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

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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.

TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

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K. Bakuła

2016-06-01

Full Text Available 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

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

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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.

Ground cover in old-growth forests of the central hardwood region

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Martin A. Spetich; Stephen R. Shifley; George R. Parker; Felix, Jr. Ponder

1997-01-01

Differences in ground cover (percent cover of litter, percent cover of vegetation and litter weight) in old-growth forests across this region are not well understood. We initiated a long-term study in a three-state region to enhance knowledge in this area. We present baseline results for ground cover and compare these data across productivity regions. Thirty 0.25-ac (0...

Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

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André Mora

2017-11-01

Full Text Available This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.

QA CLASSIFICATION ANALYSIS OF GROUND SUPPORT SYSTEMS

International Nuclear Information System (INIS)

D. W. Gwyn

1996-01-01

The purpose and objective of this analysis is to determine if the permanent function Ground Support Systems (CI: BABEEOOOO) are quality-affecting items and if so, to establish the appropriate Quality Assurance (QA) classification

Texture classification of vegetation cover in high altitude wetlands zone

International Nuclear Information System (INIS)

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

2014-01-01

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

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

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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

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

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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

Central American Vegetation/Land Cover Classification and Conservation Status

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National Aeronautics and Space Administration — The Central American Vegetation/Land Cover Classification and Conservation Status data set consists of GIS coverages of vegetation classes (forests, woodlands,...

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

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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.

LBA-ECO ND-01 Land Cover Classification, Rondonia, Brazil: 1975-2000

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National Aeronautics and Space Administration — This data set provides a time series of land cover classifications for Ariquemes, Ji-Parana, and Luiza, research sites in Rondonia, Brazil. The land cover...

Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data

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Churches, Christopher E.; Wampler, Peter J.; Sun, Wanxiao; Smith, Andrew J.

2014-08-01

This study uses 2010-2011 Landsat Thematic Mapper (TM) imagery to estimate total forested area in Haiti. The thematic map was generated using radiometric normalization of digital numbers by a modified normalization method utilizing pseudo-invariant polygons (PIPs), followed by supervised classification of the mosaicked image using the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System. Classification results were compared to other sources of land-cover data produced for similar years, with an emphasis on the statistics presented by the FAO. Three global land cover datasets (GLC2000, Globcover, 2009, and MODIS MCD12Q1), and a national-scale dataset (a land cover analysis by Haitian National Centre for Geospatial Information (CNIGS)) were reclassified and compared. According to our classification, approximately 32.3% of Haiti's total land area was tree covered in 2010-2011. This result was confirmed using an error-adjusted area estimator, which predicted a tree covered area of 32.4%. Standardization to the FAO's forest cover class definition reduces the amount of tree cover of our supervised classification to 29.4%. This result was greater than the reported FAO value of 4% and the value for the recoded GLC2000 dataset of 7.0%, but is comparable to values for three other recoded datasets: MCD12Q1 (21.1%), Globcover (2009) (26.9%), and CNIGS (19.5%). We propose that at coarse resolutions, the segmented and patchy nature of Haiti's forests resulted in a systematic underestimation of the extent of forest cover. It appears the best explanation for the significant difference between our results, FAO statistics, and compared datasets is the accuracy of the data sources and the resolution of the imagery used for land cover analyses. Analysis of recoded global datasets and results from this study suggest a strong linear relationship (R2 = 0.996 for tree cover) between spatial resolution and land cover estimates.

Measurement of semantic similarity for land use and land cover classification systems

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Deng, Dongpo

2008-12-01

Land use and land cover (LULC) data is essential to environmental and ecological research. However, semantic heterogeneous of land use and land cover classification are often resulted from different data resources, different cultural contexts, and different utilities. Therefore, there is need to develop a method to measure, compare and integrate between land cover categories. To understand the meaning and the use of terminology from different domains, the common ontology approach is used to acquire information regarding the meaning of terms, and to compare two terms to determine how they might be related. Ontology is a formal specification of a shared conceptualization of a domain of interest. LULC classification system is a ontology. The semantic similarity method is used to compare to entities of three LULC classification systems: CORINE (European Environmental Agency), Oregon State, USA), and Taiwan. The semantic properties and relations firstly have been extracted from their definitions of LULC classification systems. Then semantic properties and relations of categories in three LULC classification systems are mutually compared. The visualization of semantic proximity is finally presented to explore the similarity or dissimilarity of data. This study shows the semantic similarity method efficiently detect semantic distance in three LULC classification systems and find out the semantic similar objects.

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

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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

Evaluation of burial ground soil covers

International Nuclear Information System (INIS)

Fenimore, J.W.

1976-11-01

Solid radioactive waste burial at the Savannah River Plant between 1955 and 1972 filled a 76-acre site. Burial operations then were shifted to an adjacent site, and a program was begun to develop a land cover that would: (1) minimize soil erosion; and (2) protect the buried waste from deep-rooted plants, since radionuclides can be recycled by uptake through root systems. In anticipation of the need for a suitable soil cover, five grass species were planted on 20 plots (4 plots of each species) at the burial ground (Facility 643-G) in 1969. The grass plots were planted for evaluation of viability, root depth, and erosion protection existing under conditions of low fertility and minimum care. In addition, 16 different artificial soil covers were installed on 32 plots (each cover on two plots) to evaluate: (1) resistance of cover to deterioration from weathering; (2) resistance of cover to encroachment by deep-rooted plants; and (3) soil erosion protection provided by the cover. All test plots were observed and photographed in 1970 and in 1974. After both grass and artificial soil covers were tested five years, the following results were observed: Pensacola Bahia grass was the best of the five cover grasses tested; and fifteen of the sixteen artificial covers that were tested controlled vegetation growth and soil erosion. Photographs of the test plots will be retaken at five-year intervals for future documentation

Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

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Yongguang Zhai

2018-03-01

Full Text Available Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method.

Application of a niche-based model for forest cover classification

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Amici V

2012-05-01

Full Text Available In recent years, a surge of interest in biodiversity conservation have led to the development of new approaches to facilitate ecologically-based conservation policies and management plans. In particular, image classification and predictive distribution modeling applied to forest habitats, constitute a crucial issue as forests constitute the most widespread vegetation type and play a key role for ecosystem functioning. Then, the general purpose of this study is to develop a framework that in the absence of large amounts of field data for large areas may allow to select the most appropriate classification. In some cases, a hard division of classes is required, especially as support to environmental policies; despite this it is necessary to take into account problems which derive from a crisp view of ecological entities being mapped, since habitats are expected to be structurally complex and continuously vary within a landscape. In this paper, a niche model (MaxEnt, generally used to estimate species/habitat distribution, has been applied to classify forest cover in a complex Mediterranean area and to estimate the probability distribution of four forest types, producing continuous maps of forest cover. The use of the obtained models as validation of model for crisp classifications, highlighted that crisp classification, which is being continuously used in landscape research and planning, is not free from drawbacks as it is showing a high degree of inner variability. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, may represent an useful approach to making more efficient and effective field inventories and to developing effective forest conservation policies.

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

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Joseph St. Peter

2018-03-01

Full Text Available Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.

A hierarchical approach of hybrid image classification for land use and land cover mapping

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Rahdari Vahid

2018-01-01

Full Text Available Remote sensing data analysis can provide thematic maps describing land-use and land-cover (LULC in a short period. Using proper image classification method in an area, is important to overcome the possible limitations of satellite imageries for producing land-use and land-cover maps. In the present study, a hierarchical hybrid image classification method was used to produce LULC maps using Landsat Thematic mapper TM for the year of 1998 and operational land imager OLI for the year of 2016. Images were classified using the proposed hybrid image classification method, vegetation cover crown percentage map from normalized difference vegetation index, Fisher supervised classification and object-based image classification methods. Accuracy assessment results showed that the hybrid classification method produced maps with total accuracy up to 84 percent with kappa statistic value 0.81. Results of this study showed that the proposed classification method worked better with OLI sensor than with TM. Although OLI has a higher radiometric resolution than TM, the produced LULC map using TM is almost accurate like OLI, which is because of LULC definitions and image classification methods used.

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

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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

Impacts of land use/cover classification accuracy on regional climate simulations

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Ge, Jianjun; Qi, Jiaguo; Lofgren, Brent M.; Moore, Nathan; Torbick, Nathan; Olson, Jennifer M.

2007-03-01

Land use/cover change has been recognized as a key component in global change. Various land cover data sets, including historically reconstructed, recently observed, and future projected, have been used in numerous climate modeling studies at regional to global scales. However, little attention has been paid to the effect of land cover classification accuracy on climate simulations, though accuracy assessment has become a routine procedure in land cover production community. In this study, we analyzed the behavior of simulated precipitation in the Regional Atmospheric Modeling System (RAMS) over a range of simulated classification accuracies over a 3 month period. This study found that land cover accuracy under 80% had a strong effect on precipitation especially when the land surface had a greater control of the atmosphere. This effect became stronger as the accuracy decreased. As shown in three follow-on experiments, the effect was further influenced by model parameterizations such as convection schemes and interior nudging, which can mitigate the strength of surface boundary forcings. In reality, land cover accuracy rarely obtains the commonly recommended 85% target. Its effect on climate simulations should therefore be considered, especially when historically reconstructed and future projected land covers are employed.

High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR.

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Jimenez-Berni, Jose A; Deery, David M; Rozas-Larraondo, Pablo; Condon, Anthony Tony G; Rebetzke, Greg J; James, Richard A; Bovill, William D; Furbank, Robert T; Sirault, Xavier R R

2018-01-01

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR ( r 2 of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association ( r 2 = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass ( r 2 = 0.93 and r 2 = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

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Yang, Chun; Rottensteiner, Franz; Heipke, Christian

2018-04-01

Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

A Novel Method for Detection and Classification of Covered Conductor Faults

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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.

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

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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.

UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

Directory of Open Access Journals (Sweden)

Emily J. Sturdivant

2017-10-01

Full Text Available 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.

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...

Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

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Thilanki Dahigamuwa

2016-10-01

Full Text Available Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI, derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM, artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated.

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.

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

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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.

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

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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.

Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

Science.gov (United States)

Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

2018-05-01

Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

ERC hazard classification matrices for above ground structures and groundwater and soil remediation activities

International Nuclear Information System (INIS)

Curry, L.R.

1997-01-01

This document provides the status of the preliminary hazard classification (PHC) process for the Environmental Restoration Contractor (ERC) above ground structures and groundwater and soil remediation activities currently underway for planned for fiscal year (FY) 1997. This classification process is based on current US Department of Energy (DOE), Richland Operations Office (RL) guidance for the classification of facilities and activities containing radionuclide and nonradiological hazardous material inventories. The above ground structures presented in the matrices were drawn from the Bechtel Hanford, Inc. (BHI) Decontamination and Decommissioning (D and D) Project Facility List (DOE 1996), which identifies the facilities in the RL-Environmental Restoration baseline contract in 1997. This document contains the following two appendices: (1) Appendix A, which consists of a matrix identifying PHC documents that have been issued for BHI's above ground structures and groundwater and soil remediation activities underway or planned for FY 1997, and (2) Appendix B, which consists of a matrix showing anticipated PHCs for above ground structures, and groundwater and soil remediation activities underway or planned for FY 1997. Appendix B also shows the schedule for finalization of PHCs for above ground structures with an anticipated classification of Nuclear

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

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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

Computer-aided classification of forest cover types from small scale aerial photography

Science.gov (United States)

Bliss, John C.; Bonnicksen, Thomas M.; Mace, Thomas H.

1980-11-01

The US National Park Service must map forest cover types over extensive areas in order to fulfill its goal of maintaining or reconstructing presettlement vegetation within national parks and monuments. Furthermore, such cover type maps must be updated on a regular basis to document vegetation changes. Computer-aided classification of small scale aerial photography is a promising technique for generating forest cover type maps efficiently and inexpensively. In this study, seven cover types were classified with an overall accuracy of 62 percent from a reproduction of a 1∶120,000 color infrared transparency of a conifer-hardwood forest. The results were encouraging, given the degraded quality of the photograph and the fact that features were not centered, as well as the lack of information on lens vignetting characteristics to make corrections. Suggestions are made for resolving these problems in future research and applications. In addition, it is hypothesized that the overall accuracy is artificially low because the computer-aided classification more accurately portrayed the intermixing of cover types than the hand-drawn maps to which it was compared.

FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

A. Rizaldy

2018-05-01

Full Text Available Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs. In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN, a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher. The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

Science.gov (United States)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

International Nuclear Information System (INIS)

Deilmai, B Rokni; Ahmad, B Bin; Zabihi, H

2014-01-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification

Low-resolution Airborne Radar Air/ground Moving Target Classification and Recognition

Directory of Open Access Journals (Sweden)

Wang Fu-you

2014-10-01

Full Text Available Radar Target Recognition (RTR is one of the most important needs of modern and future airborne surveillance radars, and it is still one of the key technologies of radar. The majority of present algorithms are based on wide-band radar signal, which not only needs high performance radar system and high target Signal-to-Noise Ratio (SNR, but also is sensitive to angle between radar and target. Low-Resolution Airborne Surveillance Radar (LRASR in downward-looking mode, slow flying aircraft and ground moving truck have similar Doppler velocity and Radar Cross Section (RCS, leading to the problem that LRASR air/ground moving targets can not be distinguished, which also disturbs detection, tracking, and classification of low altitude slow flying aircraft to solve these issues, an algorithm based on narrowband fractal feature and phase modulation feature is presented for LRASR air/ground moving targets classification. Real measured data is applied to verify the algorithm, the classification results validate the proposed method, helicopters and truck can be well classified, the average discrimination rate is more than 89% when SNR ≥ 15 dB.

Source Apportionment of Annual Water Pollution Loads in River Basins by Remote-Sensed Land Cover Classification

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Yi Wang

2016-08-01

Full Text Available In this study, in order to determine the efficiency of estimating annual water pollution loads from remote-sensed land cover classification and ground-observed hydrological data, an empirical model was investigated. Remote sensing data imagery from National Oceanic and Atmospheric Administration (NOAA Advanced Very High Resolution Radiometer were applied to an 11 year (1994–2004 water quality dataset for 30 different rivers in Japan. Six water quality indicators—total nitrogen (TN, total phosphorus (TP, biochemical oxygen demand (BOD, chemical oxygen demand (COD, and dissolved oxygen (DO—were examined by using the observed river water quality data and generated land cover map. The TN, TP, BOD, COD, and DO loads were estimated for the 30 river basins using the empirical model. Calibration (1994–1999 and validation (2000–2004 results showed that the proposed simulation technique was useful for predicting water pollution loads in the river basins. We found that vegetation land cover had a larger impact on TP export into all rivers. Urban areas had a very small impact on DO export into rivers, but a relatively large impact on BOD and TN export. The results indicate that the application of land cover data generated from the remote-sensed imagery could give a useful interpretation about the river water quality.

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

a Point Cloud Classification Approach Based on Vertical Structures of Ground Objects

Science.gov (United States)

Zhao, Y.; Hu, Q.; Hu, W.

2018-04-01

This paper proposes a novel method for point cloud classification using vertical structural characteristics of ground objects. Since urbanization develops rapidly nowadays, urban ground objects also change frequently. Conventional photogrammetric methods cannot satisfy the requirements of updating the ground objects' information efficiently, so LiDAR (Light Detection and Ranging) technology is employed to accomplish this task. LiDAR data, namely point cloud data, can obtain detailed three-dimensional coordinates of ground objects, but this kind of data is discrete and unorganized. To accomplish ground objects classification with point cloud, we first construct horizontal grids and vertical layers to organize point cloud data, and then calculate vertical characteristics, including density and measures of dispersion, and form characteristic curves for each grids. With the help of PCA processing and K-means algorithm, we analyze the similarities and differences of characteristic curves. Curves that have similar features will be classified into the same class and point cloud correspond to these curves will be classified as well. The whole process is simple but effective, and this approach does not need assistance of other data sources. In this study, point cloud data are classified into three classes, which are vegetation, buildings, and roads. When horizontal grid spacing and vertical layer spacing are 3 m and 1 m respectively, vertical characteristic is set as density, and the number of dimensions after PCA processing is 11, the overall precision of classification result is about 86.31 %. The result can help us quickly understand the distribution of various ground objects.

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

2017-12-01

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 σ 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.

Finite mixture models for sub-pixel coastal land cover classification

CSIR Research Space (South Africa)

Ritchie, Michaela C

2017-05-01

Full Text Available Models for Sub- pixel Coastal Land Cover Classification M. Ritchie Dr. M. Lück-Vogel Dr. P. Debba Dr. V. Goodall ISRSE - 37 Tshwane, South Africa 10 May 2017 2Study Area Africa South Africa FALSE BAY 3Strand Gordon’s Bay Study Area WorldView-2 Image.../Urban 1 10 10 Herbaceous Vegetation 1 5 5 Shadow 1 8 8 Sparse Vegetation 1 3 3 Water 1 10 10 Woody Vegetation 1 5 5 11 Maximum Likelihood Classification (MLC) 12 Gaussian Mixture Discriminant Analysis (GMDA) 13 A B C t-distribution Mixture Discriminant...

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…

Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications

Science.gov (United States)

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. PMID:24511304

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.

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

Science.gov (United States)

Xian, George; Homer, Collin G.; Fry, Joyce

2009-01-01

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.

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...

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

Science.gov (United States)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2017-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

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...

Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

Science.gov (United States)

Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

2017-09-01

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / text-decoration: overline">α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification

POLSAR LAND COVER CLASSIFICATION BASED ON HIDDEN POLARIMETRIC FEATURES IN ROTATION DOMAIN AND SVM CLASSIFIER

Directory of Open Access Journals (Sweden)

C.-S. Tao

2017-09-01

Full Text Available Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets’ scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy

Ground penetrating radar utilization in exploring inadequate concrete covers in a new bridge deck

Directory of Open Access Journals (Sweden)

Md. Istiaque Hasan

2014-01-01

Full Text Available The reinforced concrete cast in place four span deck of a concrete bridge near Roanoke, Texas, was recently completed. Due to possible construction errors, it was suspected that the concrete covers in the deck did not conform to drawings and specifications. A full scale non-destructive evaluation of the concrete covers was carried out using ground penetrating radar (GPR equipment. Cover values were determined from the radargram generated from the scan. The estimated covers were plotted on contour maps. Migration data can substitute the drilling based ground truth data without compromising the concrete cover estimations, except for areas with very high cover values. Areas with high water content may result in inaccurate concrete dielectric constants. Based on the results, significant retrofitting of the bridge deck, such as additional overlay, was recommended.

Perch availability and ground cover: factors that may constitute ...

African Journals Online (AJOL)

In Succulent Karoe, pale chanting goshawks occupied areas where perch density (16 natural and 122 artificial/25 hal was significantly higher than in unoccupied areas (8 natural and 12 artificial/25 hal. The high proportion of cover formed by natural perches (trees and shrubs; 36%) and the low proportion of open ground ...

Testing the Potential of Vegetation Indices for Land Use/cover Classification Using High Resolution Data

Science.gov (United States)

Karakacan Kuzucu, A.; Bektas Balcik, F.

2017-11-01

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

Stratifying FIA Ground Plots Using A 3-Year Old MRLC Forest Cover Map and Current TM Derived Variables Selected By "Decision Tree" Classification

Science.gov (United States)

Michael Hoppus; Stan Arner; Andrew Lister

2001-01-01

A reduction in variance for estimates of forest area and volume in the state of Connecticut was accomplished by stratifying FIA ground plots using raw, transformed and classified Landsat Thematic Mapper (TM) imagery. A US Geological Survey (USGS) Multi-Resolution Landscape Characterization (MRLC) vegetation cover map for Connecticut was used to produce a forest/non-...

Study on Classification Accuracy Inspection of Land Cover Data Aided by Automatic Image Change Detection Technology

Science.gov (United States)

Xie, W.-J.; Zhang, L.; Chen, H.-P.; Zhou, J.; Mao, W.-J.

2018-04-01

The purpose of carrying out national geographic conditions monitoring is to obtain information of surface changes caused by human social and economic activities, so that the geographic information can be used to offer better services for the government, enterprise and public. Land cover data contains detailed geographic conditions information, thus has been listed as one of the important achievements in the national geographic conditions monitoring project. At present, the main issue of the production of the land cover data is about how to improve the classification accuracy. For the land cover data quality inspection and acceptance, classification accuracy is also an important check point. So far, the classification accuracy inspection is mainly based on human-computer interaction or manual inspection in the project, which are time consuming and laborious. By harnessing the automatic high-resolution remote sensing image change detection technology based on the ERDAS IMAGINE platform, this paper carried out the classification accuracy inspection test of land cover data in the project, and presented a corresponding technical route, which includes data pre-processing, change detection, result output and information extraction. The result of the quality inspection test shows the effectiveness of the technical route, which can meet the inspection needs for the two typical errors, that is, missing and incorrect update error, and effectively reduces the work intensity of human-computer interaction inspection for quality inspectors, and also provides a technical reference for the data production and quality control of the land cover data.

LBA-ECO LC-24 Landsat ETM+ Forest Cover Classification, Uruara, Para, Brazil: 1999

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: This data set contains a 1999 Landsat ETM+ mosaic image land of cover classification showing forested and deforestation areas in Uruara, Para, Brazil. This...

LBA-ECO LC-24 Landsat ETM+ Forest Cover Classification, Uruara, Para, Brazil: 1999

Data.gov (United States)

National Aeronautics and Space Administration — This data set contains a 1999 Landsat ETM+ mosaic image land of cover classification showing forested and deforestation areas in Uruara, Para, Brazil. This image may...

Hybrid image classification technique for land-cover mapping in the Arctic tundra, North Slope, Alaska

Science.gov (United States)

Chaudhuri, Debasish

Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was

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.

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 ...

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

Science.gov (United States)

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 classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

Science.gov (United States)

Bayoudh, Meriam; Roux, Emmanuel; Richard, Gilles; Nock, Richard

2015-03-01

The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.

Estimating cotton canopy ground cover from remotely sensed scene reflectance

International Nuclear Information System (INIS)

Maas, S.J.

1998-01-01

Many agricultural applications require spatially distributed information on growth-related crop characteristics that could be supplied through aircraft or satellite remote sensing. A study was conducted to develop and test a methodology for estimating plant canopy ground cover for cotton (Gossypium hirsutum L.) from scene reflectance. Previous studies indicated that a relatively simple relationship between ground cover and scene reflectance could be developed based on linear mixture modeling. Theoretical analysis indicated that the effects of shadows in the scene could be compensated for by averaging the results obtained using scene reflectance in the red and near-infrared wavelengths. The methodology was tested using field data collected over several years from cotton test plots in Texas and California. Results of the study appear to verify the utility of this approach. Since the methodology relies on information that can be obtained solely through remote sensing, it would be particularly useful in applications where other field information, such as plant size, row spacing, and row orientation, is unavailable

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.

Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast

International Nuclear Information System (INIS)

Escrig, H.; Batlles, F.J.; Alonso, J.; Baena, F.M.; Bosch, J.L.; Salbidegoitia, I.B.; Burgaleta, J.I.

2013-01-01

Considering that clouds are the greatest causes to solar radiation blocking, short term cloud forecasting can help power plant operation and therefore improve benefits. Cloud detection, classification and motion vector determination are key to forecasting sun obstruction by clouds. Geostationary satellites provide cloud information covering wide areas, allowing cloud forecast to be performed for several hours in advance. Herein, the methodology developed and tested in this study is based on multispectral tests and binary cross correlations followed by coherence and quality control tests over resulting motion vectors. Monthly synthetic surface albedo image and a method to reject erroneous correlation vectors were developed. Cloud classification in terms of opacity and height of cloud top is also performed. A whole-sky camera has been used for validation, showing over 85% of agreement between the camera and the satellite derived cloud cover, whereas error in motion vectors is below 15%. - Highlights: ► A methodology for detection, classification and movement of clouds is presented. ► METEOSAT satellite images are used to obtain a cloud mask. ► The prediction of cloudiness is estimated with 90% in overcast conditions. ► Results for partially covered sky conditions showed a 75% accuracy. ► Motion vectors are estimated from the clouds with a success probability of 86%

Mapping ground cover using hyperspectral remote sensing after the 2003 Simi and Old wildfires in southern California

Science.gov (United States)

Sarah A. Lewis; Leigh B. Lentile; Andrew T. Hudak; Peter R. Robichaud; Penelope Morgan; Michael J. Bobbitt

2007-01-01

Wildfire effects on the ground surface are indicative of the potential for post-fire watershed erosion response. Areas with remaining organic ground cover will likely experience less erosion than areas of complete ground cover combustion or exposed mineral soil. The Simi and Old fires burned ~67,000 ha in southern California in 2003. Burn severity indices calculated...

25 CFR 39.703 - What ground transportation costs are covered for students traveling by commercial transportation?

Science.gov (United States)

2010-04-01

... for Funds § 39.703 What ground transportation costs are covered for students traveling by commercial... 25 Indians 1 2010-04-01 2010-04-01 false What ground transportation costs are covered for students traveling by commercial transportation? 39.703 Section 39.703 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT...

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

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Directory of Open Access Journals (Sweden)

Laura Sirro

2018-06-01

Full Text Available The applicability of optical and synthetic aperture radar (SAR data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation MRV (measuring, reporting and verification services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.

IMPACTS OF PATCH SIZE AND LAND COVER HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

Science.gov (United States)

Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of miss-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support these hypotheses,...

Moss and lichen cover mapping at local and regional scales in the boreal forest ecosystem of central Canada

Science.gov (United States)

Rapalee, G.; Steyaert, L.T.; Hall, F.G.

2001-01-01

Mosses and lichens are important components of boreal landscapes [Vitt et al., 1994; Bubier et al., 1997]. They affect plant productivity and belowground carbon sequestration and alter the surface runoff and energy balance. We report the use of multiresolution satellite data to map moss and lichens over the BOREAS region at a 10 m, 30 m, and 1 km scales. Our moss and lichen classification at the 10 m scale is based on ground observations of associations among soil drainage classes, overstory composition, and cover type among four broad classes of ground cover (feather, sphagnum, and brown mosses and lichens). For our 30 m map, we used field observations of ground cover-overstory associations to map mosses and lichens in the BOREAS southern study area (SSA). To scale up to a 1 km (AVHRR) moss map of the BOREAS region, we used the TM SSA mosaics plus regional field data to identify AVHRR overstory-ground cover associations. We found that: 1) ground cover, overstory composition and density are highly correlated, permitting inference of moss and lichen cover from satellite-based land cover classifications; 2) our 1 km moss map reveals that mosses dominate the boreal landscape of central Canada, thereby a significant factor for water, energy, and carbon modeling; 3) TM and AVHRR moss cover maps are comparable; 4) satellite data resolution is important; particularly in detecting the smaller wetland features, lakes, and upland jack pine sites; and 5) distinct regional patterns of moss and lichen cover correspond to latitudinal and elevational gradients. Copyright 2001 by the American Geophysical Union.

