated including rangeland, irrigated farming and plantations land, and dry farming lands,urban. Visual interpretation, expert knowledge of the study area and ground truth information accumulated with field works to assess the accuracy of the classification results. Overall accuracy of 2001 and 2013 image classification was 81.48 (Kappa coefficient: 0.7340 and 87.04 (Kappa coefficient: 0.7841, respectively. The results showed considerable land cover changes for the given study area. Land cover change detection showed that in a period of 12 years, 277.57 hectares of dry farming lands and 340 hectares of dense range have been lost. But, 341 hectares for low dense range, 280 hectares for semi dense range and 1.4 hectares for urban areas, have been added in area.

  10. Evaluation criteria for software classification inventories, accuracies, and maps

    Science.gov (United States)

    Jayroe, R. R., Jr.

    1976-01-01

    Statistical criteria are presented for modifying the contingency table used to evaluate tabular classification results obtained from remote sensing and ground truth maps. This classification technique contains information on the spatial complexity of the test site, on the relative location of classification errors, on agreement of the classification maps with ground truth maps, and reduces back to the original information normally found in a contingency table.

  11. 7 CFR 30.31 - Classification of leaf tobacco.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Classification of leaf tobacco. 30.31 Section 30.31... REGULATIONS TOBACCO STOCKS AND STANDARDS Classification of Leaf Tobacco Covering Classes, Types and Groups of Grades § 30.31 Classification of leaf tobacco. For the purpose of this classification leaf tobacco...

  12. The Porcupine herd of barren ground caribou

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This report covers the porcupine herd of the barren ground caribou. The report covers the short history, the winter range, migration route, phenology, movements and...

  13. Classification and regression trees

    CERN Document Server

    Breiman, Leo; Olshen, Richard A; Stone, Charles J

    1984-01-01

    The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

  14. The effect of Thematic Mapper spectral properties on land cover mapping for hydrologic modeling

    Science.gov (United States)

    Gervin, J. C.; Lu, Y. C.; Gauthier, R. L.; Miller, J. R.; Irish, R. R.

    1986-01-01

    The accuracy of unsupervised land-cover classification from all seven Landsat TM bands and from six combinations of three or four bands is evaluated using images of the Clinton River Basin, a suburban watershed near Detroit. Data from aerial TMS photography, USGS topographic maps, and ground surveys are employed to determine the classification accuracy. The mapping accuracy of all seven bands is found to be significantly better (6 percent overall, 12 percent for residential areas, and 13 percent for commercial districts) than that with bands 2, 3, and 4; but almost the same accuracy is obtained by including at least one band from each major spectral region (visible, NIR, and mid-IR).

  15. Data fusion for target tracking and classification with wireless sensor network

    Science.gov (United States)

    Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

    2016-10-01

    In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

  16. Classification of remotely sensed images

    CSIR Research Space (South Africa)

    Dudeni, N

    2008-10-01

    Full Text Available (s)) is the data vector for a pixel located at s θ(s) is an unknown ground class to which pixel s belongs Objective is to classify the pixel at location s to the one of the k clusters Classification of remotely sensed images N. Dudeni, P. Debba...(s) is an unknown ground class to which pixel s belongs Objective is to classify the pixel at location s to the one of the k clusters Classification of remotely sensed images N. Dudeni, P. Debba Introduction to Remote Sensing Introduction to Image...

  17. Biogeography based Satellite Image Classification

    CERN Document Server

    Panchal, V K; Kaur, Navdeep; Kundra, Harish

    2009-01-01

    Biogeography is the study of the geographical distribution of biological organisms. The mindset of the engineer is that we can learn from nature. Biogeography Based Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. Satellite image classification is an important task because it is the only way we can know about the land cover map of inaccessible areas. Though satellite images have been classified in past by using various techniques, the researchers are always finding alternative strategies for satellite image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. This paper is focused on classification of the satellite image of a particular land cover using the theory of Biogeography based Optimization. The original BBO algorithm does not have the inbuilt property of clustering which is required during image classification. Hence modifications have been proposed to the original algorithm and...

  18. Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Thomas Möckel

    2014-08-01

    Full Text Available Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77 than one using the full set of wavebands (77%, Kappa statistic value = 0.65. Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages.

  19. Extracting Urban Ground Object Information from Images and LiDAR Data

    Science.gov (United States)

    Yi, Lina; Zhao, Xuesheng; Li, Luan; Zhang, Guifeng

    2016-06-01

    To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, this paper uses ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon. Eventually, the multiple classifier combination method is applied to get the outcome of the classification. In order to validate the feasible of this method, this paper selects two experimental regions in Stuttgart and Germany (Region A and B, covers 0.21 km2 and 1.1 km2 respectively). The aim of the first experiment on the Region A is to get the optimal segmentation scales and classification features. The overall accuracy of the classification reaches to 93.3 %. The purpose of the experiment on region B is to validate the application-ability of this method for a large area, which is turned out to be reaches 88.4 % overall accuracy. In the end of this paper, the conclusion shows that the proposed method can be performed accurately and efficiently in terms of urban ground information extraction and be of high application value.

  20. Mekong Land Cover Dasboard: Regional Land Cover Mointoring Systems

    Science.gov (United States)

    Saah, D. S.; Towashiraporn, P.; Aekakkararungroj, A.; Phongsapan, K.; Triepke, J.; Maus, P.; Tenneson, K.; Cutter, P. G.; Ganz, D.; Anderson, E.

    2016-12-01

    SERVIR-Mekong, a USAID-NASA partnership, helps decision makers in the Lower Mekong Region utilize GIS and Remote Sensing information to inform climate related activities. In 2015, SERVIR-Mekong conducted a geospatial needs assessment for the Lower Mekong countries which included individual country consultations. The team found that many countries were dependent on land cover and land use maps for land resource planning, quantifying ecosystem services, including resilience to climate change, biodiversity conservation, and other critical social issues. Many of the Lower Mekong countries have developed national scale land cover maps derived in part from remote sensing products and geospatial technologies. However, updates are infrequent and classification systems do not always meet the needs of key user groups. In addition, data products stop at political boundaries and are often not accessible making the data unusable across country boundaries and with resource management partners. Many of these countries rely on global land cover products to fill the gaps of their national efforts, compromising consistency between data and policies. These gaps in national efforts can be filled by a flexible regional land cover monitoring system that is co-developed by regional partners with the specific intention of meeting national transboundary needs, for example including consistent forest definitions in transboundary watersheds. Based on these facts, key regional stakeholders identified a need for a land cover monitoring system that will produce frequent, high quality land cover maps using a consistent regional classification scheme that is compatible with national country needs. SERVIR-Mekong is currently developing a solution that leverages recent developments in remote sensing science and technology, such as Google Earth Engine (GEE), and working together with production partners to develop a system that will use a common set of input data sources to generate high

  1. [Classification of viruses by computer].

    Science.gov (United States)

    Ageeva, O N; Andzhaparidze, O G; Kibardin, V M; Nazarova, G M; Pleteneva, E A

    1982-01-01

    The study used the information mass containing information on 83 viruses characterized by 41 markers. The suitability of one of the variants of cluster analysis for virus classification was demonstrated. It was established that certain stages of automatic allotment of viruses into groups by the degree of similarity of their properties end the formation of groups which consist of viruses sufficiently close to each other by their properties and are sufficiently isolated. Comparison of these groups with the classification proposed by the ICVT established their correspondence to individual families. Analysis of the obtained classification system permits sufficiently grounded conclusions to be drawn with regard to the classification position of certain viruses, the classification of which has not yet been completed by the ICVT.

  2. Comparison of Advanced Pixel Based (ANN and SVM and Object-Oriented Classification Approaches Using Landsat-7 Etm+ Data

    Directory of Open Access Journals (Sweden)

    Prasun Kumar Gupta

    2010-08-01

    Full Text Available In this study, the pixel-based and object-oriented image classification approaches were used for identifying different land use types in Karnal district. Imagery from Landsat-7 ETM with 6 spectral bands was used to perform the image classification.Ground truth data were collected from the available maps, personal knowledge and communication with the local people. In order to prepare land use map different approaches: Artificial Neural Network(ANN and Support Vector Machine (SVM were used. For performing object oriented classification eCognition software was used. During the object oriented classification, in first step several differentsets of parameters were used for image segmentation and in second step nearest neighbor classifier was used for classification. Outcome from the classification works show that the object-oriented approach gave more accurate results (including higher producer’s and user’s accuracy for most of the land cover classes than those achieved by pixelbased classification algorithms. It is also observed that ANN performed better as compared to SVM classification approach.

  3. 基于C5.0决策树算法的西北干旱区土地覆盖分类研究——以甘肃省武威市为例%The Study of the Northwest Arid Zone Land-Cover Classification Based on C5.0 Decision Tree Algorithm at Wuwei City,Gansu Province

    Institute of Scientific and Technical Information of China (English)

    齐红超; 祁元; 徐瑱

    2009-01-01

    西北干旱区面积广阔,由于土地利用类型多样,成因复杂,对环境变化敏感、变化过程快、幅度大、景观差异明显等特点,在影像上表现出的"同物异谱"现象明显;利用常规目视解译、监督非监督分类、人工参与的决策树分类等方法在效率或精度等方面各有其缺陷.采用机器学习C5.0决策树算法,综合利用地物波谱、NDVI、TC、纹理等信息,根据样本数据自动挖掘分类规则并对整个研究区进行地物分类.机器学习的决策树可以挖掘出更多的分类规则,C5.0算法对采样数据的分布没有要求,可以处理离散和连续数据,生成的规则易于理解,分类精度高,可以满足西北干旱区大面积的土地利用/覆被变化制图的需要.%In the broadly northwest arid regions,frequently,same object has different spectral characters because of the special characteristics of land cover change such as complex causes of formation,sensitivity to environment change,rapid and violent change and obvious differences in landscape. The conventional methods of classification including visual interpretation,supervised classification,unsupervised classification,and artificial decision tree classification have disadvantages in the efficiency or the accuracy. In this paper,machine learning algorithm based on C5. 0 decision tree was used to classify the entire study area automatically according to the sample data mining classification rules. Spectral features,NDVI,TC,texture and other informations were involved in the algorithm. More classification rules could be mined by machine learning decision tree. C5. 0 algorithm handling with both continuous and discrete data is independent of the distribution of sampling sites,The classification rules mined by this algorithm were interpretable. Other superiority of this algorithm included the fast speed of training and higher accuracy than many other classifiers. Thus,it is able to be used in the mapping of

  4. A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

    Science.gov (United States)

    Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Votava, Petr; Roy, Anshuman; Mukhopadhyay, Supratik; Nemani, Ramakrishna

    2015-01-01

    Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

  5. A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery using a Probabilistic Learning Framework

    Science.gov (United States)

    Basu, S.; Ganguly, S.; Michaelis, A.; Votava, P.; Roy, A.; Mukhopadhyay, S.; Nemani, R. R.

    2015-12-01

    Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

  6. Localizing Ground-Penetrating Radar

    Science.gov (United States)

    2014-11-01

    ing Ground-Penetrating Radar (LGPR) uses very high frequency (VHF) radar reflections of underground features to generate base- line maps and then...Innovative ground- penetrating radar that maps underground geological features provides autonomous vehicles with real-time localization. Localizing...NOV 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Localizing Ground-Penetrating Radar 5a. CONTRACT NUMBER

  7. 柔性石墨金属波齿复合垫片的分级研究%Research on Classification of Flexible Graphite Covered Wave-Serrated Metal Gaskets

    Institute of Scientific and Technical Information of China (English)

    陈元; 任建民

    2015-01-01

    Standards of flexible graphite covered wave-serrated metal gaskets were analyzed. It’s found that the gasket pretightening in the condition of same pressure levels and different nominal size fluctuated around a certain value,gasket pretightening in the condition of same nominal size and different pressure levels were not identical.The finite element simulation of flexible graphite covered wave-serrated metal gaskets was carried out by using ANSYS WORKBENCH. The compression ratio of flexible graphite corrugated metal gaskets under different structural parameters was calculated. The result shows that the gaskets can be classified by pressure through adjusting the structural parameters. Accordingly, flexible graphite covered wave-serrated metal gaskets can be classified according to pressure.%对柔性石墨金属波齿复合垫片的标准进行了剖析,发现同一压力级别不同名义尺寸的垫片,绝大部分的预紧比压在某一应力水平附近波动,同一名义尺寸不同压力级别的垫片预紧比压各不相同。利用ANSYS WORKBENCH 对柔性石墨金属波齿复合垫片进行有限元数值模拟,计算出在规定预紧比压下不同结构参数的柔性石墨金属波齿复合垫片的压缩率,结果表明在相同预紧比压作用下不同结构的垫片压缩率各不相同。即可以通过调整垫片结构参数达到调整垫片压缩回弹性能的目的。据此,可以实现柔性石墨金属波齿复合垫片生产按压力等级区分。

  8. Grounded theory.

    Science.gov (United States)

    Harris, Tina

    2015-04-29

    Grounded theory is a popular research approach in health care and the social sciences. This article provides a description of grounded theory methodology and its key components, using examples from published studies to demonstrate practical application. It aims to demystify grounded theory for novice nurse researchers, by explaining what it is, when to use it, why they would want to use it and how to use it. It should enable nurse researchers to decide if grounded theory is an appropriate approach for their research, and to determine the quality of any grounded theory research they read.

