WorldWideScience

Sample records for mapping land cover

  1. 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.

  2. Mapping land cover through time with the Rapid Land Cover Mapper—Documentation and user manual

    Science.gov (United States)

    Cotillon, Suzanne E.; Mathis, Melissa L.

    2017-02-15

    The Rapid Land Cover Mapper is an Esri ArcGIS® Desktop add-in, which was created as an alternative to automated or semiautomated mapping methods. Based on a manual photo interpretation technique, the tool facilitates mapping over large areas and through time, and produces time-series raster maps and associated statistics that characterize the changing landscapes. The Rapid Land Cover Mapper add-in can be used with any imagery source to map various themes (for instance, land cover, soils, or forest) at any chosen mapping resolution. The user manual contains all essential information for the user to make full use of the Rapid Land Cover Mapper add-in. This manual includes a description of the add-in functions and capabilities, and step-by-step procedures for using the add-in. The Rapid Land Cover Mapper add-in was successfully used by the U.S. Geological Survey West Africa Land Use Dynamics team to accurately map land use and land cover in 17 West African countries through time (1975, 2000, and 2013).

  3. Carbon Assessment of Hawaii Land Cover Map (CAH_LandCover)

    Data.gov (United States)

    Department of the Interior — While there have been many maps produced that depict vegetation for the state of Hawai‘i only a few of these display land cover for all of the main Hawaiian Islands,...

  4. 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

  5. Land cover mapping of Greater Mesoamerica using MODIS data

    Science.gov (United States)

    Giri, Chandra; Jenkins, Clinton N.

    2005-01-01

    A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: (i) qualitative assessment provided good insight in identifying and correcting gross errors; (ii) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest (r2 = 0.88), shrubland (r2 = 0.75), and cropland (r2 = 0.97) but weak positive association for grassland (r2 = 0.26); and (iii) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

  6. Next generation of global land cover characterization, mapping, and monitoring

    Science.gov (United States)

    Giri, Chandra; Pengra, Bruce; Long, J.; Loveland, Thomas R.

    2013-01-01

    Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

  7. South African National Land-Cover Change Map | Schoeman ...

    African Journals Online (AJOL)

    Globally, countries face a changing environment due to population growth, increase in agricultural production, increasing demand on natural resources, climate change and resultant degradation of the natural environment. One means of monitoring this changing scenario is through land-cover change mapping. Modern ...

  8. 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.

  9. South African National Land-Cover Change Map

    African Journals Online (AJOL)

    Fritz Schoeman

    monitoring land-cover change at a national scale over time using EO data. 2. .... assist with final results reporting and analysis on a sub-national level. ..... South African Land-Cover Characteristics Database: A synopsis of the landscape.

  10. 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...

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

    Directory of Open Access Journals (Sweden)

    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.

  12. 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...

  13. Analysis of spatial distribution of land cover maps accuracy

    Science.gov (United States)

    Khatami, R.; Mountrakis, G.; Stehman, S. V.

    2017-12-01

    Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain

  14. Land User and Land Cover Maps of Europe: a Webgis Platform

    Science.gov (United States)

    Brovelli, M. A.; Fahl, F. C.; Minghini, M.; Molinari, M. E.

    2016-06-01

    This paper presents the methods and implementation processes of a WebGIS platform designed to publish the available land use and land cover maps of Europe at continental scale. The system is built completely on open source infrastructure and open standards. The proposed architecture is based on a server-client model having GeoServer as the map server, Leaflet as the client-side mapping library and the Bootstrap framework at the core of the front-end user interface. The web user interface is designed to have typical features of a desktop GIS (e.g. activate/deactivate layers and order layers by drag and drop actions) and to show specific information on the activated layers (e.g. legend and simplified metadata). Users have the possibility to change the base map from a given list of map providers (e.g. OpenStreetMap and Microsoft Bing) and to control the opacity of each layer to facilitate the comparison with both other land cover layers and the underlying base map. In addition, users can add to the platform any custom layer available through a Web Map Service (WMS) and activate the visualization of photos from popular photo sharing services. This last functionality is provided in order to have a visual assessment of the available land coverages based on other user-generated contents available on the Internet. It is supposed to be a first step towards a calibration/validation service that will be made available in the future.

  15. Geovisualization of land use and land cover using bivariate maps and Sankey flow diagrams

    Science.gov (United States)

    Strode, Georgianna; Mesev, Victor; Thornton, Benjamin; Jerez, Marjorie; Tricarico, Thomas; McAlear, Tyler

    2018-05-01

    The terms `land use' and `land cover' typically describe categories that convey information about the landscape. Despite the major difference of land use implying some degree of anthropogenic disturbance, the two terms are commonly used interchangeably, especially when anthropogenic disturbance is ambiguous, say managed forestland or abandoned agricultural fields. Cartographically, land use and land cover are also sometimes represented interchangeably within common legends, giving with the impression that the landscape is a seamless continuum of land use parcels spatially adjacent to land cover tracts. We believe this is misleading, and feel we need to reiterate the well-established symbiosis of land uses as amalgams of land covers; in other words land covers are subsets of land use. Our paper addresses this spatially complex, and frequently ambiguous relationship, and posits that bivariate cartographic techniques are an ideal vehicle for representing both land use and land cover simultaneously. In more specific terms, we explore the use of nested symbology as ways to represent graphically land use and land cover, where land cover are circles nested with land use squares. We also investigate bivariate legends for representing statistical covariance as a means for visualizing the combinations of land use and cover. Lastly, we apply Sankey flow diagrams to further illustrate the complex, multifaceted relationships between land use and land cover. Our work is demonstrated on data representing land use and cover data for the US state of Florida.

  16. Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

    Science.gov (United States)

    Dirk Pflugmacher; Olga N. Krankina; Warren B. Cohen; Mark A. Friedl; Damien Sulla-Menashe; Robert E. Kennedy; Peder Nelson; Tatiana V. Loboda; Tobias Kuemmerle; Egor Dyukarev; Vladimir Elsadov; Viacheslav I. Kharuk

    2011-01-01

    Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse...

  17. A procedure to obtain a refined European land use/cover map

    NARCIS (Netherlands)

    Batista e Silva, F.; Lavalle, C.; Koomen, E.

    2013-01-01

    Available land use/cover maps differ in their spatial extent and in their thematic, spatial, and temporal resolutions. Due to the costs of producing such maps, there is usually a trade-off between spatial extent and resolution. The only European-wide, consistent, and multi-temporal land use/cover

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

    Directory of Open Access Journals (Sweden)

    Keigo Kitada

    2012-05-01

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

  19. 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...

  20. Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

    NARCIS (Netherlands)

    Ali, A.; de Bie, C.A.J.M.; Skidmore, A.K.; Scarrott, R.G.; Hamad, A.A.; Venus, V.; Lymberakis, P.

    2013-01-01

    The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but

  1. Land use/land cover mapping using multi-scale texture processing of high resolution data

    Science.gov (United States)

    Wong, S. N.; Sarker, M. L. R.

    2014-02-01

    Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road.

  2. Land use/land cover mapping using multi-scale texture processing of high resolution data

    International Nuclear Information System (INIS)

    Wong, S N; Sarker, M L R

    2014-01-01

    Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road

  3. Error and Uncertainty in the Accuracy Assessment of Land Cover Maps

    Science.gov (United States)

    Sarmento, Pedro Alexandre Reis

    Traditionally the accuracy assessment of land cover maps is performed through the comparison of these maps with a reference database, which is intended to represent the "real" land cover, being this comparison reported with the thematic accuracy measures through confusion matrixes. Although, these reference databases are also a representation of reality, containing errors due to the human uncertainty in the assignment of the land cover class that best characterizes a certain area, causing bias in the thematic accuracy measures that are reported to the end users of these maps. The main goal of this dissertation is to develop a methodology that allows the integration of human uncertainty present in reference databases in the accuracy assessment of land cover maps, and analyse the impacts that uncertainty may have in the thematic accuracy measures reported to the end users of land cover maps. The utility of the inclusion of human uncertainty in the accuracy assessment of land cover maps is investigated. Specifically we studied the utility of fuzzy sets theory, more precisely of fuzzy arithmetic, for a better understanding of human uncertainty associated to the elaboration of reference databases, and their impacts in the thematic accuracy measures that are derived from confusion matrixes. For this purpose linguistic values transformed in fuzzy intervals that address the uncertainty in the elaboration of reference databases were used to compute fuzzy confusion matrixes. The proposed methodology is illustrated using a case study in which the accuracy assessment of a land cover map for Continental Portugal derived from Medium Resolution Imaging Spectrometer (MERIS) is made. The obtained results demonstrate that the inclusion of human uncertainty in reference databases provides much more information about the quality of land cover maps, when compared with the traditional approach of accuracy assessment of land cover maps. None

  4. Generation and Assessment of Urban Land Cover Maps Using High-Resolution Multispectral Aerial Images

    DEFF Research Database (Denmark)

    Höhle, Joachim; Höhle, Michael

    2013-01-01

    a unique method for the automatic generation of urban land cover maps. In the present paper, imagery of a new medium-format aerial camera and advanced geoprocessing software are applied to derive normalized digital surface models and vegetation maps. These two intermediate products then become input...... to a tree structured classifier, which automatically derives land cover maps in 2D or 3D. We investigate the thematic accuracy of the produced land cover map by a class-wise stratified design and provide a method for deriving necessary sample sizes. Corresponding survey adjusted accuracy measures...... and their associated confidence intervals are used to adequately reflect uncertainty in the assessment based on the chosen sample size. Proof of concept for the method is given for an urban area in Switzerland. Here, the produced land cover map with six classes (building, wall and carport, road and parking lot, hedge...

  5. A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data

    Science.gov (United States)

    Stibig, H.-J.; Belward, A.S.; Roy, P.S.; Rosalina-Wasrin, U.; Agrawal, S.; Joshi, P.K.; ,; Beuchle, R.; Fritz, S.; Mubareka, S.; Giri, C.

    2007-01-01

    Aim  Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project.Location  The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east.Methods  The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’.Results  The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics.Main conclusions  The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub

  6. Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid SWAT Hydrologic Modeling

    Science.gov (United States)

    Spruce, J.; Bolten, J. D.; Srinivasan, R.

    2017-12-01

    This presentation discusses research to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB). Funded by a NASA ROSES Disasters grant, the main objective was to produce updated LULC maps to aid the Mekong River Commission's (MRC's) Soil and Water Assessment Tool (SWAT) hydrologic model. In producing needed LULC maps, temporally processed MODIS monthly NDVI data for 2010 were used as the primary data source for classifying regionally prominent forest and agricultural types. The MODIS NDVI data was derived from processing MOD09 and MYD09 8-day reflectance data with the Time Series Product Tool, a custom software package. Circa 2010 Landsat multispectral data from the dry season were processed into top of atmosphere reflectance mosaics and then classified to derive certain locally common LULC types, such as urban areas and industrial forest plantations. Unsupervised ISODATA clustering was used to derive most LULC classifications. GIS techniques were used to merge MODIS and Landsat classifications into final LULC maps for Sub-Basins (SBs) 1-8 of the LMB. The final LULC maps were produced at 250-meter resolution and delivered to the MRC for use in SWAT modeling for the LMB. A map accuracy assessment was performed for the SB 7 LULC map with 14 classes. This assessment was performed by comparing random locations for sampled LULC types to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data from Google Map/Earth, and other reference data from the MRC (e.g., crop calendars). LULC accuracy assessment results for SB 7 indicated an overall agreement to reference data of 81% at full scheme specificity. However, by grouping 3 deciduous forest classes into 1 class, the overall agreement improved to 87%. The project enabled updated LULC maps, plus more specific rice types were classified compared to the previous LULC maps. The LULC maps from this project should improve the use of SWAT for modeling

  7. Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

    Directory of Open Access Journals (Sweden)

    Konrad J. Wessels

    2016-10-01

    Full Text Available The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%–67% using 22 detailed classes and 72%–74% using 12 aggregated national classes. “Water”, “Plantations”, “Plantations—clearfelled”, “Orchards—trees”, “Sugarcane”, “Built-up/dense settlement”, “Cultivation—Irrigated” and “Forest (indigenous” had user’s accuracies above 70%. Other detailed classes (e.g., “Low density settlements”, “Mines and Quarries”, and “Cultivation, subsistence, drylands” which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land

  8. Towards automated statewide land cover mapping in Wisconsin using satellite remote sensing and GIS techniques

    International Nuclear Information System (INIS)

    Cosentino, B.L.; Lillesand, T.M.

    1991-01-01

    Attention is given to an initial research project being performed by the University of Wisconsin-Madison, Environmental Remote Sensing Center in conjunction with seven local, state, and federal agencies to implement automated statewide land cover mapping using satellite remote sensing and geographical information system (GIS) techniques. The basis, progress, and future research needs for this mapping program are presented. The research efforts are directed toward strategies that integrate satellite remote sensing and GIS techniques in the generation of land cover data for multiple users of land cover information. The project objectives are to investigate methodologies that integrate satellite data with other imagery and spatial data resident in emerging GISs in the state for particular program needs, and to develop techniques that can improve automated land cover mapping efficiency and accuracy. 10 refs

  9. Land Cover Mapping in Northern High Latitude Permafrost Regions with Satellite Data: Achievements and Remaining Challenges

    Directory of Open Access Journals (Sweden)

    Annett Bartsch

    2016-11-01

    Full Text Available Most applications of land cover maps that have been derived from satellite data over the Arctic require higher thematic detail than available in current global maps. A range of application studies has been reviewed, including up-scaling of carbon fluxes and pools, permafrost feature mapping and transition monitoring. Early land cover mapping studies were driven by the demand to characterize wildlife habitats. Later, in the 1990s, up-scaling of in situ measurements became central to the discipline of land cover mapping on local to regional scales at several sites across the Arctic. This includes the Kuparuk basin in Alaska, the Usa basin and the Lena Delta in Russia. All of these multi-purpose land cover maps have been derived from Landsat data. High resolution maps (from optical satellite data serve frequently as input for the characterization of periglacial features and also flux tower footprints in recent studies. The most used map to address circumpolar issues is the CAVM (Circum Arctic Vegetation Map based on AVHRR (1 km and has been manually derived. It provides the required thematic detail for many applications, but is confined to areas north of the treeline, and it is limited in spatial detail. A higher spatial resolution circumpolar land cover map with sufficient thematic content would be beneficial for a range of applications. Such a land cover classification should be compatible with existing global maps and applicable for multiple purposes. The thematic content of existing global maps has been assessed by comparison to the CAVM and regional maps. None of the maps provides the required thematic detail. Spatial resolution has been compared to used classes for local to regional applications. The required thematic detail increases with spatial resolution since coarser datasets are usually applied over larger areas covering more relevant landscape units. This is especially of concern when the entire Arctic is addressed. A spatial

  10. GlobeLand30 as an alternative fine-scale global land cover map

    DEFF Research Database (Denmark)

    Jokar Arsanjani, Jamal; Tayyebi, A.; Vaz, E.

    2016-01-01

    land cover information such as developing countries. In this study, we look at GlobeLand30 of 2010 for Iran in order to find out the accuracy of this dataset as well as its implications. By having looked at 6 selected study sites around larger cities representing dissimilar eco-regions covering rural...

  11. Deriving a per-field land use and land cover map in an agricultural mosaic catchment

    Science.gov (United States)

    Seo, B.; Bogner, C.; Poppenborg, P.; Martin, E.; Hoffmeister, M.; Jun, M.; Koellner, T.; Reineking, B.; Shope, C. L.; Tenhunen, J.

    2014-09-01

    Detailed data on land use and land cover constitute important information for Earth system models, environmental monitoring and ecosystem services research. Global land cover products are evolving rapidly; however, there is still a lack of information particularly for heterogeneous agricultural landscapes. We censused land use and land cover field by field in the agricultural mosaic catchment Haean in South Korea. We recorded the land cover types with additional information on agricultural practice. In this paper we introduce the data, their collection and the post-processing protocol. Furthermore, because it is important to quantitatively evaluate available land use and land cover products, we compared our data with the MODIS Land Cover Type product (MCD12Q1). During the studied period, a large portion of dry fields was converted to perennial crops. Compared to our data, the forested area was underrepresented and the agricultural area overrepresented in MCD12Q1. In addition, linear landscape elements such as waterbodies were missing in the MODIS product due to its coarse spatial resolution. The data presented here can be useful for earth science and ecosystem services research. The data are available at the public repository Pangaea (doi:110.1594/PANGAEA.823677).