Topsoil and fertilizer effects on ground cover growth on calcareous minesoils

International Nuclear Information System (INIS)

Kost, D.A.; Vimmerstedt, J.P.

1997-01-01

Canopy cover and above ground biomass of herbaceous species was measured in four studies for five years (1989-1993) in southeastern Ohio; on Central Ohio Coal Company's Muskingum Mine, 5 km South of Cumberland. Three studies compared graded cast overburden, standard graded topsoil (30 cm depth), and ripped topsoil. The fourth study lacked the ripped topsoil treatment. In 1987 two studies were seeded with both a standard and a modified mixture of grass and legume species, and two studies used the modified mix only. A nitrogen rate study used 45, 90 or 135 kg/ha of N applied on two occasions, and a phosphorus fertilizer study used rock phosphate amendment at 0, 1120, or 2240 kg/ha and triple superphosphate amendment at 0, 280, or 560 kg/ha. Based on one clipping per year, overall average biomass (Mg/ha dry weight) was slightly greater on standard topsoil (3.34), and ripped topsoil (3.30) than on cast overburden (3.09). Biomass did not differ significantly (p=0.05) on standard topsoil versus cast overburden for 15 of 19 comparisons. Legume biomass (Mg/ha, measured for 3 or 4 years) averaged 0.84 on standard topsoil, 0.75 on ripped topsoil, and 1.16 on cast overburden. In three studies, legume biomass was 50% higher on cast overburden than the topsoils, but differences among the soil surfaces were decreasing by 50% higher on cast overburden than the topsoils, but differences among the soil surfaces were decreasing by 1993. Nitrogen fertilizer increased ground cover only in the year when fertilizer was applied. Phosphorus fertilizer treatments had no significant effects. Ground cover showed no signs of deterioration during the last measurements in 1993. Observations in 1995 indicated dense canopy cover on all soil surfaces with substantial invasion by goldenrods (Solidago spp.) only on topsoils. 16 refs., 4 tabs

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

International Nuclear Information System (INIS)

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

2014-01-01

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

A Review of Ground Target Detection and Classification Techniques in Forward Scattering Radars

Directory of Open Access Journals (Sweden)

M. E. A. Kanona

2018-06-01

Full Text Available This paper presents a review of target detection and classification in forward scattering radar (FSR which is a special state of bistatic radars, designed to detect and track moving targets in the narrow region along the transmitter-receiver base line. FSR has advantages and incredible features over other types of radar configurations. All previous studies proved that FSR can be used as an alternative system for ground target detection and classification. The radar and FSR fundamentals were addressed and classification algorithms and techniques were debated. On the other hand, the current and future applications and the limitations of FSR were discussed.

Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation

Science.gov (United States)

Haque, Md. Enamul; Al-Ramadan, Baqer; Johnson, Brian A.

2016-07-01

Multiresolution segmentation and rule-based classification techniques are used to classify objects from very high-resolution satellite images of urban areas. Custom rules are developed using different spectral, geometric, and textural features with five scale parameters, which exploit varying classification accuracy. Principal component analysis is used to select the most important features out of a total of 207 different features. In particular, seven different object types are considered for classification. The overall classification accuracy achieved for the rule-based method is 95.55% and 98.95% for seven and five classes, respectively. Other classifiers that are not using rules perform at 84.17% and 97.3% accuracy for seven and five classes, respectively. The results exploit coarse segmentation for higher scale parameter and fine segmentation for lower scale parameter. The major contribution of this research is the development of rule sets and the identification of major features for satellite image classification where the rule sets are transferable and the parameters are tunable for different types of imagery. Additionally, the individual objectwise classification and principal component analysis help to identify the required object from an arbitrary number of objects within images given ground truth data for the training.

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.

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

[Postfire restoration of organic substance in the ground cover of the larch forests in the permafrost zone of central Evenkia].

Science.gov (United States)

Prokushkin, S G; Bogdanov, V V; Prokushkin, A S; Tokareva, I V

2011-01-01

The role of ground fires in transformation of organic substances in the ground cover of larch stands in the permafrost zone of Central Siberia was studied, as was the postfire restoration dynamics of organic substances. Ground fires lead to a considerable decrease in concentrations and resources of organic carbon and its individual fractions in the ground cover, and restoration takes many decades.

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

KAUST Repository

Jadoon, Khan; Lambot, Sé bastien; Dimitrov, Marin; Weihermü ller, Lutz

2013-01-01

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

Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the southwestern USA

Science.gov (United States)

M. A. White; J. D. Shaw; R. D. Ramsey

2005-01-01

An accuracy assessment of the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous field (VCF) tree cover product using two independent ground-based tree cover databases was conducted. Ground data included 1176 Forest Inventory and Analysis (FIA) plots for Arizona and 2778 Southwest Regional GAP (SWReGAP) plots for Utah and western Colorado....

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.

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.

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.

Comparison between Multitemporal and Polarimetric SAR Data for Land Cover Classification

DEFF Research Database (Denmark)

Skriver, Henning

2008-01-01

The investigation focuses on the determination of the land cover type using SAR data, including single polarisation, dual polarisation and fully polarimetric data, at L-band. The analysed data set was acquired during the AgriSAR 2006 campaign by the airborne ESAR system over the Gormin agricultural...... site (Northeast Germany). The multitemporal acquisitions significantly improve the classification results for single and dual polarization configurations. The best results for the single and dual polarization configurations are better than for the polarimetric mode. Overall, the cross...

Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

Science.gov (United States)

Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.

2018-02-01

The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Science.gov (United States)

Rußwurm, Marc; Körner, Marco

2018-03-01

Earth observation (EO) sensors deliver data with daily or weekly temporal resolution. Most land use and land cover (LULC) approaches, however, expect cloud-free and mono-temporal observations. The increasing temporal capabilities of today's sensors enables the use of temporal, along with spectral and spatial features. Domains, such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells, which reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, we achieved in our experiments state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing compared to other classification approaches.

Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

Directory of Open Access Journals (Sweden)

Ganchimeg Ganbold

2017-03-01

Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

Land cover mapping of North and Central America—Global Land Cover 2000

Science.gov (United States)

Latifovic, Rasim; Zhu, Zhi-Liang

2004-01-01

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Directory of Open Access Journals (Sweden)

Chao Yang

2017-11-01

Full Text Available Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D Terrain model, which was created using an image fusion digital elevation model (DEM, to select training samples (ROIs, and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

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

Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks

Science.gov (United States)

Ienco, Dino; Gaetano, Raffaele; Dupaquier, Claire; Maurel, Pierre

2017-10-01

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification (i.e. Convolutional Neural Networks - CNNs - on single images) while only very few studies exist involving temporal deep learning approaches (i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series. In this letter we evaluate the ability of Recurrent Neural Networks, in particular the Long-Short Term Memory (LSTM) model, to perform land cover classification considering multi-temporal spatial data derived from a time series of satellite images. We carried out experiments on two different datasets considering both pixel-based and object-based classification. The obtained results show that Recurrent Neural Networks are competitive compared to state-of-the-art classifiers, and may outperform classical approaches in presence of low represented and/or highly mixed classes. We also show that using the alternative feature representation generated by LSTM can improve the performances of standard classifiers.

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 objects...... of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes ‘building’ (99%, 95% CI: 95%-100%) and ‘road and parking lot’ (90%, 95% CI: 83%-95%). Some...

Vegetation Analysis and Land Use Land Cover Classification of Forest in Uttara Kannada District India Using Remote Sensign and GIS Techniques

Science.gov (United States)

Koppad, A. G.; Janagoudar, B. S.

2017-10-01

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.

An assessment of support vector machines for land cover classification

Science.gov (United States)

Huang, C.; Davis, L.S.; Townshend, J.R.G.

2002-01-01

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

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

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.

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, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

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.

Evaluating remotely sensed plant count accuracy with differing unmanned aircraft system altitudes, physical canopy separations, and ground covers

Science.gov (United States)

Leiva, Josue Nahun; Robbins, James; Saraswat, Dharmendra; She, Ying; Ehsani, Reza

2017-07-01

This study evaluated the effect of flight altitude and canopy separation of container-grown Fire Chief™ arborvitae (Thuja occidentalis L.) on counting accuracy. Images were taken at 6, 12, and 22 m above the ground using unmanned aircraft systems. Plants were spaced to achieve three canopy separation treatments: 5 cm between canopy edges, canopy edges touching, and 5 cm of canopy edge overlap. Plants were placed on two different ground covers: black fabric and gravel. A counting algorithm was trained using Feature Analyst®. Total counting error, false positives, and unidentified plants were reported for images analyzed. In general, total counting error was smaller when plants were fully separated. The effect of ground cover on counting accuracy varied with the counting algorithm. Total counting error for plants placed on gravel (-8) was larger than for those on a black fabric (-2), however, false positive counts were similar for black fabric (6) and gravel (6). Nevertheless, output images of plants placed on gravel did not show a negative effect due to the ground cover but was impacted by differences in image spatial resolution.

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

Classification of matrix-product ground states corresponding to one-dimensional chains of two-state sites of nearest neighbor interactions

International Nuclear Information System (INIS)

Fatollahi, Amir H.; Khorrami, Mohammad; Shariati, Ahmad; Aghamohammadi, Amir

2011-01-01

A complete classification is given for one-dimensional chains with nearest-neighbor interactions having two states in each site, for which a matrix product ground state exists. The Hamiltonians and their corresponding matrix product ground states are explicitly obtained.

Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China

Science.gov (United States)

Wang, Yan-ying; Wang, Jin-liang; Wang, Ping; Hu, Wen-yin; Su, Shao-hua

2015-12-01

High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.

Distributed Modelling of Stormflow Generation: Assessing the Effect of Ground Cover

Science.gov (United States)

Jarihani, B.; Sidle, R. C.; Roth, C. H.; Bartley, R.; Wilkinson, S. N.

2017-12-01

Understanding the effects of grazing management and land cover changes on surface hydrology is important for water resources and land management. A distributed hydrological modelling platform, wflow, (that was developed as part of Deltares's OpenStreams project) is used to assess the effect of land management practices on runoff generation processes. The model was applied to Weany Creek, a small catchment (13.6 km2) of the Burdekin Basin, North Australia, which is being studied to understand sources of sediment and nutrients to the Great Barrier Reef. Satellite and drone-based ground cover data, high resolution topography from LiDAR, soil properties, and distributed rainfall data were used to parameterise the model. Wflow was used to predict total runoff, peak runoff, time of rise, and lag time for several events of varying magnitudes and antecedent moisture conditions. A nested approach was employed to calibrate the model by using recorded flow hydrographs at three scales: (1) a hillslope sub-catchment: (2) a gullied sub-catchment; and the 13.6 km2 catchment outlet. Model performance was evaluated by comparing observed and predicted stormflow hydrograph attributes using the Nash Sutcliffe efficiency metric. By using a nested approach, spatiotemporal patterns of overland flow occurrence across the catchment can also be evaluated. The results show that a process-based distributed model can be calibrated to simulate spatial and temporal patterns of runoff generation processes, to help identify dominant processes which may be addressed by land management to improve rainfall retention. The model will be used to assess the effects of ground cover changes due to management practices in grazed lands on storm runoff.

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.

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.

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.

Site Classification using Multichannel Channel Analysis of Surface Wave (MASW) method on Soft and Hard Ground

Science.gov (United States)

Ashraf, M. A. M.; Kumar, N. S.; Yusoh, R.; Hazreek, Z. A. M.; Aziman, M.

2018-04-01

Site classification utilizing average shear wave velocity (Vs(30) up to 30 meters depth is a typical parameter. Numerous geophysical methods have been proposed for estimation of shear wave velocity by utilizing assortment of testing configuration, processing method, and inversion algorithm. Multichannel Analysis of Surface Wave (MASW) method is been rehearsed by numerous specialist and professional to geotechnical engineering for local site characterization and classification. This study aims to determine the site classification on soft and hard ground using MASW method. The subsurface classification was made utilizing National Earthquake Hazards Reduction Program (NERHP) and international Building Code (IBC) classification. Two sites are chosen to acquire the shear wave velocity which is in the state of Pulau Pinang for soft soil and Perlis for hard rock. Results recommend that MASW technique can be utilized to spatially calculate the distribution of shear wave velocity (Vs(30)) in soil and rock to characterize areas.

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...

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, Thomas 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

Land-Cover Change in the East Central Texas Plains, 1973-2000

Science.gov (United States)

Karstensen, Krista A.

2009-01-01

ancillary data such as census statistics and published literature are used. The sample block data are then incorporated into statistical analyses to generate an overall change matrix for the ecoregion. For example, the scalar statistics can show the spatial extent of change per cover type with time, as well as the land-cover transformations from one land-cover type to another type occurring with time. Field data of the sample blocks include direct measurements of land cover, particularly ground-survey data collected for training and validation of image classifications (Loveland and others, 2002). The field experience allows for additional observations of the character and condition of the landscape, assistance in sample block interpretation, ground truthing of Landsat imagery, and helps determine the driving forces of change identified in an ecoregion. Management and maintenance of field data, beyond initial use for training and validation of image classifications, is important as improved methods for image classification are developed, and as present-day data become part of the historical legacy for which studies of land-cover change in the future will depend (Loveland and others, 2002). The results illustrate that there is no single profile of land-cover change; instead, there is significant geographic variability that results from land uses within ecoregions continuously adapting to the resource potential created by various environmental, technological, and socioeconomic factors.

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.

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...

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).

The Classification of Ground Roasted Decaffeinated Coffee Using UV-VIS Spectroscopy and SIMCA Method

Science.gov (United States)

Yulia, M.; Asnaning, A. R.; Suhandy, D.

2018-05-01

In this work, an investigation on the classification between decaffeinated and non- decaffeinated coffee samples using UV-VIS spectroscopy and SIMCA method was investigated. Total 200 samples of ground roasted coffee were used (100 samples for decaffeinated coffee and 100 samples for non-decaffeinated coffee). After extraction and dilution, the spectra of coffee samples solution were acquired using a UV-VIS spectrometer (Genesys™ 10S UV-VIS, Thermo Scientific, USA) in the range of 190-1100 nm. The multivariate analyses of the spectra were performed using principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The SIMCA model showed that the classification between decaffeinated and non-decaffeinated coffee samples was detected with 100% sensitivity and specificity.

Multispectral and Panchromatic used Enhancement Resolution and Study Effective Enhancement on Supervised and Unsupervised Classification Land – Cover

Science.gov (United States)

Salman, S. S.; Abbas, W. A.

2018-05-01

The goal of the study is to support analysis Enhancement of Resolution and study effect on classification methods on bands spectral information of specific and quantitative approaches. In this study introduce a method to enhancement resolution Landsat 8 of combining the bands spectral of 30 meters resolution with panchromatic band 8 of 15 meters resolution, because of importance multispectral imagery to extracting land - cover. Classification methods used in this study to classify several lands -covers recorded from OLI- 8 imagery. Two methods of Data mining can be classified as either supervised or unsupervised. In supervised methods, there is a particular predefined target, that means the algorithm learn which values of the target are associated with which values of the predictor sample. K-nearest neighbors and maximum likelihood algorithms examine in this work as supervised methods. In other hand, no sample identified as target in unsupervised methods, the algorithm of data extraction searches for structure and patterns between all the variables, represented by Fuzzy C-mean clustering method as one of the unsupervised methods, NDVI vegetation index used to compare the results of classification method, the percent of dense vegetation in maximum likelihood method give a best results.

Non supervised classification of vegetable covers on digital images of remote sensors: Landsat - ETM+

International Nuclear Information System (INIS)

Arango Gutierrez, Mauricio; Branch Bedoya, John William; Botero Fernandez, Veronica

2005-01-01

The plant species diversity in Colombia and the lack of inventory 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

A Decade of Annual National Land Cover Products - the Cropland Data Layer

Science.gov (United States)

Mueller, R.; Johnson, D. M.; Sandborn, A.; Willis, P.; Ebinger, L.; Yang, Z.; Seffrin, R.; Boryan, C. G.; Hardin, R.

2017-12-01

The Cropland Data Layer (CDL) is a national land cover product produced by the US Department of Agriculture/National Agricultural Statistics Service (NASS) to assess planted crop acreage on an annual basis. The 2017 CDL product serves as the decadal anniversary for the mapping of conterminous US agriculture. The CDL is a supervised land cover classification derived from medium resolution Earth observing satellites that capture crop phenology throughout the growing season, leveraging confidentially held ground reference information from the USDA Farm Service Agency (FSA) as training data. The CDL currently uses ancillary geospatial data from the US Geological Survey's National Land Cover Database (NLCD), and Imperviousness and Forest Canopy layers as well as the National Elevation Dataset as training for the non-agricultural domain. Accuracy assessments are documented and released annually with metadata publication. NASS is currently reprocessing the 2008 and 2009 CDL products to 30m resolution. They were originally processed and released at 56m based on the Resourcesat-1 AWiFS sensor. Additionally, best practices learned from processing the FSA ground reference data were applied to the historical training set, providing an enhanced classification at 30m. The release of these reprocessed products in the fall of 2017, along with the 2017 CDL annual product will be discussed and will complete a decade's worth of annual 30m products. Discussions of change and trend analytics as well as partnerships with key industry stakeholders will be displayed on the evolution and improvements made to this decadal geospatial crop specific land cover product.

Effects of 60Co γ-rays irradiation on seed growth of ground-cover chrysanthemum

International Nuclear Information System (INIS)

Ge Weiya; Wang Tiantian; Yang Shuhua; Zhao Ying; Ge Hong; Chen Lin

2011-01-01

The seeds of ground-cover chrysanthemum were used to study the effects of different doses of 60 Co γ-rays irradiation(10-50 Gy) on seed germination and physiological characteristics. The results showed that the rate of seed germination and seedling survival decreased significantly with the irradiation doses. With the increase of irradiation dose to above 20 Gy, the content of malondialdehyde (MDA) and activity of peroxidase (POD) in seedlings significantly increased. The similar trends were found in the activities of superoxide dismutase (SOD) and glutathione reductase (GR). Catalase (CAT) activity increased at doses lower than 20 Gy, and then decreased at the higher doses, whereas ascorbate peroxidase (APX) activity did not alter except for 40 Gy. It is concluded that the suitable irradiation dose of mutation breeding is 20 Gy for the seeds of ground-cover chrysanthemum. Although 60 Co γ-rays irradiation resulted in damage of membrane lipid peroxidation in the survival seedlings, the increased activity of CAT and POD could protect them against the damage. (authors)

Land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China

Science.gov (United States)

Cheng, Tao; Zhang, Jialong; Zheng, Xinyan; Yuan, Rujin

2018-03-01

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.

Observations of temporal change of nighttime cloud cover from Himawari 8 and ground-based sky camera over Chiba, Japan

Science.gov (United States)

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

2017-12-01

Detection of nighttime cloud from Himawari 8 is implemented using the difference of digital numbers from bands 13 (10.4µm) and 7 (3.9µm). The digital number difference of -1.39x104 can be used as a threshold to separate clouds from clear sky conditions. To look at observations from the ground over Chiba, a digital camera (Canon Powershot A2300) is used to take images of the sky every 5 minutes at an exposure time of 5s at the Center for Environmental Remote Sensing, Chiba University. From these images, cloud cover values are obtained using threshold algorithm (Gacal, et al, 2016). Ten minute nighttime cloud cover values from these two datasets are compared and analyzed from 29 May to 05 June 2017 (20:00-03:00 JST). When compared with lidar data, the camera can detect thick high level clouds up to 10km. The results show that during clear sky conditions (02-03 June), both camera and satellite cloud cover values show 0% cloud cover. During cloudy conditions (05-06 June), the camera shows almost 100% cloud cover while satellite cloud cover values range from 60 to 100%. These low values can be attributed to the presence of low-level thin clouds ( 2km above the ground) as observed from National Institute for Environmental Studies lidar located inside Chiba University. This difference of cloud cover values shows that the camera can produce accurate cloud cover values of low level clouds that are sometimes not detected by satellites. The opposite occurs when high level clouds are present (01-02 June). Derived satellite cloud cover shows almost 100% during the whole night while ground-based camera shows cloud cover values that range from 10 to 100% during the same time interval. The fluctuating values can be attributed to the presence of thin clouds located at around 6km from the ground and the presence of low level clouds ( 1km). Since the camera relies on the reflected city lights, it is possible that the high level thin clouds are not observed by the camera but is

A Study on remote sensing method for drawing up and utilizing ecological and natural map - concentrated on drawing up of Land Cover Classification Map

Energy Technology Data Exchange (ETDEWEB)

Jun, Sung Woo; Chung, Sung Moon [Korea Environment Institute, Seoul (Korea)

1998-12-01

The drawing up of ecological and natural map, which is highly efficient using remote exploration method, was promoted in this study. As the first step of drawing up of ecological and natural map, this study is working on the drawing up of Land Cover using as a base map. Through the detailed and sufficient consideration on GAP analysis of USA, CORINE project of EU, and examples in Korea, it studied and proposed the Land Cover Classification system and method suitable for Korea. It will be helpful to draw up ecological and natural map by providing two strategies and principles for land cover classification. 26 refs., 33 figs., 9 tabs.

Creating high-resolution time series land-cover classifications in rapidly changing forested areas with BULC-U in Google Earth Engine

Science.gov (United States)

Cardille, J. A.; Lee, J.

2017-12-01

With the opening of the Landsat archive, there is a dramatically increased potential for creating high-quality time series of land use/land-cover (LULC) classifications derived from remote sensing. Although LULC time series are appealing, their creation is typically challenging in two fundamental ways. First, there is a need to create maximally correct LULC maps for consideration at each time step; and second, there is a need to have the elements of the time series be consistent with each other, without pixels that flip improbably between covers due only to unavoidable, stray classification errors. We have developed the Bayesian Updating of Land Cover - Unsupervised (BULC-U) algorithm to address these challenges simultaneously, and introduce and apply it here for two related but distinct purposes. First, with minimal human intervention, we produced an internally consistent, high-accuracy LULC time series in rapidly changing Mato Grosso, Brazil for a time interval (1986-2000) in which cropland area more than doubled. The spatial and temporal resolution of the 59 LULC snapshots allows users to witness the establishment of towns and farms at the expense of forest. The new time series could be used by policy-makers and analysts to unravel important considerations for conservation and management, including the timing and location of past development, the rate and nature of changes in forest connectivity, the connection with road infrastructure, and more. The second application of BULC-U is to sharpen the well-known GlobCover 2009 classification from 300m to 30m, while improving accuracy measures for every class. The greatly improved resolution and accuracy permits a better representation of the true LULC proportions, the use of this map in models, and quantification of the potential impacts of changes. Given that there may easily be thousands and potentially millions of images available to harvest for an LULC time series, it is imperative to build useful algorithms

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%.

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

Science.gov (United States)

Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai

2010-08-01

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

Analysis of effects of manhole covers on motorcycle driver maneuvers: a nonparametric classification tree approach.

Science.gov (United States)

Chang, Li-Yen

2014-01-01

A manhole cover is a removable plate forming the lid over the opening of a manhole to allow traffic to pass over the manhole and to prevent people from falling in. Because most manhole covers are placed in roadway traffic lanes, if these manhole covers are not appropriately installed or maintained, they can represent unexpected hazards on the road, especially for motorcycle drivers. The objective of this study is to identify the effects of manhole cover characteristics as well as driver factors and traffic and roadway conditions on motorcycle driver maneuvers. A video camera was used to record motorcycle drivers' maneuvers when they encountered an inappropriately installed or maintained manhole cover. Information on 3059 drivers' maneuver decisions was recorded. Classification and regression tree (CART) models were applied to explore factors that can significantly affect motorcycle driver maneuvers when passing a manhole cover. Nearly 50 percent of the motorcycle drivers decelerated or changed their driving path to reduce the effects of the manhole cover. The manhole cover characteristics including the level difference between manhole cover and pavement, the pavement condition over the manhole cover, and the size of the manhole cover can significantly affect motorcycle driver maneuvers. Other factors, including traffic conditions, lane width, motorcycle speed, and loading conditions, also have significant effects on motorcycle driver maneuvers. To reduce the effects and potential risks from the manhole covers, highway authorities not only need to make sure that any newly installed manhole covers are as level as possible but also need to regularly maintain all the manhole covers to ensure that they are in good condition. In the long run, the size of manhole covers should be kept as small as possible so that the impact of manhole covers on motorcycle drivers can be effectively reduced. Supplemental materials are available for this article. Go to the publisher

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 In this paper the authors present a 2-tier higher order Conditional Random Field which is used for land cover classification. The Conditional Random Field is based on probabilistic messages being passed along a graph to compute efficiently...

Measurement of NO2 pollutant sorption of various trees, shrubs and ground cover plants using gas NO2 labelled 15N

International Nuclear Information System (INIS)

Nasrullah, Nizar; Wungkar, Marietje; Gunawan, Andi; Gandanegara, Soertini; Suharsono, Heny

2000-01-01

The objective of this study is to measure the NO 2 pollutant sorption of various trees, shrubs and ground cover plants. 32 species of trees, 64 speceis of shrubs and 13 species of ground cover plants were exposed to 3 ppm (v / v) N- 15 O 2 in a gas chamber for 60 minutes. Experiment consisted of 2 replicates. The environment conditions in the chamber were set at 30 o C, 1000 lux, and initial relative humidity 60 %. After gas treatment, plants parts were separated into leaves, stems and roots, than dried in 70 o C for 48 hours and then weighed. After weighing, those plants parts were ground to a pine powder. After kjendhal digestion, N total content of plants were analyzed by distillation method. 15 N content of plant samples were analyzed by emission spectrometer ( Yasco, N-151). The amount of N-15 absorbed by plant was the total content of 15 N in the whole plants ( leaves, stem and root ) per gram dry weight of leaves. The amount of 15 N absorbed by plants varied among investigated plants. 15 N sorption of trees are in the range 0.28 - 68.31μg/g. The sorption of shrubs and ground cover plants varied in 1.97 - 100.02 μg/g and 2.38 - 24.06μg/g, respectively. According to the amount of 15 N sorption , the plants were divided into 3 groups of sorption level, high ( > 30.0μg/g), moderate ( 15 - 30 μg/g ), and low sorption level ( 15 μg/g). Results showed that among of 32 investigated trees, 64 shrubs and 13 ground cover plant, 4 species of trees and 13 species of shrubs performed a high sorption level and no one of ground cover plants performed a high sorption level. The species of trees and 15 species of shrubs that mention above are recommended to use as an element of landscape which to be functioned to reduce NO 2 atmospheric pollutant

Advanced Land Use Classification for Nigeriasat-1 Image of Lake Chad Basin

Science.gov (United States)

Babamaaji, R.; Park, C.; Lee, J.