  9. What Medicare Covers

    Science.gov (United States)

    ... What Part A covers Medicare Part A hospital insurance covers inpatient hospital care, skilled nursing facility, hospice, lab tests, surgery, ... Medicare Covers Drug Coverage (Part D) Supplements & Other Insurance Claims & ... doctors, providers, hospitals & plans Where can I get covered medical items? ...

  10. Analysing land cover and land use change in the Ruma National Park and surroundings in Kenya

    Science.gov (United States)

    Scharsich, Valeska; Ochuodho Otieno, Dennis; Bogner, Christina

    2017-04-01

    The change of land use and land cover (LULC) is often driven by the growth of human population. In the Lambwe valley, Kenya, the most important reason for accelerated settlement in the last decades was the control of the tsetse fly, the biological vector of trypanosomes. Since the huge efforts of tsetse control in the 1970s, the population of the Lambwe valley in Kenya increased rapidly and therefore the cultivated area expanded. This amplified the pressure on the forested areas at higher elevations and the Ruma National Park which occupies one third of the Lambwe valley. Here, we investigate possible effects of this pressure on the land cover in the Lambwe valley and in particular in the Ruma National Park. To answer this question, we analysed the surface reflectance of three Landsat images of Ruma National Park and its surroundings from 1984, 2002 and 2014. To compensate for the lack of ground data we inferred past land use and land cover from recent observations combining Google Earth images and change detection. By supervised classification with Random Forests, we identified four land use and land cover types, namely the forest dominant at the high elevation; dense shrub land; savanna; and sparsely covered soil including bare light soils with little vegetation, fields and settlements. Subsequently, we compared the three classifications and identified LULC changes that occurred between 1984 and 2014. We observed an increase of agricultural area in the western part of the Lambwe valley, where high elevation vegetation was dominant. This goes hand in hand with farming on higher slopes and a decrease of forest. In the National Park itself the savanna increased by about 8% and the proportion of sparsely covered soil decreased by about 10%. This might be due to the fire management in the park and the recovering of burned areas.

  11. LandSat-Based Land Use-Land Cover (Raster)

    Data.gov (United States)

    Minnesota Department of Natural Resources — Raster-based land cover data set derived from 30 meter resolution Thematic Mapper satellite imagery. Classification is divided into 16 classes with source imagery...

  12. LandSat-Based Land Use-Land Cover (Vector)

    Data.gov (United States)

    Minnesota Department of Natural Resources — Vector-based land cover data set derived from classified 30 meter resolution Thematic Mapper satellite imagery. Classification is divided into 16 classes with source...

  13. Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project

    Science.gov (United States)

    Reese, H.M.; Lillesand, T.M.; Nagel, D.E.; Stewart, J.S.; Goldmann, R.A.; Simmons, T.E.; Chipman, J.W.; Tessar, P.A.

    2002-01-01

    Landsat Thematic Mapper (TM) data were the basis in production of a statewide land cover data set for Wisconsin, undertaken in partnership with U.S. Geological Survey's (USGS) Gap Analysis Program (GAP). The data set contained seven classes comparable to Anderson Level I and 24 classes comparable to Anderson Level II/III. Twelve scenes of dual-date TM data were processed with methods that included principal components analysis, stratification into spectrally consistent units, separate classification of upland, wetland, and urban areas, and a hybrid supervised/unsupervised classification called "guided clustering." The final data had overall accuracies of 94% for Anderson Level I upland classes, 77% for Level II/III upland classes, and 84% for Level II/III wetland classes. Classification accuracies for deciduous and coniferous forest were 95% and 93%, respectively, and forest species' overall accuracies ranged from 70% to 84%. Limited availability of acceptable imagery necessitated use of an early May date in a majority of scene pairs, perhaps contributing to lower accuracy for upland deciduous forest species. The mixed deciduous/coniferous forest class had the lowest accuracy, most likely due to distinctly classifying a purely mixed class. Mixed forest signatures containing oak were often confused with pure oak. Guided clustering was seen as an efficient classification method, especially at the tree species level, although its success relied in part on image dates, accurate ground troth, and some analyst intervention. ?? 2002 Elsevier Science Inc. All rights reserved.

  14. Determination of Agriculture Land Use and Land Cover Change Using Remote Sensing and GIS in TROIA National Park

    Directory of Open Access Journals (Sweden)

    Y. B. Bostanci

    2007-01-01

    Full Text Available The area selected for land use land cover (LULC dynamics, TROIA national park, is located in the city of Çanakkale, TURKEY. The national park covers an area of about 13600 ha. Remote sensing studies especially multi-temporal analysis of changes provides sufficient information about the dynamics of historic landscape. Tasseled Cap Indexes and Normalized Difference Vegetation Index (NDVI were used to create the new images from Landsat TM 1987 and Landsat TM 2006 images for classification. Supervised classification was applied with ground truth data and auxiliary data collected from different sources such as air photo, cadastral information and others.Four classes of changed and unchanged multi-temporal raster were discriminated from created new images as followed: Active Agriculture, Grassland, Forestry, and Water. Classification accuracy was determined for 1987 image and 2006 image as 81% and 87% respectively. It was found that LULC change was dynamic between classes because of the land consolidation in the region. Grassland was changed to active agriculture area by 75% and to forestry class by 5%. Forested area also converted to active agriculture by 46% and to grassland by 9%. It was concluded that land consolidation project in the study area was the main force to change land cover.

  15. Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

    Science.gov (United States)

    Jürgens, Björn; Herrero-Solana, Victor

    2017-04-01

    Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

  16. ASAR analysis of the snow cover in Livingston and Deception Islands.

    Science.gov (United States)

    Mora, C.; Vieira, G.; Ramos, M.

    2009-04-01

    ASAR images from Envisat are analyzed to study the snow cover regime of Deception and Livingston Islands (South Sthetlands, Antarctic Peninsula). Data is provided by the European Space Agency in the framework of the Proposal Category-1: Snow cover characteristics and regime in the South Shetlands (Maritime Antarctic) - SnowAntar. Medium resolution images (WSW, APM and IMM) are analyzed since December 2008, and are prepared using the processing chains from BEST (Basic Envisat SAR Toolbox). The process includes the transformation of DN into power values, geometric and radiometric correction, image filtering and computation of the backscattering coefficient for each pixel. Thereafter, the imagery is analyzed in image analysis software for the classification of backscattering. A multitemporal imagery analysis is conducted in order to set a threshold on the differential backscatter between scenes under wet snow and snow free-conditions. These algorithms allow for the study of snow surface wetness and snow water equivalent. The study of snow cover regime is linked to the permafrost monitoring and modeling effort conducted in the region in the framework of the PERMANTAR-PERMAMODEL projects. The proprieties of snow are of major significance for the ground energy balance and therefore to the ground thermal regime, since thick snow provides excellent insulation. Permafrost is therefore influenced by snow cover properties, spatial distribution and regime. Snow cover maps will be produced for integration in permafrost modeling and also for comparison with re-analysis data from ERA-Interim. The poster presents the first results of the imagery analysis of the snow cover regime since December 2008. The satellite data is validated in the field with several areas of interest (AOI) with snow thickness monitoring devices based on thermal regimes at different heights.

  17. Sky cover from MFRSR observations

    Directory of Open Access Journals (Sweden)

    E. Kassianov

    2011-07-01

    Full Text Available The diffuse all-sky surface irradiances measured at two nearby wavelengths in the visible spectral range and their modeled clear-sky counterparts are the main components of a new method for estimating the fractional sky cover of different cloud types, including cumuli. The performance of this method is illustrated using 1-min resolution data from a ground-based Multi-Filter Rotating Shadowband Radiometer (MFRSR. The MFRSR data are collected at the US Department of Energy Atmospheric Radiation Measurement (ARM Climate Research Facility (ACRF Southern Great Plains (SGP site during the summer of 2007 and represent 13 days with cumuli. Good agreement is obtained between estimated values of the fractional sky cover and those provided by a well-established independent method based on broadband observations.

  18. Grounded cognition.

    Science.gov (United States)

    Barsalou, Lawrence W

    2008-01-01

    Grounded cognition rejects traditional views that cognition is computation on amodal symbols in a modular system, independent of the brain's modal systems for perception, action, and introspection. Instead, grounded cognition proposes that modal simulations, bodily states, and situated action underlie cognition. Accumulating behavioral and neural evidence supporting this view is reviewed from research on perception, memory, knowledge, language, thought, social cognition, and development. Theories of grounded cognition are also reviewed, as are origins of the area and common misperceptions of it. Theoretical, empirical, and methodological issues are raised whose future treatment is likely to affect the growth and impact of grounded cognition.

  19. The IASLC Lung Cancer Staging Project: Background Data and Proposals for the Application of TNM Staging Rules to Lung Cancer Presenting as Multiple Nodules with Ground Glass or Lepidic Features or a Pneumonic Type of Involvement in the Forthcoming Eighth Edition of the TNM Classification.

    Science.gov (United States)

    Detterbeck, Frank C; Marom, Edith M; Arenberg, Douglas A; Franklin, Wilbur A; Nicholson, Andrew G; Travis, William D; Girard, Nicolas; Mazzone, Peter J; Donington, Jessica S; Tanoue, Lynn T; Rusch, Valerie W; Asamura, Hisao; Rami-Porta, Ramón

    2016-05-01

    Application of tumor, node, and metastasis (TNM) classification is difficult in patients with lung cancer presenting as multiple ground glass nodules or with diffuse pneumonic-type involvement. Clarification of how to do this is needed for the forthcoming eighth edition of TNM classification. A subcommittee of the International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee conducted a systematic literature review to build an evidence base regarding such tumors. An iterative process that included an extended workgroup was used to develop proposals for TNM classification. Patients with multiple tumors with a prominent ground glass component on imaging or lepidic component on microscopy are being seen with increasing frequency. These tumors are associated with good survival after resection and a decreased propensity for nodal and extrathoracic metastases. Diffuse pneumonic-type involvement in the lung is associated with a worse prognosis, but also with a decreased propensity for nodal and distant metastases. For multifocal ground glass/lepidic tumors, we propose that the T category be determined by the highest T lesion, with either the number of tumors or m in parentheses to denote the multifocal nature, and that a single N and M category be used for all the lesions collectively-for example, T1a(3)N0M0 or T1b(m)N0M0. For diffuse pneumonic-type lung cancer we propose that the T category be designated by size (or T3) if in one lobe, as T4 if involving an ipsilateral different lobe, or as M1a if contralateral and that a single N and M category be used for all pulmonary areas of involvement. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

  20. Branched polynomial covering maps

    DEFF Research Database (Denmark)

    Hansen, Vagn Lundsgaard

    1999-01-01

    A Weierstrass polynomial with multiple roots in certain points leads to a branched covering map. With this as the guiding example, we formally define and study the notion of a branched polynomial covering map. We shall prove that many finite covering maps are polynomial outside a discrete branch...

  1. Branched polynomial covering maps

    DEFF Research Database (Denmark)

    Hansen, Vagn Lundsgaard

    2002-01-01

    A Weierstrass polynomial with multiple roots in certain points leads to a branched covering map. With this as the guiding example, we formally define and study the notion of a branched polynomial covering map. We shall prove that many finite covering maps are polynomial outside a discrete branch...

  2. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification...

  3. Spatial and Quantitative Comparison of Satellite-Derived Land Cover Products over China

    Institute of Scientific and Technical Information of China (English)

    GAO Hao; JIA Gen-Suo

    2012-01-01

    Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derived land cover products, especially at the regional scale. Dif- ferent classification schemes are a key obstacle to the comparison of products and are considered the main fac- tor behind the disagreement among the different products. Using a feature-based overlap metric, we investigated the degree of spatial agreement and quantified the overall and class-specific agreement among the Moderate Resolution Imaging Spectoradiometer (MODIS), Global Land Cover 2000 (GLC2000), and the National Land Cover/Use Data- sets (NLCD) products, and the author assessed the prod- ucts by ground reference data at the regional scale over China. The areas with a low degree of agreement mostly occurred in heterogeneous terrain and transition zones, while the areas with a high degree of agreement occurred in major plains and areas with homogeneous vegetation. The overall agreement of the MODIS and GLC2000 products was 50.8% and 52.9%, and the overall accuracy was 50.3% and 41.9%, respectively. Class-specific agree- ment or accuracy varied significantly. The high-agreement classes are water, grassland, cropland, snow and ice, and bare areas, whereas classes with low agreement are shru- bland and wetland in both MODIS and GLC2000. These characteristics of spatial patterns and quantitative agree- ment could be partly explained by the complex landscapes, mixed vegetation, low separability of spectro-temporal- texture signals, and coarse pixels. The differences of class definition among different the classification schemes also affects the agreement. Each product had its advantages and limitations, but neither the overall accuracy nor the class-specific accuracy could meet the requirements of climate modeling.