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

    DEFF Research Database (Denmark)

    Höhle, Joachim

    2014-01-01

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

  13. Mapping of land cover in Northern California with simulated HyspIRI images

    Science.gov (United States)

    Clark, M. L.; Kilham, N. E.

    2014-12-01

    Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (i.e., full range) of the spectrum have shown improved capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a full-range hyperspectral and thermal satellite being considered for development by NASA (hyspiri.jpl.nasa.gov). A hyperspectral satellite, such as HyspIRI, will provide detailed spectral and temporal information at global scales that could greatly improve our ability to map land cover with greater class detail and spatial and temporal accuracy than possible with conventional multispectral satellites. The broad goal of our research is to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping across a range of environmental and anthropogenic gradients in California. In this study, we mapped FAO Land Cover Classification System (LCCS) classes over 30,000 km2 in Northern California using multi-temporal HyspIRI imagery simulated from the AVIRIS airborne sensor. The Random Forests classification was applied to predictor variables derived from the multi-temporal hyperspectral data and accuracies were compared to that from Landsat 8 OLI. Results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different forest life-form types, such as mixed conifer and broadleaf forests and open- and closed-canopy forests.

  14. 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.

  15. 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...

  16. Land cover change mapping using MODIS time series to improve emissions inventories

    Science.gov (United States)

    López-Saldaña, Gerardo; Quaife, Tristan; Clifford, Debbie

    2016-04-01

    MELODIES is an FP7 funded project to develop innovative and sustainable services, based upon Open Data, for users in research, government, industry and the general public in a broad range of societal and environmental benefit areas. Understanding and quantifying land surface changes is necessary for estimating greenhouse gas and ammonia emissions, and for meeting air quality limits and targets. More sophisticated inventories methodologies for at least key emission source are needed due to policy-driven air quality directives. Quantifying land cover changes on an annual basis requires greater spatial and temporal disaggregation of input data. The main aim of this study is to develop a methodology for using Earth Observations (EO) to identify annual land surface changes that will improve emissions inventories from agriculture and land use/land use change and forestry (LULUCF) in the UK. First goal is to find the best sets of input features that describe accurately the surface dynamics. In order to identify annual and inter-annual land surface changes, a times series of surface reflectance was used to capture seasonal variability. Daily surface reflectance images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution were used to invert a Bidirectional Reflectance Distribution Function (BRDF) model to create the seamless time series. Given the limited number of cloud-free observations, a BRDF climatology was used to constrain the model inversion and where no high-scientific quality observations were available at all, as a gap filler. The Land Cover Map 2007 (LC2007) produced by the Centre for Ecology & Hydrology (CEH) was used for training and testing purposes. A land cover product was created for 2003 to 2015 and a bayesian approach was created to identified land cover changes. We will present the results of the time series development and the first exercises when creating the land cover and land cover changes products.

  17. Detailed forest formation mapping in the land cover map series for the Caribbean islands

    Science.gov (United States)

    Helmer, E. H.; Schill, S.; Pedreros, D. H.; Tieszen, L. L.; Kennaway, T.; Cushing, M.; Ruzycki, T.

    2006-12-01

    Forest formation and land cover maps for several Caribbean islands were developed from Landsat ETM+ imagery as part of a multi-organizational project. The spatially explicit data on forest formation types will permit more refined estimates of some forest attributes. The woody vegetation classification scheme relates closely to that of Areces-Malea et al. (1), who classify Caribbean vegetation according to standards of the US Federal Geographic Data Committee (FGDC, 1997), with modifications similar to those in Helmer et al. (2). For several of the islands, we developed image mosaics that filled cloudy parts of scenes with data from other scene dates after using regression tree normalization (3). The regression tree procedure permitted us to develop mosaics for wet and drought seasons for a few of the islands. The resulting multiseason imagery facilitated separation between classes such as seasonal evergreen forest, semi-deciduous forest (including semi-evergreen forest), and drought deciduous forest or woodland formations. We used decision tree classification methods to classify the Landsat image mosaics to detailed forest formations and land cover for Puerto Rico (4), St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines and Grenada. The decision trees classified a stack of raster layers for each mapping area that included the Landsat image bands and various ancillary raster data layers. For Puerto Rico, for example, the ancillary data included climate parameters (5). For some islands, the ancillary data included topographic derivatives such as aspect, slope and slope position, SRTM (6) or other topographic data. Mapping forest formations with decision tree classifiers, ancillary geospatial data, and cloud-free image mosaics, accurately distinguished spectrally similar forest formations, without the aid of ecological zone maps, on the islands where the approach was used. The approach resulted in maps of forest formations with comparable or better detail

  18. A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

    Science.gov (United States)

    Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

    2012-06-01

    Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.

  19. Detection and Mapping of Land Use and Land Cover Classes of a ...

    African Journals Online (AJOL)

    FIRST LADY

    Cover Classes of a Developing City in Southeastern. Region of Nigeria .... The emergence of small and medium sized agro-husbandry industries in the peripheral, semi- ..... lack of spatial specialization a hindrance to integrated land management and development .... Journal of Applied Sciences Asian Network for Scientific ...

  20. Application of Google Maps API service for creating web map of information retrieved from CORINE land cover databases

    Directory of Open Access Journals (Sweden)

    Kilibarda Milan

    2010-01-01

    Full Text Available Today, Google Maps API application based on Ajax technology as standard web service; facilitate users with publication interactive web maps, thus opening new possibilities in relation to the classical analogue maps. CORINE land cover databases are recognized as the fundamental reference data sets for numerious spatial analysis. The theoretical and applicable aspects of Google Maps API cartographic service are considered on the case of creating web map of change in urban areas in Belgrade and surround from 2000. to 2006. year, obtained from CORINE databases.

  1. Improving Land Cover Mapping: a Mobile Application Based on ESA Sentinel 2 Imagery

    Science.gov (United States)

    Melis, M. T.; Dessì, F.; Loddo, P.; La Mantia, C.; Da Pelo, S.; Deflorio, A. M.; Ghiglieri, G.; Hailu, B. T.; Kalegele, K.; Mwasi, B. N.

    2018-04-01

    The increasing availability of satellite data is a real value for the enhancement of environmental knowledge and land management. Possibilities to integrate different source of geo-data are growing and methodologies to create thematic database are becoming very sophisticated. Moreover, the access to internet services and, in particular, to web mapping services is well developed and spread either between expert users than the citizens. Web map services, like Google Maps or Open Street Maps, give the access to updated optical imagery or topographic maps but information on land cover/use - are not still provided. Therefore, there are many failings in the general utilization -non-specialized users- and access to those maps. This issue is particularly felt where the digital (web) maps could form the basis for land use management as they are more economic and accessible than the paper maps. These conditions are well known in many African countries where, while the internet access is becoming open to all, the local map agencies and their products are not widespread.

  2. High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa

    Science.gov (United States)

    Tatem, Andrew J.; Noor, Abdisalan M.; von Hagen, Craig; Di Gregorio, Antonio; Hay, Simon I.

    2007-01-01

    Background Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. Methodology/Principal Findings We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. Conclusions We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available. PMID:18074022

  3. High resolution population maps for low income nations: combining land cover and census in East Africa.

    Directory of Open Access Journals (Sweden)

    Andrew J Tatem

    2007-12-01

    Full Text Available Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km(2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk and are freely available.

  4. Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

    Science.gov (United States)

    Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W

    2017-01-01

    Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

  5. Annual land cover change mapping using MODIS time series to improve emissions inventories.

    Science.gov (United States)

    López Saldaña, G.; Quaife, T. L.; Clifford, D.

    2014-12-01

    Understanding and quantifying land surface changes is necessary for estimating greenhouse gas and ammonia emissions, and for meeting air quality limits and targets. More sophisticated inventories methodologies for at least key emission source are needed due to policy-driven air quality directives. Quantifying land cover changes on an annual basis requires greater spatial and temporal disaggregation of input data. The main aim of this study is to develop a methodology for using Earth Observations (EO) to identify annual land surface changes that will improve emissions inventories from agriculture and land use/land use change and forestry (LULUCF) in the UK. First goal is to find the best sets of input features that describe accurately the surface dynamics. In order to identify annual and inter-annual land surface changes, a times series of surface reflectance was used to capture seasonal variability. Daily surface reflectance images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution were used to invert a Bidirectional Reflectance Distribution Function (BRDF) model to create the seamless time series. Given the limited number of cloud-free observations, a BRDF climatology was used to constrain the model inversion and where no high-scientific quality observations were available at all, as a gap filler. The Land Cover Map 2007 (LC2007) produced by the Centre for Ecology & Hydrology (CEH) was used for training and testing purposes. A prototype land cover product was created for 2006 to 2008. Several machine learning classifiers were tested as well as different sets of input features going from the BRDF parameters to spectral Albedo. We will present the results of the time series development and the first exercises when creating the prototype land cover product.

  6. Highlighting continued uncertainty in global land cover maps for the user community

    International Nuclear Information System (INIS)

    Fritz, Steffen; See, Linda; McCallum, Ian; Schill, Christian; Obersteiner, Michael; Van der Velde, Marijn; Boettcher, Hannes; Havlík, Petr; Achard, Frédéric

    2011-01-01

    In the last 10 years a number of new global datasets have been created and new, more sophisticated algorithms have been designed to classify land cover. GlobCover and MODIS v.5 are the most recent global land cover products available, where GlobCover (300 m) has the finest spatial resolution of other comparable products such as MODIS v.5 (500 m) and GLC-2000 (1 km). This letter shows that the thematic accuracy in the cropland domain has decreased when comparing these two latest products. This disagreement is also evident spatially when examining maps of cropland and forest disagreement between GLC-2000, MODIS and GlobCover. The analysis highlights the continued uncertainty surrounding these products, with a combined forest and cropland disagreement of 893 Mha (GlobCover versus MODIS v.5). This letter suggests that data sharing efforts and the provision of more in situ data for training, calibration and validation are very important conditions for improving future global land cover products.

  7. 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.

  8. Mapping of the Land Cover Spatiotemporal Characteristics in Northern Russia Caused by Climate Change

    Science.gov (United States)

    Panidi, E.; Tsepelev, V.; Torlopova, N.; Bobkov, A.

    2016-06-01

    The study is devoted to the investigation of regional climate change in Northern Russia. Due to sparseness of the meteorological observation network in northern regions, we investigate the application capabilities of remotely sensed vegetation cover as indicator of climate change at the regional scale. In previous studies, we identified statistically significant relationship between the increase of surface air temperature and increase of the shrub vegetation productivity. We verified this relationship using ground observation data collected at the meteorological stations and Normalised Difference Vegetation Index (NDVI) data produced from Terra/MODIS satellite imagery. Additionally, we designed the technique of growing seasons separation for detailed investigation of the land cover (shrub cover) dynamics. Growing seasons are the periods when the temperature exceeds +5°C and +10°C. These periods determine the vegetation productivity conditions (i.e., conditions that allow growth of the phytomass). We have discovered that the trend signs for the surface air temperature and NDVI coincide on planes and river floodplains. On the current stage of the study, we are working on the automated mapping technique, which allows to estimate the direction and magnitude of the climate change in Northern Russia. This technique will make it possible to extrapolate identified relationship between land cover and climate onto territories with sparse network of meteorological stations. We have produced the gridded maps of NDVI and NDWI for the test area in European part of Northern Russia covered with the shrub vegetation. Basing on these maps, we may determine the frames of growing seasons for each grid cell. It will help us to obtain gridded maps of the NDVI linear trend for growing seasons on cell-by-cell basis. The trend maps can be used as indicative maps for estimation of the climate change on the studied areas.

  9. 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.

  10. 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.

  11. 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.

  12. Land Cover - Minnesota Land Cover Classification System

    Data.gov (United States)

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

  13. Urban Land Cover Mapping Accuracy Assessment - A Cost-benefit Analysis Approach

    Science.gov (United States)

    Xiao, T.

    2012-12-01

    One of the most important components in urban land cover mapping is mapping accuracy assessment. Many statistical models have been developed to help design simple schemes based on both accuracy and confidence levels. It is intuitive that an increased number of samples increases the accuracy as well as the cost of an assessment. Understanding cost and sampling size is crucial in implementing efficient and effective of field data collection. Few studies have included a cost calculation component as part of the assessment. In this study, a cost-benefit sampling analysis model was created by combining sample size design and sampling cost calculation. The sampling cost included transportation cost, field data collection cost, and laboratory data analysis cost. Simple Random Sampling (SRS) and Modified Systematic Sampling (MSS) methods were used to design sample locations and to extract land cover data in ArcGIS. High resolution land cover data layers of Denver, CO and Sacramento, CA, street networks, and parcel GIS data layers were used in this study to test and verify the model. The relationship between the cost and accuracy was used to determine the effectiveness of each sample method. The results of this study can be applied to other environmental studies that require spatial sampling.

  14. 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...

  15. 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

  16. A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses.

    Science.gov (United States)

    Estes, Lyndon; Chen, Peng; Debats, Stephanie; Evans, Tom; Ferreira, Stefanus; Kuemmerle, Tobias; Ragazzo, Gabrielle; Sheffield, Justin; Wolf, Adam; Wood, Eric; Caylor, Kelly

    2018-01-01

    Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land

  17. Land Cover Characterization and Mapping of South America for the Year 2010 Using Landsat 30 m Satellite Data

    Directory of Open Access Journals (Sweden)

    Chandra Giri

    2014-10-01

    Full Text Available Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

  18. High-Precision Land-Cover-Land-Use GIS Mapping and Land Availability and Suitability Analysis for Grass Biomass Production in the Aroostook River Valley, Maine, USA

    Directory of Open Access Journals (Sweden)

    Chunzeng Wang

    2015-03-01

    Full Text Available High-precision land-cover-land-use GIS mapping was performed in four major townships in Maine’s Aroostook River Valley, using on-screen digitization and direct interpretation of very high spatial resolution satellite multispectral imagery (15–60 cm and high spatial resolution LiDAR data (2 m and the field mapping method. The project not only provides the first-ever high-precision land-use maps for northern Maine, but it also yields accurate hectarage estimates of different land-use types, in particular grassland, defined as fallow land, pasture, and hay field. This enables analysis of potential land availability and suitability for grass biomass production and other sustainable land uses. The results show that the total area of fallow land in the four towns is 7594 hectares, which accounts for 25% of total open land, and that fallow plots equal to or over four hectares in size total 4870, or 16% of open land. Union overlay analysis, using the Natural Resources Conservation Service (NRCS soil data, indicates that only a very small percentage of grassland (4.9% is on “poorly-drained” or “very-poorly-drained” soils, and that most grassland (85% falls into the “farmland of state importance” or “prime farmland” categories, as determined by NRCS. It is concluded that Maine’s Aroostook River Valley has an ample base of suitable, underutilized land for producing grass biomass.

  19. 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%.

  20. Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015)

    Science.gov (United States)

    Li, Wei; MacBean, Natasha; Ciais, Philippe; Defourny, Pierre; Lamarche, Céline; Bontemps, Sophie; Houghton, Richard A.; Peng, Shushi

    2018-01-01

    Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013) or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992-2015 are 1.5 and 0.9 million km2 respectively, resulting in a net forest loss of 0.6 million km2, mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics

  1. 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.

  2. Utility of Mobile phones to support In-situ data collection for Land Cover Mapping

    Science.gov (United States)

    Oduor, P.; Omondi, S.; Wahome, A.; Mugo, R. M.; Flores, A.