2009-12-01

Lake Chad is a shrinking freshwater lake that has been significantly reduced to about 1/20 of its original size in the 1960’s. The severe draughts in 1970’s and 1980’s and following overexploitations of water resulted in the shortage of surface water in the lake and the surrounding rivers. Ground water resources are in scarcity too as ground water recharge is mostly made by soil infiltration through soil and land cover, but this surface cover is now experiencing siltation and expansion of wetland with invasive species. Large changes in land use and water management practices have taken place in the last 50 years including: removal of water from river systems for irrigation and consumption, degradation of forage land by overgrazing, deforestation, replacing natural ecosystems with mono-cultures, and construction of dams. Therefore, understanding the change of land use and its characteristics must be a first step to find how such changes disturb the water cycle around the lake and affect the shrinkage of the lake. Before any useful thematic information can be extracted from remote sensing data, a land cover classification system has to be developed to obtain the classes of interest. A combination of classification systems used by Global land cover, Water Resources eAtlass and Lake Chad Basin Commission gave rise to 7 land cover classes comprising of - Cropland, vegetation, grassland, water body, shrub-land, farmland ( mostly irrigated) and bareland (i.e. clear land). Supervised Maximum likelihood classification method was used with 15 reference points per class chosen. At the end of the classification, the overall accuracy is 93.33%. Producer’s accuracy for vegetation is 40% compare to the user’s accuracy that is 66.67 %. The reason is that the vegetation is similar to shrub land, it is very hard to differentiate between the vegetation and other plants, and therefore, most of the vegetation is classified as shrub land. Most of the waterbodies are occupied

Ground Pollution Science

International Nuclear Information System (INIS)

Oh, Jong Min; Bae, Jae Geun

1997-08-01

This book deals with ground pollution science and soil science, classification of soil and fundamentals, ground pollution and human, ground pollution and organic matter, ground pollution and city environment, environmental problems of the earth and ground pollution, soil pollution and development of geological features of the ground, ground pollution and landfill of waste, case of measurement of ground pollution.

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

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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

A 3D convolutional neural network approach to land cover classification using LiDAR and multi-temporal Landsat imagery

Science.gov (United States)

Xu, Z.; Guan, K.; Peng, B.; Casler, N. P.; Wang, S. W.

2017-12-01

Landscape has complex three-dimensional features. These 3D features are difficult to extract using conventional methods. Small-footprint LiDAR provides an ideal way for capturing these features. Existing approaches, however, have been relegated to raster or metric-based (two-dimensional) feature extraction from the upper or bottom layer, and thus are not suitable for resolving morphological and intensity features that could be important to fine-scale land cover mapping. Therefore, this research combines airborne LiDAR and multi-temporal Landsat imagery to classify land cover types of Williamson County, Illinois that has diverse and mixed landscape features. Specifically, we applied a 3D convolutional neural network (CNN) method to extract features from LiDAR point clouds by (1) creating occupancy grid, intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into a 3D CNN feature extractor for many epochs of learning. The learned features (e.g., morphological features, intensity features, etc) were combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. We used photo interpretation for training and testing data generation. The classification results show that our approach outperforms traditional methods using LiDAR derived feature maps, and promises to serve as an effective methodology for creating high-quality land cover maps through fusion of complementary types of remote sensing data.

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

International Nuclear Information System (INIS)

Yang, X; Leys, J

2014-01-01

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

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...

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

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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.

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.

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.

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

Science.gov (United States)

Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.

2010-01-01

-based land-use classifications. Aerial photography is typically selected for smaller landscapes (watershed-basin scale), for greater definition of the land-use categories, and for increased spatial resolution. Disadvantages of using photography include time-consuming digitization, high costs for imagery collection, and lack of seasonal data. Recently, the availability of high-resolution satellite imagery has generated a new category of LULC data product. These new datasets have similar strengths to the aerial-photo-based LULC in that they possess the potential for refined definition of land-use categories and increased spatial resolution but also have the benefit of satellite-based classifications, such as repeatability for change analysis. LULC classification based on high-resolution satellite imagery is still in the early stages of development but merits greater attention because environmental-monitoring and landscape-modeling programs rely heavily on LULC data. This publication summarizes land-use and land-cover mapping activities for Alabama and Mississippi coastal areas within the U.S. Geological Survey (USGS) Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project boundaries. Existing LULC datasets will be described, as well as imagery data sources and ancillary data that may provide ground-truth or satellite training data for a forthcoming land-cover classification. Finally, potential areas for a high-resolution land-cover classification in the Alabama-Mississippi region will be identified.

Infiltration properties of covering soil into the void of buried concrete waste due to fluctuation of ground water level and its prevention

International Nuclear Information System (INIS)

Takatsu, Tadashi; Tadano, Hideki; Abe, Satoshi; Imai, Jun; Yanagisawa, Eiji; Mitachi, Toshiyuki

1999-01-01

Low level radioactive concrete waste will be produced in future by breaking up the nuclear facilities, and the waste will be disposed in shallow depth of ground. In order to prepare for those situation, it is needed to clarify the infiltration properties of the covering soil into the void of buried concrete waste due to the fluctuation of ground water level and to develop the prevention methods against the infiltration of the covering soil. In this study, full-scale concrete structure specimens were broken up, and were compacted in large scale testing boxes and a series tests changing water level up and down in the concrete waste and covering soil were performed. From the test results, it was found that the appropriate filter installed between the covering soil and the concrete waste, enable us to prevent the infiltration of covering soil into the void of concrete waste. (author)

UV hazard on Italian Apennines under different shading and ground cover conditions during peak tourist seasons of the year.

Science.gov (United States)

Grifoni, Daniele; Carreras, Giulia; Sabatini, Francesco; Zipoli, Gaetano

2006-12-01

In solar UV irradiance monitoring and forecasting services UV information is generally expressed in terms of its effect on erythema and referred to horizontal surface. In this work we define the UV radiative regime, in terms of biologically effective UV irradiance (UVBE) for skin and eye, under full sun and shaded conditions, over a mountainous tourist area of central Italy by means of two all-day measurements (summer and early spring) with different ground albedo (grass and snow cover respectively). UV irradiance was monitored on tilted surfaces (the most frequent for people standing and walking). Results show the significant contribution of ground albedo and sun position in determining the incident UVBE irradiance. On early spring days the UVBE irradiance measured on horizontal surface was much lower than on tilted ones; the opposite condition was observed in summer. The highest UVBE irradiance values, in particular conditions of sun elevation and ground cover, were reached in periods different from the summer both in full sun and shaded condition.

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

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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.

Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes

Science.gov (United States)

Rigge, Matthew B.; Gass, Leila; Homer, Collin G.; Xian, George Z.

2017-10-26

The National Land Cover Database (NLCD) provides thematic land cover and land cover change data at 30-meter spatial resolution for the United States. Although the NLCD is considered to be the leading thematic land cover/land use product and overall classification accuracy across the NLCD is high, performance and consistency in the vast shrub and grasslands of the Western United States is lower than desired. To address these issues and fulfill the needs of stakeholders requiring more accurate rangeland data, the USGS has developed a method to quantify these areas in terms of the continuous cover of several cover components. These components include the cover of shrub, sagebrush (Artemisia spp), big sagebrush (Artemisia tridentata spp.), herbaceous, annual herbaceous, litter, and bare ground, and shrub and sagebrush height. To produce maps of component cover, we collected field data that were then associated with spectral values in WorldView-2 and Landsat imagery using regression tree models. The current report outlines the procedures and results of converting these continuous cover components to three thematic NLCD classes: barren, shrubland, and grassland. To accomplish this, we developed a series of indices and conditional models using continuous cover of shrub, bare ground, herbaceous, and litter as inputs. The continuous cover data are currently available for two large regions in the Western United States. Accuracy of the “cross-walked” product was assessed relative to that of NLCD 2011 at independent validation points (n=787) across these two regions. Overall thematic accuracy of the “cross-walked” product was 0.70, compared to 0.63 for NLCD 2011. The kappa value was considerably higher for the “cross-walked” product at 0.41 compared to 0.28 for NLCD 2011. Accuracy was also evaluated relative to the values of training points (n=75,000) used in the development of the continuous cover components. Again, the “cross-walked” product outperformed NLCD

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

African Journals Online (AJOL)

This paper attempts to derive classification rules from training data of four Landsat-8 scenes by using the classification and regression tree (CART) implementation of the decision tree algorithm. The transferability of the ruleset was evaluated by classifying two adjacent scenes. The classification of the four mosaicked scenes ...

[Application of optical flow dynamic texture in land use/cover change detection].

Science.gov (United States)

Yan, Li; Gong, Yi-Long; Zhang, Yi; Duan, Wei

2014-11-01

In the present study, a novel change detection approach for high resolution remote sensing images is proposed based on the optical flow dynamic texture (OFDT), which could achieve the land use & land cover change information automatically with a dynamic description of ground-object changes. This paper describes the ground-object gradual change process from the principle using optical flow theory, which breaks the ground-object sudden change hypothesis in remote sensing change detection methods in the past. As the steps of this method are simple, it could be integrated in the systems and software such as Land Resource Management and Urban Planning software that needs to find ground-object changes. This method takes into account the temporal dimension feature between remote sensing images, which provides a richer set of information for remote sensing change detection, thereby improving the status that most of the change detection methods are mainly dependent on the spatial dimension information. In this article, optical flow dynamic texture is the basic reflection of changes, and it is used in high resolution remote sensing image support vector machine post-classification change detection, combined with spectral information. The texture in the temporal dimension which is considered in this article has a smaller amount of data than most of the textures in the spatial dimensions. The highly automated texture computing has only one parameter to set, which could relax the onerous manual evaluation present status. The effectiveness of the proposed approach is evaluated with the 2011 and 2012 QuickBird datasets covering Duerbert Mongolian Autonomous County of Daqing City, China. Then, the effects of different optical flow smooth coefficient and the impact on the description of the ground-object changes in the method are deeply analyzed: The experiment result is satisfactory, with an 87.29% overall accuracy and an 0.850 7 Kappa index, and the method achieves better

COMBINATION OF GENETIC ALGORITHM AND DEMPSTER-SHAFER THEORY OF EVIDENCE FOR LAND COVER CLASSIFICATION USING INTEGRATION OF SAR AND OPTICAL SATELLITE IMAGERY

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H. T. Chu

2012-07-01

Full Text Available The integration of different kinds of remotely sensed data, in particular Synthetic Aperture Radar (SAR and optical satellite imagery, is considered a promising approach for land cover classification because of the complimentary properties of each data source. However, the challenges are: how to fully exploit the capabilities of these multiple data sources, which combined datasets should be used and which data processing and classification techniques are most appropriate in order to achieve the best results. In this paper an approach, in which synergistic use of a feature selection (FS methods with Genetic Algorithm (GA and multiple classifiers combination based on Dempster-Shafer Theory of Evidence, is proposed and evaluated for classifying land cover features in New South Wales, Australia. Multi-date SAR data, including ALOS/PALSAR, ENVISAT/ASAR and optical (Landsat 5 TM+ images, were used for this study. Textural information were also derived and integrated with the original images. Various combined datasets were generated for classification. Three classifiers, namely Artificial Neural Network (ANN, Support Vector Machines (SVMs and Self-Organizing Map (SOM were employed. Firstly, feature selection using GA was applied for each classifier and dataset to determine the optimal input features and parameters. Then the results of three classifiers on particular datasets were combined using the Dempster-Shafer theory of Evidence. Results of this study demonstrate the advantages of the proposed method for land cover mapping using complex datasets. It is revealed that the use of GA in conjunction with the Dempster-Shafer Theory of Evidence can significantly improve the classification accuracy. Furthermore, integration of SAR and optical data often outperform single-type datasets.

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

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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.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

Energy Technology Data Exchange (ETDEWEB)

Jürgens, Björn, E-mail: bjurgens@agenciaidea.es [Agency of Innovation and Development of Andalusia, CITPIA PATLIB Centre (Spain); Herrero-Solana, Victor, E-mail: victorhs@ugr.es [University of Granada, SCImago-UGR (SEJ036) (Spain)

2017-04-15

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.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

International Nuclear Information System (INIS)

Jürgens, Björn; Herrero-Solana, Victor

2017-01-01

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.

Evaluation of terrain geomorphometric characteristics for ground clearance charts production

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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

Reformulation of the covering and quantizer problems as ground states of interacting particles

Science.gov (United States)

Torquato, S.

2010-11-01

It is known that the sphere-packing problem and the number-variance problem (closely related to an optimization problem in number theory) can be posed as energy minimizations associated with an infinite number of point particles in d -dimensional Euclidean space Rd interacting via certain repulsive pair potentials. We reformulate the covering and quantizer problems as the determination of the ground states of interacting particles in Rd that generally involve single-body, two-body, three-body, and higher-body interactions. This is done by linking the covering and quantizer problems to certain optimization problems involving the “void” nearest-neighbor functions that arise in the theory of random media and statistical mechanics. These reformulations, which again exemplify the deep interplay between geometry and physics, allow one now to employ theoretical and numerical optimization techniques to analyze and solve these energy minimization problems. The covering and quantizer problems have relevance in numerous applications, including wireless communication network layouts, the search of high-dimensional data parameter spaces, stereotactic radiation therapy, data compression, digital communications, meshing of space for numerical analysis, and coding and cryptography, among other examples. In the first three space dimensions, the best known solutions of the sphere-packing and number-variance problems (or their “dual” solutions) are directly related to those of the covering and quantizer problems, but such relationships may or may not exist for d≥4 , depending on the peculiarities of the dimensions involved. Our reformulation sheds light on the reasons for these similarities and differences. We also show that disordered saturated sphere packings provide relatively thin (economical) coverings and may yield thinner coverings than the best known lattice coverings in sufficiently large dimensions. In the case of the quantizer problem, we derive improved upper

Analysis On Land Cover In Municipality Of Malang With Landsat 8 Image Through Unsupervised Classification

Science.gov (United States)

Nahari, R. V.; Alfita, R.

2018-01-01

Remote sensing technology has been widely used in the geographic information system in order to obtain data more quickly, accurately and affordably. One of the advantages of using remote sensing imagery (satellite imagery) is to analyze land cover and land use. Satellite image data used in this study were images from the Landsat 8 satellite combined with the data from the Municipality of Malang government. The satellite image was taken in July 2016. Furthermore, the method used in this study was unsupervised classification. Based on the analysis towards the satellite images and field observations, 29% of the land in the Municipality of Malang was plantation, 22% of the area was rice field, 12% was residential area, 10% was land with shrubs, and the remaining 2% was water (lake/reservoir). The shortcoming of the methods was 25% of the land in the area was unidentified because it was covered by cloud. It is expected that future researchers involve cloud removal processing to minimize unidentified area.

Accuracy Assessment of Lidar-Derived Digital Terrain Model (dtm) with Different Slope and Canopy Cover in Tropical Forest Region

Science.gov (United States)

Salleh, M. R. M.; Ismail, Z.; Rahman, M. Z. A.

2015-10-01

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.

ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

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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.

Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

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Hongrui Zheng

2017-12-01

Full Text Available Land Use and Land Cover (LULC classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS, nearest neighbor diffusion (NNDiffusion, PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF and high-pass filter fusion (HPF. Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM and extended attribute profiles (EAPs, are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet

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

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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

Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

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Oi, Shizuo

2011-10-01

Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

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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.

Decadal changes in tundra land cover on Yamal Peninsula, Northwest Siberia

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Forbes, B. C.; Kumpula, T.; Macias-Fauria, M.

2017-12-01

The Yamal-Nenets Okrug in Russia has experienced significant changes in land use and climate in recent decades. Average year-round air temperatures have increased ca. 2°C since the 1970's, with much - but not all - of the warming taking place in winter. In association with ongoing summer warming, the annual growth of erect deciduous shrubs has been accelerating while growing season seasonality has diminished, characterized by shifts in the spatial patterns of key phenological parameters. We prepared LANDSAT-derived land cover classifications for 1988 and 2014 using change detection analysis, supported by extensive ground truthing bolstered with data from Very High-Resolution (VHR) imagery (e.g. Quickbird-2, Worldview-2/3). Research was conducted within summer reindeer pastures utilized by the Yarsalinksi sovhoz, whose animals are collectively owned, as well as many privately-owned herds. The area represents bioclimatic Subzone D of the Circumpolar Arctic Vegetation Map and covers about 8500 km2. This is a key subzone for several reasons: (1) it includes Bovanenkovo, the first and largest gas deposit on Yamal to be developed; (2) it is a zone of extremely active periglacial processes (e.g. active layer detachment slides, lake drainage and recent methane-mediated craters); and (3) it is characterized by steadily increasing growth of tall willow shrubs (Salix spp.), which comprise an important source of fodder by reindeer migrating through the area in summer. These results are unique as our dataset: (1) covers sizable inland regions lying entirely within the Russian tundra zone; (2) derives from extensive ground truthing; and (3) treats all plant taxonomic groups (vascular, bryophytes, lichens) at the plot scale. Here we present the first such classifications, based on LANDSAT images from 1988 and 2014. We identify 16 classes ranging from bare ground and drained lakes, anthropogenic disturbances, through several wetland types, to various dwarf and erect tundra shrub

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

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Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Intrusion of soil covered uranium mill tailings by whitetail prairie dogs and Richardson's ground squirrels

International Nuclear Information System (INIS)

Shuman, R.

1984-01-01

The primary objective of the reclamation of uranium mill tailings is the long-term isolation of the matrial from the biosphere. Fossorial and semi-fossorial species represent a potentially disruptive influence as a result of their burrowing habits. The potential for intrusion was investigated with respect to two sciurids, the whitetail prairie dog (Cynomys leucurus) and Richardson's ground squirrel (Spermophilus richardsonii). Populations of prairie dogs were established on a control area, lacking a tailings layer, and two experimental areas, underlain by a waste layer, in southeastern Wyoming. Weekly measurements of prairie dog mound surface activities were conducted to demonstrate penetration, or lack thereof, of the tailings layer. Additionally, the impact of burrowing upon radon flux was determined. Limited penetration of the waste layer was noted after which frequency of inhabitance of the intruding burrow system declined. No significant changes in radon flux were detected. In another experiment, it was found that Richardson's ground squirrels burrowed to less extreme depths when confronted by mill tailings. Additional work at an inactive tailings pile in western Colorado revealed repeated intrusion through a shallow cover, and subsequent transport of radioactive material to the ground surface by prairie dogs. Radon flux from burrow entrances was significantly greater than that from undisturbed ground. Data suggested that textural and pH properties of tailings material may act to discourage repeated intrusion at some sites. 58 references

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

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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.

MANAGEMENT EFFECTS ON GROUND COVER CLUMPINESS: SCALING FROM FIELD TO SENTINEL-2 COVER ESTIMATES

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P. Scarth

2017-11-01

Full Text Available Significant progress has been made in the development of cover data and derived products based on remotely sensed fractional cover information and field data across Australia, and these cover data sets are now used for quantifying and monitoring grazing land condition. The availability of a dense time-series of nearly 30 years of cover data to describe the spatial and temporal patterns in landscape changes over time can help with monitoring the effectiveness of grazing land management practice change. With the advent of higher spatial resolution data, such as that provided by the Copernicus Sentinel 2 series of satellites, we can look beyond reporting purely on cover amount and more closely at the operational monitoring and reporting on spatial arrangement of cover and its links with land condition. We collected high spatial resolution cover transects at 20 cm intervals over the Wambiana grazing trials in the Burdekin catchment in Queensland, Australia. Spatial variance analysis was used to determine the cover autocorrelation at various support intervals. Coincident Sentinel-2 imagery was collected and processed over all the sites providing imagery to link with the field data. We show that the spatial arrangement and temporal dynamics of cover are important indicators of grazing land condition for both productivity and water quality outcomes. The metrics and products derived from this research will assist land managers to prioritize investment and practice change strategies for long term sustainability and improved water quality, particularly in the Great Barrier Reef catchments.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

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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.

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.

Land use/land cover study of urban features using spot imagery

International Nuclear Information System (INIS)

Mahmood, S.A.; Qureshi, J.; Abbas, I.

2005-01-01

This study is based on visual interpretation and classification of the urban area of Peshawar. Cloud free satellite image of the French SPOT System in panchromatic mode at 100m/pixel spatial detail was used for this purpose. The coverage area comprised nearly (7.5 x 6)sq. km. on the ground depicting the major portion of the city. Various image interpretation elements were exploited to accomplish the study, thirteen land cover classes were identified and demarcated on a tracing sheet. Having prepared the base map. Satellite image map was constructed by assigning disparate colors to the identified features. Dimensions of some of the prominent, regular and liner features were computed from the image. The results indicate that high-resolution satellite image can be effectively used for mapping and area estimation of urban land use/land cover features. (author)

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.

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ground cover and tree growth on calcareous minesoils: Greater influence of soil surface than nitrogen rate or seed mix

International Nuclear Information System (INIS)

Kost, D.A.; Vimmerstedt, J.P.

1994-01-01

Growth of ground cover and trees was evaluated for five growing seasons on calcareous coal minesoil surfaces (standard graded topsoil, graded and ripped topsoil, graded gray cast overburden) in southeastern Ohio. Soil surface plots were seeded in September 1987 with either a standard herbaceous seed mix [orchardgrass (Dactylis glomerata L.), timothy (Phleum pratense L.), perennial ryegrass (Lolium perenne L.), Kentucky bluegrass (Poa pratensis L.), Ranger alfalfa (Medicago sativa L.), Mammoth red clover (Trifolium pratense L.), Empire birdsfoot trefoil (Lotus corniculatus L.), and wheat (Triticum aestivum L.)], or a modified mix using no alfalfa and half the rate of orchardgrass. Nitrogen (45, 90, or 135 kg ha/N) was applied as ammonium nitrate in September 1987 and April 1989. White ash (Fraxinus americana L.), silver maple (Acer saccharinum L.), northern red oak (Quercus rubra L.), and eastern white pine (Pinus strobus L.) were planted in spring 1989 into 0.8 m-wide strips sprayed with glyphosate herbicide at 2.24 kg/ha in October 1988. Total cover and total biomass were highest in July 1989, following the last application of nitrogen fertilizer in April 1989. Total cover ranged from 44% to 56%, and total biomass ranged from 102 to 162 g/0.5 m 2 from 1990 to 1993. Total cover and total biomass were lower at the lowest nitrogen rate in 1989 only. Type of herbaceous seed mix did not affect growth of ground cover or trees. Overall tree survival was 82.0% the first year but declined to 40.6% after 5 yr. Survival varied significantly among all tree species (3.5% for pine, 22.2% for oak, 38.5% for maple, 98.1% for ash)

Screening of plant species as ground cover on uranium mill tailings

International Nuclear Information System (INIS)

Venu Babu, P.; Eapen, S.

2012-01-01

residual mill tailings apart from containing residual radionuclides do have the capacity to support good plant growth. Several species like Rhynchosia minima, Rhynchosia, Thysanolaena maxima, Mucuna pruriens, Desmanthus virgatus, Desmodium gangeticum, Clitoria tematea, Chrysopogon fulvus and Indigofera trita were found to be quite suitable for planting on mill tailings as ground cover while a few others that registered poor growth and/or biomass perhaps can be grown in combination with other species. On the contrary a few species viz., Bothriochloa pertusa, Cenchrus ciliaris, Panicum antidotale and Pennisetum caudatum were found to be unsuitable as they could not survive in mill tailings. A combination of several plant species tested in this study coupled with a few agronomic practices can be tried on mill tailings in Jaduguda as an appropriate vegetative cover. (author)

Analysis of Landsat-4 Thematic Mapper data for classification of forest stands in Baldwin County, Alabama

Science.gov (United States)

Hill, C. L.

1984-01-01

A computer-implemented classification has been derived from Landsat-4 Thematic Mapper data acquired over Baldwin County, Alabama on January 15, 1983. One set of spectral signatures was developed from the data by utilizing a 3x3 pixel sliding window approach. An analysis of the classification produced from this technique identified forested areas. Additional information regarding only the forested areas. Additional information regarding only the forested areas was extracted by employing a pixel-by-pixel signature development program which derived spectral statistics only for pixels within the forested land covers. The spectral statistics from both approaches were integrated and the data classified. This classification was evaluated by comparing the spectral classes produced from the data against corresponding ground verification polygons. This iterative data analysis technique resulted in an overall classification accuracy of 88.4 percent correct for slash pine, young pine, loblolly pine, natural pine, and mixed hardwood-pine. An accuracy assessment matrix has been produced for the classification.

Application of In-Segment Multiple Sampling in Object-Based Classification

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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.

Lidar-based individual tree species classification using convolutional neural network

Science.gov (United States)

Mizoguchi, Tomohiro; Ishii, Akira; Nakamura, Hiroyuki; Inoue, Tsuyoshi; Takamatsu, Hisashi

2017-06-01

Terrestrial lidar is commonly used for detailed documentation in the field of forest inventory investigation. Recent improvements of point cloud processing techniques enabled efficient and precise computation of an individual tree shape parameters, such as breast-height diameter, height, and volume. However, tree species are manually specified by skilled workers to date. Previous works for automatic tree species classification mainly focused on aerial or satellite images, and few works have been reported for classification techniques using ground-based sensor data. Several candidate sensors can be considered for classification, such as RGB or multi/hyper spectral cameras. Above all candidates, we use terrestrial lidar because it can obtain high resolution point cloud in the dark forest. We selected bark texture for the classification criteria, since they clearly represent unique characteristics of each tree and do not change their appearance under seasonable variation and aged deterioration. In this paper, we propose a new method for automatic individual tree species classification based on terrestrial lidar using Convolutional Neural Network (CNN). The key component is the creation step of a depth image which well describe the characteristics of each species from a point cloud. We focus on Japanese cedar and cypress which cover the large part of domestic forest. Our experimental results demonstrate the effectiveness of our proposed method.

Application of classification-tree methods to identify nitrate sources in ground water

Science.gov (United States)

Spruill, T.B.; Showers, W.J.; Howe, S.S.