  4. A methodology to generate a synergetic land-cover map by fusion of different land-cover products

    Science.gov (United States)

    Pérez-Hoyos, A.; García-Haro, F. J.; San-Miguel-Ayanz, J.

    2012-10-01

    The main goal of this study is to develop a general framework for building a hybrid land-cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions. The proposed approach assesses class-specific accuracies of datasets and establishes affinity between thematic legends using a common land-cover language such as the UN Land-Cover Classification System (LCCS). The approach is illustrated over a large region in Europe using four land-cover datasets (CORINE, GLC2000, MODIS and GlobCover), but it can be applied to any set of existing products. The multi-classification map is expected to improve the performance of individual classifications by reconciling their best characteristics while avoiding their main weaknesses. The intermap comparison reveals improved agreement of the hybrid map with all other land-cover products and therefore indicates the successful exploration of synergies between the different products. The approach offers also estimates for the classification confidence associated with the pixel label and flexibility to shift the balance between commission and omission errors, which are critical in order to obtain a desired reliable map.

  5. Analysis of spatio-temporal land cover changes for hydrological impact assessment within the Nyando River Basin of Kenya.

    Science.gov (United States)

    Olang, Luke Omondi; Kundu, Peter; Bauer, Thomas; Fürst, Josef

    2011-08-01

    The spatio-temporal changes in the land cover states of the Nyando Basin were investigated for auxiliary hydrological impact assessment. The predominant land cover types whose conversions could influence the hydrological response of the region were selected. Six Landsat images for 1973, 1986, and 2000 were processed to discern the changes based on a methodology that employs a hybrid of supervised and unsupervised classification schemes. The accuracy of the classifications were assessed using reference datasets processed in a GIS with the help of ground-based information obtained through participatory mapping techniques. To assess the possible hydrological effect of the detected changes during storm events, a physically based lumped approach for infiltration loss estimation was employed within five selected sub-basins. The results obtained indicated that forests in the basin declined by 20% while agricultural fields expanded by 16% during the entire period of study. Apparent from the land cover conversion matrices was that the majority of the forest decline was a consequence of agricultural expansion. The model results revealed decreased infiltration amounts by between 6% and 15%. The headwater regions with the vast deforestation were noted to be more vulnerable to the land cover change effects. Despite the haphazard land use patterns and uncertainties related to poor data quality for environmental monitoring and assessment, the study exposed the vast degradation and hence the need for sustainable land use planning for enhanced catchment management purposes.

  6. Landfill Top Covers

    DEFF Research Database (Denmark)

    Scheutz, Charlotte; Kjeldsen, Peter

    2011-01-01

    the landfill section has been filled or several years later depending on the settlement patterns. Significant differential settlements may disturb the functioning of the top cover. The specific design of the cover system depends on the type of waste landfilled (municipal, hazardous, or inert waste...... such as lowpermeability clay soils and geomembranes are required. The avoidance of water input to organic waste may impede the microbial stabilization processes including gas generation. Therefore watertight top covers may be in conflict with the purposes of reactor landfills (see Chapter 10.6). At some sites covers...... sometimes are made to include components for recirculation of landfill leachate (see Section 10.9.2 for more details). The top cover is an important factor in the water management of landfills. Details about water infiltration through top covers and its influence on the hydrology of the landfill is covered...

  7. Automatic Labelling and Selection of Training Samples for High-Resolution Remote Sensing Image Classification over Urban Areas

    Directory of Open Access Journals (Sweden)

    Xin Huang

    2015-12-01

    Full Text Available Supervised classification is the commonly used method for extracting ground information from images. However, for supervised classification, the selection and labelling of training samples is an expensive and time-consuming task. Recently, automatic information indexes have achieved satisfactory results for indicating different land-cover classes, which makes it possible to develop an automatic method for labelling the training samples instead of manual interpretation. In this paper, we propose a method for the automatic selection and labelling of training samples for high-resolution image classification. In this way, the initial candidate training samples can be provided by the information indexes and open-source geographical information system (GIS data, referring to the representative land-cover classes: buildings, roads, soil, water, shadow, and vegetation. Several operations are then applied to refine the initial samples, including removing overlaps, removing borders, and semantic constraints. The proposed sampling method is evaluated on a series of high-resolution remote sensing images over urban areas, and is compared to classification with manually labeled training samples. It is found that the proposed method is able to provide and label a large number of reliable samples, and can achieve satisfactory results for different classifiers. In addition, our experiments show that active learning can further enhance the classification performance, as active learning is used to choose the most informative samples from the automatically labeled samples.

  8. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  9. Xenolog classification.

    Science.gov (United States)

    Darby, Charlotte A; Stolzer, Maureen; Ropp, Patrick J; Barker, Daniel; Durand, Dannie

    2017-03-01

    Orthology analysis is a fundamental tool in comparative genomics. Sophisticated methods have been developed to distinguish between orthologs and paralogs and to classify paralogs into subtypes depending on the duplication mechanism and timing, relative to speciation. However, no comparable framework exists for xenologs: gene pairs whose history, since their divergence, includes a horizontal transfer. Further, the diversity of gene pairs that meet this broad definition calls for classification of xenologs with similar properties into subtypes. We present a xenolog classification that uses phylogenetic reconciliation to assign each pair of genes to a class based on the event responsible for their divergence and the historical association between genes and species. Our classes distinguish between genes related through transfer alone and genes related through duplication and transfer. Further, they separate closely-related genes in distantly-related species from distantly-related genes in closely-related species. We present formal rules that assign gene pairs to specific xenolog classes, given a reconciled gene tree with an arbitrary number of duplications and transfers. These xenology classification rules have been implemented in software and tested on a collection of ∼13 000 prokaryotic gene families. In addition, we present a case study demonstrating the connection between xenolog classification and gene function prediction. The xenolog classification rules have been implemented in N otung 2.9, a freely available phylogenetic reconciliation software package. http://www.cs.cmu.edu/~durand/Notung . Gene trees are available at http://dx.doi.org/10.7488/ds/1503 . durand@cmu.edu. Supplementary data are available at Bioinformatics online.

  10. Grau de cobertura do solo e dinâmica da vegetação em olivais de sequeiro com a introdução de herbicidas Ground cover and dynamic of weeds after the introduction of herbicides as soil management system in a rainfed olive orchard

    Directory of Open Access Journals (Sweden)

    Manuel Ângelo Rodrigues

    2009-12-01

    Full Text Available São apresentados resultados do grau de cobertura do solo e da dinâmica da vegetação num olival de sequeiro, localizado em Mirandela, após a introdução de herbicidas como estratégia de manutenção do solo. As modalidades em estudo foram: mobilização tradicional; herbicida pós-emergência (glifosato; e herbicida com componentes de acção residual e pós-emergência (diurão+glifosato+terbut ilazina. O grau de cobertura e a composição da vegetação foram avaliados desde 2002 a 2007 pelo método do ponto quadrado. Ambas as soluções herbicidas combateram adequadamente a vegetação herbácea em aplicação única anual. O grau de cobertura no talhão mobilizado, antes da primeira mobilização, oscilou entre 50 a 80 % e 30 a 60 % debaixo e fora da copa, respectivamente. O tratamento com glifosato permitiu um grau de cobertura em Abril entre 60 a 90 % debaixo da copa e 40 a 50 % fora da copa. No tratamento com herbicida residual o grau de cobertura do solo foi sempre muito baixo ao longo do ano. A gestão da vegetação com glifosato permitiu a cobertura do solo durante todo o ano, com vegetação viva desde o Outono à Primavera e um mulching de vegetação morta durante o Verão. Nas restantes modalidades o solo permaneceu descoberto durante grande parte do ano. No talhão gerido com glifosato a vegetação manteve elevada dinâmica. Um ano após o início da aplicação de glifosato apareceu a dominar o coberto Ornithopus compressus. Com o tempo ganharam importância algumas espécies de Inverno de ciclo muito cur-to (como Mibora mínima e Logfia gallicae outras de elevada produção de sementes e fácil dispersão pelo vento (como Hypochaeris radicata e Conyza canadensis com origem provável em incultos e caminhos que circundam o olival ou em plantas individuais que escaparam à acção dos herbicidas.Results of the percentage of ground cover by weeds and the dynamic of the vegetation are presented after the introduction of

  11. APPLICATION OF MODIS DATA TO ASSESS THE LATEST FOREST COVER CHANGES OF SRI LANKA

    Directory of Open Access Journals (Sweden)

    K. Perera

    2012-07-01

    Full Text Available Assessing forest cover of Sri Lanka is becoming important to lower the pressure on forest lands as well as man-elephant conflicts. Furthermore, the land access to north-east Sri Lanka after the end of 30 years long civil war has increased the need of regularly updated land cover information for proper planning. This study produced an assessment of the forest cover of Sri Lanka using two satellite data based maps within 23 years of time span. For the old forest cover map, the study used one of the first island-wide digital land cover classification produced by the main author in 1988. The old land cover classification was produced at 80 m spatial resolution, using Landsat MSS data. A previously published another study by the author has investigated the application feasibility of MODIS and Landsat MSS imagery for a selected sub-section of Sri Lanka to identify the forest cover changes. Through the light of these two studies, the assessment was conducted to investigate the application possibility of MODIS 250 m over a small island like Sri Lanka. The relation between the definition of forest in the study and spatial resolution of the used satellite data sets were considered since the 2012 map was based on MODIS data. The forest cover map of 1988 was interpolated into 250 m spatial resolution to integrate with the GIS data base. The results demonstrated the advantages as well as disadvantages of MODIS data in a study at this scale. The successful monitoring of forest is largely depending on the possibility to update the field conditions at regular basis. Freely available MODIS data provides a very valuable set of information of relatively large green patches on the ground at relatively real-time basis. Based on the changes of forest cover from 1988 to 2012, the study recommends the use of MODIS data as a resalable method to forest assessment and to identify hotspots to be re-investigated. It's noteworthy to mention the possibility of uncounted small

  12. Cover crops to improve soil health and pollinator habitat in nut orchards

    Science.gov (United States)

    Jerry. Van Sambeek

    2017-01-01

    Recently several national programs have been initiated calling for improving soil health and creating pollinator habitat using cover crops. Opportunities exist for nut growers to do both with the use of cover crops in our nut orchards. Because we can include perennial ground covers as cover crops, we have even more choices than landowners managing cover crops during...

  13. Ground Wars

    DEFF Research Database (Denmark)

    Nielsen, Rasmus Kleis

    Political campaigns today are won or lost in the so-called ground war--the strategic deployment of teams of staffers, volunteers, and paid part-timers who work the phones and canvass block by block, house by house, voter by voter. Ground Wars provides an in-depth ethnographic portrait of two...... infrastructures that utilize large databases with detailed individual-level information for targeting voters, and armies of dedicated volunteers and paid part-timers. Nielsen challenges the notion that political communication in America must be tightly scripted, controlled, and conducted by a select coterie...... of professionals. Yet he also quashes the romantic idea that canvassing is a purer form of grassroots politics. In today's political ground wars, Nielsen demonstrates, even the most ordinary-seeming volunteer knocking at your door is backed up by high-tech targeting technologies and party expertise. Ground Wars...

  14. Object-Based Land Use Classification using Airborne LiDAR

    Science.gov (United States)

    Antonarakis, A. S.; Richards, K. S.; Brasington, J.

    2007-12-01

    Better information on roughness of various types of vegetation is needed for use in resistance equations and eventually in flood modelling. These types include woody riparian species with different structural characteristics. Remote Sensing information such as 3D point cloud data from LiDAR can be used as a tool for extracting simple roughness information relevant for the condition of below canopy flow, as well as roughness relevant for more complex tree morphology that affects the flow when it enters the canopy levels. A strategy for extracting roughness parameters from remote sensing techniques is to use a data fusion object classification model. This means that multiple datasets such as LiDAR, digital aerial photography, ground data and satellite data can be combined to produce roughness parameters estimated for different vegetative patches, which can subsequently be mapped spatially using a classification methodology. Airborne LiDAR is used in this study in order to classify forest and ground types quickly and efficiently without the need for manipulating multispectral image files. LiDAR has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LiDAR intensity values, thus it is a spatial and spectral segmentation tool. This classification method also uses point distribution frequency criteria to differentiate between land cover types. The classification of three meanders of the Garonne and Allier rivers in France has demonstrated overall classification accuracies of 95%. Five types of riparian forest were classified with accuracies between 66-98%. These forest types included planted and natural forest stands of different ages. Classifications of short vegetation and bare earth also produced high accuracies averaging above 90%.

  15. Landfill Top Covers

    DEFF Research Database (Denmark)

    Scheutz, Charlotte; Kjeldsen, Peter

    2011-01-01

    is landscaped in order to fit into the surrounding area/environment or meet specific plans for the final use of the landfill. To fulfill the above listed requirements landfill covers are often multicomponent systems which are placed directly on top of the waste. The top cover may be placed immediately after...... the landfill section has been filled or several years later depending on the settlement patterns. Significant differential settlements may disturb the functioning of the top cover. The specific design of the cover system depends on the type of waste landfilled (municipal, hazardous, or inert waste...... however, top covers may be the only environmental protection measure. In some landfill regulations (for instance the Subtitle D landfills receiving municipal solid waste in the USA) it is required to minimize infiltration into the waste layers. Therefore top covers containing liner components...