    2017-12-01

    With the compelling need to create better monitoring tools for our landscapes to enhance better decision making processes, it becomes imperative to do so in much more sophisticated yet simple ways. Making it possible to leverage untapped potential of our "lay men" at the same time enabling us to respond to the complexity of the information we have to get out. SERVIR Eastern and Southern Africa has developed a mobile app that can be utilized with very little prior knowledge or no knowledge at all to collect spatial information on land cover. This set of in-situ data can be collected by masses because the tools is very simple to use, and have this information fed in classification algorithms than can then be used to map out our ever changing landscape. The LULC Mapper is a subset of JiMap system and is able to pull the google earth imagery and open street maps to enable user familiarize with their location. It uses phone GPS, phone network information to map location coordinates and at the same time gives the user sample picture of what to categorize their landscape. The system is able to work offline and when user gets access to internet they can push the information into an amazon database as bulk data. The location details including geotagged photos allows the data to be used in development of a lot of spatial information including land cover data. The app is currently available in Google Play Store and will soon be uploaded on Appstore for utilization by a wider community. We foresee a lot of potential in this tool in terms of making data collection cheaper and affordable. Taking advantage of the advances made in phone technology. We envisage to do a data collection campaign where we can have the tool used for crowdsourcing.

  3. Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

    Science.gov (United States)

    Hagensieker, Ron; Roscher, Ribana; Rosentreter, Johannes; Jakimow, Benjamin; Waske, Björn

    2017-12-01

    Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.

  4. Discovering Land Cover Web Map Services from the Deep Web with JavaScript Invocation Rules

    Directory of Open Access Journals (Sweden)

    Dongyang Hou

    2016-06-01

    Full Text Available Automatic discovery of isolated land cover web map services (LCWMSs can potentially help in sharing land cover data. Currently, various search engine-based and crawler-based approaches have been developed for finding services dispersed throughout the surface web. In fact, with the prevalence of geospatial web applications, a considerable number of LCWMSs are hidden in JavaScript code, which belongs to the deep web. However, discovering LCWMSs from JavaScript code remains an open challenge. This paper aims to solve this challenge by proposing a focused deep web crawler for finding more LCWMSs from deep web JavaScript code and the surface web. First, the names of a group of JavaScript links are abstracted as initial judgements. Through name matching, these judgements are utilized to judge whether or not the fetched webpages contain predefined JavaScript links that may prompt JavaScript code to invoke WMSs. Secondly, some JavaScript invocation functions and URL formats for WMS are summarized as JavaScript invocation rules from prior knowledge of how WMSs are employed and coded in JavaScript. These invocation rules are used to identify the JavaScript code for extracting candidate WMSs through rule matching. The above two operations are incorporated into a traditional focused crawling strategy situated between the tasks of fetching webpages and parsing webpages. Thirdly, LCWMSs are selected by matching services with a set of land cover keywords. Moreover, a search engine for LCWMSs is implemented that uses the focused deep web crawler to retrieve and integrate the LCWMSs it discovers. In the first experiment, eight online geospatial web applications serve as seed URLs (Uniform Resource Locators and crawling scopes; the proposed crawler addresses only the JavaScript code in these eight applications. All 32 available WMSs hidden in JavaScript code were found using the proposed crawler, while not one WMS was discovered through the focused crawler

  5. Mapping tsunami impacts on land cover and related ecosystem service supply in Phang Nga, Thailand

    Science.gov (United States)

    Kaiser, G.; Burkhard, B.; Römer, H.; Sangkaew, S.; Graterol, R.; Haitook, T.; Sterr, H.; Sakuna-Schwartz, D.

    2013-12-01

    The 2004 Indian Ocean tsunami caused damages to coastal ecosystems and thus affected the livelihoods of the coastal communities who depend on services provided by these ecosystems. The paper presents a case study on evaluating and mapping the spatial and temporal impacts of the tsunami on land use and land cover (LULC) and related ecosystem service supply in the Phang Nga province, Thailand. The method includes local stakeholder interviews, field investigations, remote-sensing techniques, and GIS. Results provide an ecosystem services matrix with capacity scores for 18 LULC classes and 17 ecosystem functions and services as well as pre-/post-tsunami and recovery maps indicating changes in the ecosystem service supply capacities in the study area. Local stakeholder interviews revealed that mangroves, casuarina forest, mixed beach forest, coral reefs, tidal inlets, as well as wetlands (peat swamp forest) have the highest capacity to supply ecosystem services, while e.g. plantations have a lower capacity. The remote-sensing based damage and recovery analysis showed a loss of the ecosystem service supply capacities in almost all LULC classes for most of the services due to the tsunami. A fast recovery of LULC and related ecosystem service supply capacities within one year could be observed for e.g. beaches, while mangroves or casuarina forest needed several years to recover. Applying multi-temporal mapping the spatial variations of recovery could be visualised. While some patches of coastal forest were fully recovered after 3 yr, other patches were still affected and thus had a reduced capacity to supply ecosystem services. The ecosystem services maps can be used to quantify ecological values and their spatial distribution in the framework of a tsunami risk assessment. Beyond that they are considered to be a useful tool for spatial analysis in coastal risk management in Phang Nga.

  6. 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 ...

  7. 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

  8. Using IKONOS and Aerial Videography to Validate Landsat Land Cover Maps of Central African Tropical Rain Forests

    Science.gov (United States)

    Lin, T.; Laporte, N. T.

    2003-12-01

    Compared to the traditional validation methods, aerial videography is a relatively inexpensive and time-efficient approach to collect "field" data for validating satellite-derived land cover map over large areas. In particular, this approach is valuable in remote and inaccessible locations. In the Sangha Tri-National Park region of Central Africa, where road access is limited to industrial logging sites, we are using IKONOS imagery and aerial videography to assess the accuracy of Landsat-derived land cover maps. As part of a NASA Land Cover Land Use Change project (INFORMS) and in collaboration with the Wildlife Conservation Society in the Republic of Congo, over 1500km of aerial video transects were collected in the Spring of 2001. The use of MediaMapper software combined with a VMS 200 video mapping system enabled the collection of aerial transects to be registered with geographic locations from a Geographic Positioning System. Video frame were extracted, visually interpreted, and compared to land cover types mapped by Landsat. We addressed the limitations of accuracy assessment using aerial-base data and its potential for improving vegetation mapping in tropical rain forests. The results of the videography and IKONOS image analysis demonstrate the utility of very high resolution imagery for map validation and forest resource assessment.

  9. Land cover mapping and GIS processing for the Savannah River Site Database

    International Nuclear Information System (INIS)

    Christel, L.M.; Guber, A.L.

    1994-07-01

    The Savannah River Site (SRS) is owned by the U.S. Department of Energy and operated by Westinghouse Savannah River Company. Located in Barnwell, Aiken, and Allendale counties in South Carolina, SRS covers an area of approximately 77,700 hectares. Land cover information for SRS was interpreted from color and color infrared aerial photography acquired between 1980 and 1989. The data were then used as the source of the land cover data layer for the SRS sitewide Geographic Information System database. This database provides SRS managers with recent land use information and has been successfully used to support cost-effective site characterization and reclamation

  10. Land cover change map comparisons using open source web mapping technologies

    Science.gov (United States)

    Erik Lindblom; Ian Housman; Tony Guay; Mark Finco; Kevin. Megown

    2015-01-01

    The USDA Forest Service is evaluating the status of current landscape change maps and assessing gaps in their information content. These activities have been occurring under the auspices of the Landscape Change Monitoring System (LCMS) project, which is a joint effort between USFS Research, USFS Remote Sensing Applications Center (RSAC), USGS Earth Resources...

  11. A procedure for merging land cover/use data from Landsat, aerial photography, and map sources - Compatibility, accuracy and cost

    Science.gov (United States)

    Enslin, W. R.; Tilmann, S. E.; Hill-Rowley, R.; Rogers, R. H.

    1977-01-01

    A method is developed to merge land cover/use data from Landsat, aerial photography and map sources into a grid-based geographic information system. The method basically involves computer-assisted categorization of Landsat data to provide certain user-specified land cover categories; manual interpretation of aerial photography to identify other selected land cover/use categories that cannot be obtained from Landsat data; identification of special features from aerial photography or map sources; merging of the interpreted data from all the sources into a computer compatible file under a standardized coding structure; and the production of land cover/use maps, thematic maps, and tabular data. The specific tasks accomplished in producing the merged land cover/use data file and subsequent output products are identified and discussed. It is shown that effective implementation of the merging method is critically dependent on selecting the 'best' data source for each user-specified category in terms of accuracy and time/cost tradeoffs.

  12. A method for mapping corn using the US Geological Survey 1992 National Land Cover Dataset

    Science.gov (United States)

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

    2006-01-01

    Long-term exposure to elevated nitrate levels in community drinking water supplies has been associated with an elevated risk of several cancers including non-Hodgkin's lymphoma, colon cancer, and bladder cancer. To estimate human exposure to nitrate, specific crop type information is needed as fertilizer application rates vary widely by crop type. Corn requires the highest application of nitrogen fertilizer of crops grown in the Midwest US. We developed a method to refine the US Geological Survey National Land Cover Dataset (NLCD) (including map and original Landsat images) to distinguish corn from other crops. Overall average agreement between the resulting corn and other row crops class and ground reference data was 0.79 kappa coefficient with individual Landsat images ranging from 0.46 to 0.93 kappa. The highest accuracies occurred in Regions where corn was the single dominant crop (greater than 80.0%) and the crop vegetation conditions at the time of image acquisition were optimum for separation of corn from all other crops. Factors that resulted in lower accuracies included the accuracy of the NLCD map, accuracy of corn areal estimates, crop mixture, crop condition at the time of Landsat overpass, and Landsat scene anomalies.

  13. Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

    Directory of Open Access Journals (Sweden)

    Sebastian Aleksandrowicz

    2014-06-01

    Full Text Available Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1 layer arithmetic; (2 vegetation indices (NDVI differentiating; (3 texture calculation; and methods based on (4 canonical correlation analysis (multivariate alteration detection (MAD. User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally. To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes.

  14. Meter-scale Urban Land Cover Mapping for EPA EnviroAtlas Using Machine Learning and OBIA Remote Sensing Techniques

    Science.gov (United States)

    Pilant, A. N.; Baynes, J.; Dannenberg, M.; Riegel, J.; Rudder, C.; Endres, K.

    2013-12-01

    US EPA EnviroAtlas is an online collection of tools and resources that provides geospatial data, maps, research, and analysis on the relationships between nature, people, health, and the economy (http://www.epa.gov/research/enviroatlas/index.htm). Using EnviroAtlas, you can see and explore information related to the benefits (e.g., ecosystem services) that humans receive from nature, including clean air, clean and plentiful water, natural hazard mitigation, biodiversity conservation, food, fuel, and materials, recreational opportunities, and cultural and aesthetic value. EPA developed several urban land cover maps at very high spatial resolution (one-meter pixel size) for a portion of EnviroAtlas devoted to urban studies. This urban mapping effort supported analysis of relations among land cover, human health and demographics at the US Census Block Group level. Supervised classification of 2010 USDA NAIP (National Agricultural Imagery Program) digital aerial photos produced eight-class land cover maps for several cities, including Durham, NC, Portland, ME, Tampa, FL, New Bedford, MA, Pittsburgh, PA, Portland, OR, and Milwaukee, WI. Semi-automated feature extraction methods were used to classify the NAIP imagery: genetic algorithms/machine learning, random forest, and object-based image analysis (OBIA). In this presentation we describe the image processing and fuzzy accuracy assessment methods used, and report on some sustainability and ecosystem service metrics computed using this land cover as input (e.g., carbon sequestration from USFS iTREE model; health and demographics in relation to road buffer forest width). We also discuss the land cover classification schema (a modified Anderson Level 1 after the National Land Cover Data (NLCD)), and offer some observations on lessons learned. Meter-scale urban land cover in Portland, OR overlaid on NAIP aerial photo. Streets, buildings and individual trees are identifiable.

  15. Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

    Science.gov (United States)

    Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

    2017-12-01

    Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

  16. Open land use map

    OpenAIRE

    Mildorf, T.; Charvát, K.; Jezek, J.; Templer, Simon; Malewski, Christian

    2014-01-01

    Open Land Use Map is an initiative that has been started by the Plan4business project and that will be extended as part of the SDI4Apps project in the future. This service aims to create an improved worldwide land use map. The initial map will be prepared using the CORINE Land Cover, Global Cover dataset and Open Street Map. Contributors, mainly volunteers, will able to change the geometry and assign up-to-date land use according to the HILUCS specification. For certain regions more detailed ...

  17. 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.

  18. Using the Landsat Archive to Estimate and Map Changes in Agriculture, Forests, and other Land Cover Types in East Africa

    Science.gov (United States)

    Healey, S. P.; Oduor, P.; Cohen, W. B.; Yang, Z.; Ouko, E.; Gorelick, N.; Wilson, S.

    2017-12-01

    Every country's land is distributed among different cover types, such as: agriculture; forests; rangeland; urban areas; and barren lands. Changes in the distribution of these classes can inform us about many things, including: population pressure; effectiveness of preservation efforts; desertification; and stability of the food supply. Good assessment of these changes can also support wise planning, use, and preservation of natural resources. We are using the Landsat archive in two ways to provide needed information about land cover change since the year 2000 in seven East African countries (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda, and Zambia). First, we are working with local experts to interpret historical land cover change from historical imagery at a probabilistic sample of 2000 locations in each country. This will provide a statistical estimate of land cover change since 2000. Second, we will use the same data to calibrate and validate annual land cover maps for each country. Because spatial context can be critical to development planning through the identification of hot spots, these maps will be a useful complement to the statistical, country-level estimates of change. The Landsat platform is an ideal tool for mapping land cover change because it combines a mix of appropriate spatial and spectral resolution with unparalleled length of service (Landsat 1 launched in 1972). Pilot tests have shown that time series analysis accessing the entire Landsat archive (i.e., many images per year) improves classification accuracy and stability. It is anticipated that this project will meet the civil needs of both governmental and non-governmental users across a range of disciplines.

  19. Mapping Urban Green Infrastructure: A Novel Landscape-Based Approach to Incorporating Land Use and Land Cover in the Mapping of Human-Dominated Systems

    Directory of Open Access Journals (Sweden)

    Matthew Dennis

    2018-01-01

    Full Text Available Common approaches to mapping green infrastructure in urbanised landscapes invariably focus on measures of land use or land cover and associated functional or physical traits. However, such one-dimensional perspectives do not accurately capture the character and complexity of the landscapes in which urban inhabitants live. The new approach presented in this paper demonstrates how open-source, high spatial and temporal resolution data with global coverage can be used to measure and represent the landscape qualities of urban environments. Through going beyond simple metrics of quantity, such as percentage green and blue cover, it is now possible to explore the extent to which landscape quality helps to unpick the mixed evidence presented in the literature on the benefits of urban nature to human well-being. Here we present a landscape approach, employing remote sensing, GIS and data reduction techniques to map urban green infrastructure elements in a large U.K. city region. Comparison with existing urban datasets demonstrates considerable improvement in terms of coverage and thematic detail. The characterisation of landscapes, using census tracts as spatial units, and subsequent exploration of associations with social–ecological attributes highlights the further detail that can be uncovered by the approach. For example, eight urban landscape types identified for the case study city exhibited associations with distinct socioeconomic conditions accountable not only to quantities but also qualities of green and blue space. The identification of individual landscape features through simultaneous measures of land use and land cover demonstrated unique and significant associations between the former and indicators of human health and ecological condition. The approach may therefore provide a promising basis for developing further insight into processes and characteristics that affect human health and well-being in urban areas, both in the United

  20. High spatial resolution mapping of land cover types in a priority area for conservation in the Brazilian savanna

    Science.gov (United States)

    Ribeiro, F.; Roberts, D. A.; Hess, L. L.; Davis, F. W.; Caylor, K. K.; Nackoney, J.; Antunes Daldegan, G.