2002-01-01

A study was conducted to determine if nitrate sources in ground water (fertilizer on crops, fertilizer on golf courses, irrigation spray from hog (Sus scrofa) wastes, and leachate from poultry litter and septic systems) could be classified with 80% or greater success. Two statistical classification-tree models were devised from 48 water samples containing nitrate from five source categories. Model I was constructed by evaluating 32 variables and selecting four primary predictor variables (??15N, nitrate to ammonia ratio, sodium to potassium ratio, and zinc) to identify nitrate sources. A ??15N value of nitrate plus potassium 18.2 indicated inorganic or soil organic N. A nitrate to ammonia ratio 575 indicated nitrate from golf courses. A sodium to potassium ratio 3.2 indicated spray or poultry wastes. A value for zinc 2.8 indicated poultry wastes. Model 2 was devised by using all variables except ??15N. This model also included four variables (sodium plus potassium, nitrate to ammonia ratio, calcium to magnesium ratio, and sodium to potassium ratio) to distinguish categories. Both models were able to distinguish all five source categories with better than 80% overall success and with 71 to 100% success in individual categories using the learning samples. Seventeen water samples that were not used in model development were tested using Model 2 for three categories, and all were correctly classified. Classification-tree models show great potential in identifying sources of contamination and variables important in the source-identification process.

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...

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...

Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey.

Science.gov (United States)

Reis, Selçuk

2008-10-01

Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel). In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude) by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2%) (especially in tea gardens), urban (117%), pasture (-72.8%) and forestry (-12.8%) areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values.

Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey

Directory of Open Access Journals (Sweden)

Selçuk Reis

2008-10-01

Full Text Available Mapping land use/land cover (LULC changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities, negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel. In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2% (especially in tea gardens, urban (117%, pasture (-72.8% and forestry (-12.8% areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values.

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

Using cluster analysis and a classification and regression tree model to developed cover types in the Sky Islands of southeastern Arizona [Abstract

Science.gov (United States)

Jose M. Iniguez; Joseph L. Ganey; Peter J. Daugherty; John D. Bailey

2005-01-01

The objective of this study was to develop a rule based cover type classification system for the forest and woodland vegetation in the Sky Islands of southeastern Arizona. In order to develop such system we qualitatively and quantitatively compared a hierarchical (Ward’s) and a non-hierarchical (k-means) clustering method. Ecologically, unique groups and plots...

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

Combining low level features and visual attributes for VHR remote sensing image classification

Science.gov (United States)

Zhao, Fumin; Sun, Hao; Liu, Shuai; Zhou, Shilin

2015-12-01

Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

A high accuracy land use/cover retrieval system

Directory of Open Access Journals (Sweden)

Alaa Hefnawy

2012-03-01

Full Text Available The effects of spatial resolution on the accuracy of mapping land use/cover types have received increasing attention as a large number of multi-scale earth observation data become available. Although many methods of semi automated image classification of remotely sensed data have been established for improving the accuracy of land use/cover classification during the past 40 years, most of them were employed in single-resolution image classification, which led to unsatisfactory results. In this paper, we propose a multi-resolution fast adaptive content-based retrieval system of satellite images. Through our proposed system, we apply a Super Resolution technique for the Landsat-TM images to have a high resolution dataset. The human–computer interactive system is based on modified radial basis function for retrieval of satellite database images. We apply the backpropagation supervised artificial neural network classifier for both the multi and single resolution datasets. The results show significant improved land use/cover classification accuracy for the multi-resolution approach compared with those from single-resolution approach.

Land cover change of watersheds in Southern Guam from 1973 to 2001.

Science.gov (United States)

Wen, Yuming; Khosrowpanah, Shahram; Heitz, Leroy

2011-08-01

Land cover change can be caused by human-induced activities and natural forces. Land cover change in watershed level has been a main concern for a long time in the world since watersheds play an important role in our life and environment. This paper is focused on how to apply Landsat Multi-Spectral Scanner (MSS) satellite image of 1973 and Landsat Thematic Mapper (TM) satellite image of 2001 to determine the land cover changes of coastal watersheds from 1973 to 2001. GIS and remote sensing are integrated to derive land cover information from Landsat satellite images of 1973 and 2001. The land cover classification is based on supervised classification method in remote sensing software ERDAS IMAGINE. Historical GIS data is used to replace the areas covered by clouds or shadows in the image of 1973 to improve classification accuracy. Then, temporal land cover is utilized to determine land cover change of coastal watersheds in southern Guam. The overall classification accuracies for Landsat MSS image of 1973 and Landsat TM image of 2001 are 82.74% and 90.42%, respectively. The overall classification of Landsat MSS image is particularly satisfactory considering its coarse spatial resolution and relatively bad data quality because of lots of clouds and shadows in the image. Watershed land cover change in southern Guam is affected greatly by anthropogenic activities. However, natural forces also affect land cover in space and time. Land cover information and change in watersheds can be applied for watershed management and planning, and environmental modeling and assessment. Based on spatio-temporal land cover information, the interaction behavior between human and environment may be evaluated. The findings in this research will be useful to similar research in other tropical islands.

Automated cloud classification using a ground based infra-red camera and texture analysis techniques

Science.gov (United States)

Rumi, Emal; Kerr, David; Coupland, Jeremy M.; Sandford, Andrew P.; Brettle, Mike J.

2013-10-01

Clouds play an important role in influencing the dynamics of local and global weather and climate conditions. Continuous monitoring of clouds is vital for weather forecasting and for air-traffic control. Convective clouds such as Towering Cumulus (TCU) and Cumulonimbus clouds (CB) are associated with thunderstorms, turbulence and atmospheric instability. Human observers periodically report the presence of CB and TCU clouds during operational hours at airports and observatories; however such observations are expensive and time limited. Robust, automatic classification of cloud type using infrared ground-based instrumentation offers the advantage of continuous, real-time (24/7) data capture and the representation of cloud structure in the form of a thermal map, which can greatly help to characterise certain cloud formations. The work presented here utilised a ground based infrared (8-14 μm) imaging device mounted on a pan/tilt unit for capturing high spatial resolution sky images. These images were processed to extract 45 separate textural features using statistical and spatial frequency based analytical techniques. These features were used to train a weighted k-nearest neighbour (KNN) classifier in order to determine cloud type. Ground truth data were obtained by inspection of images captured simultaneously from a visible wavelength colour camera at the same installation, with approximately the same field of view as the infrared device. These images were classified by a trained cloud observer. Results from the KNN classifier gave an encouraging success rate. A Probability of Detection (POD) of up to 90% with a Probability of False Alarm (POFA) as low as 16% was achieved.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

Science.gov (United States)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

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.

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

KAUST Repository

Jadoon, Khan; Weihermller, Lutz; McCabe, Matthew; Moghadas, Davood; Vereecken, Harry; Lambot, Sbastien

2015-01-01

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.

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.

Evaluating rapid ground sampling and scaling estimated plant cover using UAV imagery up to Landsat for mapping arctic vegetation

Science.gov (United States)

Nelson, P.; Paradis, D. P.

2017-12-01

The small stature and spectral diversity of arctic plant taxa presents challenges in mapping arctic vegetation. Mapping vegetation at the appropriate scale is needed to visualize effects of disturbance, directional vegetation change or mapping of specific plant groups for other applications (eg. habitat mapping). Fine spatial grain of remotely sensed data (ca. 10 cm pixels) is often necessary to resolve patches of many arctic plant groups, such as bryophytes and lichens. These groups are also spectrally different from mineral, litter and vascular plants. We sought to explore method to generate high-resolution spatial and spectral data to explore better mapping methods for arctic vegetation. We sampled ground vegetation at seven sites north or west of tree-line in Alaska, four north of Fairbanks and three northwest of Bethel, respectively. At each site, we estimated cover of plant functional types in 1m2 quadrats spaced approximately every 10 m along a 100 m long transect. Each quadrat was also scanned using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm range) and photographed from multiple perspectives. We then flew our small UAV with a RGB camera over the transect and at least 50 m on either side collecting on imagery of the plot, which were used to generate a image mosaic and digital surface model of the plot. We compare plant functional group cover ocular estimated in situ to post-hoc estimation, either automated or using a human observer, using the quadrat photos. We also compare interpolated lichen cover from UAV scenes to estimated lichen cover using a statistical models using Landsat data, with focus on lichens. Light and yellow lichens are discernable in the UAV imagery but certain lichens, especially dark colored lichens or those with spectral signatures similar to graminoid litter, present challenges. Future efforts will focus on integrating UAV-upscaled ground cover estimates to hyperspectral sensors (eg. AVIRIS ng) for better combined

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.

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.

Solubility classification of airborne products from uranium ores and tailings piles

International Nuclear Information System (INIS)

Kalkwarf, D.R.

1979-01-01

Airborne products generated at uranium mills were assigned solubility classifications for use in the ICRP Task Group Lung Model. No significant difference was seen between the dissolution behavior of airborne samples and sieved ground samples of the same product. If the product contained radionuclides that dissolved at different rates, composite classifications were assigned to show the solubility class of each component. If the dissolution data indicated that a radionuclide was present in two chemical forms that dissolved at different rates, a mixed classification was assigned to show the percentage of radionuclide in each solubility class. Uranium-ore dust was assigned the composite classification: ( 235 U, 238 U) W; ( 226 Ra) 10% D, 90% Y; ( 230 Th, 210 Pb, 210 Po) Y. Tailings-pile dust was classified: ( 226 Ra) 10% D, 90% Y; ( 230 Th, 210 Pb, 210 Po) Y. Uranium octoxide was classified Y, uranium tetrafluoride was also classified Y, ammonium diuranate was classified D, and yellow-cake dust was classified ( 235 U, 238 U) 60% D, 40% W. The term yellow cake, however, covers a variety of materials which differ significantly in dissolution rate. Solubility classifications based on the dissolution half-times of particular yellow-cake products should, thus, be used when available. The D, W, and Y classifications refer to biological half-times for clearance from the human respiratory tract of 0 to 10 days, 11 to 100 days, and > 100 days, respectively

Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7(7, 8368–8390

Directory of Open Access Journals (Sweden)

Brian A. Johnson

2015-10-01

Full Text Available Much remote sensing (RS research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution and thermal infrared (TIR; 100–120 m spatial resolution Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research [2]. This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR/TIR classification result, can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated.

Spatial and Temporal Land Cover Changes in the Simen Mountains National Park, a World Heritage Site in Northwestern Ethiopia

Directory of Open Access Journals (Sweden)

Menale Wondie

2011-04-01

Full Text Available The trend of land cover (LC and land cover change (LCC, both in time and space, was investigated at the Simen Mountains National Park (SMNP, a World Heritage Site located in northern Ethiopia, between 1984 and 2003 using Geographical Information System (GIS and remote sensing (RS. The objective of the study was to generate spatially and temporally quantified information on land cover dynamics, providing the basis for policy/decision makers and resource managers to facilitate biodiversity conservation, including wild animals. Two satellite images (Landsat TM of 1984 and Landsat ETM+ of 2003 were acquired and supervised classification was used to categorize LC types. Ground Control Points were obtained in field condition for georeferencing and accuracy assessment. The results showed an increase in the areas of pure forest (Erica species dominated and shrubland but a decrease in the area of agricultural land over the 20 years. The overall accuracy and the Kappa value of classification results were 88 and 85%, respectively. The spatial setting of the LC classes was heterogeneous and resulted from the biophysical nature of SMNP and anthropogenic activities. Further studies are suggested to evaluate the existing LC and LCC in connection with wildlife habitat, conservation and management of SMNP.

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 shall...

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.

Strategies for cloud-top phase determination: differentiation between thin cirrus clouds and snow in manual (ground truth) analyses

Science.gov (United States)

Hutchison, Keith D.; Etherton, Brian J.; Topping, Phillip C.

1996-12-01

Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create ground truth analyses for the evaluation of cloud detection algorithms is relatively straightforward. However, when focus shifts toward quantifying the performance of automated cloud classification algorithms, the task of creating ground truth images becomes much more complicated since these CNC analyses must differentiate between water and ice cloud tops while ensuring that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. The process of creating these ground truth CNC analyses may become particularly difficult when little or no spectral signature is evident between a cloud and its background, as appears to be the case when thin cirrus is present over snow-covered surfaces. In this paper, procedures are described that enhance the researcher's ability to manually interpret and differentiate between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery. The methodology uses data in up to six AVHRR spectral bands, including an additional band derived from the daytime 3.7 micron channel, which has proven invaluable for the manual discrimination between thin cirrus clouds and snow. It is concluded that while the 1.6 micron channel remains essential to differentiate between thin ice clouds and snow. However, this capability that may be lost if the 3.7 micron data switches to a nighttime-only transmission with the launch of future NOAA satellites.

Classification of movement disorders.

Science.gov (United States)

Fahn, Stanley

2011-05-01

The classification of movement disorders has evolved. Even the terminology has shifted, from an anatomical one of extrapyramidal disorders to a phenomenological one of movement disorders. The history of how this shift came about is described. The history of both the definitions and the classifications of the various neurologic conditions is then reviewed. First is a review of movement disorders as a group; then, the evolving classifications for 3 of them--parkinsonism, dystonia, and tremor--are covered in detail. Copyright © 2011 Movement Disorder Society.

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

Na Li

2018-03-01

Full Text Available The diverse density (DD algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels. However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS sensor and the Push-broom Hyperspectral Imager (PHI are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively.

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.

Surface covering of downed logs: drivers of a neglected process in dead wood ecology.

Science.gov (United States)

Dynesius, Mats; Gibb, Heloise; Hjältén, Joakim

2010-10-07

Many species use coarse woody debris (CWD) and are disadvantaged by the forestry-induced loss of this resource. A neglected process affecting CWD is the covering of the surfaces of downed logs caused by sinking into the ground (increasing soil contact, mostly covering the underside of the log), and dense overgrowth by ground vegetation. Such cover is likely to profoundly influence the quality and accessibility of CWD for wood-inhabiting organisms, but the factors affecting covering are largely unknown. In a five-year experiment we determined predictors of covering rate of fresh logs in boreal forests and clear-cuts. Logs with branches were little covered because they had low longitudinal ground contact. For branchless logs, longitudinal ground contact was most strongly related to estimated peat depth (positive relation). The strongest predictor for total cover of branchless logs was longitudinal ground contact. To evaluate the effect on cover of factors other than longitudinal ground contact, we separately analyzed data from only those log sections that were in contact with the ground. Four factors were prominent predictors of percentage cover of such log sections: estimated peat depth, canopy shade (both increasing cover), potential solar radiation calculated from slope and slope aspect, and diameter of the log (both reducing cover). Peat increased cover directly through its low resistance, which allowed logs to sink and soil contact to increase. High moisture and low temperatures in pole-ward facing slopes and under a canopy favor peat formation through lowered decomposition and enhanced growth of peat-forming mosses, which also proved to rapidly overgrow logs. We found that in some boreal forests, peat and fast-growing mosses can rapidly cover logs lying on the ground. When actively introducing CWD for conservation purposes, we recommend that such rapid covering is avoided, thereby most likely improving the CWD's longevity as habitat for many species.

Coefficient of variation for use in crop area classification across multiple climates

Science.gov (United States)

Whelen, Tracy; Siqueira, Paul

2018-05-01

In this study, the coefficient of variation (CV) is introduced as a unitless statistical measurement for the classification of croplands using synthetic aperture radar (SAR) data. As a measurement of change, the CV is able to capture changing backscatter responses caused by cycles of planting, growing, and harvesting, and thus is able to differentiate these areas from a more static forest or urban area. Pixels with CV values above a given threshold are classified as crops, and below the threshold are non-crops. This paper uses cross-polarized L-band SAR data from the ALOS PALSAR satellite to classify eleven regions across the United States, covering a wide range of major crops and climates. Two separate sets of classification were done, with the first targeting the optimum classification thresholds for each dataset, and the second using a generalized threshold for all datasets to simulate a large-scale operationalized situation. Overall accuracies for the first phase of classification ranged from 66%-81%, and 62%-84% for the second phase. Visual inspection of the results shows numerous possibilities for improving the classifications while still using the same classification method, including increasing the number and temporal frequency of input images in order to better capture phenological events and mitigate the effects of major precipitation events, as well as more accurate ground truth data. These improvements would make the CV method a viable tool for monitoring agriculture throughout the year on a global scale.

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.

Application of Modis Data to Assess the Latest Forest Cover Changes of Sri Lanka

Science.gov (United States)

Perera, K.; Herath, S.; Apan, A.; Tateishi, R.

2012-07-01

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 isolated pockets of

Land-cover change in the Ozark Highlands, 1973-2000

Science.gov (United States)

Karstensen, Krista A.

2010-01-01

Led by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land-Cover Trends Project was initiated in 1999 and aims to document the types, geographic distributions, and rates of land-cover change on a region by region basis for the conterminous United States, and to determine some of the key drivers and consequences of the change (Loveland and others, 2002). For 1973, 1980, 1986, 1992, and 2000 land-cover maps derived from the Landsat series are classified by visual interpretation, inspection of historical aerial photography and ground survey, into 11 land-cover classes. The classes are defined to capture land cover that is discernable in Landsat data. A stratified probability-based sampling methodology undertaken within the 84 Omernik Level III Ecoregions (Omernik, 1987) was used to locate the blocks, with 9 to 48 blocks per ecoregion. The sampling was designed to enable a statistically robust 'scaling up' of the sample-classification data to estimate areal land-cover change within each ecoregion (Loveland and others, 2002; Stehman and others, 2005). At the time of writing, approximately 90 percent of the 84 conterminous United States ecoregions have been processed by the Land-Cover Trends Project. Results from these completed ecoregions illustrate that across the conterminous United States there is no single profile of land-cover/land-use change, rather, there are varying pulses affected by clusters of change agents (Loveland and others, 2002). Land-Cover Trends Project results for the conterminous United States to-date are being used for collaborative environmental change research with partners such as; the National Science Foundation, the National Oceanic and Atmospheric Administration, and the U.S. Fish and Wildlife Service. The strategy has also been adapted for use in a NASA global

Soil respiration and photosynthetic uptake of carbon dioxide by ground-cover plants in four ages of jack pine forest

Science.gov (United States)

Striegl, Robert G.; Wickland, K.P.

2001-01-01

Soil carbon dioxide (CO2) emission (soil respiration), net CO2 exchange after photosynthetic uptake by ground-cover plants, and soil CO2 concentration versus depth below land surface were measured at four ages of jack pine (Pinus banksiana Lamb.) forest in central Saskatchewan. Soil respiration was smallest at a clear-cut site, largest in an 8-year-old stand, and decreased with stand age in 20-year-old and mature (60-75 years old) stands during May-September 1994 (12.1, 34.6, 31.5, and 24.9 mol C??m-2, respectively). Simulations of soil respiration at each stand based on continuously recorded soil temperature were within one standard deviation of measured flux for 48 of 52 measurement periods, but were 10%-30% less than linear interpolations of measured flux for the season. This was probably due to decreased soil respiration at night modeled by the temperature-flux relationships, but not documented by daytime chamber measurements. CO2 uptake by ground-cover plants ranged from 0 at the clear-cut site to 29, 25, and 9% of total growing season soil respiration at the 8-year, 20-year, and mature stands. CO2 concentrations were as great as 7150 ppmv in the upper 1 m of unsaturated zone and were proportional to measured soil respiration.

Monitoring of Conservation Tillage and Tillage Intensity by Ground and Satellite Imagery

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M.A Rostami

2014-09-01

Full Text Available Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA. The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81. The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75. Two effective indices named as Infrared Percentage Vegetation Index (IPVI and Ratio Vegetation Index (RVI with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.

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

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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.

Assessment of the thematic accuracy of land cover maps

DEFF Research Database (Denmark)

Høhle, Joachim

2015-01-01

were applied (‘Decision Tree’ and ‘Support Vector Machine’) using only two attributes (height above ground and normalized difference vegetation index) which both are derived from the images. The assessment of the thematic accuracy applied a stratified design and was based on accuracy measures...... methods perform equally for five classes. Trees are classified with a much better accuracy and a smaller confidence interval by means of the decision tree method. Buildings are classified by both methods with an accuracy of 99% (95% CI: 95%-100%) using independent 3D checkpoints. The average width......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...

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.

Ground water

International Nuclear Information System (INIS)

Osmond, J.K.; Cowart, J.B.

1982-01-01

The subject is discussed under the headings: background and theory (introduction; fractionation in the hydrosphere; mobility factors; radioisotope evolution and aquifer classification; aquifer disequilibria and geochemical fronts); case studies (introduction; (a) conservative, and (b) non-conservative, behaviour); ground water dating applications (general requirements; radon and helium; radium isotopes; uranium isotopes). (U.K.)

Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery

Science.gov (United States)

Helmer, E.H.; Kennaway, T.A.; Pedreros, D.H.; Clark, M.L.; Marcano-Vega, H.; Tieszen, L.L.; Ruzycki, T.R.; Schill, S.R.; Carrington, C.M.S.

2008-01-01

Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius, testing a more detailed classification than earlier work in the latter three islands. Secondly, we estimate the extents of land cover and protected forest by formation for five islands and ask how land cover has changed over the second half of the 20th century. The image interpretation approach combines image mosaics and ancillary geographic data, classifying the resulting set of raster data with decision tree software. Cloud-free image mosaics for one or two seasons were created by applying regression tree normalization to scene dates that could fill cloudy areas in a base scene. Such mosaics are also known as cloud-filled, cloud-minimized or cloud-cleared imagery, mosaics, or composites. The approach accurately distinguished several classes that more standard methods would confuse; the seamless mosaics aided reference data collection; and the multiseason imagery allowed us to separate drought deciduous forests and woodlands from semi-deciduous ones. Cultivated land areas declined 60 to 100 percent from about 1945 to 2000 on several islands. Meanwhile, forest cover has increased 50 to 950%. This trend will likely continue where sugar cane cultivation has dominated. Like the island of Puerto Rico, most higher-elevation forest formations are protected in formal or informal reserves. Also similarly, lowland forests, which are drier forest types on these islands, are not well represented in reserves. Former cultivated lands in lowland areas could provide lands for new reserves of drier forest types. The land-use history of these islands may provide insight for planners in countries currently considering

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

Science.gov (United States)

Crawford, Christopher J.; Manson, Steven M.; Bauer, Marvin E.; Hall, Dorothy K.

2013-01-01

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

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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

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

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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.

Modified Marsh Classification of the Duodenal Biopsies of a Large Database Covering 10 Years

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Cansu Abayli

2014-02-01

Full Text Available Purpose: Celiac is an autoimmune disease caused by of gluten proteins which can be found in multi-grain food like wheat, barley and oat. The disease affects more than 1% of population and characterized by intestinal inflammation. In celiac disease, mucosal damage is a dynamic process. It is shown that it has autoimmune components. It is also T-Cell mediated and can be categorised as a chronic inflammatory disease. The purpose of this study is to make modified Marsh classification of the duodenal biopsies that came to our department in the 10 years. The study deals with reassessment of all events and uncovering the low graded events that were not diagnosed. Material and Methods: 467 biopsies (diagnosed between 2001 and 2011 at the Cukurova University, Faculty of Medicine, Department of Pathology were taken and analyzed by two pathologists. Each sample was reevaluated without taking the previous reports into consideration and scored by using modified Marsh classification. Results: According to Modified Marsh Classification total of 48 cases were diagnosed as Type 1. Total of 6 cases according to Modified Marsh Classification was diagnosed as Type 2. Total of 11 cases according to Modified Marsh Classification was diagnosed as Type 3a. Total of 5 cases, according to Modified Marsh Classification, was diagnosed as Type 3b. Total of 6 cases according to Modified Marsh Classification was diagnosed as Type 3c. Conclusion: As a result of this study, it has been found that Modified Marsh Classification is a very important standardization tool for detection of suspicious duodenal biopsies and for early case examinations.

Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

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Chunyang Wang

2015-01-01

Full Text Available Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution. The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields. Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction. In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method. The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results.

IRSeL-An approach to enhance continuity and accuracy of remotely sensed land cover data

Science.gov (United States)

Rathjens, H.; Dörnhöfer, K.; Oppelt, N.

2014-09-01

Land cover data gives the opportunity to study interactions between land cover status and environmental issues such as hydrologic processes, soil properties, or biodiversity. Land cover data often are based on classification of remote sensing data that seldom provides the requisite accuracy, spatial availability and temporal observational frequency for environmental studies. Thus, there is a high demand for accurate and spatio-temporal complete time series of land cover. In the past considerable research was undertaken to increase land cover classification accuracy, while less effort was spent on interpolation techniques. The purpose of this article is to present a space-time interpolation and revision approach for remotely sensed land cover data. The approach leverages special properties known for agricultural areas such as crop rotations or temporally static land cover classes. The newly developed IRSeL-tool (Interpolation and improvement of Remotely Sensed Land cover) corrects classification errors and interpolates missing land cover pixels. The easy-to-use tool solely requires an initial land cover data set. The IRSeL specific interpolation and revision technique, the data input requirements and data output structure are described in detail. A case study in an area around the city of Neumünster in Northern Germany from 2006 to 2012 was performed for IRSeL validation with initial land cover data sets (Landsat TM image classifications) for the years 2006, 2007, 2009, 2010 and 2011. The results of the case study showed that IRSeL performs well; including years with no classification data overall accuracy values for IRSeL interpolated pixels range from 0.63 to 0.81. IRSeL application significantly increases the accuracy of the land cover data; overall accuracy values rise 0.08 in average resulting in overall accuracy values of at least 0.86. Considering estimated reliabilities, the IRSeL tool provides a temporally and spatially completed and revised land cover

Expenses for creating software as an object of accounting: the nature and classification

Directory of Open Access Journals (Sweden)

I.V. Perviy

2015-12-01

Full Text Available The reasons of development of the accounting classification of expenses for computer programs have been grounded. The features of the market of IT services in Ukraine have been analyzed and its future prospects have been determined. The reasons to provide users with information of the costs of creating computer programs have been defined and grounded. Complex classification of computer software creating costs based on the selection of six characteristics (for stage by creating a computer program; for the ability to be expenced on specific computer program; for the connection of costs with created computer program; for constancy; for the elements of costs; for the value chain have been developed and grounded. Information prerequisites to ensure the effectiveness of strategic management process of creating computer software based on the classification of costs in the value chain have been allocated. Role of complex classification of computer software creating costs in accounting have been grounded.

A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

Science.gov (United States)

Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

2014-12-01

Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. 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 (CRF), 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 incorporating expert knowledge through the relabeling of 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 state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

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Ramesh K. Ningthoujam

2016-07-01

Full Text Available Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41 and 20 m (63% overall accuracy, κ = 0.27. The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes.

Optimization of a Non-traditional Unsupervised Classification Approach for Land Cover Analysis

Science.gov (United States)

Boyd, R. K.; Brumfield, J. O.; Campbell, W. J.