  16. Application of In-Segment Multiple Sampling in Object-Based Classification

    Directory of Open Access Journals (Sweden)

    Nataša Đurić

    2014-12-01

    Full Text Available When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN and Support Vector Machine (SVM. Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.

  17. Percent Forest Cover

    Data.gov (United States)

    U.S. Environmental Protection Agency — Forests provide economic and ecological value. High percentages of forest cover (FORPCT) generally indicate healthier ecosystems and cleaner surface water. More...

  18. Saturated Domino Coverings

    CERN Document Server

    Buchanan, Andrew; Ryba, Alex

    2011-01-01

    A domino covering of a board is saturated if no domino is redundant. We introduce the concept of a fragment tiling and show that a minimal fragment tiling always corresponds to a maximal saturated domino covering. The size of a minimal fragment tiling is the domination number of the board. We define a class of regular boards and show that for these boards the domination number gives the size of a minimal X-pentomino covering. Natural sequences that count maximal saturated domino coverings of square and rectangular boards are obtained. These include the new sequences A193764, A193765, A193766, A193767, and A193768 of OEIS.

  19. Percent Forest Cover (Future)

    Data.gov (United States)

    U.S. Environmental Protection Agency — Forests provide economic and ecological value. High percentages of forest cover (FORPCTFuture) generally indicate healthier ecosystems and cleaner surface water....

  20. The potential of more accurate InSAR covariance matrix estimation for land cover mapping

    Science.gov (United States)

    Jiang, Mi; Yong, Bin; Tian, Xin; Malhotra, Rakesh; Hu, Rui; Li, Zhiwei; Yu, Zhongbo; Zhang, Xinxin

    2017-04-01

    Synthetic aperture radar (SAR) and Interferometric SAR (InSAR) provide both structural and electromagnetic information for the ground surface and therefore have been widely used for land cover classification. However, relatively few studies have developed analyses that investigate SAR datasets over richly textured areas where heterogeneous land covers exist and intermingle over short distances. One of main difficulties is that the shapes of the structures in a SAR image cannot be represented in detail as mixed pixels are likely to occur when conventional InSAR parameter estimation methods are used. To solve this problem and further extend previous research into remote monitoring of urban environments, we address the use of accurate InSAR covariance matrix estimation to improve the accuracy of land cover mapping. The standard and updated methods were tested using the HH-polarization TerraSAR-X dataset and compared with each other using the random forest classifier. A detailed accuracy assessment complied for six types of surfaces shows that the updated method outperforms the standard approach by around 9%, with an overall accuracy of 82.46% over areas with rich texture in Zhuhai, China. This paper demonstrates that the accuracy of land cover mapping can benefit from the 3 enhancement of the quality of the observations in addition to classifiers selection and multi-source data ingratiation reported in previous studies.

  1. Land Use Cover Mapping of Water Melon and Cereals in Southern Italy

    Directory of Open Access Journals (Sweden)

    Costanza Fiorentino

    2010-06-01

    Full Text Available The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the opportunity to map ground features, which were not detectable in the past, by using medium resolution remote sensed data (LANDSAT. More accurate and reliable maps of land cover can then be produced. However, classification procedure with these images is more complex than with the medium resolution remote sensing data for two main reasons: firstly, because of their exiguous number of spectral bands, secondly, owing to high spatial resolution, the assumption of pixel independence does not generally hold. It is then necessary to have a multi-temporal series of images or to use classifiers taking into account also proximal information. The data in this study were (i a remote sensing image taken by SPOT5 satellite in July 2007 and used to discriminate the water melon cover class and, (ii three multi-temporal remote sensing images taken by SPOT5 satellite in May, June and July 2008 used to discriminate water melon and cereal crop cover classes. For water melon recognition, providing a single image in 2007, an object-oriented technique was applied instead of a traditional, per pixel technique obtaining an increase of overall accuracy of 15%. In 2008, since it was available a multi-temporal data set, a traditional ‘Maximum Likelihood’ technique was applied for both water melon and cereal crop cover class. The overall accuracy is greater than 95%.

  2. Woody vegetation and land cover changes in the Sahel of Mali (1967-2011)

    Science.gov (United States)

    Spiekermann, Raphael; Brandt, Martin; Samimi, Cyrus

    2015-02-01

    In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (-4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.

  3. Analysing land cover and land use change in the Matobo National Park and surroundings in Zimbabwe

    Science.gov (United States)

    Scharsich, Valeska; Mtata, Kupakwashe; Hauhs, Michael; Lange, Holger; Bogner, Christina

    2016-04-01

    Natural forests are threatened worldwide, therefore their protection in National Parks is essential. Here, we investigate how this protection status affects the land cover. To answer this question, we analyse the surface reflectance of three Landsat images of Matobo National Park and surrounding in Zimbabwe from 1989, 1998 and 2014 to detect changes in land cover in this region. To account for the rolling countryside and the resulting prominent shadows, a topographical correction of the surface reflectance was required. To infer land cover changes it is not only necessary to have some ground data for the current satellite images but also for the old ones. In particular for the older images no recent field study could help to reconstruct these data reliably. In our study we follow the idea that land cover classes of pixels in current images can be transferred to the equivalent pixels of older ones if no changes occurred meanwhile. Therefore we combine unsupervised clustering with supervised classification as follows. At first, we produce a land cover map for 2014. Secondly, we cluster the images with clara, which is similar to k-means, but suitable for large data sets. Whereby the best number of classes were determined to be 4. Thirdly, we locate unchanged pixels with change vector analysis in the images of 1989 and 1998. For these pixels we transfer the corresponding cluster label from 2014 to 1989 and 1998. Subsequently, the classified pixels serve as training data for supervised classification with random forest, which is carried out for each image separately. Finally, we derive land cover classes from the Landsat image in 2014, photographs and Google Earth and transfer them to the other two images. The resulting classes are shrub land; forest/shallow waters; bare soils/fields with some trees/shrubs; and bare light soils/rocks, fields and settlements. Subsequently the three different classifications are compared and land changes are mapped. The main changes are

  4. NatureServe International Ecological Classification Standard: Terrestrial Ecological Classifications of Vegetation Alliances and Associations at Prime Hook National Wildlife Refuge

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This subset of the International Ecological Classification Standard represents the National Vegetation Classification Standard (NVCS) and covers vegetation alliances...

  5. Object-Based Classification as an Alternative Approach to the Traditional Pixel-Based Classification to Identify Potential Habitat of the Grasshopper Sparrow

    Science.gov (United States)

    Jobin, Benoît; Labrecque, Sandra; Grenier, Marcelle; Falardeau, Gilles

    2008-01-01

    The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. Object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Québec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.

  6. Evaluation of terrain geomorphometric characteristics for ground clearance charts production

    Directory of Open Access Journals (Sweden)

    Mirko A. Borisov

    2011-01-01

    into the standard military procedure OCOKA (Observation and fields of fires; Cover and concealment; Obstacles and movement; Key terrain; Avenues of approach. A few parameters of relief significantly influencing the possibilities for cover and concealment (visibility, slope and aspect were included into the definition of the model of terrain spatial analysis The morphometric data included in partial assessment categories were determined on the basis of the digital model relief analysis and by using GIS tools and given morphometric relief exploration methods. Analysis of vegetation effects on ground clearance for military forces Vegetation, in addition to terrain slope, presents one of the main factors in cross-country analyses and ground clearance assessments. In classification and extraction of vegetation from satellite images, numerous algorithms of two basic classification types, supervised and unsupervised classification, are applied. Supervised classification requires the identification of cover types of interest by user. Samples of pixels are then selected, based on available ground real information to represent each cover type. These samples are called training areas. The selection of appropriate training areas is based on the analyst's familiarity with the geographical area and his knowledge of the actual surface cover types presented in the image. Thus, the analyst 'supervises' the categorization of a set of specific classes. Unsupervised classification basically reverses the supervised classification process. Spectral classes are grouped first, based solely on the numerical information in the data, and then they are matched by the analyst to information classes (if possible. Programs, called clustering algorithms, are used to determine the natural (statistical groupings or structures in the data. The analyst usually specifies how many groups or clusters are to be looked for in the data. In addition to specifying the desired number of classes, the analyst may

  7. Land Cover Characterization Program

    Science.gov (United States)

    ,

    1997-01-01

    The U.S. Geological Survey (USGS) has a long heritage of leadership and innovation in land use and land cover mapping. The USGS Anderson system defined the principles for land use and land cover mapping that have been the model both nationally and internationally for more than 20 years. The Land Cover Characterization Program (LCCP) is founded on the premise that the Nation's needs for land cover and land use data are diverse and increasingly sophisticated. The range of projects, programs, and organizations that use land cover data to meet their planning, management, development, and assessment objectives has expanded significantly. The reasons for this are numerous, and include the improved capabilities provided by geographic information systems, better and more data-intensive analytic models, and increasing requirements for improved information for decision making. The overall goals of the LCCP are to:

  8. Land Cover Trends Project

    Science.gov (United States)

    Acevedo, William

    2006-01-01

    The Land Cover Trends Project is designed to document the types, rates, causes, and consequences of land cover change from 1973 to 2000 within each of the 84 U.S. Environmental Protection Agency (EPA) Level III ecoregions that span the conterminous United States. The project's objectives are to: * Develop a comprehensive methodology using probability sampling and change analysis techniques and Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM) data for estimating regional land cover change. * Characterize the spatial and temporal characteristics of conterminous U.S. land cover change for five periods from 1973 to 2000 (nominally 1973, 1980, 1986, 1992, and 2000). * Document the regional driving forces and consequences of change. * Prepare a national synthesis of land cover change.

  9. Flat covers of modules

    CERN Document Server

    Xu, Jinzhong

    1996-01-01

    Since the injective envelope and projective cover were defined by Eckmann and Bas in the 1960s, they have had great influence on the development of homological algebra, ring theory and module theory. In the 1980s, Enochs introduced the flat cover and conjectured that every module has such a cover over any ring. This book provides the uniform methods and systematic treatment to study general envelopes and covers with the emphasis on the existence of flat cover. It shows that Enochs' conjecture is true for a large variety of interesting rings, and then presents the applications of the results. Readers with reasonable knowledge in rings and modules will not have difficulty in reading this book. It is suitable as a reference book and textbook for researchers and graduate students who have an interest in this field.

  10. SNOW COVER MONITORING BY REMOTE SENSING AND SNOWMELT RUNOFF CALCULATION IN THE UPPER HUANGHE RIVER BASIN

    Institute of Scientific and Technical Information of China (English)

    LANYong-chao; MAQua-jie; 等

    2002-01-01

    The upper Huanghe(Yellow) River basin is situated in the northeast of the Qinghai-Xizang(Tibet)Plateau of China.The melt-water from the snow-cover is main water supply for the rivers in the region during springtime and other arid regions of the northwestern China, and the hydrological conditions of the rivers are directly controlled by the snowmelt water in spring .So snowmelt runoff forecast has importance for hydropower,flood prevention and water resources utilize-tion.The application of remote sensing and Geographic Information System(GIS) techniques in snow cover monitoring and snowmelt runoff calculation in the upper Huanghe River basin are introduced amply in this paper.The key parame-ter-snow cover area can be computed by satellite images from multi-platform,multi-templral and multi-spectral.A clus-ter of snow-cover data can be yielded by means of the classification filter method.Meanwhile GIS will provide relevant information for obtaining the parameters and also for zoning .According to the typical samples extracting snow covered moun-tained in detail also.The runoff snowmelt models based on the snow-cover data from NOAA images and observation data of runoff,precipitation and air temperature have been satisfactorily used for predicting the inflow to the Longyangxia Reser-voir,which is located at lower end of snow cover region and is one of the largest reservoirs on the upper Huanghe River, during late March to early June.The result shows that remote sensing techniques combined with the ground meteorological and hydrological observation is of great potential in snowmelt runoff forecasting for a large river basin.With the develop-ment of remote sensing technique and the progress of the interpretation method,the forecast accuracy of snowmelt runoff will be improved in the near future .Large scale extent and few stations are two objective reality situations in Chian,so they should be considered in simulation and forecast.Apart from dividing ,the derivation of

  11. SNOW COVER MONITORING BY REMOTE SENSING AND SNOWMELT RUNOFF CALCULATION IN THE UPPER HUANGHE RIVER BASIN

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The upper Huanghe(Yellow) River basin is situated in the northeast of the Qinghai-Xizang(Tibet)Plateau of China. The melt-water from the snow-cover is main water supply for the rivers in the region during springtime and other arid regions of the northwestern China, and the hydrological conditions of the rivers are directly controlled by the snowmelt water in spring. So snowmelt runoff forecast has importance for hydropower, flood prevention and water resources utilization. The application of remote sensing and Geographic Information System (GIS) techniques in snow cover monitoring and snowmelt runoff calculation in the upper Huanghe River basin are introduced amply in this paper. The key parameter- snow cover area can be computed by satellite images from multi-platform, multi-temporal and multi-spectral. A cluster of snow-cover data can be yielded by means of the classification filter method. Meanwhile GIS will provide relevant information for obtaining the parameters and also for zoning. According to the typical samples extracting snow covered mountainous region, the snowmelt runoff calculation models in the upper Huanghe River basin are presented and they are mentioned in detail also. The runoff snowmelt models based on the snow-cover data from NOAA images and observation data of runoff, precipitation and air temperature have been satisfactorily used for predicting the inflow to the Longyangxia Reservoir , which is located at lower end of snow cover region and is one of the largest reservoirs on the upper Huanghe River, during late March to early June. The result shows that remote sensing techniques combined with the ground meteorological and hydrological observation is of great potential in snowmelt runoff forecasting for a large river basin. With the development of remote sensing technique and the progress of the interpretation method, the forecast accuracy of snowmelt runoff will be improved in the near future. Large scale extent and few stations are two

  12. Basis of Criminalistic Classification of a Person in Republic Kazakhstan and Republic Mongolia

    Science.gov (United States)

    Abdilov, Kanat S.; Zusbaev, Baurzan T.; Naurysbaev, Erlan A.; Nukiev, Berik A.; Nurkina, Zanar B.; Myrzahanov, Erlan N.; Urazalin, Galym T.