    2017-12-01

    Savannas are heterogeneous landscapes consisting of highly mixed land cover types that lack clear distinct boundaries. The Brazilian Cerrado is a Neotropical savanna considered a biodiversity hotspot for conservation due to its biodiversity richness and rapid transformation of its landscape by crop and pasture activities. The Cerrado is one of the most threatened Brazilian biomes and only 2.2% of its original extent is strictly protected. Accurate mapping and monitoring of its ecosystems and adjacent land use are important to select areas for conservation and to improve our understanding of the dynamics in this biome. Land cover mapping of savannas is difficult due to spectral similarity between land cover types resulting from similar vegetation structure, floristically similar components, generalization of land cover classes, and heterogeneity usually expressed as small patch sizes within the natural landscape. These factors are the major contributor to misclassification and low map accuracies among remote sensing studies in savannas. Specific challenges to map the Cerrado's land cover types are related to the spectral similarity between classes of land use and natural vegetation, such as natural grassland vs. cultivated pasture, and forest ecosystem vs. crops. This study seeks to classify and evaluate the land cover patterns across an area ranked as having extremely high priority for future conservation in the Cerrado. The main objective of this study is to identify the representativeness of each vegetation type across the landscape using high to moderate spatial resolution imagery using an automated scheme. A combination of pixel-based and object-based approaches were tested using RapidEye 3A imagery (5m spatial resolution) to classify the Cerrado's major land cover types. The random forest classifier was used to map the major ecosystems present across the area, and demonstrated to have an effective result with 68% of overall accuracy. Post

  1. Fusion Approaches for Land Cover Map Production Using High Resolution Image Time Series without Reference Data of the Corresponding Period

    Directory of Open Access Journals (Sweden)

    Benjamin Tardy

    2017-11-01

    Full Text Available Optical sensor time series images allow one to produce land cover maps at a large scale. The supervised classification algorithms have been shown to be the best to produce maps automatically with good accuracy. The main drawback of these methods is the need for reference data, the collection of which can introduce important production delays. Therefore, the maps are often available too late for some applications. Domain adaptation methods seem to be efficient for using past data for land cover map production. According to this idea, the main goal of this study is to propose several simple past data fusion schemes to override the current land cover map production delays. A single classifier approach and three voting rules are considered to produce maps without reference data of the corresponding period. These four approaches reach an overall accuracy of around 80% with a 17-class nomenclature using Formosat-2 image time series. A study of the impact of the number of past periods used is also done. It shows that the overall accuracy increases with the number of periods used. The proposed methods require at least two or three previous years to be used.

  2. Land cover mapping with emphasis to burnt area delineation using co-orbital ALI and Landsat TM imagery

    Science.gov (United States)

    Petropoulos, George P.; Kontoes, Charalambos C.; Keramitsoglou, Iphigenia

    2012-08-01

    In this study, the potential of EO-1 Advanced Land Imager (ALI) radiometer for land cover and especially burnt area mapping from a single image analysis is investigated. Co-orbital imagery from the Landsat Thematic Mapper (TM) was also utilised for comparison purposes. Both images were acquired shortly after the suppression of a fire occurred during the summer of 2009 North-East of Athens, the capital of Greece. The Maximum Likelihood (ML), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) classifiers were parameterised and subsequently applied to the acquired satellite datasets. Evaluation of the land use/cover mapping accuracy was based on the error matrix statistics. Also, the McNemar test was used to evaluate the statistical significance of the differences between the approaches tested. Derived burnt area estimates were validated against the operationally deployed Services and Applications For Emergency Response (SAFER) Burnt Scar Mapping service. All classifiers applied to either ALI or TM imagery proved flexible enough to map land cover and also to extract the burnt area from other land surface types. The highest total classification accuracy and burnt area detection capability was returned from the application of SVMs to ALI data. This was due to the SVMs ability to identify an optimal separating hyperplane for best classes' separation that was able to better utilise ALI's advanced technological characteristics in comparison to those of TM sensor. This study is to our knowledge the first of its kind, effectively demonstrating the benefits of the combined application of SVMs to ALI data further implying that ALI technology may prove highly valuable in mapping burnt areas and land use/cover if it is incorporated into the development of Landsat 8 mission, planned to be launched in the coming years.

  3. Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Auxiliary Data

    Science.gov (United States)

    Aristizabal, F.; Judge, J.

    2017-12-01

    Synthetic Aperture Radar (SAR) has been widely used to detect surface water inundation and provides an advantage over multi-spectral instruments due to cloud penetration and higher spatial resolutions. However, detecting inundation for densely vegetated and urban areas with SAR remains a challenge due to corner reflection and diffuse scattering. Additionally, flat urban surfaces such as roads exhibit similar backscatter coefficients as urban surface water. Differences between inundated and non-inundated backscatter over vegetated land covers of static spatial domains have been demonstrated in previous studies. However, these backscatter differences are sensitive to changes in water depth, soil moisture, SAR sensor parameters, terrain, and vegetation properties. These factors tend to make accurate inundation mapping of heterogeneous regions across varying spatial and temporal extents difficult with exclusive use of SAR. This study investigates the utility of auxiliary data specifically high-resolution (10m) terrain information in conjunction with SAR (10m) for detecting inundated areas. Digital elevation models provide an absolute elevation which could enhance inundation mapping given a limited study extent with similar topography. To counter this limitation, a hydrologically relevant terrain index is proposed known as the Height Above Nearest Drainage (HAND) which normalizes topography to the local relative elevation of the nearest point along the relevant drainage line. HAND has been used for assisting remote sensing inundation mapping in the pre-processing stage as a terrain correction tool and as a post-processing mask that eliminates areas of low inundation risk. While the latter technique is useful for reduction of commission errors, it does not employ HAND for reducing omission errors that can occur from dense vegetation, spectral noise, and urban features. Sentinel-1 dual-pol SAR as well as auxiliary HAND will be used as predictors by various supervised and

  4. VT National Land Cover Dataset - 2001

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) The NLCD2001 layer available from VCGI is a subset of the the National Land Cover Database 2001 land cover layer for mapping zone 65 was produced...

  5. Trajectory analysis of land use and land cover maps to improve spatial-temporal patterns, and impact assessment on groundwater recharge

    Science.gov (United States)

    Zomlot, Z.; Verbeiren, B.; Huysmans, M.; Batelaan, O.

    2017-11-01

    Land use/land cover (LULC) change is a consequence of human-induced global environmental change. It is also considered one of the major factors affecting groundwater recharge. Uncertainties and inconsistencies in LULC maps are one of the difficulties that LULC timeseries analysis face and which have a significant effect on hydrological impact analysis. Therefore, an accuracy assessment approach of LULC timeseries is needed for a more reliable hydrological analysis and prediction. The objective of this paper is to assess the impact of land use uncertainty and to improve the accuracy of a timeseries of CORINE (coordination of information on the environment) land cover maps by using a new approach of identifying spatial-temporal LULC change trajectories as a pre-processing tool. This ensures consistency of model input when dealing with land-use dynamics and as such improves the accuracy of land use maps and consequently groundwater recharge estimation. As a case study the impact of consistent land use changes from 1990 until 2013 on groundwater recharge for the Flanders-Brussels region is assessed. The change trajectory analysis successfully assigned a rational trajectory to 99% of all pixels. The methodology is shown to be powerful in correcting interpretation inconsistencies and overestimation errors in CORINE land cover maps. The overall kappa (cell-by-cell map comparison) improved from 0.6 to 0.8 and from 0.2 to 0.7 for forest and pasture land use classes respectively. The study shows that the inconsistencies in the land use maps introduce uncertainty in groundwater recharge estimation in a range of 10-30%. The analysis showed that during the period of 1990-2013 the LULC changes were mainly driven by urban expansion. The results show that the resolution at which the spatial analysis is performed is important; the recharge differences using original and corrected CORINE land cover maps increase considerably with increasing spatial resolution. This study indicates

  6. Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

    Science.gov (United States)

    Hester, David Barry

    The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the

  7. The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

    Science.gov (United States)

    Hogan, Christine A.

    1996-01-01

    A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation

  8. Branched polynomial covering maps

    DEFF Research Database (Denmark)

    Hansen, Vagn Lundsgaard

    1999-01-01

    A Weierstrass polynomial with multiple roots in certain points leads to a branched covering map. With this as the guiding example, we formally define and study the notion of a branched polynomial covering map. We shall prove that many finite covering maps are polynomial outside a discrete branch...... set. Particular studies are made of branched polynomial covering maps arising from Riemann surfaces and from knots in the 3-sphere....

  9. Image analysis for facility siting: a comparison of low- and high-altitude image interpretability for land use/land cover mapping

    International Nuclear Information System (INIS)

    Borella, H.M.; Estes, J.E.; Ezra, C.E.; Scepan, J.; Tinney, L.R.

    1982-01-01

    For two test sites in Pennsylvania the interpretability of commercially acquired low-altitude and existing high-altitude aerial photography are documented in terms of time, costs, and accuracy for Anderson Level II land use/land cover mapping. Information extracted from the imagery is to be used in the evaluation process for siting energy facilities. Land use/land cover maps were drawn at 1:24,000 scale using commercially flown color infrared photography obtained from the United States Geological Surveys' EROS Data Center. Detailed accuracy assessment of the maps generated by manual image analysis was accomplished employing a stratified unaligned adequate class representation. Both are-weighted and by-class accuracies were documented and field-verified. A discrepancy map was also drawn to illustrate differences in classifications between the two map scales. Results show that the 1:24,000 scale map set was accurate (99% to 94% area-weighted) than the 1:62,500 scale set, especially when sampled by class (96% to 66%). The 1:24,000 scale maps were also more time-consuming and costly to produce, due mainly to higher image acquisition costs

  10. Image Analysis for Facility Siting: a Comparison of Lowand High-altitude Image Interpretability for Land Use/land Cover Mapping

    Science.gov (United States)

    Borella, H. M.; Estes, J. E.; Ezra, C. E.; Scepan, J.; Tinney, L. R.

    1982-01-01

    For two test sites in Pennsylvania the interpretability of commercially acquired low-altitude and existing high-altitude aerial photography are documented in terms of time, costs, and accuracy for Anderson Level II land use/land cover mapping. Information extracted from the imagery is to be used in the evaluation process for siting energy facilities. Land use/land cover maps were drawn at 1:24,000 scale using commercially flown color infrared photography obtained from the United States Geological Surveys' EROS Data Center. Detailed accuracy assessment of the maps generated by manual image analysis was accomplished employing a stratified unaligned adequate class representation. Both 'area-weighted' and 'by-class' accuracies were documented and field-verified. A discrepancy map was also drawn to illustrate differences in classifications between the two map scales. Results show that the 1:24,000 scale map set was more accurate (99% to 94% area-weighted) than the 1:62,500 scale set, especially when sampled by class (96% to 66%). The 1:24,000 scale maps were also more time-consuming and costly to produce, due mainly to higher image acquisition costs.

  11. Branched polynomial covering maps

    DEFF Research Database (Denmark)

    Hansen, Vagn Lundsgaard

    2002-01-01

    A Weierstrass polynomial with multiple roots in certain points leads to a branched covering map. With this as the guiding example, we formally define and study the notion of a branched polynomial covering map. We shall prove that many finite covering maps are polynomial outside a discrete branch ...... set. Particular studies are made of branched polynomial covering maps arising from Riemann surfaces and from knots in the 3-sphere. (C) 2001 Elsevier Science B.V. All rights reserved.......A Weierstrass polynomial with multiple roots in certain points leads to a branched covering map. With this as the guiding example, we formally define and study the notion of a branched polynomial covering map. We shall prove that many finite covering maps are polynomial outside a discrete branch...

  12. Land Cover Mapping Analysis and Urban Growth Modelling Using Remote Sensing Techniques in Greater Cairo Region—Egypt

    Directory of Open Access Journals (Sweden)

    Yasmine Megahed

    2015-09-01

    Full Text Available This study modeled the urban growth in the Greater Cairo Region (GCR, one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC maps (1984, 2003 and 2014 were produced from satellite images by using Support Vector Machines (SVM. Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modeled to predict the future scenarios for year 2025 using Land Change Modeler (LCM embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.

  13. 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...

  14. 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...

  15. Use of multispectral satellite imagery and hyperspectral endmember libraries for urban land cover mapping at the metropolitan scale

    Science.gov (United States)

    Priem, Frederik; Okujeni, Akpona; van der Linden, Sebastian; Canters, Frank

    2016-10-01

    The value of characteristic reflectance features for mapping urban materials has been demonstrated in many experiments with airborne imaging spectrometry. Analysis of larger areas requires satellite-based multispectral imagery, which typically lacks the spatial and spectral detail of airborne data. Consequently the need arises to develop mapping methods that exploit the complementary strengths of both data sources. In this paper a workflow for sub-pixel quantification of Vegetation-Impervious-Soil urban land cover is presented, using medium resolution multispectral satellite imagery, hyperspectral endmember libraries and Support Vector Regression. A Landsat 8 Operational Land Imager surface reflectance image covering the greater metropolitan area of Brussels is selected for mapping. Two spectral libraries developed for the cities of Brussels and Berlin based on airborne hyperspectral APEX and HyMap data are used. First the combined endmember library is resampled to match the spectral response of the Landsat sensor. The library is then optimized to avoid spectral redundancy and confusion. Subsequently the spectra of the endmember library are synthetically mixed to produce training data for unmixing. Mapping is carried out using Support Vector Regression models trained with spectra selected through stratified sampling of the mixed library. Validation on building block level (mean size = 46.8 Landsat pixels) yields an overall good fit between reference data and estimation with Mean Absolute Errors of 0.06, 0.06 and 0.08 for vegetation, impervious and soil respectively. Findings of this work may contribute to the use of universal spectral libraries for regional scale land cover fraction mapping using regression approaches.

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

    Science.gov (United States)

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

    2017-06-01

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

  17. 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

  18. Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

    Directory of Open Access Journals (Sweden)

    Brian A. Johnson

    2018-01-01

    Full Text Available The advent of very high resolution (VHR satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters. The scale parameter (SP is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water through meta-analysis and nonlinear regression tree (RT modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions, and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our

  19. National Land Cover Database (NLCD) Land Cover Collection

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The National Land Cover Database (NLCD) Land Cover Collection is produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC)...

  20. Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

    Science.gov (United States)

    Giri, Chandra; Long, Jordan

    2014-01-01

    Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

  1. Using indigenous knowledge to link hyper-temporal land cover mapping with land use in the Venezuelan Amazon: "The Forest Pulse".

    Science.gov (United States)

    Olivero, Jesús; Ferri, Francisco; Acevedo, Pelayo; Lobo, Jorge M; Fa, John E; Farfán, Miguel Á; Romero, David; Real, Raimundo

    2016-12-01

    Remote sensing and traditional ecological knowledge (TEK) can be combined to advance conservation of remote tropical regions, e.g. Amazonia, where intensive in situ surveys are often not possible. Integrating TEK into monitoring and management of these areas allows for community participation, as well as for offering novel insights into sustainable resource use. In this study, we developed a 250 m resolution land-cover map of the Western Guyana Shield (Venezuela) based on remote sensing, and used TEK to validate its relevance for indigenous livelihoods and land uses. We first employed a hyper-temporal remotely sensed vegetation index to derive a land classification system. During a 1 300 km, eight day fluvial expedition in roadless areas in the Amazonas State (Venezuela), we visited six indigenous communities who provided geo-referenced data on hunting, fishing and farming activities. We overlaid these TEK data onto the land classification map, to link land classes with indigenous use. We characterized land classes using patterns of greenness temporal change and topo-hydrological information, and proposed 12 land-cover types, grouped into five main landscapes: 1) water bodies; 2) open lands/forest edges; 3) evergreen forests; 4) submontane semideciduous forests, and 5) cloud forests. Each land cover class was identified with a pulsating profile describing temporal changes in greenness, hence we labelled our map as "The Forest Pulse". These greenness profiles showed a slightly increasing trend, for the period 2000 to 2009, in the land classes representing grassland and scrubland, and a slightly decreasing trend in the classes representing forests. This finding is consistent with a gain in carbon in grassland as a consequence of climate warming, and also with some loss of vegetation in the forests. Thus, our classification shows potential to assess future effects of climate change on landscape. Several classes were significantly connected with agriculture, fishing

  2. Linking Land Cover Data and Crop Yields for Mapping and Assessment of Pollination Services in Europe

    Directory of Open Access Journals (Sweden)

    Maria Luisa Paracchini

    2013-09-01

    Full Text Available Pollination is a key ecosystem service as many crops but in particular, fruits and vegetables are partially dependent on pollinating insects to produce food for human consumption. Here we assessed how pollination services are delivered at the European scale. We used this assessment to estimate the relative contribution of wild pollinators to crop production. We developed an index of relative pollination potential, which is defined as the relative potential or relative capacity of ecosystems to support crop pollination. The model for relative pollination potential is based on the assumption that different habitats, but in particular forest edges, grasslands rich in flowers and riparian areas, offer suitable sites for wild pollinator insects. Using data of the foraging range of wild bees with short flight distances, we linked relative pollination potential to regional statistics of crop production. At aggregated EU level, the absence of insect pollination would result in a reduction of between 25% and 32% of the total production of crops which are partially dependent on insect pollination, depending on the data source used for the assessment. This production deficit decreases to 2.5% if only the relative pollination potential of a single guild of pollinators is considered. A strength of our approach is the spatially-explicit link between land cover based relative pollination potential and crop yield which enables a general assessment of the benefits that are derived from pollination services in Europe while providing insight where pollination gaps in the landscape occur.