1982-01-01

The conditions under which a hybrid of clustering and canonical analysis for image classification produce optimum results were analyzed. The approach involves generation of classes by clustering for input to canonical analysis. The importance of the number of clusters input and the effect of other parameters of the clustering algorithm (ISOCLS) were examined. The approach derives its final result by clustering the canonically transformed data. Therefore the importance of number of clusters requested in this final stage was also examined. The effect of these variables were studied in terms of the average separability (as measured by transformed divergence) of the final clusters, the transformation matrices resulting from different numbers of input classes, and the accuracy of the final classifications. The research was performed with LANDSAT MSS data over the Hazleton/Berwick Pennsylvania area. Final classifications were compared pixel by pixel with an existing geographic information system to provide an indication of their accuracy.

Land Use and Land Cover Change Analysis along the Coastal ...

African Journals Online (AJOL)

Agribotix GCS 077

are carried out on the land usually effect changes in its cover. ... The FAO document on land cover classification systems, (2000) partly answers this ... over the surface land, including water, vegetation, bare soils and or artificial structures. ... diseases may occur more readily in areas exposed by Land Use and Land Cover ...

Territorial pattern and classification of soils of Kryvyi Rih Iron-Ore Basin

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О. О. Dolina

2014-10-01

Full Text Available The authors developed the classification of soils and adapted it to the conditions of Krivyi Rih industrial region. It became the basis for determining the degree of soil cover transformation in the iron-ore basin under technogenesis. The classification represents the system of hierarchical objects of different taxonomic levels. It allows determination of relationships between objects and their properties. Researched patterns of soil cover structures’ distribution were the basis for the relevant mapping and classification of soils. The classification is adapted to highly-influential industrial conditions of soils formation in the region. The adaptation measures were specific classification levels and units, which provided more detailed differentiation of soils. The authors proposed to separate the soils by the degree of soil formation potential realization for super-divisions. The potential determination allowed predicting the outcome of soil formation and identification of transformation degree of soil cover structures in the region. The results indicated that the main type of soil structures in the industrial region was represented by primitive soils (indicated as a separate type. These soils were determined as dynamic elements in the structure of industrial region soil cover. The article indicated that presence of soil cover structures with the domination of technogenic soils, particularly post-technogenic soils, was the marker of the soil cover in Krivyi Rih Iron-Ore Basin

Track classification within wireless sensor network

Science.gov (United States)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.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 this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Validation of MODIS snow cover images over Austria

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J. Parajka

2006-01-01

Full Text Available This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS snow cover product over the territory of Austria. The aims are (a to analyse the spatial and temporal variability of the MODIS snow product classes, (b to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4 and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m. and the MODIS acquisition time (early afternoon. The comparison of daily air temperature maps with MODIS snow cover images indicates that almost all MODIS overestimation errors are caused by the misclassification of cirrus clouds as snow.

Accuracy assessment between different image classification ...

African Journals Online (AJOL)

What image classification does is to assign pixel to a particular land cover and land use type that has the most similar spectral signature. However, there are possibilities that different methods or algorithms of image classification of the same data set could produce appreciable variant results in the sizes, shapes and areas of ...

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.

Detecting and quantifying land use/land cover dynamics in Wadla ...

African Journals Online (AJOL)

A study was conducted in Wadla Delanta Massif to investigate land use/cover dynamics over the last four decades (1973-2014) using satellite images (1973 MSS, 1995 TM and 2014 ETM+). Global positioning system ... in the study area. Keywords: GIS, Image classification, Remote sensing, Supervised classification ...

Potential and limitations of webcam images for snow cover monitoring in the Swiss Alps

Science.gov (United States)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2017-04-01

In Switzerland, several thousands of outdoor webcams are currently connected to the Internet. They deliver freely available images that can be used to analyze snow cover variability on a high spatio-temporal resolution. To make use of this big data source, we have implemented a webcam-based snow cover mapping procedure, which allows to almost automatically derive snow cover maps from such webcam images. As there is mostly no information about the webcams and its parameters available, our registration approach automatically resolves these parameters (camera orientation, principal point, field of view) by using an estimate of the webcams position, the mountain silhouette, and a high-resolution digital elevation model (DEM). Combined with an automatic snow classification and an image alignment using SIFT features, our procedure can be applied to arbitrary images to generate snow cover maps with a minimum of effort. Resulting snow cover maps have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or hidden from webcams' positions. Up to now, we processed images of about 290 webcams from our archive, and evaluated images of 20 webcams using manually selected ground control points (GCPs) to evaluate the mapping accuracy of our procedure. We present methodological limitations and ongoing improvements, show some applications of our snow cover maps, and demonstrate that webcams not only offer a great opportunity to complement satellite-derived snow retrieval under cloudy conditions, but also serve as a reference for improved validation of satellite-based approaches.

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

Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

Science.gov (United States)

Shupe, Scott Marshall

2000-10-01

Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers

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.

Land cover classification using reformed fuzzy C-means

Indian Academy of Sciences (India)

This paper explains the task of land cover classification using reformed fuzzy C means. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. The most basic attribute for clustering of an image is its luminance ...

CLASSIFICATION BY USING MULTISPECTRAL POINT CLOUD DATA

Directory of Open Access Journals (Sweden)

C. T. Liao

2012-07-01

Full Text Available Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

Classification by Using Multispectral Point Cloud Data

Science.gov (United States)

Liao, C. T.; Huang, H. H.

2012-07-01

Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

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%.

Object Classification Using Airborne Multispectral LiDAR Data

Directory of Open Access Journals (Sweden)

PAN Suoyan

2018-02-01

Full Text Available Airborne multispectral LiDAR system,which obtains surface geometry and spectral data of objects,simultaneously,has become a fast effective,large-scale spatial data acquisition method.Multispectral LiDAR data are characteristics of completeness and consistency of spectrum and spatial geometric information.Support vector machine (SVM,a machine learning method,is capable of classifying objects based on small samples.Therefore,by means of SVM,this paper performs land cover classification using multispectral LiDAR data. First,all independent point cloud with different wavelengths are merged into a single point cloud,where each pixel contains the three-wavelength spectral information.Next,the merged point cloud is converted into range and intensity images.Finally,land-cover classification is performed by means of SVM.All experiments were conducted on the Optech Titan multispectral LiDAR data,containing three individual point cloud collected by 532 nm,1024 nm,and 1550 nm laser beams.Experimental results demonstrate that ①compared to traditional single-wavelength LiDAR data,multispectral LiDAR data provide a promising solution to land use and land cover applications;②SVM is a feasible method for land cover classification of multispectral LiDAR data.

Classification of Gait Types Based on the Duty-factor

DEFF Research Database (Denmark)

Fihl, Preben; Moeslund, Thomas B.

2007-01-01

on the speed of the human, the cameras setup etc. and hence a robust descriptor for gait classification. The dutyfactor is basically a matter of measuring the ground support of the feet with respect to the stride. We estimate this by comparing the incoming silhouettes to a database of silhouettes with known...... ground support. Silhouettes are extracted using the Codebook method and represented using Shape Contexts. The matching with database silhouettes is done using the Hungarian method. While manually estimated duty-factors show a clear classification the presented system contains misclassifications due...

The Regional Land Cover Monitoring System: Building regional capacity through innovative land cover mapping approaches

Science.gov (United States)

Saah, D.; Tenneson, K.; Hanh, Q. N.; Aekakkararungroj, A.; Aung, K. S.; Goldstein, J.; Cutter, P. G.; Maus, P.; Markert, K. N.; Anderson, E.; Ellenburg, W. L.; Ate, P.; Flores Cordova, A. I.; Vadrevu, K.; Potapov, P.; Phongsapan, K.; Chishtie, F.; Clinton, N.; Ganz, D.

2017-12-01

Earth observation and Geographic Information System (GIS) tools, products, and services are vital to support the environmental decision making by governmental institutions, non-governmental agencies, and the general public. At the heart of environmental decision making is the monitoring land cover and land use change (LCLUC) for land resource planning and for ecosystem services, including biodiversity conservation and resilience to climate change. A major challenge for monitoring LCLUC in developing regions, such as Southeast Asia, is inconsistent data products at inconsistent intervals that have different typologies across the region and are typically made in without stakeholder engagement or input. Here we present the Regional Land Cover Monitoring System (RLCMS), a novel land cover mapping effort for Southeast Asia, implemented by SERVIR-Mekong, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries. The RLCMS focuses on mapping biophysical variables (e.g. canopy cover, tree height, or percent surface water) at an annual interval and in turn using those biophysical variables to develop land cover maps based on stakeholder definitions of land cover classes. This allows for flexible and consistent land cover classifications that can meet the needs of different institutions across the region. Another component of the RLCMS production is the stake-holder engagement through co-development. Institutions that directly benefit from this system have helped drive the development for regional needs leading to services for their specific uses. Examples of services for regional stakeholders include using the RLCMS to develop maps using the IPCC classification scheme for GHG emission reporting and developing custom annual maps as an input to hydrologic modeling/flood forecasting systems. In addition to the implementation of this system and the service stemming from the RLCMS in Southeast Asia, it is

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, Alexey; Roy, David P.; Potapov, P.; Ju, J.; Turubanova, S.; Kommareddy, I.; Loveland, Thomas 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 the Web-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 perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.

Guidance on classification for reproductive toxicity under the globally harmonized system of classification and labelling of chemicals (GHS).

Science.gov (United States)

Moore, Nigel P; Boogaard, Peter J; Bremer, Susanne; Buesen, Roland; Edwards, James; Fraysse, Benoit; Hallmark, Nina; Hemming, Helena; Langrand-Lerche, Carole; McKee, Richard H; Meisters, Marie-Louise; Parsons, Paul; Politano, Valerie; Reader, Stuart; Ridgway, Peter; Hennes, Christa

2013-11-01

The Globally Harmonised System of Classification (GHS) is a framework within which the intrinsic hazards of substances may be determined and communicated. It is not a legislative instrument per se, but is enacted into national legislation with the appropriate legislative instruments. GHS covers many aspects of effects upon health and the environment, including adverse effects upon sexual function and fertility or on development. Classification for these effects is based upon observations in humans or from properly designed experiments in animals, although only the latter is covered herein. The decision to classify a substance based upon experimental data, and the category of classification ascribed, is determined by the level of evidence that is available for an adverse effect on sexual function and fertility or on development that does not arise as a secondary non-specific consequence of other toxic effect. This document offers guidance on the determination of level of concern as a measure of adversity, and the level of evidence to ascribe classification based on data from tests in laboratory animals.

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...

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...

PROGRESSIVE DENSIFICATION AND REGION GROWING METHODS FOR LIDAR DATA CLASSIFICATION

Directory of Open Access Journals (Sweden)

J. L. Pérez-García

2012-07-01

Full Text Available At present, airborne laser scanner systems are one of the most frequent methods used to obtain digital terrain elevation models. While having the advantage of direct measurement on the object, the point cloud obtained has the need for classification of their points according to its belonging to the ground. This need for classification of raw data has led to appearance of multiple filters focused LiDAR classification information. According this approach, this paper presents a classification method that combines LiDAR data segmentation techniques and progressive densification to carry out the location of the points belonging to the ground. The proposed methodology is tested on several datasets with different terrain characteristics and data availability. In all case, we analyze the advantages and disadvantages that have been obtained compared with the individual techniques application and, in a special way, the benefits derived from the integration of both classification techniques. In order to provide a more comprehensive quality control of the classification process, the obtained results have been compared with the derived from a manual procedure, which is used as reference classification. The results are also compared with other automatic classification methodologies included in some commercial software packages, highly contrasted by users for LiDAR data treatment.

Detecting Arctic Climate Change Using Koeppen Climate Classification

Energy Technology Data Exchange (ETDEWEB)

Wang, M. [Joint Institute for the Study of Atmosphere and Oceans, University of Washington, Seattle, Washington (United States); Overland, J.E. [NOAA/Pacific Marine Environmental Laboratory, Sand Point Way NE, Seattle, Washington (United States)

2004-11-01

Ecological impacts of the recent warming trend in the Arctic are already noted as changes in tree line and a decrease in tundra area with the replacement of ground cover by shrubs in northern Alaska and several locations in northern Eurasia. The potential impact of vegetation changes to feedbacks on the atmospheric climate system is substantial because of the large land area impacted and the multi-year persistence of the vegetation cover. Satellite NDVI estimates beginning in 1981 and the Koeppen climate classification, which relates surface types to monthly mean air temperatures from 1901 onward, track these changes on an Arctic-wide basis. Temperature fields from the NCEP/NCAR reanalysis and CRU analysis serve as proxy for vegetation cover over the century. A downward trend in the coverage of tundra group for the first 40 yr of the twentieth century was followed by two increases during 1940s and early 1960s, and then a rapid decrease in the last 20 yr. The decrease of tundra group in the 1920-40 period was localized, mostly over Scandinavia; whereas the decrease since 1990 is primarily pan-Arctic, but largest in NW Canada, and eastern and coastal Siberia. The decrease in inferred tundra coverage from 1980 to 2000 was 1.4 x 106 km{sup 2}, or about a 20% reduction in tundra area based on the CRU analyses. This rate of decrease is confirmed by the NDVI data. These tundra group changes in the last 20 yr are accompanied by increase in the area of both the boreal and temperate groups. During the tundra group decrease in the first half of the century boreal group area also decreased while temperate group area increased. The calculated minimum coverage of tundra group from both the Koeppen classification and NDVI indicates that the impact of warming on the spatial coverage of the tundra group in the 1990s is the strongest in the century, and will have multi-decadal consequences for the Arctic.

Applications of UAS-SfM for coastal vulnerability assessment: Geomorphic feature extraction and land cover classification from fine-scale elevation and imagery data

Science.gov (United States)

Sturdivant, E. J.; Lentz, E. E.; Thieler, E. R.; Remsen, D.; Miner, S.

2016-12-01

Characterizing the vulnerability of coastal systems to storm events, chronic change and sea-level rise can be improved with high-resolution data that capture timely snapshots of biogeomorphology. Imagery acquired with unmanned aerial systems (UAS) coupled with structure from motion (SfM) photogrammetry can produce high-resolution topographic and visual reflectance datasets that rival or exceed lidar and orthoimagery. Here we compare SfM-derived data to lidar and visual imagery for their utility in a) geomorphic feature extraction and b) land cover classification for coastal habitat assessment. At a beach and wetland site on Cape Cod, Massachusetts, we used UAS to capture photographs over a 15-hectare coastal area with a resulting pixel resolution of 2.5 cm. We used standard SfM processing in Agisoft PhotoScan to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM). The SfM-derived products have a horizontal uncertainty of +/- 2.8 cm. Using the point cloud in an extraction routine developed for lidar data, we determined the position of shorelines, dune crests, and dune toes. We used the output imagery and DEM to map land cover with a pixel-based supervised classification. The dense and highly precise SfM point cloud enabled extraction of geomorphic features with greater detail than with lidar. The feature positions are reported with near-continuous coverage and sub-meter accuracy. The orthomosaic image produced with SfM provides visual reflectance with higher resolution than those available from aerial flight surveys, which enables visual identification of small features and thus aids the training and validation of the automated classification. We find that the high-resolution and correspondingly high density of UAS data requires some simple modifications to existing measurement techniques and processing workflows, and that the types of data and the quality provided is equivalent to, and in some cases surpasses, that of data

Temporal change detection of land use/land cover using GIS and ...

African Journals Online (AJOL)

Satellite images for the years 1972, 1989, 1999 and 2016 were used for LULC ... built-up areas, pastures and bare land, agricultural land and water bodies. For the accuracy of assessment classifications, matrix error and KAPPA ... Keywords: land use/land cover change; change detection; classification; remote sensing; GIS ...

Classification of huminite-ICCP System 1994

Energy Technology Data Exchange (ETDEWEB)

Sykorova, I. [Institute of Rock Structure and Mechanics, Academy of Science of the Czech Republic, V Holesovicka 41, 182 09 Prague 8 (Czech Republic); Pickel, W. [Coal and Organic Petrology Services Pty Ltd, 23/80 Box Road, Taren Point, NSW 2229 (Australia); Christanis, K. [Department of Geology, University of Patras, 26500 Rio-Patras (Greece); Wolf, M. [Mergelskull 29, 47802 Krefeld (Germany); Taylor, G.H. [15 Hawkesbury Cres, Farrer Act 2607 (Australia); Flores, D. [Departamento de Geologia, Faculdade de Ciencias do Porto, Praca de Gomes Teixeira, 4099-002 Porto (Portugal)

2005-04-12

In the new classification (ICCP System 1994), the maceral group huminite has been revised from the previous classification (ICCP, 1971. Int. Handbook Coal Petr., suppl. to 2nd ed.) to accommodate the nomenclature to changes in the other maceral groups, especially the changes in the vitrinite classification (ICCP, 1998. The new vitrinite classification (ICCP System 1994). Fuel 77, 349-358.). The vitrinite and huminite systems have been correlated so that down to the level of sub-maceral groups, the two systems can be used in parallel. At the level of macerals and for finer classifications, the analyst now has, according to the nature of the coal and the purpose of the analysis, a choice of using either of the two classification systems for huminite and vitrinite. This is in accordance with the new ISO Coal Classification that covers low rank coals as well and allows for the simultaneous use of the huminite and vitrinite nomenclature for low rank coals.

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...

Classification of radar echoes using fractal geometry

International Nuclear Information System (INIS)

Azzaz, Nafissa; Haddad, Boualem

2017-01-01

Highlights: • Implementation of two concepts of fractal geometry to classify two types of meteorological radar echoes. • A new approach, called a multi-scale fractal dimension is used for classification between fixed echoes and rain echoes. • An Automatic identification system of meteorological radar echoes was proposed using fractal geometry. - Abstract: This paper deals with the discrimination between the precipitation echoes and the ground echoes in meteorological radar images using fractal geometry. This study aims to improve the measurement of precipitations by weather radars. For this, we considered three radar sites: Bordeaux (France), Dakar (Senegal) and Me lbourne (USA). We showed that the fractal dimension based on contourlet and the fractal lacunarity are pertinent to discriminate between ground and precipitation echoes. We also demonstrated that the ground echoes have a multifractal structure but the precipitations are more homogeneous than ground echoes whatever the prevailing climate. Thereby, we developed an automatic classification system of radar using a graphic interface. This interface, based on the fractal geometry makes possible the identification of radar echoes type in real time. This system can be inserted in weather radar for the improvement of precipitation estimations.

MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION

Directory of Open Access Journals (Sweden)

T. Yamamoto

2015-05-01

Full Text Available A frequent map revision is required in GIS applications, such as disaster prevention and urban planning. In general, airborne photogrammetry and LIDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, these approaches classify geometrical attributes. Moreover, ground survey and manual editing works are finally required in attribute data classification. On the other hand, although geometrical data extraction is difficult, SAR data have a possibility to automate the attribute data acquisition and classification. The SAR data represent microwave reflections on various surfaces of ground and buildings. There are many researches related to monitoring activities of disaster, vegetation, and urban. Moreover, we have an opportunity to acquire higher resolution data in urban areas with new sensors, such as ALOS2 PALSAR2. Therefore, in this study, we focus on an integration of airborne LIDAR data and satellite SAR data for building extraction and classification.

Lymphoma classification update: B-cell non-Hodgkin lymphomas.

Science.gov (United States)

Jiang, Manli; Bennani, N Nora; Feldman, Andrew L

2017-05-01

Lymphomas are classified based on the normal counterpart, or cell of origin, from which they arise. Because lymphocytes have physiologic immune functions that vary both by lineage and by stage of differentiation, the classification of lymphomas arising from these normal lymphoid populations is complex. Recent genomic data have contributed additional complexity. Areas covered: Lymphoma classification follows the World Health Organization (WHO) system, which reflects international consensus and is based on pathological, genetic, and clinical factors. A 2016 revision to the WHO classification of lymphoid neoplasms recently was reported. The present review focuses on B-cell non-Hodgkin lymphomas, the most common group of lymphomas, and summarizes recent changes most relevant to hematologists and other clinicians who care for lymphoma patients. Expert commentary: Lymphoma classification is a continually evolving field that needs to be responsive to new clinical, pathological, and molecular understanding of lymphoid neoplasia. Among the entities covered in this review, the 2016 revision of the WHO classification particularly impact the subclassification and genetic stratification of diffuse large B-cell lymphoma and high-grade B-cell lymphomas, and reflect evolving criteria and nomenclature for indolent B-cell lymphomas and lymphoproliferative disorders.

Operational monitoring of land-cover change using multitemporal remote sensing data

Science.gov (United States)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation

Generation of Land Cover Maps Using High-Resolution Multispectral Aerial Cameras

DEFF Research Database (Denmark)

Höhle, Joachim

2013-01-01

. The classification had an overall accuracy of 79%. Suggestions for the improvements in the applied methodology are made. The potential of land cover maps lies in updating of topographic databases, quality control of maps, studies of town development, and other geo-spatial domain applications. The automatic...... for classification of land cover. A high degree of automation can be achieved. The obtained results of a practical example are checked with reference values derived from ortho-images in natural colour and from colour images using stereo-vision. An error matrix is applied in the evaluation of the results...

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

Science.gov (United States)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Completion of the National Land Cover Database (NLCD) 1992–2001 Land Cover Change Retrofit product

Science.gov (United States)

Fry, J.A.; Coan, Michael; Homer, Collin G.; Meyer, Debra K.; Wickham, J.D.

2009-01-01

The Multi-Resolution Land Characteristics Consortium has supported the development of two national digital land cover products: the National Land Cover Dataset (NLCD) 1992 and National Land Cover Database (NLCD) 2001. Substantial differences in imagery, legends, and methods between these two land cover products must be overcome in order to support direct comparison. The NLCD 1992-2001 Land Cover Change Retrofit product was developed to provide more accurate and useful land cover change data than would be possible by direct comparison of NLCD 1992 and NLCD 2001. For the change analysis method to be both national in scale and timely, implementation required production across many Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) path/rows simultaneously. To meet these requirements, a hybrid change analysis process was developed to incorporate both post-classification comparison and specialized ratio differencing change analysis techniques. At a resolution of 30 meters, the completed NLCD 1992-2001 Land Cover Change Retrofit product contains unchanged pixels from the NLCD 2001 land cover dataset that have been cross-walked to a modified Anderson Level I class code, and changed pixels labeled with a 'from-to' class code. Analysis of the results for the conterminous United States indicated that about 3 percent of the land cover dataset changed between 1992 and 2001.

DECISION LEVEL FUSION OF ORTHOPHOTO AND LIDAR DATA USING CONFUSION MATRIX INFORMATION FOR LNAD COVER CLASSIFICATION

Directory of Open Access Journals (Sweden)

S. Daneshtalab

2017-09-01

Full Text Available Automatic urban objects extraction from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Lidar data available today. The aim of this study is to propose a new approach for the integration of high-resolution aerial imagery and Lidar data to improve the accuracy of classification in the city complications. In the proposed method, first, the classification of each data is separately performed using Support Vector Machine algorithm. In this case, extracted Normalized Digital Surface Model (nDSM and pulse intensity are used in classification of LiDAR data, and three spectral visible bands (Red, Green, Blue are considered as feature vector for the orthoimage classification. Moreover, combining the extracted features of the image and Lidar data another classification is also performed using all the features. The outputs of these classifications are integrated in a decision level fusion system according to the their confusion matrices to find the final classification result. The proposed method was evaluated using an urban area of Zeebruges, Belgium. The obtained results represented several advantages of image fusion with respect to a single shot dataset. With the capabilities of the proposed decision level fusion method, most of the object extraction difficulties and uncertainty were decreased and, the overall accuracy and the kappa values were improved 7% and 10%, respectively.

Grounded meets floating

Science.gov (United States)

Walker, Ryan T.

2018-04-01

A comprehensive assessment of grounding-line migration rates around Antarctica, covering a third of the coast, suggests retreat in considerable portions of the continent, beyond the rates expected from adjustment following the Last Glacial Maximum.

Is our Ground-Truth for Traffic Classification Reliable?

DEFF Research Database (Denmark)

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

2014-01-01

. 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...

Data mining and model adaptation for the land use and land cover classification of a Worldview 2 image

Science.gov (United States)

Nascimento, L. C.; Cruz, C. B. M.; Souza, E. M. F. R.

2013-10-01

Forest fragmentation studies have increased since the last 3 decades. Land use and land cover maps (LULC) are important tools for this analysis, as well as other remote sensing techniques. The object oriented analysis classifies the image according to patterns as texture, color, shape, and context. However, there are many attributes to be analyzed, and data mining tools helped us to learn about them and to choose the best ones. In this way, the aim of this paper is to describe data mining techniques and results of a heterogeneous area, as the municipality of Silva Jardim, Rio de Janeiro, Brazil. The municipality has forest, urban areas, pastures, water bodies, agriculture and also some shadows as objects to be represented. Worldview 2 satellite image from 2010 was used and LULC classification was processed using the values that data mining software has provided according to the J48 method. Afterwards, this classification was analyzed, and the verification was made by the confusion matrix, being possible to evaluate the accuracy (58,89%). The best results were in classes "water" and "forest" which have more homogenous reflectance. Because of that, the model has been adapted, in order to create a model for the most homogeneous classes. As result, 2 new classes were created, some values and some attributes changed, and others added. In the end, the accuracy was 89,33%. It is important to highlight this is not a conclusive paper; there are still many steps to develop in highly heterogeneous surfaces.

Feasibility of using pyranometers for continuous estimation of ground cover fraction in table grape vineyards

Directory of Open Access Journals (Sweden)

Antonio Martinez-Cob

2014-06-01

Full Text Available This paper evaluates the feasibility of using pyranometers for continuous estimation of ground cover fraction (GCF at remote, unattended sites. Photographical techniques were used for measuring GCF (GCFref at a table grape vineyard grown under a net. Daily pyranometer-driven GCF estimates (GCFpyr were obtained from solar radiation measurements above and below the canopy. For GCFpyr computation, solar radiation was averaged for two hours around solar noon (midday periods and for daylight periods (8:00 to 18:00 Universal Time Coordinated. GCFpyr and GCFref (daylight periods showed a good agreement: mean estimation error, 0.000; root mean square error, 0.113; index of agreement, 0.967. The high GCF attained, the large measurement range for GCF and the presence of the net above the table grape were the likely reasons for the good performance of GCFpyr in this crop despite the short number of pyranometers used. Further research is required to develop more appropriate calibration equations of GCFpyr and for a more detailed evaluation of using a short number of pyranometers to estimate GCF.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

Science.gov (United States)

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Final hazard classification and auditable safety analysis for the 300-FF-1 Operable Unit liquid waste sites, landfills, and Burial Ground 618-4

International Nuclear Information System (INIS)

Adam, W.J.; Larson, A.R.