    2016-01-01

    In this article reviewed problems of the criminalistic classification building of a person. In the work were used legal formal, logical, comparative legal methods. The author describes classification kinds. Reveal the meaning of classification in criminalistic systematics. Shows types of grounds of criminalistic classification of a person.…

  13. Manhole cover point locations across Guam's developed areas

    Data.gov (United States)

    U.S. Environmental Protection Agency — This is a point feature dataset with points across Guam's developed areas. The points represent manhole cover locations, ground elevation and manhole access depth.

  14. Land Cover Trends Geotagged Photography: 1999-2007

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The United States Geological Survey (USGS) Land Cover Trends field photography collection is a national-scale, ground-reference dataset which initially served as a...

  15. Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project

    Science.gov (United States)

    Hansen, M.C.; Egorov, A.; Roy, D.P.; Potapov, P.; Ju, J.; Turubanova, S.; Kommareddy, I.; Loveland, T.R.

    2011-01-01

    Vegetation Continuous Field (VCF) layers of 30 m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from theWeb-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to performLandsat land cover characterizations at continental scales that ar einternally consistent while retaining local spatial and thematic detail. ?? 2011 Taylor & Francis.

  16. Propagation of Sound Through the Atmosphere: Effects of Ground Cover

    Science.gov (United States)

    1978-06-19

    surface. The impedance measurements were limited to the -f -quency range 220 Hz to 1000 Hz due to the experimental saometry. In this region, however...frequency limit of 100 Hz. In this range, the surface wave predicted by the theory used to analyze the data was not calculated to be a significant fraction...RETURN rND FUNCION EAST611101,D31*1 DIME~ýIOW 11M(4 LEAST52 =IS MAE IS BXZST QUALITY FRLI=40 C D)UNA4I𔃻 AS TEVFITF 1,FD)AN4fN C(~r PHECTLO T( US AI

  17. BRS Centauro – oat cultivar for ground cover and grazing

    Directory of Open Access Journals (Sweden)

    Alfredo do Nascimento Junior

    2015-04-01

    Full Text Available Plants and seeds of oat cultivar BRS Centauro, of the species Avena brevis Roth., are highly uniform. The crop cycle is long, the suitability as fodder excellent, and leaf production particularly high, resulting in better quality forage than that of the black oat forage controls.

  18. An Automated Algorithm for Producing Land Cover Information from Landsat Surface Reflectance Data Acquired Between 1984 and Present

    Science.gov (United States)

    Rover, J.; Goldhaber, M. B.; Holen, C.; Dittmeier, R.; Wika, S.; Steinwand, D.; Dahal, D.; Tolk, B.; Quenzer, R.; Nelson, K.; Wylie, B. K.; Coan, M.

    2015-12-01

    Multi-year land cover mapping from remotely sensed data poses challenges. Producing land cover products at spatial and temporal scales required for assessing longer-term trends in land cover change are typically a resource-limited process. A recently developed approach utilizes open source software libraries to automatically generate datasets, decision tree classifications, and data products while requiring minimal user interaction. Users are only required to supply coordinates for an area of interest, land cover from an existing source such as National Land Cover Database and percent slope from a digital terrain model for the same area of interest, two target acquisition year-day windows, and the years of interest between 1984 and present. The algorithm queries the Landsat archive for Landsat data intersecting the area and dates of interest. Cloud-free pixels meeting the user's criteria are mosaicked to create composite images for training the classifiers and applying the classifiers. Stratification of training data is determined by the user and redefined during an iterative process of reviewing classifiers and resulting predictions. The algorithm outputs include yearly land cover raster format data, graphics, and supporting databases for further analysis. Additional analytical tools are also incorporated into the automated land cover system and enable statistical analysis after data are generated. Applications tested include the impact of land cover change and water permanence. For example, land cover conversions in areas where shrubland and grassland were replaced by shale oil pads during hydrofracking of the Bakken Formation were quantified. Analytical analysis of spatial and temporal changes in surface water included identifying wetlands in the Prairie Pothole Region of North Dakota with potential connectivity to ground water, indicating subsurface permeability and geochemistry.

  19. Classifying Multi-year Land Use and Land Cover using Deep Convolutional Neural Networks

    Science.gov (United States)

    Seo, B.

    2015-12-01

    Cultivated ecosystems constitute a particularly frequent form of human land use. Long-term management of a cultivated ecosystem requires us to know temporal change of land use and land cover (LULC) of the target system. Land use and land cover changes (LUCC) in agricultural ecosystem is often rapid and unexpectedly occurs. Thus, longitudinal LULC is particularly needed to examine trends of ecosystem functions and ecosystem services of the target system. Multi-temporal classification of land use and land cover (LULC) in complex heterogeneous landscape remains a challenge. Agricultural landscapes often made up of a mosaic of numerous LULC classes, thus spatial heterogeneity is large. Moreover, temporal and spatial variation within a LULC class is also large. Under such a circumstance, standard classifiers would fail to identify the LULC classes correctly due to the heterogeneity of the target LULC classes. Because most standard classifiers search for a specific pattern of features for a class, they fail to detect classes with noisy and/or transformed feature data sets. Recently, deep learning algorithms have emerged in the machine learning communities and shown superior performance on a variety of tasks, including image classification and object recognition. In this paper, we propose to use convolutional neural networks (CNN) to learn from multi-spectral data to classify agricultural LULC types. Based on multi-spectral satellite data, we attempted to classify agricultural LULC classes in Soyang watershed, South Korea for the three years' study period (2009-2011). The classification performance of support vector machine (SVM) and CNN classifiers were compared for different years. Preliminary results demonstrate that the proposed method can improve classification performance compared to the SVM classifier. The SVM classifier failed to identify classes when trained on a year to predict another year, whilst CNN could reconstruct LULC maps of the catchment over the study

  20. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    Science.gov (United States)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  1. Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Péter Burai

    2015-02-01

    Full Text Available Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier—MLC, machine learning algorithms (support vector machine—SVM, random forest—RF and feature extraction (minimum noise fraction (MNF-transformation on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm, a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2–15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%, but the use of a smaller training dataset (10 training pixels significantly reduced the accuracy of classification (52.56%. Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC

  2. Covering folded shapes

    Directory of Open Access Journals (Sweden)

    Oswin Aichholzer

    2014-05-01

    Full Text Available Can folding a piece of paper flat make it larger? We explore whether a shape S must be scaled to cover a flat-folded copy of itself. We consider both single folds and arbitrary folds (continuous piecewise isometries \\(S\\to\\mathbb{R}^2\\. The underlying problem is motivated by computational origami, and is related to other covering and fixturing problems, such as Lebesgue's universal cover problem and force closure grasps. In addition to considering special shapes (squares, equilateral triangles, polygons and disks, we give upper and lower bounds on scale factors for single folds of convex objects and arbitrary folds of simply connected objects.

  3. Analysis of Causes of Karst Collapse and the Corresponding Countermeasures During the Running of Ground Source Heat Pump in the Covered Karst Areas%覆盖型岩溶区地下水地源热泵工程岩溶塌陷成因及对策研究

    Institute of Scientific and Technical Information of China (English)

    段启杉; 曹振东; 孟凡涛; 宋小庆

    2015-01-01

    地下水地源热泵工程是抽取地下水,以地下水为换热介质,提取能量之后全部回灌于含水层中。因此地下水地源热泵是改变地下水局部水位高程,整体上不影响地下水流场的变化。覆盖型岩溶区地质环境承载力较差,地下水的开发利用过程,可能加强诱发岩溶塌陷的因素,发生岩溶塌陷。在地下水地源热泵的建设和运行中,控制水位降深、尽量避免水位波动和瞬间水位变化过大等措施,达到预防岩溶塌陷的目的。而岩溶塌陷预防措施的研究给覆盖型岩溶区地下水地源热泵的建设和运行提供技术支撑。%Ground source heat pump engineering is pumping groundwater, groundwater as heat transfer medium, extract the energy after full recharge in the aquifer. So the groundwater source heat pump is to change the local groundwater level elevation, on the whole does not affect the change of the groundwater flow field. Covered karst area with poor bearing capacity of geological envi-ronment, the development and utilization of groundwater, karst collapse could strengthen induced factors, karst collapse occurred. In groundwater in the construction and operation of ground source heat pump, control the water level drawdown, avoid water level fluctuation and instantaneous water level change is too big, so as to achieve the purpose of prevention of karst collapse, and preven-tive measures of karst collapse research covered karst area groundwater source heat pump with pumping and technical support for the construction and operation.

  4. Agriculture classification using POLSAR data

    DEFF Research Database (Denmark)

    Skriver, Henning; Dall, Jørgen; Ferro-Famil, Laurent

    2005-01-01

    , and a very important class of algorithms is the knowledge-based approaches. Here, generic characteristics of different cover types are derived by combining physical reasoning with the available empirical evidence. These are then used to define classification rules. Because of their emphasis on the physical...... of their components) show strongly preferred orientations, such as the stalks or ears of cereals. The importance of SAR polarimetry in crop classification arises principally because polarisation is sen-sitive to orientation. Hence it provides a means to distinguish crops with different canopy archi-tectures. Detailed...... in the crop canopy, particularly between the response of the canopy itself and soil response. It is expected that PolInSAR data will add to the classification potential of POLSAR data by their sensitivity to the vertical distribution of scatterers. Different approaches have been used to classify SAR data...

  5. Differential productivity of Bristol Bay spawning grounds

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — Bristol Bay escapement surveys covering a period of several years show that, irrespective of fluctuations in total numbers on a system, certain grounds display a...

  6. Measurements of radar ground returns

    NARCIS (Netherlands)

    Loor, G.P. de

    1974-01-01

    The ground based measurement techniques for the determination of the radar back-scatter of vegetation and soils as used in The Netherlands will be described. Two techniques are employed: one covering a large sample area (> 1000 m2) but working at low grazing angels only and one (short range) coverin

  7. Percent Wetland Cover

    Data.gov (United States)

    U.S. Environmental Protection Agency — Wetlands act as filters, removing or diminishing the amount of pollutants that enter surface water. Higher values for percent of wetland cover (WETLNDSPCT) may be...

  8. Percent of Impervious Cover

    Data.gov (United States)

    U.S. Environmental Protection Agency — High amounts of impervious cover (parking lots, rooftops, roads, etc.) can increase water runoff, which may directly enter surface water. Runoff from roads often...

  9. Percent Wetland Cover (Future)

    Data.gov (United States)

    U.S. Environmental Protection Agency — Wetlands act as filters, removing or diminishing the amount of pollutants that enter surface water. Higher values for percent of wetland cover (WETLNDSPCT) may be...

  10. Projected 2020 Land Cover

    Data.gov (United States)

    U.S. Environmental Protection Agency — Projected 2020 land cover was developed to provide one scenario of development in the year 2020. It was used to generate several metrics to compare to 1992 metrics...

  11. Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries

    Science.gov (United States)

    Le Bris, A.; Chehata, N.; Briottet, X.; Paparoditis, N.

    2016-06-01

    In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000-2400 nm) to material

  12. Spectral Classification Beyond M

    CERN Document Server

    Leggett, S K; Burgasser, A J; Jones, H R A; Marley, M S; Tsuji, T

    2004-01-01

    Significant populations of field L and T dwarfs are now known, and we anticipate the discovery of even cooler dwarfs by Spitzer and ground-based infrared surveys. However, as the number of known L and T dwarfs increases so does the range in their observational properties, and difficulties have arisen in interpreting the observations. Although modellers have made significant advances, the complexity of the very low temperature, high pressure, photospheres means that problems remain such as the treatment of grain condensation as well as incomplete and non-equilibrium molecular chemistry. Also, there are several parameters which control the observed spectral energy distribution - effective temperature, grain sedimentation efficiency, metallicity and gravity - and their effects are not well understood. In this paper, based on a splinter session, we discuss classification schemes for L and T dwarfs, their dependency on wavelength, and the effects of the parameters T_eff, f_sed, [m/H] and log g on optical and infra...