  3. On the spatial and temporal resolution of land cover products for applied use in wind resource mapping

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay; Badger, Merete; Dellwik, Ebba

    as input for modelling the wind conditions over a Danish near-coastal region. The flow model results were compared to alternative use of USGS land cover. Significant variations in the wind speed were found between the two atmospheric flow model results. Furthermore the wind speed from the flow model...... was compared to meteorological observations taken in a tall mast and from ground based remote-sensing wind profiling lidars. It is shown that simulations using CORINE provide better wind flow results close to the surface as compared to those using USGS on the investigated site. The next step towards...... improvement of flow model inputs is to investigate in further detail applied use of satellite maps in forested areas. 75% of new land-based wind farms are planned in or near forests in Europe. In forested areas the near surface atmospheric flow is more challenging to calculate than in regions with low...

  4. Land Use and Land Cover - MO 2015 Silver Land Cover (GDB)

    Data.gov (United States)

    NSGIC State | GIS Inventory — MoRAP produced and integrated data to map land cover and wetlands for the Upper Silver Creek Watershed in Illinois. LiDAR elevation and vegetation height information...

  5. Land Use and Land Cover - MO 2015 Meramec Land Cover (GDB)

    Data.gov (United States)

    NSGIC State | GIS Inventory — MoRAP produced and integrated data to map land cover and wetlands for the Meramec River bottomland in Missouri. LiDAR elevation and vegetation height information and...

  6. Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

    Science.gov (United States)

    LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

    2001-01-01

    The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

  7. Allegheny County Land Cover Areas

    Data.gov (United States)

    Allegheny County / City of Pittsburgh / Western PA Regional Data Center — The Land Cover dataset demarcates 14 land cover types by area; such as Residential, Commercial, Industrial, Forest, Agriculture, etc. If viewing this description on...

  8. Remote sensing and GIS for land use/cover mapping and integrated land management: case from the middle Ganga plain

    Science.gov (United States)

    Singh, R. B.; Kumar, Dilip

    2012-06-01

    In India, land resources have reached a critical stage due to the rapidly growing population. This challenge requires an integrated approach toward harnessing land resources, while taking into account the vulnerable environmental conditions. Remote sensing and Geographical Information System (GIS) based technologies may be applied to an area in order to generate a sustainable development plan that is optimally suited to the terrain and to the productive potential of the local resources. The present study area is a part of the middle Ganga plain, known as Son-Karamnasa interfluve, in India. Alternative land use systems and the integration of livestock enterprises with the agricultural system have been suggested for land resources management. The objective of this paper is to prepare a land resource development plan in order to increase the productivity of land for sustainable development. The present study will contribute necessary input for policy makers to improve the socio-economic and environmental conditions of the region.

  9. Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

    Science.gov (United States)

    Tamiminia, H.; Homayouni, S.; Safari, A.

    2015-12-01

    Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

  10. High Resolution Urban Land Cover Mapping Using NAIP Aerial Photography and Image Processing for the USEPA National Atlas of Sustainability and Ecosystem Services

    Science.gov (United States)

    Pilant, A. N.; Baynes, J.; Dannenberg, M.

    2012-12-01

    The US EPA National Atlas for Sustainability is a web-based, easy-to-use, mapping application that allows users to view and analyze multiple ecosystem services in a specific region. The Atlas provides users with a visual method for interpreting ecosystem services and understanding how they can be conserved and enhanced for a sustainable future. The Urban Atlas component of the National Atlas will provide fine-scale information linking human health and well-being to environmental conditions such as urban heat islands, near-road pollution, resource use, access to recreation, drinking water quality and other quality of life indicators. The National Land Cover Data (NLCD) derived from 30 m scale 2006 Landsat imagery provides the land cover base for the Atlas. However, urban features and phenomena occur at finer spatial scales, so higher spatial resolution and more current LC maps are required. We used 4 band USDA NAIP imagery (1 m pixel size) and various classification approaches to produce urban land cover maps with these classes: impervious surface, grass and herbaceous, trees and forest, soil and barren, and water. Here we present the remote sensing methods used and results from four pilot cities in this effort, highlighting the pros and cons of the approach, and the benefits to sustainability and ecosystem services analysis. Example of high resolution land cover map derived from USDA NAIP aerial photo. Compare 30 m and 1 m resolution land cover maps of downtown Durham, NC.

  11. From land cover change to land function dynamics: A major challenge to improve land characterization

    NARCIS (Netherlands)

    Verburg, P.H.; Steeg, van de J.; Veldkamp, A.; Willemen, L.

    2009-01-01

    Land cover change has always had a central role in land change science. This central role is largely the result of the possibilities to map and characterize land cover based on observations and remote sensing. This paper argues that more attention should be given to land use and land functions and

  12. Mapping Land Cover and Estimating the Grassland Structure in a Priority Area of the Chihuahuan Desert

    Directory of Open Access Journals (Sweden)

    Alberto Rodríguez-Maturino

    2017-10-01

    Full Text Available A field characterization of the grassland vegetation structure, represented by the coverage of grass canopy (CGC and the grass height, was carried out during three years (2009–2011 in a priority area for the conservation of grasslands of North America. Landsat Thematic Mapper (TM5 images were selected and the information of reflectance was obtained based on the geographical location of each field-sampling site. Linear models, constructed with field and satellite data, with high coefficients of determination for CGC (R2 = 0.81, R2 = 0.81 and R2 = 0.72 and grass height (R2 = 0.82, R2 = 0.79 and R2 = 0.73 were obtained. The maps showed a good level of CGC (>25% and grass height (>25 cm, except for the year 2009, which presented the lowest values of grass height in the area. According to the Kappa Index, a moderate concordance among the three CGC maps was presented (0.49–0.59. Conversely, weak and moderate concordances were found among the grass height maps (0.36–0.59. It was observed that areas with a high CGC do not necessarily correspond to areas with greater grass height values. Based on the data analyzed in this study, the grassland areas are highly dynamic, structurally heterogeneous and the spatial distribution of the variables does not show a definite pattern. From the information generated, it is possible to determine those areas that are the most important for monitoring to then establish effective strategies for the conservation of these grasslands and the protection of threatened migratory bird species.

  13. Evaluating The Land Use And Land Cover Dynamics In Borena ...

    African Journals Online (AJOL)

    The integration of satellite remote sensing and GIS was an effective approach for analyzing the direction, rate, and spatial pattern of land use change. Three land use and land cover maps were produced by analyzing remotely sensed images of Landsat satellite imageries at three time points (1972,1985,and 2003) .

  14. Removing non-urban roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011

    Science.gov (United States)

    Soulard, Christopher E.; Acevedo, William; Stehman, Stephen V.

    2018-01-01

    Quantifying change in urban land provides important information to create empirical models examining the effects of human land use. Maps of developed land from the National Land Cover Database (NLCD) of the conterminous United States include rural roads in the developed land class and therefore overestimate the amount of urban land. To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7 percent) than the NLCD maps (96.2 percent). More importantly, the urban maps resulted in lower commission error of the urban class (23 percent versus 57 percent for the NLCD in 2006) with the trade-off of slightly inflated omission error (20 percent for the urban map, 16 percent for NLCD in 2006). The removal of approximately 230,000 km2 of rural roads from the NLCD developed class resulted in maps that better characterize the urban footprint. These urban maps are more suited to modeling applications and policy decisions that rely on quantitative and spatially explicit information regarding urban lands.

  15. Mapping decadal land cover changes in the woodlands of north eastern Namibia using the Landsat satellite archive (1975-2014)

    Science.gov (United States)

    Wingate, Vladimir; Phinn, Stuart; Kuhn, Nikolaus

    2016-04-01

    Woodland savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to intensive land uses. This study investigates the land cover changes over 108,038 km2 in NE Namibia using multi-sensor Landsat imagery, at decadal intervals from 1975 to 2014, with a post-classification change detection method and supervised Regression Tree classifiers. We discuss likely impacts of land tenure and reforms over the past four decades on changes in land use and land cover. These included losses, gains and exchanges between predominant land cover classes. Exchanges comprised logical conversions between woodland and agricultural classes, implying woodland clearing for arable farming, cropland abandonment and vegetation succession. The dominant change was a reduction in the area of the woodland class due to the expansion of the agricultural class, specifically, small-scale cereal and pastoral production. Woodland area decreased from 90% of the study area in 1975 to 83% in 2014, while cleared land increased from 9% to 14%. We found that the main land cover changes are conversion from woodland to agricultural and urban land uses, driven by urban expansion and woodland clearing for subsistence-based agriculture and pastoralism.

  16. High spatial resolution mapping of the Cerrado's land cover and land use types in the priority area for conservation Chapada da Contagem, Brazil.

    Science.gov (United States)

    Ribeiro, F.; Roberts, D. A.; Davis, F. W.; Antunes Daldegan, G.; Nackoney, J.; Hess, L. L.

    2016-12-01

    The Brazilian savanna, Cerrado, is the second largest biome over South America and the most floristically diverse savanna in the world. This biome is considered a conservation hotspot in respect to its biodiversity importance and rapid transformation of its landscape. The Cerrado's natural vegetation has been severely transformed by agriculture and pasture activities. Currently it is the main agricultural frontier in Brazil and one of the most threatened Brazilian biomes. This scenario results in environmental impacts such as ecosystems fragmentation as well as losses in connectivity, biodiversity and gene flow, changes in the microclimate and energy, carbon and nutrients cycles, among others. The Priority Areas for Conservation is a governmental program from Brazil that identifies areas with high conservation priority. One of this program's recommendation is a natural vegetation map including their major ecosystem classes. This study aims to generate more precise information for the Cerrado's vegetation. The main objective of this study is to identify which ecosystems are being prioritized and/or threatened by land use, refining information for further protection. In order to test methods, the priority area for conservation Chapada da Contagem was selected as the study site. This area is ranked as "extremely high priority" by the government and is located in the Federal District and Goias State, Brazil. Satellites with finer spatial resolution may improve the classification of the Cerrado's vegetation. Remote sensing methods and two criteria were tested using RapidEye 3A imagery (5m spatial resolution) collected in 2014 in order to classify the Cerrado's major land cover types of this area, as well as its land use. One criterion considers the Cerrado's major terrestrial ecosystems, which are divided into forest, savanna and grassland. The other involves scaling it down to the major physiognomic groups of each ecosystem. Other sources of environmental dataset such

  17. 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

  18. 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.

  19. Use of Land Use Land Cover Change Mapping Products in Aiding Coastal Habitat Conservation and Restoration Efforts of the Mobile Bay NEP

    Science.gov (United States)

    Spruce, Joseph P.; Swann, Roberta; Smooth, James

    2010-01-01

    The Mobile Bay region has undergone significant land use land cover change (LULC) over the last 35 years, much of which is associated with urbanization. These changes have impacted the region s water quality and wildlife habitat availability. In addition, much of the region is low-lying and close to the Gulf, which makes the region vulnerable to hurricanes, climate change (e.g., sea level rise), and sometimes man-made disasters such as the Deepwater Horizon (DWH) oil spill. Land use land cover change information is needed to help coastal zone managers and planners to understand and mitigate the impacts of environmental change on the region. This presentation discusses selective results of a current NASA-funded project in which Landsat data over a 34-year period (1974-2008) is used to produce, validate, refine, and apply land use land cover change products to aid coastal habitat conservation and restoration needs of the Mobile Bay National Estuary Program (MB NEP). The project employed a user defined classification scheme to compute LULC change mapping products for the entire region, which includes the majority of Mobile and Baldwin counties. Additional LULC change products have been computed for select coastal HUC-12 sub-watersheds adjacent to either Mobile Bay or the Gulf of Mexico, as part of the MB NEP watershed profile assessments. This presentation will include results of additional analyses of LULC change for sub-watersheds that are currently high priority areas, as defined by MB NEP. Such priority sub-watersheds include those that are vulnerable to impacts from the DWH oil spill, as well as sub-watersheds undergoing urbanization. Results demonstrating the nature and permanence of LULC change trends for these higher priority sub-watersheds and results characterizing change for the entire 34-year period and at approximate 10-year intervals across this period will also be presented. Future work will include development of value-added coastal habitat quality

  20. Mapping the Influence of Land Use/Land Cover Changes on the Urban Heat Island Effect—A Case Study of Changchun, China

    Directory of Open Access Journals (Sweden)

    Chaobin Yang

    2017-02-01

    Full Text Available The spatio-temporal patterns of land use/land cover changes (LUCC can significantly affect the distribution and intensity of the urban heat island (UHI effect. However, few studies have mapped a clear picture of the influence of LUCC on UHI. In this study, both qualitative and quantitative models are employed to explore the effect of LUCC on UHI. UHI and LUCC maps were retrieved from Landsat data acquired from 1984, 1992, 2000, 2007, and 2014 to show their spatiotemporal patterns. The results showed that: (1 both the patterns of LUCC and UHI have had dramatic changes in the past 30 years. The urban area of Changchun increased more than four times, from 143.15 km2 in 1984 to 577.45 km2 in 2014, and the proportion of UHI regions has increased from 15.27% in 1984 to 29.62% in 2014; (2 the spatiotemporal changes in thermal environment were consistent with the process of urbanization. The average LST of the study area has been continuously increasing as many other land use types have been transformed to urban regions. The mean temperatures were higher in urban regions than rural areas over all of the periods, but the UHI intensity varied based on different measurements; and (3 the thermal environment inside the city varied widely even within a small area. The LST possesses a very strong positive relationship with impervious surface area (ISA, and the relationship has become stronger in recent years. The UHI we employ, specifically in this study, is SUHI (surface urban heat island.

  1. Mapping Decadal Land Cover Changes in the Woodlands of North Eastern Namibia from 1975 to 2014 Using the Landsat Satellite Archived Data

    Directory of Open Access Journals (Sweden)

    Vladimir R. Wingate

    2016-08-01

    Full Text Available Woodlands and savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to subsistence and intensive agriculture or urbanized. This study investigates changes in land cover over four administrative regions of North Eastern Namibia within the Kalahari woodland savannah biome, covering a total of 107,994 km2. Land cover is mapped using multi-sensor Landsat imagery at decadal intervals from 1975 to 2014, with a post-classification change detection method. The dominant change observed was a reduction in the area of woodland savannah due to the expansion of agriculture, primarily in the form of small-scale cereal and pastoral production. More specifically, woodland savannah area decreased from 90% of the study area in 1975 to 83% in 2004, and then increased to 86% in 2014, while agricultural land increased from 6% to 12% between 1975 and 2014. We assess land cover changes in relation to towns, villages, rivers and roads and find most changes occurred in proximity to these. In addition, we find that most land cover changes occur within land designated as communally held, followed by state protected land. With widespread changes occurring across the African continent, this study provides important data for understanding drivers of change in the region and their impacts on the distribution of woodland savannahs.