1996-12-01

This document provides the hazard categorizations and classifications for the activities associated with the 300-FF-1 Operable Unit (OU) remediation. Categories and classifications presented are applicable only to the 300-FF-1 OU waste sites specifically listed in the inventory. The purpose of this remedial action is to remove contaminated soil, debris, and solid waste from liquid waste sites, landfills, and Burial Ground 618-4 within the 300-FF-1 OU. Resulting waste from this project will be sent to the Environmental Restoration Disposal Facility (ERDF) in the 200 West Area. The 300-FF-1 OU is part of the 300 Area of the Hanford Site and is next to the Columbia River. The objective of this remedial action is to reduce contamination at these waste sites to levels that are acceptable for industrial purposes. Specific remedial objectives (cleanup goals) for each contaminant of concern (COC) are provided in a table, along with the maximum soil concentration detected

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.

Exploring dust emission responses to land cover change using an ecological land classification

Science.gov (United States)

Galloza, Magda S.; Webb, Nicholas P.; Bleiweiss, Max P.; Winters, Craig; Herrick, Jeffrey E.; Ayers, Eldon

2018-06-01

Despite efforts to quantify the impacts of land cover change on wind erosion, assessment uncertainty remains large. We address this uncertainty by evaluating the application of ecological site concepts and state-and-transition models (STMs) for detecting and quantitatively describing the impacts of land cover change on wind erosion. We apply a dust emission model over a rangeland study area in the northern Chihuahuan Desert, New Mexico, USA, and evaluate spatiotemporal patterns of modelled horizontal sediment mass flux and dust emission in the context of ecological sites and their vegetation states; representing a diversity of land cover types. Our results demonstrate how the impacts of land cover change on dust emission can be quantified, compared across land cover classes, and interpreted in the context of an ecological model that encapsulates land management intensity and change. Results also reveal the importance of established weaknesses in the dust model soil characterisation and drag partition scheme, which appeared generally insensitive to the impacts of land cover change. New models that address these weaknesses, coupled with ecological site concepts and field measurements across land cover types, could significantly reduce assessment uncertainties and provide opportunities for identifying land management options.

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

A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series

Science.gov (United States)

Salmon, B. P.; Kleynhans, W.; Olivier, J. C.; van den Bergh, F.; Wessels, K. J.

2018-05-01

Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer (MODIS) land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance values. The proper fitting of a parametric model to these time series usually requires several adjustments to the regression method. To reduce the workload, a global setting of parameters is done to the regression method for a geographical area. In this work we have modified a meta-optimization approach to setting a regression method to extract the parameters on a per time series basis. The standard deviation of the model parameters and magnitude of residuals are used as scoring function. We successfully fitted a triply modulated model to the seasonal patterns of our study area using a non-linear extended Kalman filter (EKF). The approach uses temporal information which significantly reduces the processing time and storage requirements to process each time series. It also derives reliability metrics for each time series individually. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data.

USGS National Land Cover Dataset (NLCD) Downloadable Data Collection

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...

Classification of high resolution imagery based on fusion of multiscale texture features

International Nuclear Information System (INIS)

Liu, Jinxiu; Liu, Huiping; Lv, Ying; Xue, Xiaojuan

2014-01-01

In high resolution data classification process, combining texture features with spectral bands can effectively improve the classification accuracy. However, the window size which is difficult to choose is regarded as an important factor influencing overall classification accuracy in textural classification and current approaches to image texture analysis only depend on a single moving window which ignores different scale features of various land cover types. In this paper, we propose a new method based on the fusion of multiscale texture features to overcome these problems. The main steps in new method include the classification of fixed window size spectral/textural images from 3×3 to 15×15 and comparison of all the posterior possibility values for every pixel, as a result the biggest probability value is given to the pixel and the pixel belongs to a certain land cover type automatically. The proposed approach is tested on University of Pavia ROSIS data. The results indicate that the new method improve the classification accuracy compared to results of methods based on fixed window size textural classification

BENCHMARK OF MACHINE LEARNING METHODS FOR CLASSIFICATION OF A SENTINEL-2 IMAGE

Directory of Open Access Journals (Sweden)

F. Pirotti

2016-06-01

Full Text Available Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels for testing and validating subsets. The classes used are the following: (i urban (ii sowable areas (iii water (iv tree plantations (v grasslands. Validation is carried out using three different approaches: (i using pixels from the training dataset (train, (ii using pixels from the training dataset and applying cross-validation with the k-fold method (kfold and (iii using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train, the whole control dataset (full and with k-fold cross-validation (kfold with ten folds. Results from validation of predictions of the whole dataset (full show the

Generating Ground Reference Data for a Global Impervious Surface Survey

Science.gov (United States)

Tilton, James C.; deColstoun, Eric Brown; Wolfe, Robert E.; Tan, Bin; Huang, Chengquan

2012-01-01

We are engaged in a project to produce a 30m impervious cover data set of the entire Earth for the years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. The GLS data from Landsat provide an unprecedented opportunity to map global urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such as buildings, roads and parking lots. Finally, with GLS data available for the 1975, 1990, 2000, and 2005 time periods, and soon for the 2010 period, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. Our approach works across spatial scales using very high spatial resolution commercial satellite data to both produce and evaluate continental scale products at the 30m spatial resolution of Landsat data. We are developing continental scale training data at 1m or so resolution and aggregating these to 30m for training a regression tree algorithm. Because the quality of the input training data are critical, we have developed an interactive software tool, called HSegLearn, to facilitate the photo-interpretation of high resolution imagery data, such as Quickbird or Ikonos data, into an impervious versus non-impervious map. Previous work has shown that photo-interpretation of high resolution data at 1 meter resolution will generate an accurate 30m resolution ground reference when coarsened to that resolution. Since this process can be very time consuming when using standard clustering classification algorithms, we are looking at image segmentation as a potential avenue to not only improve the training process but also provide a semi-automated approach for generating the ground reference data. HSegLearn takes as its input a hierarchical set of image segmentations produced by the HSeg image segmentation program [1, 2]. HSegLearn lets an analyst specify pixel locations as being

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.

Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data

Science.gov (United States)

Steyaert, L.T.; Hall, F.G.; Loveland, Thomas R.

1997-01-01

A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach 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. The land cover classification was developed 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). Quantitative areal proportions of the major boreal forest components were determined for a 821 km ?? 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within

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.

Territorial pattern and classification of soils of Kryvyi Rih Iron-Ore Basin

OpenAIRE

О. О. Dolina; О. М. Smetana

2014-01-01

The authors developed the classification of soils and adapted it to the conditions of Krivyi Rih industrial region. It became the basis for determining the degree of soil cover transformation in the iron-ore basin under technogenesis. The classification represents the system of hierarchical objects of different taxonomic levels. It allows determination of relationships between objects and their properties. Researched patterns of soil cover structures’ distribution were the basis for the relev...

Classification of High Spatial Resolution, Hyperspectral ...

Science.gov (United States)

EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.

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

Distribution of female genital tract anomalies in two classifications.

Science.gov (United States)

Heinonen, Pentti K

2016-11-01

This study assessed the distribution of Müllerian duct anomalies in two verified classifications of female genital tract malformations, and the presence of associated renal defects. 621 women with confirmed female genital tract anomalies were retrospectively grouped under the European (ESHRE/ESGE) and the American (AFS) classification. The diagnosis of uterine malformation was based on findings in hysterosalpingography, two-dimensional ultrasonography, endoscopies, laparotomy, cesarean section and magnetic resonance imaging in 97.3% of cases. Renal status was determined in 378 patients, including 5 with normal uterus and vagina. The European classification covered all 621 women studied. Uterine anomalies without cervical or vaginal anomaly were found in 302 (48.6%) patients. Uterine anomaly was associated with vaginal anomaly in 45.2%, and vaginal anomaly alone was found in 26 (4.2%) cases. Septate uterus was the most common (49.1%) of all genital tract anomalies, followed by bicorporeal uteri (18.2%). The American classification covered 590 (95%) out of the 621 women with genital tract anomalies. The American system did not take into account vaginal anomalies in 170 (34.7%) and cervical anomalies in 174 (35.5%) out of 490 cases with uterine malformations. Renal abnormalities were found in 71 (18.8%) out of 378 women, unilateral renal agenesis being the most common defect (12.2%), also found in 4 women without Müllerian duct anomaly. The European classification sufficiently covered uterine and vaginal abnormalities. The distribution of the main uterine anomalies was equal in both classifications. The American system missed cervical and vaginal anomalies associated with uterine anomalies. Evaluation of renal system is recommended for all patients with genital tract anomalies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Classification of parotidectomy: a proposed modification to the European Salivary Gland Society classification system.

Science.gov (United States)

Wong, Wai Keat; Shetty, Subhaschandra

2017-08-01

Parotidectomy remains the mainstay of treatment for both benign and malignant lesions of the parotid gland. There exists a wide range of possible surgical options in parotidectomy in terms of extent of parotid tissue removed. There is increasing need for uniformity of terminology resulting from growing interest in modifications of the conventional parotidectomy. It is, therefore, of paramount importance for a standardized classification system in describing extent of parotidectomy. Recently, the European Salivary Gland Society (ESGS) proposed a novel classification system for parotidectomy. The aim of this study is to evaluate this system. A classification system proposed by the ESGS was critically re-evaluated and modified to increase its accuracy and its acceptability. Modifications mainly focused on subdividing Levels I and II into IA, IB, IIA, and IIB. From June 2006 to June 2016, 126 patients underwent 130 parotidectomies at our hospital. The classification system was tested in that cohort of patient. While the ESGS classification system is comprehensive, it does not cover all possibilities. The addition of Sublevels IA, IB, IIA, and IIB may help to address some of the clinical situations seen and is clinically relevant. We aim to test the modified classification system for partial parotidectomy to address some of the challenges mentioned.

Isotope hydrology of ground waters of the Kalahari, Gordonia

International Nuclear Information System (INIS)

Verhagen, B.Th.

1985-01-01

Environmental isotope observations were conducted on ground waters from approximately 50 boreholes covering a substantial part of Gordonia. The quality of these waters ranges from fresh to saline. The observed isotope ratios cover a wide range of values, indicating varied hydrological conditions. The most important conclusions arrived at by this study are: 1. no important regional movement of ground water occurs at present; 2. there is widespread evidence of diffuse rainfall recharge; and 3. an important part of ground-water salinity is derived from the unsaturated zone, during such recharge

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

The Land Cover Dynamics and Conversion of Agricultural Land in Northwestern Bangladesh, 1973-2003.

Science.gov (United States)

Pervez, M.; Seelan, S. K.; Rundquist, B. C.

2006-05-01

The importance of land cover information describing the nature and extent of land resources and changes over time is increasing; this is especially true in Bangladesh, where land cover is changing rapidly. This paper presents research into the land cover dynamics of northwestern Bangladesh for the period 1973-2003 using Landsat satellite images in combination with field survey data collected in January and February 2005. Land cover maps were produced for eight different years during the study period with an average 73 percent overall classification accuracy. The classification results and post-classification change analysis showed that agriculture is the dominant land cover (occupying 74.5 percent of the study area) and is being reduced at a rate of about 3,000 ha per year. In addition, 6.7 percent of the agricultural land is vulnerable to temporary water logging annually. Despite this loss of agricultural land, irrigated agriculture increased substantially until 2000, but has since declined because of diminishing water availability and uncontrolled extraction of groundwater driven by population pressures and the extended need for food. A good agreement (r = 0.73) was found between increases in irrigated land and the depletion of the shallow groundwater table, a factor affecting widely practiced small-scale irrigation in northwestern Bangladesh. Results quantified the land cover change patterns and the stresses placed on natural resources; additionally, they demonstrated an accurate and economical means to map and analyze changes in land cover over time at a regional scale, which can assist decision makers in land and natural resources management decisions.

Spectral unmixing of urban land cover using a generic library approach

Science.gov (United States)

Degerickx, Jeroen; Lordache, Marian-Daniel; Okujeni, Akpona; Hermy, Martin; van der Linden, Sebastian; Somers, Ben

2016-10-01

Remote sensing based land cover classification in urban areas generally requires the use of subpixel classification algorithms to take into account the high spatial heterogeneity. These spectral unmixing techniques often rely on spectral libraries, i.e. collections of pure material spectra (endmembers, EM), which ideally cover the large EM variability typically present in urban scenes. Despite the advent of several (semi-) automated EM detection algorithms, the collection of such image-specific libraries remains a tedious and time-consuming task. As an alternative, we suggest the use of a generic urban EM library, containing material spectra under varying conditions, acquired from different locations and sensors. This approach requires an efficient EM selection technique, capable of only selecting those spectra relevant for a specific image. In this paper, we evaluate and compare the potential of different existing library pruning algorithms (Iterative Endmember Selection and MUSIC) using simulated hyperspectral (APEX) data of the Brussels metropolitan area. In addition, we develop a new hybrid EM selection method which is shown to be highly efficient in dealing with both imagespecific and generic libraries, subsequently yielding more robust land cover classification results compared to existing methods. Future research will include further optimization of the proposed algorithm and additional tests on both simulated and real hyperspectral data.

An automated approach to mapping corn from Landsat imagery

Science.gov (United States)

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.; Hoffer, R.M.

2004-01-01

Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.

Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

Science.gov (United States)

Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

2016-06-01

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

Rohingya Refugee Crisis and Forest Cover Change in Teknaf, Bangladesh

Directory of Open Access Journals (Sweden)

Mohammad Mehedy Hassan

2018-04-01

Full Text Available Following a targeted campaign of violence by Myanmar military, police, and local militias, more than half a million Rohingya refugees have fled to neighboring Bangladesh since August 2017, joining thousands of others living in overcrowded settlement camps in Teknaf. To accommodate this mass influx of refugees, forestland is razed to build spontaneous settlements, resulting in an enormous threat to wildlife habitats, biodiversity, and entire ecosystems in the region. Although reports indicate that this rapid and vast expansion of refugee camps in Teknaf is causing large scale environmental destruction and degradation of forestlands, no study to date has quantified the camp expansion extent or forest cover loss. Using remotely sensed Sentinel-2A and -2B imagery and a random forest (RF machine learning algorithm with ground observation data, we quantified the territorial expansion of refugee settlements and resulting degradation of the ecological resources surrounding the three largest concentrations of refugee camps—Kutupalong–Balukhali, Nayapara–Leda and Unchiprang—that developed between pre- and post-August of 2017. Employing RF as an image classification approach for this study with a cross-validation technique, we obtained a high overall classification accuracy of 94.53% and 95.14% for 2016 and 2017 land cover maps, respectively, with overall Kappa statistics of 0.93 and 0.94. The producer and user accuracy for forest cover ranged between 92.98–98.21% and 96.49–92.98%, respectively. Results derived from the thematic maps indicate a substantial expansion of refugee settlements in the three refugee camp study sites, with an increase of 175 to 1530 hectares between 2016 and 2017, and a net growth rate of 774%. The greatest camp expansion is observed in the Kutupalong–Balukhali site, growing from 146 ha to 1365 ha with a net increase of 1219 ha (total growth rate of 835% in the same time period. While the refugee camps’ occupancy

Common occupational classification system - revision 3

Energy Technology Data Exchange (ETDEWEB)

Stahlman, E.J.; Lewis, R.E.

1996-05-01

Workforce planning has become an increasing concern within the DOE community as the Office of Environmental Restoration and Waste Management (ER/WM or EM) seeks to consolidate and refocus its activities and the Office of Defense Programs (DP) closes production sites. Attempts to manage the growth and skills mix of the EM workforce while retaining the critical skills of the DP workforce have been difficult due to the lack of a consistent set of occupational titles and definitions across the complex. Two reasons for this difficulty may be cited. First, classification systems commonly used in industry often fail to cover in sufficient depth the unique demands of DOE`s nuclear energy and research community. Second, the government practice of contracting the operation of government facilities to the private sector has introduced numerous contractor-specific classification schemes to the DOE complex. As a result, sites/contractors report their workforce needs using unique classification systems. It becomes difficult, therefore, to roll these data up to the national level necessary to support strategic planning and analysis. The Common Occupational Classification System (COCS) is designed to overcome these workforce planning barriers. The COCS is based on earlier workforce planning activities and the input of technical, workforce planning, and human resource managers from across the DOE complex. It provides a set of mutually-exclusive occupation titles and definitions that cover the broad range of activities present in the DOE complex. The COCS is not a required record-keeping or data management guide. Neither is it intended to replace contractor/DOE-specific classification systems. Instead, the system provides a consistent, high- level, functional structure of occupations to which contractors can crosswalk (map) their job titles.

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..

Incorporating Open Source Data for Bayesian Classification of Urban Land Use From VHR Stereo Images

NARCIS (Netherlands)

Li, Mengmeng; De Beurs, Kirsten M.; Stein, Alfred; Bijker, Wietske

2017-01-01

This study investigates the incorporation of open source data into a Bayesian classification of urban land use from very high resolution (VHR) stereo satellite images. The adopted classification framework starts from urban land cover classification, proceeds to building-type characterization, and

Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks

Science.gov (United States)

Xu, Xin; Gui, Rong; Pu, Fangling

2018-01-01

Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification methods can only classify one pixel each time. Because all the pixels of a PolSAR image are classified independently, the inherent interrelation of different land covers is ignored. We use a fixed-feature-size CNN (FFS-CNN) to classify all pixels in a patch simultaneously. The proposed method has several advantages. First, FFS-CNN can classify all the pixels in a small patch simultaneously. When classifying a whole PolSAR image, it is faster than common CNNs. Second, FFS-CNN is trained to learn the interrelation of different land covers in a patch, so it can use the interrelation of land covers to improve the classification results. The experiments of FFS-CNN are evaluated on a Chinese Gaofen-3 PolSAR image and other two real PolSAR images. Experiment results show that FFS-CNN is comparable with the state-of-the-art PolSAR image classification methods. PMID:29510499

Planning School Grounds for Outdoor Learning

Science.gov (United States)

Wagner, Cheryl; Gordon, Douglas

2010-01-01

This publication covers the planning and design of school grounds for outdoor learning in new and existing K-12 facilities. Curriculum development as well as athletic field planning and maintenance are not covered although some references on these topics are provided. It discusses the different types of outdoor learning environments that can be…

Multispectral Image classification using the theories of neural networks

International Nuclear Information System (INIS)

Ardisasmita, M.S.; Subki, M.I.R.

1997-01-01

Image classification is the one of the important part of digital image analysis. the objective of image classification is to identify and regroup the features occurring in an image into one or several classes in terms of the object. basic to the understanding of multispectral classification is the concept of the spectral response of an object as a function of the electromagnetic radiation and the wavelength of the spectrum. new approaches to classification has been developed to improve the result of analysis, these state-of-the-art classifiers are based upon the theories of neural networks. Neural network classifiers are algorithmes which mimic the computational abilities of the human brain. Artificial neurons are simple emulation's of biological neurons; they take in information from sensors or other artificial neurons, perform very simple operations on this data, and pass the result to other recognize the spectral signature of each image pixel. Neural network image classification has been divided into supervised and unsupervised training procedures. In the supervised approach, examples of each cover type can be located and the computer can compute spectral signatures to categorize all pixels in a digital image into several land cover classes. In supervised classification, spectral signatures are generated by mathematically grouping and it does not require analyst-specified training data. Thus, in the supervised approach we define useful information categories and then examine their spectral reparability; in the unsupervised approach the computer determines spectrally sapable classes and then we define thei information value

Estimating Snow Cover from Publicly Available Images

OpenAIRE

Fedorov, Roman; Camerada, Alessandro; Fraternali, Piero; Tagliasacchi, Marco

2016-01-01

In this paper we study the problem of estimating snow cover in mountainous regions, that is, the spatial extent of the earth surface covered by snow. We argue that publicly available visual content, in the form of user generated photographs and image feeds from outdoor webcams, can both be leveraged as additional measurement sources, complementing existing ground, satellite and airborne sensor data. To this end, we describe two content acquisition and processing pipelines that are tailored to...

Thrips (Thysanoptera: Thripidae) mitigation in seedling cotton using strip tillage and winter cover crops.

Science.gov (United States)

Toews, Michael D; Tubbs, R Scott; Wann, Dylan Q; Sullivan, Dana

2010-10-01

Thrips are the most consistent insect pests of seedling cotton in the southeastern United States, where symptoms can range from leaf curling to stand loss. In a 2 year study, thrips adults and immatures were sampled at 14, 21 and 28 days after planting on cotton planted with a thiamethoxam seed treatment in concert with crimson clover, wheat or rye winter cover crops and conventional or strip tillage to investigate potential differences in thrips infestations. Densities of adult thrips, primarily Frankliniella fusca (Hinds), peaked on the first sampling date, whereas immature densities peaked on the second sampling date. Regardless of winter cover crop, plots that received strip tillage experienced significantly fewer thrips at each sampling interval. In addition, assessment of percentage ground cover 42 days after planting showed that there was more than twice as much ground cover in the strip-tilled plots compared with conventionally tilled plots. Correlation analyses showed that increased ground cover was inversely related to thrips densities that occurred on all three sampling dates in 2008 and the final sampling date in 2009. Growers who utilize strip tillage and a winter cover crop can utilize seed treatments for mitigation of early-season thrips infestation.

Topographic gradient based site characterization in India complemented by strong ground-motion spectral attributes

KAUST Repository

Nath, Sankar Kumar; Thingbaijam, Kiran Kumar; Adhikari, M. D.; Nayak, Avinash; Devaraj, N.; Ghosh, Soumalya K.; Mahajan, Arun K.

2013-01-01

We appraise topographic-gradient approach for site classification that employs correlations between 30. m column averaged shear-wave velocity and topographic gradients. Assessments based on site classifications reported from cities across India indicate that the approach is reasonably viable at regional level. Additionally, we experiment three techniques for site classification based on strong ground-motion recordings, namely Horizontal-to-Vertical Spectral Ratio (HVSR), Response Spectra Shape (RSS), and Horizontal-to-Vertical Response Spectral Ratio (HVRSR) at the strong motion stations located across the Himalayas and northeast India. Statistical tests on the results indicate that these three techniques broadly differentiate soil and rock sites while RSS and HVRSR yield better signatures. The results also support the implemented site classification in the light of strong ground-motion spectral attributes observed in different parts of the globe. © 2013 Elsevier Ltd.

Topographic gradient based site characterization in India complemented by strong ground-motion spectral attributes

KAUST Repository

Nath, Sankar Kumar

2013-12-01

We appraise topographic-gradient approach for site classification that employs correlations between 30. m column averaged shear-wave velocity and topographic gradients. Assessments based on site classifications reported from cities across India indicate that the approach is reasonably viable at regional level. Additionally, we experiment three techniques for site classification based on strong ground-motion recordings, namely Horizontal-to-Vertical Spectral Ratio (HVSR), Response Spectra Shape (RSS), and Horizontal-to-Vertical Response Spectral Ratio (HVRSR) at the strong motion stations located across the Himalayas and northeast India. Statistical tests on the results indicate that these three techniques broadly differentiate soil and rock sites while RSS and HVRSR yield better signatures. The results also support the implemented site classification in the light of strong ground-motion spectral attributes observed in different parts of the globe. © 2013 Elsevier Ltd.

VOCAL SEGMENT CLASSIFICATION IN POPULAR MUSIC

DEFF Research Database (Denmark)

Feng, Ling; Nielsen, Andreas Brinch; Hansen, Lars Kai

2008-01-01

This paper explores the vocal and non-vocal music classification problem within popular songs. A newly built labeled database covering 147 popular songs is announced. It is designed for classifying signals from 1sec time windows. Features are selected for this particular task, in order to capture...

A comparison of photointerpretation and ground measurements of forest structure

International Nuclear Information System (INIS)

Biging, G.S.; Congalton, R.G.; Murphy, E.C.

1991-01-01

Traditional forest inventory methods are compared with photointerpreted results. The accuracy of photointerpretation for forest-type classification is assessed in test locations in northern California. If the accuracy of photointerpretation is not sufficiently high, then the traditional practice of comparing satellite classification to photointerpretation is not justified. If this hypothesis is true, it is speculated that spectral analysis of advanced digital satellite data (SPOT and TM) can be used in conjunction with ancillary ground data to produce forest classifications of the same or better accuracy than by traditional photointerpretation techniques. Results of the accuracy assessment of three levels of classification - species, size class, and density - are presented in tables. 5 refs

Fluorescence imaging to quantify crop residue cover

Science.gov (United States)

Daughtry, C. S. T.; Mcmurtrey, J. E., III; Chappelle, E. W.

1994-01-01

Crop residues, the portion of the crop left in the field after harvest, can be an important management factor in controlling soil erosion. Methods to quantify residue cover are needed that are rapid, accurate, and objective. Scenes with known amounts of crop residue were illuminated with long wave ultraviolet (UV) radiation and fluorescence images were recorded with an intensified video camera fitted with a 453 to 488 nm band pass filter. A light colored soil and a dark colored soil were used as background for the weathered soybean stems. Residue cover was determined by counting the proportion of the pixels in the image with fluorescence values greater than a threshold. Soil pixels had the lowest gray levels in the images. The values of the soybean residue pixels spanned nearly the full range of the 8-bit video data. Classification accuracies typically were within 3(absolute units) of measured cover values. Video imaging can provide an intuitive understanding of the fraction of the soil covered by residue.

Automatic Classification of Attacks on IP Telephony

Directory of Open Access Journals (Sweden)

Jakub Safarik

2013-01-01

Full Text Available This article proposes an algorithm for automatic analysis of attack data in IP telephony network with a neural network. Data for the analysis is gathered from variable monitoring application running in the network. These monitoring systems are a typical part of nowadays network. Information from them is usually used after attack. It is possible to use an automatic classification of IP telephony attacks for nearly real-time classification and counter attack or mitigation of potential attacks. The classification use proposed neural network, and the article covers design of a neural network and its practical implementation. It contains also methods for neural network learning and data gathering functions from honeypot application.

Classification of Dust Days by Satellite Remotely Sensed Aerosol Products

Science.gov (United States)

Sorek-Hammer, M.; Cohen, A.; Levy, Robert C.; Ziv, B.; Broday, D. M.

2013-01-01

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 nm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88 percent occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary

Landuse/Cover Change Trend in Soroti District Eastern Uganda ...

African Journals Online (AJOL)

This study assessed the extent and trend of landuse/cover change in Soroti District, Uganda. A series of systematically corrected Orthorectified Landsat imageries of 1973, 1986 and 2001 were downloaded from the Landsat website. The images were analysed using unsupervised classification approach and the land-use/ ...