  13. Sensitivity of Support Vector Machine Classification to Various Training Features

    Directory of Open Access Journals (Sweden)

    Fuling Bian

    2013-07-01

    Full Text Available Remote sensing image classification is one of the most important techniques in image interpretation, which can be used for environmental monitoring, evaluation and prediction. Many algorithms have been developed for image classification in the literature. Support vector machine (SVM is a kind of supervised classification that has been widely used recently. The classification accuracy produced by SVM may show variation depending on the choice of training features. In this paper, SVM was used for land cover classification using Quickbird images. Spectral and textural features were extracted for the classification and the results were analyzed thoroughly. Results showed that the number of features employed in SVM was not the more the better. Different features are suitable for different type of land cover extraction. This study verifies the effectiveness and robustness of SVM in the classification of high spatial resolution remote sensing images.    

  14. Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

    Science.gov (United States)

    Lee, Kyeong-sang; Choi, Sungwon; Seo, Minji; Lee, Chang suk; Seong, Noh-hun; Han, Kyung-Soo

    2016-10-01

    Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth's energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer's Accuracy), UA (User's Accuracy) and CI (Comparison Index) as validation criteria

  15. Evaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration

    Directory of Open Access Journals (Sweden)

    Marvin E. Bauer

    2013-09-01

    Full Text Available Shadows in high resolution imagery create significant problems for urban land cover classification and environmental application. We first investigated whether shadows were intrinsically different and hypothetically possible to separate from each other with ground spectral measurements. Both pixel-based and object-oriented methods were used to evaluate the effects of shadow detection on QuickBird image classification and spectroradiometric restoration. In each method, shadows were detected and separated either with or without histogram thresholding, and subsequently corrected with a k-nearest neighbor algorithm and a linear correlation correction. The results showed that shadows had distinct spectroradiometric characteristics, thus, could be detected with an optimal brightness threshold and further differentiated with a scene-based near infrared ratio. The pixel-based methods generally recognized more shadow areas and with statistically higher accuracy than the object-oriented methods. The effects of the prior shadow thresholding were not statistically significant. The accuracy of the final land cover classification, after accounting for the shadow detection and separation, was significantly higher for the pixel-based methods than for the object-oriented methods, although both achieved similar accuracy for the non-shadow classes. Both radiometric restoration algorithms significantly reduced shadow areas in the original satellite images.

  16. Interactive exploration of uncertainty in fuzzy classifications by isosurface visualization of class clusters

    NARCIS (Netherlands)

    Lucieer, A.; Veen, L.E.

    2009-01-01

    Uncertainty and vagueness are important concepts when dealing with transition zones between vegetation communities or land-cover classes. In this study, classification uncertainty is quantified by applying a supervised fuzzy classification algorithm. New visualization techniques are proposed and pre

  17. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  18. Classification in Australia.

    Science.gov (United States)

    McKinlay, John

    Despite some inroads by the Library of Congress Classification and short-lived experimentation with Universal Decimal Classification and Bliss Classification, Dewey Decimal Classification, with its ability in recent editions to be hospitable to local needs, remains the most widely used classification system in Australia. Although supplemented at…

  19. SpinSat Mission Ground Truth Characterization

    Science.gov (United States)

    2014-09-01

    SpinSat Mission Ground Truth Characterization Andrew Nicholas, Ted Finne, Ivan Galysh, Anthony Mai, Jim Yen Naval Research Laboratory, Washington...mission overview, ground truth characterization and unique SSA observation opportunities of the mission. 1. MISSION CONCEPT The Naval Research...2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE SpinSat Mission Ground Truth Characterization 5a. CONTRACT

  20. Climate under cover

    CERN Document Server

    Takakura, Tadashi

    2002-01-01

    1.1. INTRODUCTION Plastic covering, either framed or floating, is now used worldwide to protect crops from unfavorable growing conditions, such as severe weather and insects and birds. Protected cultivation in the broad sense, including mulching, has been widely spread by the innovation of plastic films. Paper, straw, and glass were the main materials used before the era of plastics. Utilization of plastics in agriculture started in the developed countries and is now spreading to the developing countries. Early utilization of plastic was in cold regions, and plastic was mainly used for protection from the cold. Now plastic is used also for protection from wind, insects and diseases. The use of covering techniques started with a simple system such as mulching, then row covers and small tunnels were developed, and finally plastic houses. Floating mulch was an exception to this sequence: it was introduced rather recently, although it is a simple structure. New development of functional and inexpensive films trig...

  1. Snow cover thickness estimation by using radial basis function networks

    Directory of Open Access Journals (Sweden)

    A. Guidali

    2012-07-01

    Full Text Available This work investigates learning and generalisation capabilities of radial basis function networks (RBFN used to solve snow cover thickness estimation model as regression and classification. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes in both regression and classification tasks. The snow cover thickness estimation by RBFN has been proved a valuable tool able to deal with several critical aspects arising from the specific experimental context.

  2. Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery.

    Science.gov (United States)

    Marapareddy, Ramakalavathi; Aanstoos, James V; Younan, Nicolas H

    2016-06-16

    Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H), anisotropy (A), alpha (α), and eigenvalues (λ, λ₁, λ₂, and λ₃), we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory's (JPL's) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers.

  3. Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery

    Directory of Open Access Journals (Sweden)

    Ramakalavathi Marapareddy

    2016-06-01

    Full Text Available Fully polarimetric Synthetic Aperture Radar (polSAR data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H, anisotropy (A, alpha (α, and eigenvalues (λ, λ1, λ2, and λ3, we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR. The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers.

  4. Reusable pipe flange covers

    Energy Technology Data Exchange (ETDEWEB)

    Holden, James Elliott (Simpsonville, SC); Perez, Julieta (Houston, TX)

    2001-01-01

    A molded, flexible pipe flange cover for temporarily covering a pipe flange and a pipe opening includes a substantially round center portion having a peripheral skirt portion depending from the center portion, the center portion adapted to engage a front side of the pipe flange and to seal the pipe opening. The peripheral skirt portion is formed to include a plurality of circumferentially spaced tabs, wherein free ends of the flexible tabs are formed with respective through passages adapted to receive a drawstring for pulling the tabs together on a back side of the pipe flange.

  5. Systematic classification of hazards in underground mining

    Energy Technology Data Exchange (ETDEWEB)

    Ryncarz, T.

    1983-01-01

    Hazards in underground coal mines are analyzed. A general definition of a hazard is given as a physical process or interaction between environment and men which can harm miners. The following classification of mine environment is given: lithosphere, atmosphere, and so-called technosphere (equipment, machines and processes associated with mining operations in underground mines). It is stated that the traditional classification of hazards in underground mining which divides the hazards into two groups: natural hazards and other hazards, is not precise. The hazards classification proposed by the author uses three criteria: criterion of mining environment (lithosphere, atmosphere and technosphere), criterion of physical process development (mechanical process, thermal process), and criterion of process intensity (slow or rapid flow). The classification, presented in a table, covers all hazards in underground mining such as rock bursts, water influx, fires, dusts, rock falls etc. Practical use of the classification system in coal mining is discussed. 3 references.

  6. Detection of Shoreline and Land Cover Changes around Rosetta Promontory, Egypt, Based on Remote Sensing Analysis

    Directory of Open Access Journals (Sweden)

    Ali Masria

    2015-03-01

    Full Text Available Rosetta Promontory, Egypt has been suffering from a continuous erosion problem. The dramatic retreatment was observed during the last century. It is basically due to the construction of Aswan High Dam in 1964, which reduced the flow and sediment discharges. In this paper, four Landsat images (two Thematic Mapper and two Enhanced Thematic Mapper covering the period from 1984 to 2014 were used. These Landsat images were radio-metrically and geometrically corrected, and then, multi-temporal post-classification analysis was performed to detect land cover changes, extracting shoreline positions to estimate shoreline change rates of the Nile delta coast around Rosetta Promontory. This method provides a viable means for examining long-term shoreline changes. Four categories, including seawater, developed (agriculture and urban, sabkhas (salt-flat, and undeveloped areas, were selected to evaluate their temporal changes by comparing the four selected images. Supervised classification technique was used with support vector machine algorithm to detect temporal changes. The overall accuracy assessment of this method ranged from 97% to 100%. In addition, the shoreline was extracted by applying two different techniques. The first method is based on a histogram threshold of Band 5, and the other uses the combination of histogram threshold of Band 5 and two band ratios (Band 2/Band 4 and Band 2/Band 5. For land cover change detection from 1984 to 2014, it was found that the developed area that increased by 9% although the land in the study area has been contracted by 1.6% due to coastal erosion. The shoreline retreat rate has decreased more than 70% from 1984 to 2014. Nevertheless, it still suffers from significant erosion with a maximum rate of 37 m/year. In comparison to ground survey and different remote sensing techniques, the established trend of shoreline change extracted using histogram threshold was found to be closely consistent with these studies

  7. 'Grounded' Politics

    DEFF Research Database (Denmark)

    Schmidt, Garbi

    2012-01-01

    play within one particular neighbourhood: Nørrebro in the Danish capital, Copenhagen. The article introduces the concept of grounded politics to analyse how groups of Muslim immigrants in Nørrebro use the space, relationships and history of the neighbourhood for identity political statements....... The article further describes how national political debates over the Muslim presence in Denmark affect identity political manifestations within Nørrebro. By using Duncan Bell’s concept of mythscape (Bell, 2003), the article shows how some political actors idealize Nørrebro’s past to contest the present...

  8. Covering tree with stars

    DEFF Research Database (Denmark)

    Baumbach, Jan; Guo, Jian-Ying; Ibragimov, Rashid

    2013-01-01

    We study the tree edit distance problem with edge deletions and edge insertions as edit operations. We reformulate a special case of this problem as Covering Tree with Stars (CTS): given a tree T and a set of stars, can we connect the stars in by adding edges between them such that the resulting ...

  9. Covering tree with stars

    DEFF Research Database (Denmark)

    Baumbach, Jan; Guo, Jiong; Ibragimov, Rashid

    2015-01-01

    We study the tree edit distance problem with edge deletions and edge insertions as edit operations. We reformulate a special case of this problem as Covering Tree with Stars (CTS): given a tree T and a set of stars, can we connect the stars in by adding edges between them such that the resulting ...

  10. Covering All Options

    Science.gov (United States)

    Kennedy, Mike

    2011-01-01

    The day a school opens its doors for the first time, the flooring will be new and untarnished. When the flooring is in such pristine condition, many flooring materials--carpeting, vinyl, terrazzo, wood or some other surface--will look good. But school and university planners who decide what kind of material covers the floors of their facilities…

  11. CORINE Land Cover 2006

    DEFF Research Database (Denmark)

    Stjernholm, Michael

    "CORINE land cover" er en fælleseuropæisk kortlægning af arealanvendelse/arealdække. Arealanvendelse/arealdække er i Danmark kortlagt efter CORINE metode og klasseopdeling med satellitbilleder fra 3 forskellige tidsperioder, fra begyndelsen af 1990'erne (CLC90), fra år 2000 (CLC2000) og fra år 2006...

  12. CORINE Land Cover 2006

    DEFF Research Database (Denmark)

    Stjernholm, Michael

    "CORINE land cover" er en fælleseuropæisk kortlægning af arealanvendelse/arealdække. Arealanvendelse/arealdække er i Danmark kortlagt efter CORINE metode og klasseopdeling med satellitbilleder fra 3 forskellige tidsperioder, fra begyndelsen af 1990'erne (CLC90), fra år 2000 (CLC2000) og fra år 2006...

  13. USGS Land Cover (NLCD) Overlay Map Service from The National Map - National Geospatial Data Asset (NGDA) National Land Cover Database (NLCD)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — NLCD 1992, NLCD 2001, NLCD 2006, and NLCD 2011 are National Land Cover Database classification schemes based primarily on Landsat data along with ancillary data...

  14. Effective UV surface albedo of seasonally snow-covered lands

    Science.gov (United States)

    Tanskanen, A.; Manninen, T.

    2007-05-01

    At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.

  15. Dielectric Covered Planar Antennas

    Science.gov (United States)

    Llombart Juan, Nuria (Inventor); Lee, Choonsup (Inventor); Chattopadhyay, Goutam (Inventor); Gill, John J. (Inventor); Skalare, Anders J. (Inventor); Siegel, Peter H. (Inventor)

    2014-01-01

    An antenna element suitable for integrated arrays at terahertz frequencies is disclosed. The antenna element comprises an extended spherical (e.g. hemispherical) semiconductor lens, e.g. silicon, antenna fed by a leaky wave waveguide feed. The extended spherical lens comprises a substantially spherical lens adjacent a substantially planar lens extension. A couple of TE/TM leaky wave modes are excited in a resonant cavity formed between a ground plane and the substantially planar lens extension by a waveguide block coupled to the ground plane. Due to these modes, the primary feed radiates inside the lens with a directive pattern that illuminates a small sector of the lens. The antenna structure is compatible with known semiconductor fabrication technology and enables production of large format imaging arrays.