  2. 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

  3. The National Land Cover Database

    Science.gov (United States)

    Homer, Collin G.; Fry, Joyce A.; Barnes, Christopher A.

    2012-01-01

    The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution, land cover database for the Nation. NLCD provides spatial reference and descriptive data for characteristics of the land surface such as thematic class (for example, urban, agriculture, and forest), percent impervious surface, and percent tree canopy cover. NLCD supports a wide variety of Federal, State, local, and nongovernmental applications that seek to assess ecosystem status and health, understand the spatial patterns of biodiversity, predict effects of climate change, and develop land management policy. NLCD products are created by the Multi-Resolution Land Characteristics (MRLC) Consortium, a partnership of Federal agencies led by the U.S. Geological Survey. All NLCD data products are available for download at no charge to the public from the MRLC Web site: http://www.mrlc.gov.

  4. Land Cover Mapping using GEOBIA to Estimate Loss of Salacca zalacca Trees in Landslide Area of Clapar, Madukara District of Banjarnegara

    Science.gov (United States)

    Permata, Anggi; Juniansah, Anwar; Nurcahyati, Eka; Dimas Afrizal, Mousafi; Adnan Shafry Untoro, Muhammad; Arifatha, Na'ima; Ramadhani Yudha Adiwijaya, Raden; Farda, Nur Mohammad

    2016-11-01

    Landslide is an unpredictable natural disaster which commonly happens in highslope area. Aerial photography in small format is one of acquisition method that can reach and obtain high resolution spatial data faster than other methods, and provide data such as orthomosaic and Digital Surface Model (DSM). The study area contained landslide area in Clapar, Madukara District of Banjarnegara. Aerial photographs of landslide area provided advantage in objects visibility. Object's characters such as shape, size, and texture were clearly seen, therefore GEOBIA (Geography Object Based Image Analysis) was compatible as method for classifying land cover in study area. Dissimilar with PPA (PerPixel Analyst) method that used spectral information as base object detection, GEOBIA could use spatial elements as classification basis to establish a land cover map with better accuracy. GEOBIA method used classification hierarchy to divide post disaster land cover into three main objects: vegetation, landslide/soil, and building. Those three were required to obtain more detailed information that can be used in estimating loss caused by landslide and establishing land cover map in landslide area. Estimating loss in landslide area related to damage in Salak (Salacca zalacca) plantations. This estimation towards quantity of Salak tree that were drifted away by landslide was calculated in assumption that every tree damaged by landslide had same age and production class with other tree that weren't damaged. Loss calculation was done by approximating quantity of damaged trees in landslide area with data of trees around area that were acquired from GEOBIA classification method.

  5. Meta-Analysis of Land Use / Land Cover Change Factors in the Conterminous US and Prediction of Potential Working Timberlands in the US South from FIA Inventory Plots and NLCD Cover Maps

    Science.gov (United States)

    Jeuck, James A.

    -analysis provides insight into the general success of econometric independent variables for future forest use or cover change research. The second part of this dissertation developed a method for predicting area estimates and spatial distribution of PWT in the US South. This technique determined land use from USFS Forest Inventory and Analysis (FIA) and land cover from the National Land Cover Database (NLCD). Three dependent variable forms (DV Forms) were derived from the FIA data: DV Form 1, timberland, other; DV Form 2, short timberland, tall timberland, agriculture, other; and DV Form 3, short hardwood (HW) timberland, tall HW timberland, short softwood (SW) timberland, tall SW timberland, agriculture, other. The prediction accuracy of each DV Form was investigated using both random forest model and logistic regression model specifications and data optimization techniques. Model verification employing a "leave-group-out" Monte Carlo simulation determined the selection of a stratified version of the random forest model using one-year NLCD observations with an overall accuracy of 0.53-0.94. The lower accuracy side of the range was when predictions were made from an aggregated NLCD land cover class "grass_shrub". The selected model specification was run using 2011 NLCD and the other predictor variables to produce three levels of timberland prediction and probability maps for the US South. Spatial masks removed areas unlikely to be working forests (protected and urbanized lands) resulting in PWT maps. The area of the resulting maps compared well with USFS area estimates and masked PWT maps and had an 8-11% reduction of the USFS timberland estimate for the US South compared to the DV Form. Change analysis of the 2011 NLCD to PWT showed (1) the majority of the short timberland came from NLCD grass_shrub; (2) the majority of NLCD grass_shrub predicted into tall timberland, and (3) NLCD grass_shrub was more strongly associated with timberland in the Coastal Plain. Resulting map products

  6. National Land Cover Database 2001 (NLCD01)

    Science.gov (United States)

    LaMotte, Andrew E.

    2016-01-01

    This 30-meter data set represents land use and land cover for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System (see http://water.usgs.gov/GIS/browse/nlcd01-partition.jpg). The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp). The NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see http://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

  7. Land-cover change detection

    Science.gov (United States)

    Chen, Xuexia; Giri, Chandra; Vogelmann, James

    2012-01-01

    Land cover is the biophysical material on the surface of the earth. Land-cover types include grass, shrubs, trees, barren, water, and man-made features. Land cover changes continuously.  The rate of change can be either dramatic and abrupt, such as the changes caused by logging, hurricanes and fire, or subtle and gradual, such as regeneration of forests and damage caused by insects (Verbesselt et al., 2001).  Previous studies have shown that land cover has changed dramatically during the past sevearal centuries and that these changes have severely affected our ecosystems (Foody, 2010; Lambin et al., 2001). Lambin and Strahlers (1994b) summarized five types of cause for land-cover changes: (1) long-term natural changes in climate conditions, (2) geomorphological and ecological processes, (3) human-induced alterations of vegetation cover and landscapes, (4) interannual climate variability, and (5) human-induced greenhouse effect.  Tools and techniques are needed to detect, describe, and predict these changes to facilitate sustainable management of natural resources.

  8. Moving from proprietary to open-source solutions for academic research in remote sensing: Example with semi-automated land cover mapping

    OpenAIRE

    Grippa, Taïs

    2017-01-01

    GRASS GIS has recently experienced significant improvements for Object-Based Image Analysis. At ULB the choice was made to combine GRASS GIS and Python in a semi-automated processing chain for land-cover mapping. The later proved its ability of being quickly customized in order to match the requirements of different projects. In order to promote the OSGEO software, we decided to make it freely available, allowing anyone interested to review, reuse and/or enhance it for further studies.

  9. Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data

    Science.gov (United States)

    Beaumont, Benjamin; Grippa, Tais; Lennert, Moritz; Vanhuysse, Sabine; Stephenne, Nathalie; Wolff, Eléonore

    2017-07-01

    Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15 m2. Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed.

  10. 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

  11. Mapping and analysis land-use and land-cover changes during 1996-2016 in Lubuk Kertang mangrove forest, North Sumatra, Indonesia

    Science.gov (United States)

    Basyuni, M.; Fitri, A.; Harahap, Z. A.

    2018-03-01

    Mangrove forest plays a significant role for biogeochemical carbon cycle in the context of climate change along the tropical coastal area. The present study analyzed the land-use and land-cover changes from 1996, 2006 and 2016 in Lubuk Kertang mangrove forest, Langkat, North Sumatra, Indonesia. Mangrove diversity in Lubuk Kertang consists of fifteen species, Acanthus ilicifolius, Avicennia marina, A. lanata, A. officinalis, Bruguiera gymnorrhiza, B. sexangula, Ceriops tagal, Excoecaria agallocha, Lumnitzera racemosa, L. littorea, R. apiculata, R. mucronata, Scyphiphora hydrophyllacea, Sonneratia caseolaris, and Xylocarpus granatum. The land use/land cover consists of seven classes namely, mangrove forest, river, residential, paddy field, oil palm plantation, aquaculture, and open space area. A land use change matrix showed that the decrease of mangrove forest 109.4 ha from 1996-2006 converted to aquaculture 51.5 ha (47.1%). By contrast, mangrove lost 291.2 ha during 2006-2016, with main driver deforestation was oil palm plantation 128.1 ha (44%). During twenty years mangrove forest has been lost more than 400.4 ha, which is equal to 20.02 ha/year. On the other hand, oil palm plantation and aquaculture have been increased 155.3 ha and 114.1 ha during 1996-2016, respectively, suggested that both land-uses are mainly responsible for mangrove deforestation. These data are likely to contribute towards coastal management planning and practice and mitigating actions for emission reduction scenario.

  12. the implications of land use/cover dynamics on resources

    African Journals Online (AJOL)

    2017-12-04

    Dec 4, 2017 ... Land use maps were produced using the GIS software packages of ... Keywords: Land use/cover, Dynamics, Remote Sensing Techniques, Geographic Information System, .... sporadic floods and landslides in Bambui which.

  13. 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.

  14. Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set

    Directory of Open Access Journals (Sweden)

    Jinshui Zhang

    2017-04-01

    Full Text Available This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD, to determine optimal parameters for support vector data description (SVDD model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C and kernel width (s, in mapping homogeneous specific land cover.

  15. Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data

    Science.gov (United States)

    Ghrefat, Habes A.; Goodell, Philip C.

    2011-08-01

    The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR-SWIR (0.4-2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen

  16. 2005 Kansas Land Cover Patterns, Level I, Kansas River Watershed

    Data.gov (United States)

    Kansas Data Access and Support Center — The Upper Kansas River Watershed Land Cover Patterns map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period as part of a...

  17. 2005 Kansas Land Cover Patterns, Level IV, Kansas River Watershed

    Data.gov (United States)

    Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns (KLCP) Mapping Initiative was a two-phase mapping endeavor that occurred over a three-year period (2007-2009). Note that while...

  18. A Tool for Creating Regionally Calibrated High-Resolution Land Cover Data Sets for the West African Sahel: Using Machine Learning to Scale Up Hand-Classified Maps in a Data-Sparse Environment

    Science.gov (United States)

    Van Gordon, M.; Van Gordon, S.; Min, A.; Sullivan, J.; Weiner, Z.; Tappan, G. G.

    2017-12-01

    Using support vector machine (SVM) learning and high-accuracy hand-classified maps, we have developed a publicly available land cover classification tool for the West African Sahel. Our classifier produces high-resolution and regionally calibrated land cover maps for the Sahel, representing a significant contribution to the data available for this region. Global land cover products are unreliable for the Sahel, and accurate land cover data for the region are sparse. To address this gap, the U.S. Geological Survey and the Regional Center for Agriculture, Hydrology and Meteorology (AGRHYMET) in Niger produced high-quality land cover maps for the region via hand-classification of Landsat images. This method produces highly accurate maps, but the time and labor required constrain the spatial and temporal resolution of the data products. By using these hand-classified maps alongside SVM techniques, we successfully increase the resolution of the land cover maps by 1-2 orders of magnitude, from 2km-decadal resolution to 30m-annual resolution. These high-resolution regionally calibrated land cover datasets, along with the classifier we developed to produce them, lay the foundation for major advances in studies of land surface processes in the region. These datasets will provide more accurate inputs for food security modeling, hydrologic modeling, analyses of land cover change and climate change adaptation efforts. The land cover classification tool we have developed will be publicly available for use in creating additional West Africa land cover datasets with future remote sensing data and can be adapted for use in other parts of the world.

  19. A procedure for merging land cover/use data from LANDSAT, aerial photography, and map sources: Compatibility, accuracy, and cost. Remote Sensing Project

    Science.gov (United States)

    Enslin, W. R.; Tilmann, S. E.; Hill-Rowley, R.; Rogers, R. H.

    1977-01-01

    Regional planning agencies are currently expressing a need for detailed land cover/use information to effectively meet the requirements of various federal programs. Individual data sources have advantages and limitations in fulfilling this need, both in terms of time/cost and technological capability. A methodology has been developed to merge land cover/use data from LANDSAT, aerial photography and map sources to maximize the effective use of a variety of data sources in the provision of an integrated information system for regional analysis. A test of the proposed inventory method is currently under way in four central Michigan townships. This test will evaluate the compatibility, accuracy and cost of the integrated method with reference to inventories developed from a single data source, and determine both the technological feasibility and analytical potential of such a system.

  20. Monitoring land Cover Changes and Fragmentation dynamics in the ...

    African Journals Online (AJOL)

    Monitoring land Cover Changes and Fragmentation dynamics in the subtropical thicket of the Eastern Cape Province, South Africa. ... Baseline land use/cover maps and fragmentation analyses in a temporal framework are valuable for gaining insights into, among other things, carbon stock change trends. Keywords: Land ...

  1. Completion of the National Land Cover Database (NLCD) 1992-2001 Land Cover Change Retrofit Product

    Science.gov (United States)

    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 betwe...

  2. QUALITY ASSESSMENT AND CONTROL OF OUTPUTS OF A NATIONWIDE AGRICULTURAL LAND COVER MAPPING PROGRAM USING LIDAR: PHIL-LIDAR 2 PARMAP EXPERIENCE

    Directory of Open Access Journals (Sweden)

    H. M. Pagkalinawan

    2017-11-01

    Full Text Available The Agricultural Resources Extraction from LiDAR Surveys (PARMAP project component of the Nationwide Detailed Resources Assessment using LiDAR (Phil-LiDAR 2 Program aims to produce detailed agricultural maps using LiDAR. Agricultural land cover at crop level was classified through object based image analysis using Support Vector Machine as classifier and LiDAR derivatives from point cloud (2 points per sq.m. and orthophoto (0.5-meter resolution as inputs. An accuracy of at least 90 %, assessed using validation points from the field and through image interpretation, was required before proceeding to post-processing and map lay-out. Knowledge sharing and capacity development facilitated by the University of the Philippines Diliman (UPD enabled partner universities across the Philippines to produce outputs for their assigned region. Considering output layers were generated by multiple teams working on different landscape complexities with some degree of data quality variability, quality checking is crucial to ensure accuracy standards were met. UPD PARMap devised a centralized and end-to-end scheme divided into four steps – land classification, GIS post-processing, schema application, and map lay-out. At each step, a block is reviewed and, subsequently, either approved or returned with documentation on required revisions. Turnaround time of review is at least one block (area ranging from 10 to 580 sq. km. per day. For coastal municipalities, an additional integration process to incorporate mapped coastal features was applied. Common problems observed during quality checking include misclassifications, gaps between features, incomplete attributes and missing map elements. Some issues are particular to specific blocks such as problematic LiDAR derivatives. UPD addressed these problems through discussion and mentoring visits to partner universities. As of March 2017, a total of 336 municipal agricultural maps have been turned-over to various

  3. 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 ...

  4. 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...

  5. 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...

  6. An operational methodology for riparian land cover fine scale regional mapping for the study of landscape influence on river ecological status

    Science.gov (United States)

    Tormos, T.; Kosuth, P.; Souchon, Y.; Villeneuve, B.; Durrieu, S.; Chandesris, A.

    2010-12-01

    Preservation and restoration of river ecosystems require an improved understanding of the mechanisms through which they are influenced by landscape at multiple spatial scales and particularly at river corridor scale considering the role of riparian vegetation for regulating and protecting river ecological status and the relevance of this specific area for implementing efficient and realistic strategies. Assessing correctly this influence over large river networks involves accurate broad scale (i.e. at least regional) information on Land Cover within Riparian Areas (LCRA). As the structure of land cover along rivers is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose we developed a generic multi-scale Object Based Image Analysis (OBIA) scheme able to produce LCRA maps in different geographic context by exploiting information available from very high spatial resolution imagery (satellite or airborne) and/or metric to decametric spatial thematic data on a given study zone thanks to fuzzy expert knowledge classification rules. A first experimentation was carried out on the Herault river watershed (southern of France), a 2650 square kilometers basin that presents a contrasted landscape (different ecoregions) and a total stream length of 1150 Km, using high and very high multispectral remotely-sensed images (10m Spot5 multispectral images and 0.5m aerial photography) and existing spatial thematic data. Application of the OBIA scheme produced a detailed (22 classes) LCRA map with an overall accuracy of 89% and a Kappa index of 83% according to a land cover pressures typology (six categories). A second experimentation (using the same data sources) was carried out on a larger test zone, a part of the Normandy river network (25 000 square kilometers basin; 6000 km long river network; 155 ecological stations). This second work aimed at elaborating a robust statistical

  7. It's time for a crisper image of the Face of the Earth: Landsat and climate time series for massive land cover & climate change mapping at detailed resolution.