An ecological classification system for the central hardwoods region: The Hoosier National Forest

Science.gov (United States)

James E. Van Kley; George R. Parker

1993-01-01

This study, a multifactor ecological classification system, using vegetation, soil characteristics, and physiography, was developed for the landscape of the Hoosier National Forest in Southern Indiana. Measurements of ground flora, saplings, and canopy trees from selected stands older than 80 years were subjected to TWINSPAN classification and DECORANA ordination....

Multi-temporal Assessment of Forest Cover, Stocking parameters ...

African Journals Online (AJOL)

user

The study assessed forest cover, stocking parameters and above-ground tree .... deration new emerging ideas on REDD+, this study .... representing areas of change and zero values representing no ..... John Wiley & Sons, Inc. New York.

Biomass burning aerosols characterization from ground based and profiling measurements

Science.gov (United States)

Marin, Cristina; Vasilescu, Jeni; Marmureanu, Luminita; Ene, Dragos; Preda, Liliana; Mihailescu, Mona

2018-04-01

The study goal is to assess the chemical and optical properties of aerosols present in the lofted layers and at the ground. The biomass burning aerosols were evaluated in low level layers from multi-wavelength lidar measurements, while chemical composition at ground was assessed using an Aerosol Chemical Speciation Monitor (ACSM) and an Aethalometer. Classification of aerosol type and specific organic markers were used to explore the potential to sense the particles from the same origin at ground base and on profiles.

Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

Science.gov (United States)

Comiso, J. C.

1990-08-01

The ability to classify and monitor Arctic multiyear sea ice cover using multispectral passive microwave data is studied. Sea ice concentration maps during several summer minima have been analyzed to obtain estimates of ice surviving the summer. The results are compared with multiyear ice concentrations derived from data the following winter, using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data is approximately 25 to 40% less than the summer ice cover minimum, suggesting that even during winter when the emissivity of sea ice is most stable, passive microwave data may account for only a fraction of the total multiyear ice cover. The difference of about 2×106 km2 is considerably more than estimates of advection through Fram Strait during the intervening period. It appears that as in the Antarctic, some multiyear ice floes in the Arctic, especially those near the summer marginal ice zone, have first-year ice or intermediate signatures in the subsequent winter. A likely mechanism for this is the intrusion of seawater into the snow-ice interface, which often occurs near the marginal ice zone or in areas where snow load is heavy. Spatial variations in melt and melt ponding effects also contribute to the complexity of the microwave emissivity of multiyear ice. Hence the multiyear ice data should be studied in conjunction with the previous summer ice data to obtain a more complete characterization of the state of the Arctic ice cover. The total extent and actual areas of the summertime Arctic pack ice were estimated to be 8.4×106 km2 and 6.2×106 km2, respectively, and exhibit small interannual variability during the years 1979 through 1985, suggesting a relatively stable ice cover.

15th Conference of the International Federation of Classification Societies

CERN Document Server

Montanari, Angela; Vichi, Maurizio

2017-01-01

This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.

Shallow ground disposal of radioactive wastes

International Nuclear Information System (INIS)

1981-01-01

This guidebook outlines the factors to be considered in site selection, design, operation, shut-down and surveillance as well as the regulatory requirements of repositories for safe disposal of radioactive waste in shallow ground. No attempt is made to summarize the existing voluminous literature on the many facets of radioactive waste disposal. In the context of this guidebook, shallow ground disposal refers to the emplacement of radioactive waste, with or without engineered barriers, above or below the ground surface, where the final protective covering is of the order of a few metres thick. Deep geological disposal and other underground disposal methods, management of mill tailings and disposal into the sea have been or will be considered in other IAEA publications. These guidelines have been made sufficiently general to cover a broad variety of climatic, hydrogeological and biological conditions. They may need to be interpreted or modified to reflect local conditions and national regulations

Land cover classification of VHR airborne images for citrus grove identification

Science.gov (United States)

Amorós López, J.; Izquierdo Verdiguier, E.; Gómez Chova, L.; Muñoz Marí, J.; Rodríguez Barreiro, J. Z.; Camps Valls, G.; Calpe Maravilla, J.

Managing land resources using remote sensing techniques is becoming a common practice. However, data analysis procedures should satisfy the high accuracy levels demanded by users (public or private companies and governments) in order to be extensively used. This paper presents a multi-stage classification scheme to update the citrus Geographical Information System (GIS) of the Comunidad Valenciana region (Spain). Spain is the first citrus fruit producer in Europe and the fourth in the world. In particular, citrus fruits represent 67% of the agricultural production in this region, with a total production of 4.24 million tons (campaign 2006-2007). The citrus GIS inventory, created in 2001, needs to be regularly updated in order to monitor changes quickly enough, and allow appropriate policy making and citrus production forecasting. Automatic methods are proposed in this work to facilitate this update, whose processing scheme is summarized as follows. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution aerial images (0.5 m). Next, several automatic classifiers (decision trees, artificial neural networks, and support vector machines) are trained and combined to improve the final classification accuracy. Finally, the citrus GIS is automatically updated if a high enough level of confidence, based on the agreement between classifiers, is achieved. This is the case for 85% of the parcels and accuracy results exceed 94%. The remaining parcels are classified by expert photo-interpreters in order to guarantee the high accuracy demanded by policy makers.

Open land cover from OpenStreetMap and remote sensing

Science.gov (United States)

Schultz, Michael; Voss, Janek; Auer, Michael; Carter, Sarah; Zipf, Alexander

2017-12-01

OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011-2016, and 46% of the area was representative of 2016-2017.

Investigation of snow cover dust pollution by contact and satellite observations

Science.gov (United States)

Raputa, Vladimir F.; Yaroslavtseva, Tatyana V.

2015-11-01

The problems of reconstructing the snow cover pollution fields from dusting, point, linear and area sources according to ground and satellite observations are considered. Using reconstruction models, the methods of the combined analysis of the characteristic images of snow cover pollution haloes in the vicinity of sources of dust and contact data observations have been developed. On the basis of the numerical data analysis of ground monitoring and satellite imagery, the stable quantitative regularities between the fields of dust fallouts and the intensity of a change of tones of gray in the radial directions relative to the main sources are identified.

Hanford site ground water protection management plan

International Nuclear Information System (INIS)

1994-10-01

Ground water protection at the Hanford Site consists of preventative and remedial measures that are implemented in compliance with a variety of environmental regulations at local, state, and federal levels. These measures seek to ensure that the resource can sustain a broad range of beneficial uses. To effectively coordinate and ensure compliance with applicable regulations, the U.S. Department of Energy has issued DOE Order 5400.1 (DOE 1988a). This order requires all U.S. Department of Energy facilities to prepare separate ground water protection program descriptions and plans. This document describes the Ground Water Protection Management Plan (GPMP) for the Hanford Site located in the state of Washington. DOE Order 5400.1 specifies that the GPMP covers the following general topical areas: (1) documentation of the ground water regime; (2) design and implementation of a ground water monitoring program to support resource management and comply with applicable laws and regulations; (3) a management program for ground water protection and remediation; (4) a summary and identification of areas that may be contaminated with hazardous waste; (5) strategies for controlling hazardous waste sources; (6) a remedial action program; and (7) decontamination, decommissioning, and related remedial action requirements. Many of the above elements are currently covered by existing programs at the Hanford Site; thus, one of the primary purposes of this document is to provide a framework for coordination of existing ground water protection activities. The GPMP provides the ground water protection policy and strategies for ground water protection/management at the Hanford Site, as well as an implementation plan to improve coordination of site ground water activities

Assessing the land cover situation in Surkhang, Upper Mustang, Nepal, using an ASTER image

NARCIS (Netherlands)

Sharma, B.D.; Clevers, J.G.P.W.; Graaf, de N.R.; Chapagain, N.R.

2003-01-01

This paper describes the remote sensing technique used to prepare a land cover map of Surkhang, Upper Mustang Nepal. The latest ASTER image (October 2002) and an ASTER DEM were used for the land cover classification. The study was carried out in Surkhang Village Development Committee (area 799 km2)

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...

Survey of Ground Dwelling Arthropods Associated with Two Habitat ...

African Journals Online (AJOL)

Survey of Ground Dwelling Arthropods Associated with Two Habitat Types in the Jos ... in the mean abundance of ground dwelling arthropods in relation to taxa. ... Food availability and vegetation cover were found to be critical to arthropods ...

Thermal Environmental Design in Outdoor Space Focusing on Radiation Environment Influenced by Ground Cover Material and Solar Shading, through the Examination on the Redevelopment Buildings in Front of Central Osaka Station

Directory of Open Access Journals (Sweden)

Hideki Takebayashi

2018-01-01

Full Text Available The outdoor open space is used for various purposes, e.g., to walk, rest, talk, meet, study, exercise, play, perform, eat, and drink. Therefore, it is desirable to provide various thermal environments according to users’ needs and their actual conditions. In this study, the radiation environment was evaluated, focusing on ground cover materials and solar radiation shading, through the examination on the redevelopment buildings in front of Central Osaka Station. The spatial distribution of solar radiation shading was calculated using ArcGIS and building shape data. Surface temperatures on the ground and wall are calculated based on the surface heat budget equation. MRT (Mean Radiant Temperature of the human body is calculated assuming that the human body is a sphere. The most dominant factor for the radiant environment is solar radiation shielding and the next is the improvement of surface cover. It is difficult to make SET* (Standard new Effective Temperature comfortable in the afternoon by both solar radiation shielding and improved surface cover because the air temperature is too high on a typical summer day (August. However, particularly in Rooftop Gardens and Green Garden, because the areas of shade grass and water are large, there are several places where people do not feel uncomfortable.

Meta-language for land use classification systems

CSIR Research Space (South Africa)

Cooper, Antony K

2014-04-01

Full Text Available This presentation provides an overview of a meta-language for land use classification. It also explains why land use can’t always be determined from imagery, and why land use is not the same as land cover, zoning or planning - though...

Global land cover mapping at 30 m resolution: A POK-based operational approach

Science.gov (United States)

Chen, Jun; Chen, Jin; Liao, Anping; Cao, Xin; Chen, Lijun; Chen, Xuehong; He, Chaoying; Han, Gang; Peng, Shu; Lu, Miao; Zhang, Weiwei; Tong, Xiaohua; Mills, Jon

2015-05-01

Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 m and 1000 m, a 30 m resolution mapping approach is now a feasible option for the next generation of GLC products. Since most significant human impacts on the land system can be captured at this scale, a number of researchers are focusing on such products. This paper reports the operational approach used in such a project, which aims to deliver reliable data products. Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. To handle the classification process of 10 land cover types, a split-and-merge strategy was employed, i.e. firstly each class identified in a prioritized sequence and then results are merged together. For the identification of each class, a robust integration of pixel-and object-based classification was developed. To improve the quality of the classification results, a knowledge-based interactive verification procedure was developed with the support of web service technology. The performance of the POK-based approach was tested using eight selected areas with differing landscapes from five different continents. An overall classification accuracy of over 80% was achieved. This indicates that the developed POK-based approach is effective and feasible

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...

Detection and Classification of Objects in Synthetic Aperture Radar Imagery

National Research Council Canada - National Science Library

Cooke, Tristrom

2006-01-01

.... The reports concern the detection of faint trails, and the theory and evaluation of a number of existing and novel methods for the detection and classification of ground and maritime targets with SAR imagery...

Aircraft and ground vehicle friction measurements obtained under winter runway conditions

Science.gov (United States)

Yager, Thomas J.

1989-01-01

Tests with specially instrumented NASA B-737 and B-727 aircraft together with several different ground friction measuring devices have been conducted for a variety of runway surface types and wetness conditions. This effort is part of the Joint FAA/NASA Aircraft/Ground Vehicle Runway Friction Program aimed at obtaining a better understanding of aircraft ground handling performance under adverse weather conditions, and defining relationships between aircraft and ground vehicle tire friction measurements. Aircraft braking performance on dry, wet, snow-, and ice-covered runway conditions is discussed together with ground vehicle friction data obtained under similar runway conditions. For the wet, compacted snow- and ice-covered runway conditions, the relationship between ground vehicles and aircraft friction data is identified. The influence of major test parameters on friction measurements such as speed, test tire characteristics, and surface contaminant-type are discussed. The test results indicate that use of properly maintained and calibrated ground vehicles for monitoring runway friction conditions should be encouraged particularly under adverse weather conditions.

Design of a High Resolution Open Access Global Snow Cover Web Map Service Using Ground and Satellite Observations

Science.gov (United States)

Kadlec, J.; Ames, D. P.

2014-12-01

The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by

Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

Science.gov (United States)

Friesz, Aaron M.; Wylie, Bruce K.; Howard, Daniel M.

2017-01-01

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

Science.gov (United States)

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O`Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. PMID:26262681

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Directory of Open Access Journals (Sweden)

Gillian V. Lui

2015-03-01

Full Text Available Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite software package and the Global Forest Change dataset (GFCD being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001–2007 and 2007–2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite’s comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today’s continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application.

7 CFR 30.1 - Definitions of terms used in classification of leaf tobacco.

Science.gov (United States)

2010-01-01

... tobacco. 30.1 Section 30.1 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING... STANDARD CONTAINER REGULATIONS TOBACCO STOCKS AND STANDARDS Classification of Leaf Tobacco Covering Classes, Types and Groups of Grades § 30.1 Definitions of terms used in classification of leaf tobacco. For the...

Characterization of subarctic vegetation using ground based remote sensing methods

Science.gov (United States)

Finnell, D.; Garnello, A.; Palace, M. W.; Sullivan, F.; Herrick, C.; Anderson, S. M.; Crill, P. M.; Varner, R. K.

2014-12-01

Stordalen mire is located at 68°21'N and 19°02'E in the Swedish subarctic. Climate monitoring has revealed a warming trend spanning the past 150 years affecting the mires ability to hold stable palsa/hummock mounds. The micro-topography of the landscape has begun to degrade into thaw ponds changing the vegetation cover from ombrothrophic to minerotrophic. Hummocks are ecologically important due to their ability to act as a carbon sinks. Thaw ponds and sphagnum rich transitional zones have been documented as sources of atmospheric CH4. An objective of this project is to determine if a high resolution three band camera (RGB) and a RGNIR camera could detect differences in vegetation over five different site types. Species composition was collected for 50 plots with ten repetitions for each site type: palsa/hummock, tall shrub, semi-wet, tall graminoid, and wet. Sites were differentiated based on dominating species and features consisting of open water presence, sphagnum spp. cover, graminoid spp. cover, or the presence of dry raised plateaus/mounds. A pole based camera mount was used to collect images at a height of ~2.44m from the ground. The images were cropped in post-processing to fit a one-square meter quadrat. Texture analysis was performed on all images, including entropy, lacunarity, and angular second momentum. Preliminary results suggested that site type influences the number of species present. The p-values for the ability to predict site type using a t-test range from use of a stepwise regression of texture variables, actual vs. predicted percent of vegetation coverage provided R squared values of 0.73, 0.71, 0.67, and 0.89 for C. bigelowii, R. chamaemorus, Sphagnum spp., and open water respectively. These data have provided some support to the notion that texture analyses can be used for classification of mire site types. Future work will involve scaling up from the 50 plots through the use of data collected from two unmanned aerial systems (UAS), as

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

Science.gov (United States)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to

Land Use and Land Cover (LULC) Change Detection in Islamabad and its Comparison with Capital Development Authority (CDA) 2006 Master Plan

Science.gov (United States)

Hasaan, Zahra

2016-07-01

Remote sensing is very useful for the production of land use and land cover statistics which can be beneficial to determine the distribution of land uses. Using remote sensing techniques to develop land use classification mapping is a convenient and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region. In Islamabad city and surrounding the land use has been changing, every day new developments (urban, industrial, commercial and agricultural) are emerging leading to decrease in vegetation cover. The purpose of this work was to develop the land use of Islamabad and its surrounding area that is an important natural resource. For this work the eCognition Developer 64 computer software was used to develop a land use classification using SPOT 5 image of year 2012. For image processing object-based classification technique was used and important land use features i.e. Vegetation cover, barren land, impervious surface, built up area and water bodies were extracted on the basis of object variation and compared the results with the CDA Master Plan. The great increase was found in built-up area and impervious surface area. On the other hand vegetation cover and barren area followed a declining trend. Accuracy assessment of classification yielded 92% accuracies of the final land cover land use maps. In addition these improved land cover/land use maps which are produced by remote sensing technique of class definition, meet the growing need of legend standardization.

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

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

Simple Fully Automated Group Classification on Brain fMRI

International Nuclear Information System (INIS)

Honorio, J.; Goldstein, R.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

2010-01-01

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

Simple Fully Automated Group Classification on Brain fMRI

Energy Technology Data Exchange (ETDEWEB)

Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

2010-04-14

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

EXTRACTION OF BENTHIC COVER INFORMATION FROM VIDEO TOWS AND PHOTOGRAPHS USING OBJECT-BASED IMAGE ANALYSIS

Directory of Open Access Journals (Sweden)

M. T. L. Estomata

2012-07-01

Full Text Available Mapping benthic cover in deep waters comprises a very small proportion of studies in the field of research. Majority of benthic cover mapping makes use of satellite images and usually, classification is carried out only for shallow waters. To map the seafloor in optically deep waters, underwater videos and photos are needed. Some researchers have applied this method on underwater photos, but made use of different classification methods such as: Neural Networks, and rapid classification via down sampling. In this study, accurate bathymetric data obtained using a multi-beam echo sounder (MBES was attempted to be used as complementary data with the underwater photographs. Due to the absence of a motion reference unit (MRU, which applies correction to the data gathered by the MBES, accuracy of the said depth data was compromised. Nevertheless, even with the absence of accurate bathymetric data, object-based image analysis (OBIA, which used rule sets based on information such as shape, size, area, relative distance, and spectral information, was still applied. Compared to pixel-based classifications, OBIA was able to classify more specific benthic cover types other than coral and sand, such as rubble and fish. Through the use of rule sets on area, less than or equal to 700 pixels for fish and between 700 to 10,000 pixels for rubble, as well as standard deviation values to distinguish texture, fish and rubble were identified. OBIA produced benthic cover maps that had higher overall accuracy, 93.78±0.85%, as compared to pixel-based methods that had an average accuracy of only 87.30±6.11% (p-value = 0.0001, α = 0.05.

Ground-water monitoring under RCRA

International Nuclear Information System (INIS)

Coalgate, J.

1993-11-01

In developing a regulatory strategy for the disposal of hazardous waste under the Resource Conservation and Recovery Act (RCRA), protection of ground-water resources was the primary goal of the Environmental Protection Agency (EPA). EPA's ground-water protection strategy seeks to minimize the potential for hazardous wastes and hazardous constituents in waste placed in land disposel units to migrate into the environment. This is achieved through liquids management (limiting the placement of liquid wastes in or on the land, requiring the use of liners beneath waste, installing leachate collection systems and run-on and run-off controls, and covering wastes at closure). Ground-water monitoring serves to detect any failure in EPA's liquids management strategy so that ground-water contamination can be detected and addressed as soon as possible

Land use and land cover mapping: City of Palm Bay, Florida

Science.gov (United States)

Barile, D. D.; Pierce, R.

1977-01-01

Two different computer systems were compared for use in making land use and land cover maps. The Honeywell 635 with the LANDSAT signature development program (LSDP) produced a map depicting general patterns, but themes were difficult to classify as specific land use. Urban areas were unclassified. The General Electric Image 100 produced a map depicting eight land cover categories classifying 68 percent of the total area. Ground truth, LSDP, and Image 100 maps were all made to the same scale for comparison. LSDP agreed with the ground truth 60 percent and 64 percent within the two test areas compared and Image 100 was in agreement 70 percent and 80 percent.

Segmentation schema for enhancing land cover identification: A case study using Sentinel 2 data

Science.gov (United States)

Mongus, Domen; Žalik, Borut

2018-04-01

Land monitoring is performed increasingly using high and medium resolution optical satellites, such as the Sentinel-2. However, optical data is inevitably subjected to the variable operational conditions under which it was acquired. Overlapping of features caused by shadows, soft transitions between shadowed and non-shadowed regions, and temporal variability of the observed land-cover types require radiometric corrections. This study examines a new approach to enhancing the accuracy of land cover identification that resolves this problem. The proposed method constructs an ensemble-type classification model with weak classifiers tuned to the particular operational conditions under which the data was acquired. Iterative segmentation over the learning set is applied for this purpose, where feature space is partitioned according to the likelihood of misclassifications introduced by the classification model. As these are a consequence of overlapping features, such partitioning avoids the need for radiometric corrections of the data, and divides land cover types implicitly into subclasses. As a result, improved performance of all tested classification approaches were measured during the validation that was conducted on Sentinel-2 data. The highest accuracies in terms of F1-scores were achieved using the Naive Bayes Classifier as the weak classifier, while supplementing original spectral signatures with normalised difference vegetation index and texture analysis features, namely, average intensity, contrast, homogeneity, and dissimilarity. In total, an F1-score of nearly 95% was achieved in this way, with F1-scores of each particular land cover type reaching above 90%.

Classification and mapping of rangeland vegetation physiognomic ...

African Journals Online (AJOL)

Plot vegetation species growth form, cover and height data were collected from 450 sampling sites based on eight spectral strata generated using unsupervised image classification. Field data were grouped at four levels of seven, six, three and two vegetation physiognomic classes which were subjected to both ML and ...

Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery

Science.gov (United States)

Nascimento, Wilson R.; Souza-Filho, Pedro Walfir M.; Proisy, Christophe; Lucas, Richard M.; Rosenqvist, Ake

2013-01-01

Mapping and monitoring mangrove ecosystems is a crucial objective for tropical countries, particularly where human disturbance occurs and because of uncertainties associated with sea level and climatic fluctuation. In many tropical regions, such efforts have focused largely on the use of optical data despite low capture rates because of persistent cloud cover. Recognizing the ability of Synthetic Aperture Radar (SAR) for providing cloud-free observations, this study investigated the use of JERS-1 SAR and ALOS PALSAR data, acquired in 1996 and 2008 respectively, for mapping the extent of mangroves along the Brazilian coastline, from east of the Amazon River mouth, Pará State, to the Bay of São José in Maranhão. For each year, an object-orientated classification of major land covers (mangrove, secondary vegetation, gallery and swamp forest, open water, intermittent lakes and bare areas) was performed with the resulting maps then compared to quantify change. Comparison with available ground truth data indicated a general accuracy in the 2008 image classification of all land covers of 96% (kappa = 90.6%, tau = 92.6%). Over the 12 year period, the area of mangrove increased by 718.6 km2 from 6705 m2 to 7423.60 km2, with 1931.0 km² of expansion and 1213 km² of erosion noted; 5493 km² remained unchanged in extent. The general accuracy relating to changes in mangroves was 83.3% (Kappa 66.1%; tau 66.7%). The study confirmed that these mangroves constituted the largest continuous belt globally and were experiencing significant change because of the dynamic coastal environment and the influence of sedimentation from the Amazon River along the shoreline. The study recommends continued observations using combinations of SAR and optical data to establish trends in mangrove distributions and implications for provision of ecosystem services (e.g., fish/invertebrate nurseries, carbon storage and coastal protection).

Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

Directory of Open Access Journals (Sweden)

Fan Yang

2015-07-01

Full Text Available Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC. Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR sensor of the Jet Propulsion Laboratory (JPL and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR sensor of the Technical University of Denmark (DTU. Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers.

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.

Land cover fire proneness in Europe

Directory of Open Access Journals (Sweden)

Mario Gonzalez Pereira

2014-12-01

Full Text Available Aim of study: This study aims to identify and characterize the spatial and temporal evolution of the types of vegetation that are most affected by forest fires in Europe. The characterization of the fuels is an important issue of the fire regime in each specific ecosystem while, on the other hand, fire is an important disturbance for global vegetation dynamics.Area of study: Southern European countries: Portugal, Spain, France, Italy and Greece.Material and Methods: Corine Land Cover maps for 2000 and 2006 (CLC2000, CLC2006 and burned area (BA perimeters, from 2000 to 2013 in Europe are combined to access the spatial and temporal evolution of the types of vegetation that are most affected by wild fires using descriptive statistics and Geographical Information System (GIS techniques.Main results: The spatial and temporal distribution of BA perimeters, vegetation and burnt vegetation by wild fires was performed and different statistics were obtained for Mediterranean and entire Europe, confirming the usefulness of the proposed land cover system. A fire proneness index is proposed to assess the fire selectivity of land cover classes. The index allowed to quantify and to compare the propensity of vegetation classes and countries to fire.Research highlights: The usefulness and efficiency of the land cover classification scheme and fire proneness index. The differences between northern Europe and southern Europe and among the Mediterranean region in what concerns to vegetation cover, fire incidence, area burnt in land cover classes and fire proneness between classes for the different countries.Keywords: Fire proneness; Mixed forests; Land cover/land use; Fire regime; Europe; GIS; Corine land cover. 

Estimating pinyon and juniper cover across Utah using NAIP imagery

Directory of Open Access Journals (Sweden)

Darrell B. Roundy

2016-11-01

Full Text Available Expansion of Pinus L. (pinyon and Juniperus L. (juniper (P-J trees into sagebrush (Artemisia L. steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA software (Feature AnalystTM for ArcMap 10.1®, ENVI Feature Extraction®, and Trimble eCognition Developer 8.2® to extract tree canopy cover using NAIP (National Agricultural Imagery Program imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature AnalystTM, and 0.92 for eCognition. Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature AnalystTM, and 0.83 for eCognition. All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry and Quercus gambelii (Gambel’s oak, which had similar spectral values as P-J.

Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

Science.gov (United States)

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

2018-01-01

The growing use of optimization for geographic object-based image analysis and the possibility to derive a wide range of information about the image in textual form makes machine learning (data mining) a versatile tool for information extraction from multiple data sources. This paper presents application of data mining for land-cover classification by fusing SPOT-6, RADARSAT-2, and derived dataset. First, the images and other derived indices (normalized difference vegetation index, normalized difference water index, and soil adjusted vegetation index) were combined and subjected to segmentation process with optimal segmentation parameters obtained using combination of spatial and Taguchi statistical optimization. The image objects, which carry all the attributes of the input datasets, were extracted and related to the target land-cover classes through data mining algorithms (decision tree) for classification. To evaluate the performance, the result was compared with two nonparametric classifiers: support vector machine (SVM) and random forest (RF). Furthermore, the decision tree classification result was evaluated against six unoptimized trials segmented using arbitrary parameter combinations. The result shows that the optimized process produces better land-use land-cover classification with overall classification accuracy of 91.79%, 87.25%, and 88.69% for SVM and RF, respectively, while the results of the six unoptimized classifications yield overall accuracy between 84.44% and 88.08%. Higher accuracy of the optimized data mining classification approach compared to the unoptimized results indicates that the optimization process has significant impact on the classification quality.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

Science.gov (United States)

Belgiu, Mariana; ǎguţ, Lucian, , Dr; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty

NARCIS (Netherlands)

Beekhuizen, J.; Clarke, K.C.