  16. Expenses classification, oriented to the effective company management

    OpenAIRE

    Ющак, Жанна Миколаївна

    2015-01-01

    The detailed classification of expenses by the management functions that is the most adequate for the effectivemanagement of company’s activities has been presented.The development mechanism of the expenses classification of a company while information forming in the accountingsystem has been grounded

  17. Application of Neutral Network by EEG Signal Classification

    Directory of Open Access Journals (Sweden)

    Michal Gala

    2008-01-01

    Full Text Available Analysis of long-term EEG requires that it is segmented into piece-wise stationary sections and classified. Neural network architecture is introduced for the problem of classification of EEG signals. This paper deals with basic signal classification into two classes. This work is a ground towards creating an algorithm to sleep status analysis. Signal is first worked by signal segmentation and then is used a neural network to classification into two class.

  18. Application of neutral network by EEG signal classification

    OpenAIRE

    Michal Gala; Vladimir Krajca; Jitka Mohylova

    2008-01-01

    Analysis of long-term EEG requires that it is segmented into piece-wise stationary sections and classified. Neural network architecture is introduced for the problem of classification of EEG signals. This paper deals with basic signal classification into two classes. This work is a ground towards creating an algorithm to sleep status analysis. Signal is first worked by signal segmentation and then is used a neural network to classification into two class.

  19. UNCERTAINTY ASSESSMENT OF GLOBELAND30 LAND COVER DATA SET OVER CENTRAL ASIA

    Directory of Open Access Journals (Sweden)

    B. Sun

    2016-06-01

    Full Text Available GlobeLand30, the world’s first 30m-resolution global land cover data set, has recently been issued for research on global change at a fine resolution. Given the accuracy of GlobeLand30 data may show significant variation in different parts of the world and data quality at continental scale has not been validated yet, this study aims to evaluate the uncertainty of the data over Central Asia. Since it is difficult to get long-term historical ground references, GlobeLand30 data at the most recent epoch (i.e., GlobeLand30-2010 was assessed. In the test, a large sample size was adopted, and more than 25 thousand samples were selected by a random sampling scheme and interpreted manually as ground references based on higher resolution imagery at the same epoch, such as images from ZY-3 (China Resources Series satellite and Google earth. Cross validation of image interpretation by three well-trained interpreters was adopted to make the references more reliable. Error matrix and Kappa coefficient were utilized to quantify data accuracies in terms of classification accuracy. Results show that the GlobeLand30-2010 data presents an overall accuracy of 46% in the study area. As for specific land cover types, bare land illustrates a high user’s accuracy but a lower producer’s accuracy. At the same time, the accuracies of grassland and forest are significantly lower than other types. The majority of misclassification types come from bare land. It implies a difficulty of distinguishing grassland or forest from bare land in the study area. In addition, the confusion between shrub land and grassland also results in the misclassification. The results serve as a useful reference of data accuracy for further analysis of land cover change in Central Asia as well as the applications of GlobeLand30 data at a regional or continental scale.

  20. Classification of authors by literary prestige

    NARCIS (Netherlands)

    Verboord, Marc

    2003-01-01

    In this study, I investigated a new system to classify authors by literary prestige. The notion of ‘canon’ was considered to lackclear theoretical and empirical grounding. Evaluation and classification practices were examined and operationalized from the perspective of literary field theory. The val

  1. Singular coverings of toposes

    CERN Document Server

    Bunge, Marta

    2006-01-01

    The self-contained theory of certain singular coverings of toposes called complete spreads, that is presented in this volume, is a field of interest to topologists working in knot theory, as well as to various categorists. It extends the complete spreads in topology due to R. H. Fox (1957) but, unlike the classical theory, it emphasizes an unexpected connection with topos distributions in the sense of F. W. Lawvere (1983). The constructions, though often motivated by classical theories, are sometimes quite different from them. Special classes of distributions and of complete spreads, inspired respectively by functional analysis and topology, are studied. Among the former are the probability distributions; the branched coverings are singled out amongst the latter. This volume may also be used as a textbook for an advanced one-year graduate course introducing topos theory with an emphasis on geometric applications. Throughout the authors emphasize open problems. Several routine proofs are left as exercises, but...

  2. On directed coverings

    DEFF Research Database (Denmark)

    Fajstrup, Lisbeth

    In [1], we study coverings in the setting of directed topology. Unfortunately, there is a condition missing in the definition of a directed covering. Some of the results in [1] require this extra condition and in fact it was claimed to follow from the original definition. It is the purpose...... of this note to give the right definition and point out how this affects the statements in that paper. Moreover, we give an example of a dicovering in the sense of [1], which does not satisfy the extra condition. Fortunately, with the extra condition, the subsequent results are now correct. [1] L. Fajstrup......, Dicovering spaces, Homology Homotopy Appl. 5 (2003), no. 2, 1-17....

  3. Modeling and synthesis of strong ground motion

    Indian Academy of Sciences (India)

    S T G Raghu Kanth

    2008-11-01

    Success of earthquake resistant design practices critically depends on how accurately the future ground motion can be determined at a desired site. But very limited recorded data are available about ground motion in India for engineers to rely upon. To identify the needs of engineers, under such circumstances, in estimating ground motion time histories, this article presents a detailed review of literature on modeling and synthesis of strong ground motion data. In particular, modeling of seismic sources and earth medium, analytical and empirical Green’s functions approaches for ground motion simulation, stochastic models for strong motion and ground motion relations are covered. These models can be used to generate realistic near-field and far-field ground motion in regions lacking strong motion data. Numerical examples are shown for illustration by taking Kutch earthquake-2001 as a case study.

  4. The EO-1 hyperion and advanced land imager sensors for use in tundra classification studies within the Upper Kuparuk River Basin, Alaska

    Science.gov (United States)

    Hall-Brown, Mary

    The heterogeneity of Arctic vegetation can make land cover classification vey difficult when using medium to small resolution imagery (Schneider et al., 2009; Muller et al., 1999). Using high radiometric and spatial resolution imagery, such as the SPOT 5 and IKONOS satellites, have helped arctic land cover classification accuracies rise into the 80 and 90 percentiles (Allard, 2003; Stine et al., 2010; Muller et al., 1999). However, those increases usually come at a high price. High resolution imagery is very expensive and can often add tens of thousands of dollars onto the cost of the research. The EO-1 satellite launched in 2002 carries two sensors that have high specral and/or high spatial resolutions and can be an acceptable compromise between the resolution versus cost issues. The Hyperion is a hyperspectral sensor with the capability of collecting 242 spectral bands of information. The Advanced Land Imager (ALI) is an advanced multispectral sensor whose spatial resolution can be sharpened to 10 meters. This dissertation compares the accuracies of arctic land cover classifications produced by the Hyperion and ALI sensors to the classification accuracies produced by the Systeme Pour l' Observation de le Terre (SPOT), the Landsat Thematic Mapper (TM) and the Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. Hyperion and ALI images from August 2004 were collected over the Upper Kuparuk River Basin, Alaska. Image processing included the stepwise discriminant analysis of pixels that were positively classified from coinciding ground control points, geometric and radiometric correction, and principle component analysis. Finally, stratified random sampling was used to perform accuracy assessments on satellite derived land cover classifications. Accuracy was estimated from an error matrix (confusion matrix) that provided the overall, producer's and user's accuracies. This research found that while the Hyperion sensor produced classfication accuracies that were

  5. Covering R-trees

    CERN Document Server

    Berestovskii, V N

    2007-01-01

    We show that every inner metric space X is the metric quotient of a complete R-tree via a free isometric action, which we call the covering R-tree of X. The quotient mapping is a weak submetry (hence, open) and light. In the case of compact 1-dimensional geodesic space X, the free isometric action is via a subgroup of the fundamental group of X. In particular, the Sierpin'ski gasket and carpet, and the Menger sponge all have the same covering R-tree, which is complete and has at each point valency equal to the continuum. This latter R-tree is of particular interest because it is "universal" in at least two senses: First, every R-tree of valency at most the continuum can be isometrically embedded in it. Second, every Peano continuum is the image of it via an open light mapping. We provide a sketch of our previous construction of the uniform universal cover in the special case of inner metric spaces, the properties of which are used in the proof.

  6. Fish Creek Watershed Lake Classification; NPRA, Alaska, 2016

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This study focuses on the development of a 20 attribute lake cover classification scheme for the Fish Creek Watershed (FCW), which is located in the National...

  7. Bering Land Bridge Lake Classification; Seward Peninsula, Alaska, 2012-2016

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This study focuses on a 67 attribute lake cover classification scheme covering the Bering Land Bridge area of the Seward Peninsula, Alaska. The GIS dataset consists...

  8. One-Class Classification Algorithm Based on Sparse Minimum Spanning Tree Adaptive Covering Model in High-Dimensional Space%基于高维空间稀疏最小生成树自适应覆盖模型的一类分类算法

    Institute of Scientific and Technical Information of China (English)

    胡正平; 路亮; 许成谦

    2011-01-01

    Minimum spanning tree class descriptor (MSTCD) describes the target class with the assumption that all the edges of the graph are basic elements of the classifier, which offers additional virtual training data for a description of sample distribution in high-dimensional space. However, this descriptive model has too many branches, which results in the model being more complicated. According to the continuity law of the feature space of similar samples, a one-class classification algorithm based on sparse minimum spanning tree covering model is presented. The method firstly constructs sparse A-nearest-neighbor graph representation for the target class. Then, a recursive graph bipartition algorithm is introduced to find the micro-cluster. Finally, it builds sparse minimum spanning tree on the graph nodes which are centers of micro-cluster. Experimental results show that the presented algorithm performs better than MSTCD and other one-class classifiers.%最小生成树数据描述( MSTCD)在刻画高维空间样本点分布时,将所有图形的边作为新增虚拟样本以提供目标类样本分布描述,这种描述存在分支多、覆盖模型复杂的问题.针对该问题,依据特征空间中同类样本分布的连续性规律,文中提出基于稀疏最小生成树覆盖模型的一类分类算法.该方法首先构建目标类数据集的稀疏k近邻图表示,通过递归图分割算法发现数据分布的微聚类,再以微聚类的中心为图节点构建目标类的稀疏最小生成树覆盖模型.实验结果表明,文中方法与MSTCD和其它一类分类器相比有较优的描述性能和较低的模型复杂度.

  9. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...

  10. Assessment of the thematic accuracy of land cover maps

    DEFF Research Database (Denmark)

    Høhle, Joachim

    2015-01-01

    Several land cover maps are generated from aerial imagery and assessed by different approaches. The test site is an urban area in Europe for which six classes (‘building’, ‘hedge and bush’, ‘grass’, ‘road and parking lot’, ‘tree’, ‘wall and car port’) had to be derived. Two classification methods...

  11. Studies on deposition, adhesion and resuspension of radioactive substances on the ground surface and ground cover

    Energy Technology Data Exchange (ETDEWEB)

    Kurita, Susumu; Kurihara, Kazuo [Meteorological Research Inst., Tsukuba, Ibaraki (Japan)

    1999-03-01

    After the Chernobyl` nuclear power plant accident, resuspension of radioactive nuclei into the atmosphere is recognized as the one of the important processes that must be considered in the estimation of inhalation doses to humans. In this study, resuspensions of particles from soil and grass have been studied. The resuspension of particles from bare soil was modelized by using Shao`s method. The resuspension of particles from grass was studied by a wind tunnel and a field experiment. Dependencies of the resuspension rate on time and on friction velocity were obtained clearly. And it was also found that the other meteorological parameters, such as temperature, relative humidity, solar radiation and condensation, affected the resuspension rate in the field. (author)

  12. Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia

    Science.gov (United States)

    Westermann, Sebastian; Peter, Maria; Langer, Moritz; Schwamborn, Georg; Schirrmeister, Lutz; Etzelmüller, Bernd; Boike, Julia

    2017-06-01

    Permafrost is a sensitive element of the cryosphere, but operational monitoring of the ground thermal conditions on large spatial scales is still lacking. Here, we demonstrate a remote-sensing-based scheme that is capable of estimating the transient evolution of ground temperatures and active layer thickness by means of the ground thermal model CryoGrid 2. The scheme is applied to an area of approximately 16 000 km2 in the Lena River delta (LRD) in NE Siberia for a period of 14 years. The forcing data sets at 1 km spatial and weekly temporal resolution are synthesized from satellite products and fields of meteorological variables from the ERA-Interim reanalysis. To assign spatially distributed ground thermal properties, a stratigraphic classification based on geomorphological observations and mapping is constructed, which accounts for the large-scale patterns of sediment types, ground ice and surface properties in the Lena River delta. A comparison of the model forcing to in situ measurements on Samoylov Island in the southern part of the study area yields an acceptable agreement for the purpose of ground thermal modeling, for surface temperature, snow depth, and timing of the onset and termination of the winter snow cover. The model results are compared to observations of ground temperatures and thaw depths at nine sites in the Lena River delta, suggesting that thaw depths are in most cases reproduced to within 0.1 m or less and multi-year averages of ground temperatures within 1-2 °C. Comparison of monthly average temperatures at depths of 2-3 m in five boreholes yielded an RMSE of 1.1 °C and a bias of -0.9 °C for the model results. The highest ground temperatures are calculated for grid cells close to the main river channels in the south as well as areas with sandy sediments and low organic and ice contents in the central delta, where also the largest thaw depths occur. On the other hand, the lowest temperatures are modeled for the eastern part, which is an

  13. 7 CFR 51.1904 - Maturity classification.