    Science.gov (United States)

    Pons, Xavier; Miquel, Ninyerola; Oscar, González-Guerrero; Cristina, Cea; Pere, Serra; Alaitz, Zabala; Lluís, Pesquer; Ivette, Serral; Joan, Masó; Cristina, Domingo; Maria, Serra Josep; Jordi, Cristóbal; Chris, Hain; Martha, Anderson; Juanjo, Vidal

    2014-05-01

    Combining climate dynamics and land cover at a relative coarse resolution allows a very interesting approach to global studies, because in many cases these studies are based on a quite high temporal resolution, but they may be limited in large areas like the Mediterranean. However, the current availability of long time series of Landsat imagery and spatially detailed surface climate models allow thinking on global databases improving the results of mapping in areas with a complex history of landscape dynamics, characterized by fragmentation, or areas where relief creates intricate climate patterns that can be hardly monitored or modeled at coarse spatial resolutions. DinaCliVe (supported by the Spanish Government and ERDF, and by the Catalan Government, under grants CGL2012-33927 and SGR2009-1511) is the name of the project that aims analyzing land cover and land use dynamics as well as vegetation stress, with a particular emphasis on droughts, and the role that climate variation may have had in such phenomena. To meet this objective is proposed to design a massive database from long time series of Landsat land cover products (grouped in quinquennia) and monthly climate records (in situ climate data) for the Iberian Peninsula (582,000 km2). The whole area encompasses 47 Landsat WRS2 scenes (Landsat 4 to 8 missions, from path 197 to 202 and from rows 30 to 34), and 52 Landsat WRS1 scenes (for the previous Landsat missions, 212 to 221 and 30 to 34). Therefore, a mean of 49.5 Landsat scenes, 8 quinquennia per scene and a about 6 dates per quinquennium , from 1975 to present, produces around 2376 sets resulting in 30 m x 30 m spatial resolution maps. Each set is composed by highly coherent geometric and radiometric multispectral and multitemporal (to account for phenology) imagery as well as vegetation and wetness indexes, and several derived topographic information (about 10 Tbyte of data). Furthermore, on the basis on a previous work: the Digital Climatic Atlas of

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  9. Statistical Monitoring of Changes to Land Cover

    KAUST Repository

    Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying

    2018-01-01

    Accurate detection of changes in land cover leads to better understanding of the dynamics of landscapes. This letter reports the development of a reliable approach to detecting changes in land cover based on remote sensing and radiometric data

  10. 1990 Kansas Land Cover Patterns Update

    Data.gov (United States)

    Kansas Data Access and Support Center — In 2008, an update of the 1990 Kansas Land Cover Patterns (KLCP) database was undertaken. The 1990 KLCP database depicts 10 general land cover classes for the State...

  11. Land cover changes in central Sonora Mexico

    Science.gov (United States)

    Diego Valdez-Zamudio; Alejandro Castellanos-Villegas; Stuart Marsh

    2000-01-01

    Remote sensing techniques have been demonstrated to be very effective tools to help detect, analyze, and evaluate land cover changes in natural areas of the world. Changes in land cover can generally be attributed to either natural or anthropogenic forces. Multitemporal satellite imagery and airborne videography were used to detect, analyze, and evaluate land cover...

  12. Data mining algorithms for land cover change detection: a review

    Indian Academy of Sciences (India)

    Sangram Panigrahi

    2017-11-24

    Nov 24, 2017 ... values, poor quality measurement, high resolution and high dimensional data. The land cover .... These data sets also include quality assurance information, ...... 2012 A new data mining framework for forest fire mapping.

  13. Effect of Feature Dimensionality on Object-based Land Cover ...

    African Journals Online (AJOL)

    Myburgh, G, Mnr

    features, it has not been demonstrated with land cover mapping in an ... classifiers were chosen for benchmarking as the latter is the most commonly .... Additional open-source libraries were acquired to complete the implementation of the.

  14. Multi-Temporal Landslide Susceptibility Maps and Future Scenarios for Expected Land Cover Changes (Southern Apennines, Italy)

    NARCIS (Netherlands)

    Pisano, Luca; Zumpano, Veronica; Malek, Ziga; Micu, Mihai; Rosskopf, Carmen Maria; Parise, Mario; Mikoš, Matjaž; Vilímek, Vít; Yin, Yueping; Sassa, Kyoji

    2017-01-01

    Human activities, including extensive land use practices, such as deforestation and intensive cultivation, may severely affect the landscape, and have caused important changes to the extent of natural forests during the last century in Southern Italy. Such changes had a strong influence on the

  15. Validation of land use / land cover changes for Denmark

    DEFF Research Database (Denmark)

    Levin, Gregor; Johannsen, Vivian Kvist; Caspersen, Ole Hjort

    2018-01-01

    This report presents applied methods and results for a validation of land use and land cover changes for 1990 and 2014-2016. Results indicate that generally, accuracies of land use and land cover. However, afforestation and particularly deforestation are significantly overestimated....

  16. Mapping the Forest Type and Land Cover of Puerto Rico, a Component of the Caribbean Biodiversity Hotspot

    Science.gov (United States)

    Eileen Helmer; Olga Ramos; T. DEL M. LÓPEZ; Maya Quinones; W. DIAZ

    2002-01-01

    The Caribbean is one of the world’s centers of biodiversity and endemism. As in similar regions, many of its islands have complex topography, climate and soils, and ecological zones change over small areas. A segmented, supervised classification approach using Landsat TM imagery enabled us to develop the most detailed island-wide map of Puerto Rico’s extremely complex...

  17. CORINE land cover and floristic variation in a Mediterranean wetland.

    Science.gov (United States)

    Giallonardo, Tommaso; Landi, Marco; Frignani, Flavio; Geri, Francesco; Lastrucci, Lorenzo; Angiolini, Claudia

    2011-11-01

    The aims of the present study were to: (1) investigate whether CORINE land cover classes reflect significant differences in floristic composition, using a very detailed CORINE land cover map (scale 1:5000); (2) decompose the relationships between floristic assemblages and three groups of explanatory variables (CORINE land cover classes, environmental characteristics and spatial structure) into unique and interactive components. Stratified sampling was used to select a set of 100-m(2) plots in each land cover class identified in the semi-natural wetland surrounding a lake in central Italy. The following six classes were considered: stable meadows, deciduous oak dominated woods, hygrophilous broadleaf dominated woods, heaths and shrublands, inland swamps, canals or watercourses. The relationship between land cover classes and floristic composition was tested using several statistical techniques in order to determine whether the results remained consistent with different procedures. The variation partitioning approach was applied to identify the relative importance of three groups of explanatory variables in relation to floristic variation. The most important predictor was land cover, which explained 20.7% of the variation in plant distribution, although the hypothesis that each land cover class could be associated with a particular floristic pattern was not verified. Multi Response Permutation Analysis did not indicate a strong floristic separability between land cover classes and only 9.5% of species showed a significant indicator value for a specific land cover class. We suggest that land cover classes linked with hygrophilous and herbaceous communities in a wetland may have floristic patterns that vary with fine scale and are not compatible with a land cover map.

  18. International Coalition Land Use/Land Cover

    Data.gov (United States)

    Minnesota Department of Natural Resources — This data set is a product of an effort to update Minnesota's 1969 land use inventory. The project was funded in 1989 by the State Legislature per recommendation...

  19. Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research

    Science.gov (United States)

    Young, Steven

    2017-05-02

    When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting.Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields.In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information.Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand.Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.

  20. Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

    Science.gov (United States)

    Xu, Feinan; Wang, Weizhen; Wang, Jiemin; Xu, Ziwei; Qi, Yuan; Wu, Yueru

    2017-08-01

    The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC) sites and four groups of large-aperture scintillometers (LASs), were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this work will be

  1. Assessment of environmental responses to land use/land cover ...

    African Journals Online (AJOL)

    aghomotsegin

    2013-12-17

    Dec 17, 2013 ... 49.86% of the land cover has been converted to other land uses, ... management information system and policies that will ensure sustainable management of fragile ...... growth in agricultural output such as food and fiber.

  2. Border Lakes land-cover classification

    Science.gov (United States)

    Marvin Bauer; Brian Loeffelholz; Doug. Shinneman

    2009-01-01

    This document contains metadata and description of land-cover classification of approximately 5.1 million acres of land bordering Minnesota, U.S.A. and Ontario, Canada. The classification focused on the separation and identification of specific forest-cover types. Some separation of the nonforest classes also was performed. The classification was derived from multi-...

  3. Development of a 30 m Spatial Resolution Land Cover of Canada: Contribution to the Harmonized North America Land Cover Dataset

    Science.gov (United States)

    Pouliot, D.; Latifovic, R.; Olthof, I.

    2017-12-01

    Land cover is needed for a large range of environmental applications regarding climate impacts and adaption, emergency response, wildlife habitat, air quality, water yield, etc. In Canada a 2008 user survey revealed that the most practical scale for provision of land cover data is 30 m, nationwide, with an update frequency of five years (Ball, 2008). In response to this need the Canada Centre for Remote Sensing has generated a 30 m land cover of Canada for the base year 2010 as part of a planned series of maps at the recommended five year update frequency. This land cover is the Canadian contribution to the North American Land Change Monitoring System initiative, which seeks to provide harmonized land cover across Canada, the United States, and Mexico. The methodology developed in this research utilized a combination of unsupervised and machine learning techniques to map land cover, blend results between mapping units, locally optimize results, and process some thematic attributes with specific features sets. Accuracy assessment with available field data shows it was on average 75% for the five study areas assessed. In this presentation an overview of the unique processing aspects, example results, and initial accuracy assessment will be discussed.

  4. 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.

  5. 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

  6. 2005 Kansas Land Cover Patterns, Level I, State of Kansas (300m buffer)

    Data.gov (United States)

    Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly...

  7. 2005 Kansas Land Cover Patterns, Level I, Kansas River Watershed (1,000m buffer)

    Data.gov (United States)

    Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly...

  8. Simulating feedbacks in land use and land cover change models

    NARCIS (Netherlands)

    Verburg, P.H.

    2006-01-01

    In spite of the many advances in land use and land cover change modelling over the past decade many challenges remain. One of these challenges relates to the explicit treatment of feedback mechanisms in descriptive models of the land use system. This paper argues for model-based analysis to explore

  9. Remote Sensing Image Fusion at the Segment Level Using a Spatially-Weighted Approach: Applications for Land Cover Spectral Analysis and Mapping

    Directory of Open Access Journals (Sweden)

    Brian Johnson

    2015-01-01

    Full Text Available Segment-level image fusion involves segmenting a higher spatial resolution (HSR image to derive boundaries of land cover objects, and then extracting additional descriptors of image segments (polygons from a lower spatial resolution (LSR image. In past research, an unweighted segment-level fusion (USF approach, which extracts information from a resampled LSR image, resulted in more accurate land cover classification than the use of HSR imagery alone. However, simply fusing the LSR image with segment polygons may lead to significant errors due to the high level of noise in pixels along the segment boundaries (i.e., pixels containing multiple land cover types. To mitigate this, a spatially-weighted segment-level fusion (SWSF method was proposed for extracting descriptors (mean spectral values of segments from LSR images. SWSF reduces the weights of LSR pixels located on or near segment boundaries to reduce errors in the fusion process. Compared to the USF approach, SWSF extracted more accurate spectral properties of land cover objects when the ratio of the LSR image resolution to the HSR image resolution was greater than 2:1, and SWSF was also shown to increase classification accuracy. SWSF can be used to fuse any type of imagery at the segment level since it is insensitive to spectral differences between the LSR and HSR images (e.g., different spectral ranges of the images or different image acquisition dates.

  10. Maps From Mud—Using the Multiple Scenario Approach to Reconstruct Land Cover Dynamics From Pollen Records: A Case Study of Two Neolithic Landscapes

    Directory of Open Access Journals (Sweden)

    M. Jane Bunting

    2018-04-01

    Full Text Available Pollen records contain a wide range of information about past land cover, but translation from the pollen diagram to other formats remains a challenge. In this paper, we present LandPolFlow, a software package enabling Multiple Scenario Approach (MSA based land cover reconstruction from pollen records for specific landscapes. It has two components: a basic Geographic Information System which takes grids of landscape constraints (e.g., topography, geology and generates possible “scenarios” of past land cover using a combination of probabilistic and deterministic placement rules to distribute defined plant communities within the landscape, and a pollen dispersal and deposition model which simulates pollen loading at specified points within each scenario and compares that statistically with actual pollen assemblages from the same location. Goodness of fit statistics from multiple pollen site locations are used to identify which scenarios are likely reconstructions of past land cover. We apply this approach to two case studies of Neolithisation in Britain, the first from the Somerset Levels and Moors and the second from Mainland, Orkney. Both landscapes contain significant evidence of Neolithic activity, but present contrasting contexts. In Somerset, wet-preserved Neolithic remains such as trackways are abundant, but little dry land settlement archaeology is known, and the pre-Neolithic landscape was extensively wooded. In Orkney, the Neolithic archaeology includes domestic and monumental stone-built structures forming a UNESCO World Heritage Site, and the pre-Neolithic landscape was largely treeless. Existing pollen records were collated from both landscapes and correlated within new chronological frameworks (presented elsewhere. This allowed pollen data to be grouped into 200 year periods, or “timeslices,” for reconstruction of land cover through time using the MSA. Reconstruction suggests that subtle but clear and persistent impacts of

  11. Scenarios of land cover in China

    Science.gov (United States)

    Yue, Tian Xiang; Fan, Ze Meng; Liu, Ji Yuan

    2007-02-01

    A method for surface modeling of land cover change (SMLC) is developed on the basis of establishing transition probability matrixes between land cover types and HLZ types. SMLC is used to simulate land cover scenarios of China for the years 2039, 2069 and 2099, for which HLZ scenarios are first simulated in terms of HadCM3 climatic scenarios that are downscaled in zonal model of spatial climate change in China. This paper also analyzes spatial distribution of land cover types, area change and mean center shift of each land cover type, ecotope diversity, and patch connectivity under the land cover scenarios. The results show that cultivated land would decrease and woodland would expand greatly with climatic change, which coincides with consequences expected by implementation of Grain-for-Green policy. Nival area would shrink, and desertification area would expand at a comparatively slow rate in future 100 years. Climate change would generally cause less ecotope diversity and more patch connectivity. Ecosystems in China would have a pattern of beneficial cycle after efficient ecological conservation and restoration. However, if human activities would exceed regulation capacity of ecosystems themselves, the ecosystems in China might deteriorate more seriously.

  12. EnviroAtlas - Land Cover for the Conterminous United States

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset represents the percentage of land area that is classified as forest land cover, modified forest land cover, and natural land cover using the 2006...

  13. CLC2000 land cover database of the Netherlands; monitoring land cover changes between 1986 and 2000

    OpenAIRE

    Hazeu, G.W.

    2003-01-01

    The 1986 CORINE land cover database of the Netherlands was revised and updated on basis of Landsat satellite images and ancillary data. Interpretation of satellite images from 1986 and 2000 resulted in the CLC2000, CLC1986rev and CLCchange databases. A standard European legend and production methodology was applied. Thirty land cover classes were discerned. Most extended land cover types were pastures (231), arable land (211) and complex cultivation patterns (242). Between 1986 and 2000 aroun...

  14. 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.

  15. C-CAP Niihau 2005 Land Cover

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land cover derived from high resolution imagery according to the Coastal Change Analysis Program (C-CAP) protocol. This data set utilized 1...