2010-01-01

The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by

BOREAS TE-18 Landsat TM Physical Classification Image of the NSA

Science.gov (United States)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training data to classify the image into the different land cover classes. The data are provided in a binary, image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Classification of permafrost active layer depth from remotely sensed and topographic evidence

International Nuclear Information System (INIS)

Peddle, D.R.; Franklin, S.E.

1993-01-01

The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to this study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth

Classification of quantum phases and topology of logical operators in an exactly solved model of quantum codes

International Nuclear Information System (INIS)

Yoshida, Beni

2011-01-01

Searches for possible new quantum phases and classifications of quantum phases have been central problems in physics. Yet, they are indeed challenging problems due to the computational difficulties in analyzing quantum many-body systems and the lack of a general framework for classifications. While frustration-free Hamiltonians, which appear as fixed point Hamiltonians of renormalization group transformations, may serve as representatives of quantum phases, it is still difficult to analyze and classify quantum phases of arbitrary frustration-free Hamiltonians exhaustively. Here, we address these problems by sharpening our considerations to a certain subclass of frustration-free Hamiltonians, called stabilizer Hamiltonians, which have been actively studied in quantum information science. We propose a model of frustration-free Hamiltonians which covers a large class of physically realistic stabilizer Hamiltonians, constrained to only three physical conditions; the locality of interaction terms, translation symmetries and scale symmetries, meaning that the number of ground states does not grow with the system size. We show that quantum phases arising in two-dimensional models can be classified exactly through certain quantum coding theoretical operators, called logical operators, by proving that two models with topologically distinct shapes of logical operators are always separated by quantum phase transitions.

ASSESSING LAND COVER CHANGES CAUSED BY GRANITE QUARRYING USING REMOTE SENSING

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R. S. Moeletsi

2017-11-01

Full Text Available Dimension stone quarrying in the area between Rustenburg and Brits in the North West Province of South Africa has been in existence for over 70 decades. The unique characteristics of the granite deposits in South Africa resulted in making the country a global producer of the granite rocks. This led to intensified quarrying activities between Rustenburg and Brits town. However, this surface mining method, has a potential to impact the environment in a negative way causing loss in vegetation, depletion of natural resources, loss of scenic beauty and contamination of surface water resources. To assess the land cover changes caused by granite quarrying activities, remotely sensed data in the form of Landsat images between 1998 and 2015 were used. Supervised classification was used to create maps. Accuracy assessment using Google EarthTM as a reference data yielded an overall accuracy of 78 %. The post classification change detection method was used to assess land cover changes within the granite quarries. Granite quarries increased by 1174.86 ha while formation of quarry lakes increased to 5.3 ha over the 17-year period. Vegetation cover decreased by 1308 ha in area while 18.3 ha bare land was lost during the same period. This study demonstrated the utility of remote sensing to detect changes in land cover within granite quarries.

Spatial and temporal variability of grass cover in two olive grove catchments on contrasting soil types

Science.gov (United States)

Aguilera, Laura; Taguas, Encarnación V.; Gimeno, Enrique; Gómez, José A.

2013-04-01

Mediterranean climate conditions -characterized by the concentration of the precipitation in the seasons of autumn and spring, the low temperatures in winter and extremely warm and dry summers- determine that ground cover by adventitious (or cover crop) vegetation shows significant seasonal and annual variability. In addition, its spatial variability associates also, partially, to water availability among the landscape. This is especially relevant in olive orchards, an agricultural system under high erosion risk in the region where the establishment of herbaceous cover has proved to improve soil protection reducing erosion risk, as well as the improvement of soil properties (Gómez et al., 2009). All these benefits are based on small scale studies where full ground cover by the cover crop is relatively easy to obtain. However, few information is available about the actual ground cover achieved at farm scale, although preliminary observations suggests that this might be extremely variable (Gómez and Giráldez, 2009). This study presents the preliminary results evaluating the spatial and temporal evolution of ground cover by adventitious vegetation (the preferred option by farmers to achieve a cover crop) in two commercial olive farms during 2 hydrological years (2011-2012). The study was conducted in two farms located in the province of Cordoba, Southern Spain. Both were olive orchards grown under deficit irrigation systems and present a gauge station where rainfall, runoff and sediment loads have been measured from the year 2005. The soil management in "La Conchuela" farm was based in the use of herbicide in the line of olive trees to keep the bare soil all year round, and the application of selective herbicide in the lane between the olive trees to promote the grown of graminaceae grasses . In addition, the grass is mechanically killed in June. In the another farm, "Arroyo Blanco", the grass spontaneous cover is allowed until mid-spring in which is also

Agricultural crop mapping and classification by Landsat images to evaluate water use in the Lake Urmia basin, North-west Iran

Science.gov (United States)

Fazel, Nasim; Norouzi, Hamid; Madani, Kaveh; Kløve, Bjørn

2016-04-01

Lake Urmia, once one of the largest hypersaline lakes in the world has lost more than 90% of its surface body mainly due to the intensive expansion of agriculture, using more than 90% of all water in the region. Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Lake Urmia basin. The mapping approach consists of a two-stage supervised classification of multi-temporal multi-spectral high resolution images obtained from Landsat imagery archive. Irrigated and non-irrigated croplands and orchards were separated from other major land covers (urban, ranges, bare-lands, and water) in the region by means of maximum Likelihood supervised classification method. The field data collected during 2015 and land use maps generated in 2007 and Google Earth comparisons were used to form a training data set to perform the supervised classification. In the second stage, non-agricultural lands were masked and the supervised classification was applied on the Landsat images stack to identify seven major croplands in the region (wheat and barley, beetroot, corn, sunflower, alfalfa, vineyards, and apple orchards). The obtained results can be of significant value to the Urmia Lake restoration efforts which

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.

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Martin Längkvist

2016-04-01

Full Text Available The availability of high-resolution remote sensing (HRRS data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN can be applied to multispectral orthoimagery and a digital surface model (DSM of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water, and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

Detection and classification of pole-like road objects from mobile LiDAR data in motorway environment

Science.gov (United States)

Yan, Li; Li, Zan; Liu, Hua; Tan, Junxiang; Zhao, Sainan; Chen, Changjun

2017-12-01

Mobile LiDAR Scanning (MLS) can collect 3-dimensional (3D) road and road-related geospatial information accurately and efficiently. Pole-like objects located in road environment are important street furniture and they are necessary information in road inventory and road mapping. The automatic detection and classification of pole-like road objects from mobile LiDAR data can greatly reduce the cost and improve the efficiency. This paper provides a complete workflow for the detection and classification of pole-like road objects from mobile LiDAR data in motorway environment. The major workflow includes three steps: data preprocessing, pole-like road objects detection and pole-like road objects classification. In data preprocessing step, ground points are removed by an automatic ground filtering algorithm, and then off-ground points are clustered into segments and the overlapped segments containing pole-like road objects are further separated through an iterative min-cut based segmentation approach. In detection step, filters utilizing both prior and shape information are used to detect the target objects. In classification step, features of objects are calculated and classified using Random Forest classifier. Our method was tested on two datasets scanned in motorway environment, and the results showed that the Matthews correlation coefficient of the two datasets in detection step was 93.7% and 95.9% respectively and the overall accuracy of the two datasets in classification step was 96.5% and 97.9% respectively.

A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

Directory of Open Access Journals (Sweden)

Apisit Eiumnoh

2013-10-01

Full Text Available Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF model to simultaneously align two or more images and obtain a land cover map (LCM of the scene. The expectation maximization (EM algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

Energy Technology Data Exchange (ETDEWEB)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); McEwen, Jason D., E-mail: dr.michelle.lochner@gmail.com [Mullard Space Science Laboratory, University College London, Surrey RH5 6NT (United Kingdom)

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

International Nuclear Information System (INIS)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.; McEwen, Jason D.

2016-01-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

Science.gov (United States)

Albright, Thomas P.; Painter, Thomas H.; Roberts, Dar A.; Shi, Jiancheng; Dozier, Jeff; Fielding, Eric

1998-01-01

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologic and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover.

ASSESSMENT OF LANDSCAPE CHARACTERISTICS ON THEMATIC IMAGE CLASSIFICATION ACCURACY

Science.gov (United States)

Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of misclassifying pixels during thematic image classification. However, there has been a lack of empirical evidence, to support these hypotheses. This...

Mapping a classification system to architectural education

DEFF Research Database (Denmark)

Hermund, Anders; Klint, Lars; Rostrup, Nicolai

2015-01-01

This paper examines to what extent a new classification system, Cuneco Classification System, CCS, proves useful in the education of architects, and to what degree the aim of an architectural education, rather based on an arts and crafts approach than a polytechnic approach, benefits from...... the distinct terminology of the classification system. The method used to examine the relationship between education, practice and the CCS bifurcates in a quantitative and a qualitative exploration: Quantitative comparison of the curriculum with the students’ own descriptions of their studies through...... a questionnaire survey among 88 students in graduate school. Qualitative interviews with a handful of practicing architects, to be able to cross check the relevance of the education with the profession. The examination indicates the need of a new definition, in addition to the CCS’s scale, covering the earliest...

Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (uav) Images

Science.gov (United States)

Chenari, A.; Erfanifard, Y.; Dehghani, M.; Pourghasemi, H. R.

2017-09-01

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

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.

Using Winter Annual Cover Crops in a Virginia No-till Cotton Production System

OpenAIRE

Daniel, James B. II

1997-01-01

Cotton (Gossypium hirsutum L.) is a low residue crop, that may not provide sufficient surface residue to reduce erosion and protect the soil. A winter annual cover crop could alleviate erosion between cotton crops. Field experiments were conducted to evaluate selected winter annual cover crops for biomass production, ground cover, and N assimilation. The cover crop treatments were monitored under no-till and conventional tillage systems for the effects on soil moisture, cotton yield and qu...

Solving satisfiability problems by the ground-state quantum computer

International Nuclear Information System (INIS)

Mao Wenjin

2005-01-01

A quantum algorithm is proposed to solve the satisfiability (SAT) problems by the ground-state quantum computer. The scale of the energy gap of the ground-state quantum computer is analyzed for the 3-bit exact cover problem. The time cost of this algorithm on the general SAT problems is discussed

Minimum triplet covers of binary phylogenetic X-trees.

Science.gov (United States)

Huber, K T; Moulton, V; Steel, M

2017-12-01

Trees with labelled leaves and with all other vertices of degree three play an important role in systematic biology and other areas of classification. A classical combinatorial result ensures that such trees can be uniquely reconstructed from the distances between the leaves (when the edges are given any strictly positive lengths). Moreover, a linear number of these pairwise distance values suffices to determine both the tree and its edge lengths. A natural set of pairs of leaves is provided by any 'triplet cover' of the tree (based on the fact that each non-leaf vertex is the median vertex of three leaves). In this paper we describe a number of new results concerning triplet covers of minimum size. In particular, we characterize such covers in terms of an associated graph being a 2-tree. Also, we show that minimum triplet covers are 'shellable' and thereby provide a set of pairs for which the inter-leaf distance values will uniquely determine the underlying tree and its associated branch lengths.

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.

Virtual Satellite Construction and Application for Image Classification

International Nuclear Information System (INIS)

Su, W G; Su, F Z; Zhou, C H

2014-01-01

Nowadays, most remote sensing image classification uses single satellite remote sensing data, so the number of bands and band spectral width is consistent. In addition, observed phenomenon such as land cover have the same spectral signature, which causes the classification accuracy to decrease as different data have unique characteristic. Therefore, this paper analyzes different optical remote sensing satellites, comparing the spectral differences and proposes the ideas and methods to build a virtual satellite. This article illustrates the research on the TM, HJ-1 and MODIS data. We obtained the virtual band X 0 through these satellites' bands combined it with the 4 bands of a TM image to build a virtual satellite with five bands. Based on this, we used these data for image classification. The experimental results showed that the virtual satellite classification results of building land and water information were superior to the HJ-1 and TM data respectively

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Science.gov (United States)

Hoffer, R. M.; Dean, M. E.; Knowlton, D. J.; Latty, R. S.

1982-01-01

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

Assessing the Approaches to Classification of the State Financial Control

OpenAIRE

Baraniuk Yurii R.

2017-01-01

The article is aimed at assessing the approaches to classification of the State financial control, as well as disclosing the relationship and differences between its forms, types and methods. The results of comparative analysis of existing classifications of the State financial control have been covered. The substantiation of its identification by forms, types and methods of control was explored. Clarification of the interpretation of the concepts of «form of control», «type of control», «sub...

USING OF THE MULTITEMPORAL THERMAL INFRARED SATELLITE IMAGERY FOR NATURAL AREAS MAPPING (CASE OF MENDELEEV VOLCANO

Directory of Open Access Journals (Sweden)

M. Y. Grishchenko

2014-01-01

Full Text Available In the paper authors examine the mountain group of Mendeleev volcano situated on the Kunashir island, Kuril archipelago, Russia. Ground observations were led to examine the vegetation cover of the area as well as its typical landscapes. The other type of used data is Landsat imagery. Images were combined into multitemporal thermal infrared and multispectral pictures, which were classified to reveal the heterogeneity of the study area. Ground observations and comparison of the classification results with landscape map derive that the multitemporal thermal infrared image classification result describes better the vegetation cover structure of the area and particularity of its typical landscapes distribution. It leads to the proposition that miltitemporal thermal infrared imagery can be used to refine landscape and vegetation cover contours. 

Review of Land Use and Land Cover Change research progress

Science.gov (United States)

Chang, Yue; Hou, Kang; Li, Xuxiang; Zhang, Yunwei; Chen, Pei

2018-02-01

Land Use and Land Cover Change (LUCC) can reflect the pattern of human land use in a region, and plays an important role in space soil and water conservation. The study on the change of land use patterns in the world is of great significance to cope with global climate change and sustainable development. This paper reviews the main research progress of LUCC at home and abroad, and suggests that land use change has been shifted from land use planning and management to land use change impact and driving factors. The development of remote sensing technology provides the basis and data for LUCC with dynamic monitoring and quantitative analysis. However, there is no uniform standard for land use classification at present, which brings a lot of inconvenience to the collection and analysis of land cover data. Globeland30 is an important milestone contribution to the study of international LUCC system. More attention should be paid to the accuracy and results contrasting test of land use classification obtained by remote sensing technology.

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...

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for the Pattern Consistency of Multi Scale Landcover

Science.gov (United States)

Juniati, E.; Arrofiqoh, E. N.

2017-09-01

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

Measuring methods and classification in the muscoskeletal radiology

International Nuclear Information System (INIS)

Waldt, Simone; Eiber, Matthias; Woertler, Klaus

2011-01-01

The book on measuring methods and classification in the musculoskeletal radiology covers the following topics: legs; hip joint; knee joint; foot; shoulder joint; elbow joint; wrist joint; spinal column; craniocervical transition region and cervical spine; muscular-skeletal carcinomas; osteoporosis; arthrosis; articular cartilage; hemophilia; rheumatic arthritis; muscular injuries; skeleton age.

Investigation of accuracy of CORINE 2006 land cover data used in watershed studies

Directory of Open Access Journals (Sweden)

Ayhan Ateşoğlu

2016-01-01

Full Text Available There have been many studies concerning the use of sustainable natural resources. The planning concerning the results of watershed-based studies is made for the future. The issue to be considered in these studies, is obtaining accurate data. The most important data of the studies in the watershed basin is obtaining land cover/use data. Land cover / land classification done by using remote sensing and GIS and monitoring the change periodically are both easy and economical. To this end, CORINE (Coordination of Information on the Environment land cover program was initiated by The European Commission (CEC. The accuracy of CORINE 2006 land cover data was evaluated using high resolution Google Earth data in two separate test areas located in the Black Sea and Central Anatolia region. Random 5000 points for each test area were assigned to classes according to the CORINE classification method using Google Earth and were compared with the CORINE 2006 data. The accuracy of first test area in Black Sea region was calculated as 51.80% the accuracy of second test area in Central Anatolia region was calculated as 55.32%. For each test area, CORINE 2006 data has not been found to be up to date and has been detected to have low accuracy.

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Directory of Open Access Journals (Sweden)

Shaohong Tian

2016-11-01

Full Text Available The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%–10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest

Creating Space Plasma from the Ground

Science.gov (United States)

2016-05-12

AFRL-AFOSR-VA-TR-2016-0179 CREATING SPACE PLASMA FROM THE GROUND Herbert C Carlson UTAH STATE UNIVERSITY Final Report 05/12/2016 DISTRIBUTION A...DATE (DD-MM-YYYY) 05/14/2016 2. REPORT TYPE Final 3. DATES COVERED (From - To) 08/14/2012-05/14/2016 4. TITLE AND SUBTITLE Creating space plasma from...Report (2016) Creating Space Plasma from the Ground Grant FA9550-11-1-0236 AFOSR Program Manager Dr. Kent Miller PI: Herbert C. Carlson Center for

Enhancement of force patterns classification based on Gaussian distributions.

Science.gov (United States)

Ertelt, Thomas; Solomonovs, Ilja; Gronwald, Thomas

2018-01-23

Description of the patterns of ground reaction force is a standard method in areas such as medicine, biomechanics and robotics. The fundamental parameter is the time course of the force, which is classified visually in particular in the field of clinical diagnostics. Here, the knowledge and experience of the diagnostician is relevant for its assessment. For an objective and valid discrimination of the ground reaction force pattern, a generic method, especially in the medical field, is absolutely necessary to describe the qualities of the time-course. The aim of the presented method was to combine the approaches of two existing procedures from the fields of machine learning and the Gauss approximation in order to take advantages of both methods for the classification of ground reaction force patterns. The current limitations of both methods could be eliminated by an overarching method. Twenty-nine male athletes from different sports were examined. Each participant was given the task of performing a one-legged stopping maneuver on a force plate from the maximum possible starting speed. The individual time course of the ground reaction force of each subject was registered and approximated on the basis of eight Gaussian distributions. The descriptive coefficients were then classified using Bayesian regulated neural networks. The different sports served as the distinguishing feature. Although the athletes were all given the same task, all sports referred to a different quality in the time course of ground reaction force. Meanwhile within each sport, the athletes were homogeneous. With an overall prediction (R = 0.938) all subjects/sports were classified correctly with 94.29% accuracy. The combination of the two methods: the mathematical description of the time course of ground reaction forces on the basis of Gaussian distributions and their classification by means of Bayesian regulated neural networks, seems an adequate and promising method to discriminate the

On the impact of snow cover on daytime pollution dispersion

Science.gov (United States)

Segal, M.; Garratt, J. R.; Pielke, R. A.; Hildebrand, P.; Rogers, F. A.; Cramer, J.; Schanot, A.

A preliminary evaluation of the impact of snow cover on daytime pollutant dispersion conditions is made by using conceptual, scaling, and observational analyses. For uniform snow cover and synoptically unperturbed sunny conditions, observations indicate a considerate suppression of the surface sensible heat flux, the turbulence, and the development of the daytime atmospheric boundary layer (ABL) when compared to snow-free conditions. However, under conditions of non-uniform snow cover, as in urban areas, or associated with vegetated areas or bare ground patches, a milder effect on pollutant dispersion conditions would be expected. Observed concentrations of atmospheric particles within the ABL, and surface pollutant concentrations in urban areas, reflect the impact of snow cover on the modification of ABL characteristics.

Shallow ground disposal of radioactive wastes. A guidebook

Energy Technology Data Exchange (ETDEWEB)

1981-01-01

This guidebook outlines the factors to be considered in site selection, design, operation, shut-down and surveillance as well as the regulatory requirements of repositories for safe disposal of radioactive waste in shallow ground. No attempt is made to summarize the existing voluminous literature on the many facets of radioactive waste disposal. In the context of this guidebook, shallow ground disposal refers to the emplacement of radioactive waste, with or without engineered barriers, above or below the ground surface, where the final protective covering is of the order of a few metres thick. Deep geological disposal and other underground disposal methods, management of mill tailings and disposal into the sea have been or will be considered in other IAEA publications. These guidelines have been made sufficiently general to cover a broad variety of climatic, hydrogeological and biological conditions. They may need to be interpreted or modified to reflect local conditions and national regulations.

Spatiotemporal Change Detection in Forest Cover Dynamics Along Landslide Susceptible Region of Karakoram Highway, Pakistan

Science.gov (United States)

Rashid, Barira; Iqbal, Javed

2018-04-01

Forest Cover dynamics and its understanding is essential for a country's social, environmental, and political engagements. This research provides a methodical approach for the assessment of forest cover along Karakoram Highway. It has great ecological and economic significance because it's a part of China-Pakistan Economic Corridor. Landsat 4, 5 TM, Landsat 7 ETM and Landsat 8 OLI imagery for the years 1990, 2000, 2010 and 2016 respectively were subjected to supervised classification in ArcMap 10.5 to identify forest change. The study area was categorized into five major land use land cover classes i.e., Forest, vegetation, urban, open land and snow cover. Results from post classification forest cover change maps illustrated notable decrease of almost 26 % forest cover over the time period of 26 years. The accuracy assessment revealed the kappa coefficients 083, 0.78, 0.77 and 0.85, respectively. Major reason for this change is an observed replacement of native forest cover with urban areas (12.5 %) and vegetation (18.6 %) However, there is no significant change in the reserved forests along the study area that contributes only 2.97 % of the total forest cover. The extensive forest degradation and risk prone topography of the region has increased the environmental risk of landslides. Hence, effective policies and forest management is needed to protect not only the environmental and aesthetic benefits of the forest cover but also to manage the disaster risks. Apart from the forest assessment, this research gives an insight of land cover dynamics, along with causes and consequences, thereby showing the forest degradation hotspots.

An Initial Analysis of LANDSAT-4 Thematic Mapper Data for the Discrimination of Agricultural, Forested Wetland, and Urban Land Covers

Science.gov (United States)

Quattrochi, D. A.

1984-01-01

An initial analysis of LANDSAT 4 Thematic Mapper (TM) data for the discrimination of agricultural, forested wetland, and urban land covers is conducted using a scene of data collected over Arkansas and Tennessee. A classification of agricultural lands derived from multitemporal LANDSAT Multispectral Scanner (MSS) data is compared with a classification of TM data for the same area. Results from this comparative analysis show that the multitemporal MSS classification produced an overall accuracy of 80.91% while the TM classification yields an overall classification accuracy of 97.06% correct.

Land-cover mapping using multitemporal, dual-frequency polarimetric SAR data

DEFF Research Database (Denmark)

Skriver, Henning; Schou, Jesper; Dierking, Wolfgang

2000-01-01

during the growing season acquired a lot of data over a Danish agricultural site. The data acquisitions were co-ordinated with ground surveys to obtain a detailed land cover map. The test area contains a large number of different land cover classes, such as more than 10 different crop types, deciduous......The Danish Center for Remote Sensing (DCRS) is, in collaboration with the Danish mapping agency, conducting a study on topographic mapping using SAR data, and land cover mapping results are presented. The Danish EMISAR system (an L- and C-band, fully polarimetric, airborne SAR) have in 1994 to 1999...

Towards Seamless Validation of Land Cover Data

Science.gov (United States)

Chuprikova, Ekaterina; Liebel, Lukas; Meng, Liqiu

2018-05-01

This article demonstrates the ability of the Bayesian Network analysis for the recognition of uncertainty patterns associated with the fusion of various land cover data sets including GlobeLand30, CORINE (CLC2006, Germany) and land cover data derived from Volunteered Geographic Information (VGI) such as Open Street Map (OSM). The results of recognition are expressed as probability and uncertainty maps which can be regarded as a by-product of the GlobeLand30 data. The uncertainty information may guide the quality improvement of GlobeLand30 by involving the ground truth data, information with superior quality, the know-how of experts and the crowd intelligence. Such an endeavor aims to pave a way towards a seamless validation of global land cover data on the one hand and a targeted knowledge discovery in areas with higher uncertainty values on the other hand.

Classification of sources of municipal solid wastes in developing countries

Energy Technology Data Exchange (ETDEWEB)

Buenrostro, O. [Instituto de Investigaciones sobre los Recursos Naturales, Universidad Michoacana de San Nicolas de Hidalgo, Apartado Postal 2-105, 58400, Michoacan, Morelia (Mexico); Bocco, G. [Departamento de Ecologia de los Recursos Naturales, Instituto de Ecologia, Universidad Nacional Autonoma de Mexico, Campus Morelia, Apartado Postal 27-3 Xangari, 58089, Michoacan, Morelia (Mexico); Cram, S. [Departamento de Geografia Fisica, Instituto de Geografia, Universidad Nacional Autonoma de Mexico, Circuito Exterior, C.P. 04510 Ciudad Universitaria, Mexico City (Mexico)

2001-05-01

The existence of different classifications of municipal solid waste (MSW) creates confusion and makes it difficult to interpret and compare the results of generation analyses. In this paper, MSW is conceptualized as the solid waste generated within the territorial limits of a municipality, independently of its source of generation. Grounded on this assumption, and based on the economic activity that generates a solid waste with determinate physical and chemical characteristics, a hierarchical source classification of MSW is suggested. Thus, a connection between the source and the type of waste is established. The classification categorizes the sources into three divisions and seven classes of sources: residential, commercial, institutional, construction/demolition, agricultural-animal husbandry, industrial, and special. When applied at different geographical scales, this classification enables the assessment of the volume of MSW generated, and provides an overview of the types of residues expected to be generated in a municipality, region or state.

Crop Classification Using Short-Revisit Multitemporal SAR Data

DEFF Research Database (Denmark)

Skriver, Henning; Mattia, Francesco; Satalino, Giuseppe

2011-01-01

Classification of crops and other land cover types is an important application of both optical/infrared and SAR satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. An airborne SAR data set with a short revisi...

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 Accessibility to higher resolution earth observation satellites suggests an improvement in the potential for fine scale image classification. In this comparative study, imagery from three optical satellites (WorldView-2, Pléiades and RapidEye) were...

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

Science.gov (United States)

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

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 landscape features such as beaded stream pools may be important because of their distribution and role in connecting other water bodies on the landscape. These findings advance techniques for detecting and knowledge associated with potential winter habitat distribution for fish and invertebrates at the local scale in a region of the Arctic with increasing stressors related to climate and land use change.

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

Science.gov (United States)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Accuracy Assessment of Satellite Derived Forest Cover Products in South and Southeast Asia

Science.gov (United States)

Gilani, H.; Xu, X.; Jain, A. K.