    Science.gov (United States)

    2010-01-01

    ... Maturity classification. Tomatoes which are characteristically red when ripe, but are not overripe or soft... or red color. (c) Hard ripe, when the tomato shows three-fourths or more of the surface in the aggregate covered with pink or red color. (d) Firm ripe, when the tomato shows three-fourths or more of...

  14. Covered Clause Elimination

    CERN Document Server

    Heule, Marijn; Biere, Armin

    2010-01-01

    Generalizing the novel clause elimination procedures developed in [M. Heule, M. J\\"arvisalo, and A. Biere. Clause elimination procedures for CNF formulas. In Proc. LPAR-17, volume 6397 of LNCS, pages 357-371. Springer, 2010.], we introduce explicit (CCE), hidden (HCCE), and asymmetric (ACCE) variants of a procedure that eliminates covered clauses from CNF formulas. We show that these procedures are more effective in reducing CNF formulas than the respective variants of blocked clause elimination, and may hence be interesting as new preprocessing/simplification techniques for SAT solving.

  15. GEOTECNOLOGY FOR FOREST COVER TEMPORAL ANALISYS

    Directory of Open Access Journals (Sweden)

    Nathália Suemi Saito

    2016-03-01

    Full Text Available The landscape ecology metrics associated with data mining can be used to increase the potential of remote sensing data analysis and applications, being an important tool for decision making. The present study aimed to use data mining techniques and landscape ecology metrics to classify and quantify different types of vegetation using a multitemporal analysis (2001 and 2011, in São Luís do Paraitinga city, São Paulo, Brazil. Object-based image analyses and the C4.5 data-mining algorithm were used for automated classification. Classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Four land use and land cover classes were mapped, including Eucalyptus plantations, whose area increased from 4.4% to 8.6%. The automatic classification showed a kappa index of 0.79 and 0.80, quantity disagreements of 2% e 3.5% and allocation measures of 5.5% and 5% for 2001 and 2011, respectively. We therefore concluded that the data mining method and landscape ecology metrics were efficient in separating vegetation classes.

  16. Detecting land cover change using a sliding window temporal autocorrelation approach

    CSIR Research Space (South Africa)

    Kleynhans, W

    2012-07-01

    Full Text Available There has been recent developments in the use of hypertemporal satellite time series data for land cover change detection and classification. Recently, an Autocorrelation function (ACF) change detection method was proposed to detect the development...

  17. EnviroAtlas - Fresno, CA - Meter-Scale Urban Land Cover (MULC) Data (2010)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Fresno, CA EnviroAtlas Meter-Scale Urban Land Cover (MULC) Data were generated via supervised classification of combined aerial photography and LiDAR data. The...

  18. EnviroAtlas -- Fresno, California -- One Meter Resolution Urban Land Cover Data (2010)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Fresno, CA EnviroAtlas One-Meter-scale Urban Land Cover Data were generated via supervised classification of combined aerial photography and LiDAR data. The air...

  19. Validation of Land Cover Products Using Reliability Evaluation Methods

    Directory of Open Access Journals (Sweden)

    Wenzhong Shi

    2015-06-01

    Full Text Available Validation of land cover products is a fundamental task prior to data applications. Current validation schemes and methods are, however, suited only for assessing classification accuracy and disregard the reliability of land cover products. The reliability evaluation of land cover products should be undertaken to provide reliable land cover information. In addition, the lack of high-quality reference data often constrains validation and affects the reliability results of land cover products. This study proposes a validation schema to evaluate the reliability of land cover products, including two methods, namely, result reliability evaluation and process reliability evaluation. Result reliability evaluation computes the reliability of land cover products using seven reliability indicators. Process reliability evaluation analyzes the reliability propagation in the data production process to obtain the reliability of land cover products. Fuzzy fault tree analysis is introduced and improved in the reliability analysis of a data production process. Research results show that the proposed reliability evaluation scheme is reasonable and can be applied to validate land cover products. Through the analysis of the seven indicators of result reliability evaluation, more information on land cover can be obtained for strategic decision-making and planning, compared with traditional accuracy assessment methods. Process reliability evaluation without the need for reference data can facilitate the validation and reflect the change trends of reliabilities to some extent.

  20. Lossless Compression of Classification-Map Data

    Science.gov (United States)

    Hua, Xie; Klimesh, Matthew

    2009-01-01

    A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

  1. Terra Incognita: Absence of Concentrated Animal Feeding Operations from the National Land Cover Database and Implications for Environmental Risk

    Science.gov (United States)

    Martin, K. L.; Emanuel, R. E.; Vose, J. M.

    2016-12-01

    The number of concentrated animal feeding operations (CAFOs) has increased rapidly in recent decades. Although important to food supplies, CAFOs may present significant risks to human health and environmental quality. The National land cover database (NLCD) is a publically available database of land cover whose purpose is to provide assessment of ecosystem health, facilitate nutrient modeling, land use planning, and developing land management practices. However, CAFOs do not align with any existing NLCD land cover classes. This is especially concerning due to their distinct nutrient loading characteristics, potential for other environmental impacts, and given that individual CAFOs may occupy several NLCD pixels worth of ground area. Using 2011 NLCD data, we examined the land cover classification of CAFO sites in North Carolina (USA). Federal regulations require CAFOs with a liquid waste disposal system to obtain a water quality permit. In North Carolina, there were 2679 permitted sites as of 2015, primarily in the southeastern part of the state. As poultry operations most frequently use dry waste disposal systems, they are not required to obtain a permit and thus, their locations are undocumented. For each permitted CAFO, we determined the mode of the NLCD land uses within a 50m buffer surrounding point coordinates. We found permitted CAFOS were most likely to be classified as hay/pasture (58%). An additional 13% were identified as row crops, leaving 29% as a non-agricultural land cover class, including wetlands (12%). This misclassification of CAFOs can have implications for environmental management and public policy. Scientists and land managers need access to better spatial data on the distribution of these operations to monitor the environmental impacts and identify the best landscape scale mitigation strategies. We recommend adding a new land cover class (concentrated animal operations) to the NLCD database.

  2. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

    2011-01-01

    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases th...... datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset....

  3. Using satellite data to monitor land-use land-cover change in North-eastern Latvia.

    Science.gov (United States)

    Fonji, Simon Foteck; Taff, Gregory N

    2014-01-01

    Land-use and land-cover change (LULCC), especially those caused by human activities, is one of the most important components of global environmental change (Jessen 3(rd) edition: 1-526 2005). In this study the effects of geographic and demographic factors on LULCC are analyzed in northeastern Latvia using official estimates from census and vital statistics data, and using remotely sensed satellite imagery (Landsat Thematic Mapper) acquired from 1992 and 2007. The remote sensing images, elevation data, in-situ ground truth and ground control data (using GPS), census and vital statistics data were processed, integrated, and analyzed in a geographic information system (GIS). Changes in six categories of land-use and land-cover (wetland, water, agriculture, forest, bare field and urban/suburban) were studied to determine their relationship to demographic and geographic factors between 1992 and 2007. Supervised classifications were performed on the Landsat images. Analysis of land cover change based on "change-to" categories between the 1992 and 2007 images revealed that changes to forest were the most common type of change (17.1% of pixels), followed by changes to agriculture (8.6%) and the fewest were changes to urban/suburban (0.8%). Integration of population data and land-cover change data revealed key findings: areas near to roads underwent more LULCC and areas far away from Riga underwent less LULCC. Range in elevation was positively correlated with all LULCC categories. Population density was found to be associated with most LULCC categories but the direction of effect was scale dependent. This paper shows how socio-demographic data can be integrated with satellite image data and cartographic data to analyze drivers of LULCC at multiple spatial scales.

  4. Covering walks in graphs

    CERN Document Server

    Fujie, Futaba

    2014-01-01

    Covering Walks  in Graphs is aimed at researchers and graduate students in the graph theory community and provides a comprehensive treatment on measures of two well studied graphical properties, namely Hamiltonicity and traversability in graphs. This text looks into the famous Kӧnigsberg Bridge Problem, the Chinese Postman Problem, the Icosian Game and the Traveling Salesman Problem as well as well-known mathematicians who were involved in these problems. The concepts of different spanning walks with examples and present classical results on Hamiltonian numbers and upper Hamiltonian numbers of graphs are described; in some cases, the authors provide proofs of these results to illustrate the beauty and complexity of this area of research. Two new concepts of traceable numbers of graphs and traceable numbers of vertices of a graph which were inspired by and closely related to Hamiltonian numbers are introduced. Results are illustrated on these two concepts and the relationship between traceable concepts and...

  5. Vegetation cover analysis using a low budget hyperspectral proximal sensing system

    Directory of Open Access Journals (Sweden)

    C. Daquino

    2006-06-01

    Full Text Available This report describes the implementation of a hyperspectral proximal sensing low-budget acquisition system and its application to the detection of terrestrian vegetation cover anomalies in sites of high environmental quality. Anomalies can be due to stress for lack of water and/or pollution phenomena and weed presence in agricultural fields. The hyperspectral cube (90-bands ranging from 450 to 900 nm was acquired from the hill near Segni (RM, approximately 500 m far from the target, by means of electronically tunable filters and 8 bit CCD cameras. Spectral libraries were built using both endmember identification method and extraction of centroids of the clusters obtained from a k-means analysis of the image itself. Two classification methods were applied on the hyperspectral cube: Spectral Angle Mapper (hard and Mixed Tuned Matching Filters (MTMF. Results show the good capability of the system in detecting areas with an arboreal, shrub or leafage cover, distinguishing between zones with different spectral response. Better results were obtained using spectral library originated by the k-means method. The detected anomalies not correlated to seasonal phenomena suggest a ground true analysis to identify their origin.

  6. Improving rainfall estimation from commercial microwave links using METEOSAT SEVIRI cloud cover information

    Science.gov (United States)

    Boose, Yvonne; Doumounia, Ali; Chwala, Christian; Moumouni, Sawadogo; Zougmoré, François; Kunstmann, Harald

    2017-04-01

    The number of rain gauges is declining worldwide. A recent promising method for alternative precipitation measurements is to derive rain rates from the attenuation of the microwave signal between remote antennas of mobile phone base stations, so called commercial microwave links (CMLs). In European countries, such as Germany, the CML technique can be used as a complementary method to the existing gauge and radar networks improving their products, for example, in mountainous terrain and urban areas. In West African countries, where a dense gauge or radar network is absent, the number of mobile phone users is rapidly increasing and so are the CML networks. Hence, the CML-derived precipitation measurements have high potential for applications such as flood warning and support of agricultural planning in this region. For typical CML bandwidths (10-40 GHz), the relationship of attenuation to rain rate is quasi-linear. However, also humidity, wet antennas or electronic noise can lead to signal interference. To distinguish these fluctuations from actual attenuation due to rain, a temporal wet (rain event occurred)/ dry (no rain event) classification is usually necessary. In dense CML networks this is possible by correlating neighboring CML time series. Another option is to use the correlation between signal time series of different frequencies or bidirectional signals. The CML network in rural areas is typically not dense enough for correlation analysis and often only one polarization and one frequency are available along a CML. In this work we therefore use cloud cover information derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer onboard the geostationary satellite METEOSAT for a wet (pixels along link are cloud covered)/ dry (no cloud along link) classification. We compare results for CMLs in Burkina Faso and Germany, which differ meteorologically (rain rate and duration, droplet size distributions) and technically (CML frequencies

  7. Land Use and Land Cover - Montana Land Cover Framework 2013

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This statewide land cover theme is a baseline digital map of Montana's natural and human land cover. The baseline map is adapted from the Northwest ReGAP project...

  8. Sky cover from MFRSR observations: cumulus clouds

    Directory of Open Access Journals (Sweden)

    E. Kassianov

    2011-01-01

    Full Text Available The diffuse all-sky surface irradiances measured at two nearby wavelengths in the visible spectral range and their model clear-sky counterparts are two main components of a new method for estimating the fractional sky cover of different cloud types, including cumulus clouds. The performance of this method is illustrated using 1-min resolution data from ground-based Multi-Filter Rotating Shadowband Radiometer (MFRSR. The MFRSR data are collected at the US Department of Energy Atmospheric Radiation Measurement (ARM Climate Research Facility (ACRF Southern Great Plains (SGP site during the summer of 2007 and represent 13 days with cumulus clouds. Good agreement is obtained between estimated values of the fractional sky cover and those provided by a well-established independent method based on broadband observations.

  9. Georadar Measurements for the Snow Cover Density

    Directory of Open Access Journals (Sweden)

    A. Godio

    2009-01-01

    Full Text Available Ground Probing Radar (GPR devices is adopted for the analysis of thickness and the mechanical properties (density of the snow cover in some test site in Alps, in Northern Italy. The performances of standard radar systems for the snow cover characterisation are analysed, the main aim is to assess the reliability of the method to estimate the snow dens