  16. Statistical Monitoring of Changes to Land Cover

    KAUST Repository

    Zerrouki, Nabil

    2018-04-06

    Accurate detection of changes in land cover leads to better understanding of the dynamics of landscapes. This letter reports the development of a reliable approach to detecting changes in land cover based on remote sensing and radiometric data. This approach integrates the multivariate exponentially weighted moving average (MEWMA) chart with support vector machines (SVMs) for accurate and reliable detection of changes to land cover. Here, we utilize the MEWMA scheme to identify features corresponding to changed regions. Unfortunately, MEWMA schemes cannot discriminate between real changes and false changes. If a change is detected by the MEWMA algorithm, then we execute the SVM algorithm that is based on features corresponding to detected pixels to identify the type of change. We assess the effectiveness of this approach by using the remote-sensing change detection database and the SZTAKI AirChange benchmark data set. Our results show the capacity of our approach to detect changes to land cover.

  17. Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

    Directory of Open Access Journals (Sweden)

    F. Xu

    2017-08-01

    Full Text Available The determination of area-averaged evapotranspiration (ET at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC sites and four groups of large-aperture scintillometers (LASs, were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this

  18. Building a Continental Scale Land Cover Monitoring Framework for Australia

    Science.gov (United States)

    Thankappan, Medhavy; Lymburner, Leo; Tan, Peter; McIntyre, Alexis; Curnow, Steven; Lewis, Adam

    2012-04-01

    Land cover information is critical for national reporting and decision making in Australia. A review of information requirements for reporting on national environmental indicators identified the need for consistent land cover information to be compared against a baseline. A Dynamic Land Cover Dataset (DLCD) for Australia has been developed by Geoscience Australia and the Australian Bureau of Agriculture and Resource Economics and Sciences (ABARES) recently, to provide a comprehensive and consistent land cover information baseline to enable monitoring and reporting for sustainable farming practices, water resource management, soil erosion, and forests at national and regional scales. The DLCD was produced from the analysis of Enhanced Vegetation Index (EVI) data at 250-metre resolution derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period from 2000 to 2008. The EVI time series data for each pixel was modelled as 12 coefficients based on the statistical, phenological and seasonal characteristics. The time series were then clustered in coefficients spaces and labelled using ancillary information on vegetation and land use at the catchment scale. The accuracy of the DLCD was assessed using field survey data over 25,000 locations provided by vegetation and land management agencies in State and Territory jurisdictions, and by ABARES. The DLCD is seen as the first in a series of steps to build a framework for national land cover monitoring in Australia. A robust methodology to provide annual updates to the DLCD is currently being developed at Geoscience Australia. There is also a growing demand from the user community for land cover information at better spatial resolution than currently available through the DLCD. Global land cover mapping initiatives that rely on Earth observation data offer many opportunities for national and international programs to work in concert and deliver better outcomes by streamlining efforts on development and

  19. Estimating Accuracy of Land-Cover Composition From Two-Stage Clustering Sampling

    Science.gov (United States)

    Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), ...

  20. Use of UAV-Borne Spectrometer for Land Cover Classification

    Directory of Open Access Journals (Sweden)

    Sowmya Natesan

    2018-04-01

    Full Text Available Unmanned aerial vehicles (UAV are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification.

  1. Louisiana Land Cover Data Set, UTM Zone 15 NAD83, USGS [landcover_la_nlcd_usgs_2001.tif

    Data.gov (United States)

    Louisiana Geographic Information Center — The National Land Cover Database 2001 land cover layer for mapping zone 37A was produced through a cooperative project conducted by the Multi-Resolution Land...

  2. 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.

  3. LandSense: A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring

    Science.gov (United States)

    Moorthy, Inian; Fritz, Steffen; See, Linda; McCallum, Ian

    2017-04-01

    Currently within the EU's Earth Observation (EO) monitoring framework, there is a need for low-cost methods for acquiring high quality in-situ data to create accurate and well-validated environmental monitoring products. To help address this need, a new four year Horizon 2020 project entitled LandSense will link remote sensing data with modern participatory data collection methods that involve citizen scientists. This paper will describe the citizen science activities within the LandSense Observatory that aim to deliver concrete, measurable and quality-assured ground-based data that will complement existing satellite monitoring systems. LandSense will deploy advanced tools, services and resources to mobilize and engage citizens to collect in-situ observations (i.e. ground-based data and visual interpretations of EO imagery). Integrating these citizen-driven in-situ data collections with established authoritative and open access data sources will help reduce costs, extend GEOSS and Copernicus capacities, and support comprehensive environmental monitoring systems. Policy-relevant campaigns will be implemented in close collaboration with multiple stakeholders to ensure that citizen observations address user requirements and contribute to EU-wide environmental governance and decision-making. Campaigns for addressing local and regional Land Use and Land Cover (LULC) issues are planned for select areas in Austria, France, Germany, Spain, Slovenia and Serbia. Novel LandSense services (LandSense Campaigner, FarmLand Support, Change Detector and Quality Assurance & Control) will be deployed and tested in these areas to address critical LULC issues (i.e. urbanization, agricultural land use and forest/habitat monitoring). For example, local residents in the cities of Vienna, Tulln, and Heidelberg will help cooperatively detect and map changes in land cover and green space to address key issues of urban sprawl, land take and flooding. Such campaigns are facilitated through

  4. Land use, land cover, and drainage on the Albemarle-Pamlico Peninsula, Eastern North Carolina, 1974

    Science.gov (United States)

    Daniel, C.C.

    1978-01-01

    A land use, land cover, and drainage map of the 2,000-square-mile Albermarle-Pamlico peninsula of eastern North Carolina has been prepared, at a scale of 1:125,000, as part of a larger study of the effects of large-scale land clearing on regional hydrology. The peninsula includes the most extensive area of wetland in North Carolina and one of the largest in the country. In recent years the pace of land clearing on the peninsula has accelerated as land is being converted from forest, swamp, and brushland to agricultural use. Conversion of swamps to intensive farming operations requires profound changes in the landscape. Vegetation is uprooted and burned and ditches and canals are dug to remove excess water. What is the impact of these changes on ground-water supplies and on the streams and surrounding coastal waters which receive the runoff This map will aid in answering these and similar questions that have arisen about the patterns of land use and the artificial drainage system that removes excess water from the land. By showing both land use and drainage, this map can be used to identify those areas where water-related problems may occur and help assess the nature and causes of these problems. The map covers the entire area east of the Suffolk Scarp, an area of about 2,000 square miles, for the year 1974 using data from 1974-76. Land use and land cover were compiled and modified from the U.S. Geological Survey 's Rocky Mount and Manteo LUDA maps. Additional information came from U.S. Geological Survey orthophotoquads, Landsat imagery, and field checking. Drainage was mapped from orthophotoquads, some field inspection, and 7-1/2 minute topographic quadrangle maps.

  5. 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.

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Recep Gundogan

    2008-02-01

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

  8. Land Cover Monitoring for Water Resources Management in Angola

    Science.gov (United States)

    Miguel, Irina; Navarro, Ana; Rolim, Joao; Catalao, Joao; Silva, Joel; Painho, Marco; Vekerdy, Zoltan

    2016-08-01

    The aim of this paper is to assess the impact of improved temporal resolution and multi-source satellite data (SAR and optical) on land cover mapping and monitoring for efficient water resources management. For that purpose, we developed an integrated approach based on image classification and on NDVI and SAR backscattering (VV and VH) time series for land cover mapping and crop's irrigation requirements computation. We analysed 28 SPOT-5 Take-5 images with high temporal revisiting time (5 days), 9 Sentinel-1 dual polarization GRD images and in-situ data acquired during the crop growing season. Results show that the combination of images from different sources provides the best information to map agricultural areas. The increase of the images temporal resolution allows the improvement of the estimation of the crop parameters, and then, to calculate of the crop's irrigation requirements. However, this aspect was not fully exploited due to the lack of EO data for the complete growing season.

  9. Tree Cover Mapping Tool—Documentation and user manual

    Science.gov (United States)

    Cotillon, Suzanne E.; Mathis, Melissa L.

    2016-06-02

    The Tree Cover Mapping (TCM) tool was developed by scientists at the U.S. Geological Survey Earth Resources Observation and Science Center to allow a user to quickly map tree cover density over large areas using visual interpretation of high resolution imagery within a geographic information system interface. The TCM tool uses a systematic sample grid to produce maps of tree cover. The TCM tool allows the user to define sampling parameters to estimate tree cover within each sample unit. This mapping method generated the first on-farm tree cover maps of vast regions of Niger and Burkina Faso. The approach contributes to implementing integrated landscape management to scale up re-greening and restore degraded land in the drylands of Africa. The TCM tool is easy to operate, practical, and can be adapted to many other applications such as crop mapping, settlements mapping, or other features. This user manual provides step-by-step instructions for installing and using the tool, and creating tree cover maps. Familiarity with ArcMap tools and concepts is helpful for using the tool.

  10. 2005 Kansas Land Cover Patterns, Level IV, State of Kansas (300m buffer)

    Data.gov (United States)

    Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns (KLCP) Mapping Initiative was a two-phase mapping endeavor that occurred over a three-year period (2007-2009). Note that while...

  11. 2005 Kansas Land Cover Patterns, Level IV, Kansas River Watershed (1,000m buffer)

    Data.gov (United States)

    Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns (KLCP) Mapping Initiative was a two-phase mapping endeavor that occurred over a three-year period (2007-2009). Note that while...

  12. Land cover and water yield: inference problems when comparing catchments with mixed land cover

    Directory of Open Access Journals (Sweden)

    A. I. J. M. van Dijk

    2012-09-01

    Full Text Available Controlled experiments provide strong evidence that changing land cover (e.g. deforestation or afforestation can affect mean catchment streamflow (Q. By contrast, a similarly strong influence has not been found in studies that interpret Q from multiple catchments with mixed land cover. One possible reason is that there are methodological issues with the way in which the Budyko framework was used in the latter type studies. We examined this using Q data observed in 278 Australian catchments and by making inferences from synthetic Q data simulated by a hydrological process model (the Australian Water Resources Assessment system Landscape model. The previous contrasting findings could be reproduced. In the synthetic experiment, the land cover influence was still present but not accurately detected with the Budyko- framework. Likely sources of interpretation bias demonstrated include: (i noise in land cover, precipitation and Q data; (ii additional catchment climate characteristics more important than land cover; and (iii covariance between Q and catchment attributes. These methodological issues caution against the use of a Budyko framework to quantify a land cover influence in Q data from mixed land-cover catchments. Importantly, however, our findings do not rule out that there may also be physical processes that modify the influence of land cover in mixed land-cover catchments. Process model simulations suggested that lateral water redistribution between vegetation types and recirculation of intercepted rainfall may be important.

  13. Towards a global land subsidence map

    NARCIS (Netherlands)

    Erkens, G.; Sutanudjaja, E. H.

    2015-01-01

    Land subsidence is a global problem, but a global land subsidence map is not available yet. Such map is crucial to raise global awareness of land subsidence, as land subsidence causes extensive damage (probably in the order of billions of dollars annually). With the global land subsidence map

  14. The Significance of Land Cover Delineation on Soil Erosion Assessment.

    Science.gov (United States)

    Efthimiou, Nikolaos; Psomiadis, Emmanouil

    2018-04-25

    The study aims to evaluate the significance of land cover delineation on soil erosion assessment. To that end, RUSLE (Revised Universal Soil Loss Equation) was implemented at the Upper Acheloos River catchment, Western Central Greece, annually and multi-annually for the period 1965-92. The model estimates soil erosion as the linear product of six factors (R, K, LS, C, and P) considering the catchment's climatic, pedological, topographic, land cover, and anthropogenic characteristics, respectively. The C factor was estimated using six alternative land use delineations of different resolution, namely the CORINE Land Cover (CLC) project (2000, 2012 versions) (1:100,000), a land use map conducted by the Greek National Agricultural Research Foundation (NAGREF) (1:20,000), a land use map conducted by the Greek Payment and Control Agency for Guidance and Guarantee Community Aid (PCAGGCA) (1:5,000), and the Landsat 8 16-day Normalized Difference Vegetation Index (NDVI) dataset (30 m/pixel) (two approximations) based on remote sensing data (satellite image acquired on 07/09/2016) (1:40,000). Since all other factors remain unchanged per each RUSLE application, the differences among the yielded results are attributed to the C factor (thus the land cover pattern) variations. Validation was made considering the convergence between simulated (modeled) and observed sediment yield. The latter was estimated based on field measurements conducted by the Greek PPC (Public Power Corporation). The model performed best at both time scales using the Landsat 8 (Eq. 13) dataset, characterized by a detailed resolution and a satisfactory categorization, allowing the identification of the most susceptible to erosion areas.

  15. Nielsen number of a covering map

    Directory of Open Access Journals (Sweden)

    Jezierski Jerzy

    2006-01-01

    Full Text Available We consider a finite regular covering over a compact polyhedron and a map admitting a lift . We show some formulae expressing the Nielsen number as a linear combination of the Nielsen numbers of its lifts.

  16. a Free and Open Source Tool to Assess the Accuracy of Land Cover Maps: Implementation and Application to Lombardy Region (italy)

    Science.gov (United States)

    Bratic, G.; Brovelli, M. A.; Molinari, M. E.

    2018-04-01

    The availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community.

  17. 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)

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

    Directory of Open Access Journals (Sweden)

    Dong Jiang

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

  19. The GOFC-GOLD/CEOS Land Cover Harmonization and Validation Initiative: Technical Design and Implementation

    Science.gov (United States)

    Herold, M.; Woodcock, C.; Stehman, S.; Nightingale, J.; Friedl, M.; Schmullius, C.

    2010-12-01

    A global effort to assess the accuracy of existing and future land cover products derived from a variety of satellite sensors over a range of spatial resolutions is being led by the Land Cover Implementation Team (LC-IT) of GOFC/GOLD (Global Observation of Land Cover Dynamics) in conjunction with the CEOS (Committee on Earth Observation Satellites) WGCV (Working Group on Calibration and Validation) LPV (Land Product Validation) subgroup. The first phase of this effort is complete and culminated in a publication of community consensus "best practices" for validation of global land cover datasets (2). The next phase is to implement the recommendations outlined in the "best practices" document. A "living database" of global randomized sample sites will form the basis of accuracy assessment for a host of global land cover products (GLC2000, MODIS land cover, GLOBCOVER, United Nation's Forest Resource Assessment (FRA2010), and the Mid-Decadal Global Land Survey. This "living dataset" will also be a community resource available for use in validation of regional or national mapping efforts using LCCS (UN FAO's Land Cover Classification System). Based on the known accuracy of existing land cover products, GOFC/GOLD will to develop and update a "best currently available" global land cover map. Individual geographic regions may be selected from different land cover products (global, national or regional), or they may be combined in various ways

  20. 81 An Assessment of the land use and land cover changes in ...

    African Journals Online (AJOL)

    `123456789jkl''''#

    Three satellite images for three different years (1991, 2000 and 2009) were used to come up with a ... land cover change maps among other data resources that are .... in agro-ecological region 3 that receives an average rainfall of .... After field observation and collection of .... the image along with statistical comparison of.

  1. Conversion of land use and cover in northwest Amazon (Brazil

    Directory of Open Access Journals (Sweden)

    Carlos Antonio da Silva Junior

    2014-09-01

    Full Text Available The increasing use of natural resources in a disorderly way has been demanding constant monitoring and ecological-economic zoning. The knowledge on land use and cover allows that measures that guarantee the preservation, maintenance of the environment and space management be appropriate to the reality, since through these factors it is possible to follow the probable environmental impacts and the socioeconomic development of a place in several contexts. The Geographical Information System (GIS and remote sensing techniques have been applied to land use and land cover mapping. This study aimed to analyze the conversion of land use from different perspectives, concerning geoprocessing techniques, in the southeastern of Roraima State, Brazil, in two distinct periods. In order to verify the land use and cover, two analyses were conducted, using the Spring and TerraView softwares. Great part of the cultivated areas was converted into capoeira, what probably denotes an ending of profitable agriculture, as well as its abandonment caused by the nutritional deficiency of the soil, that became inappropriate for cultivation in the subsequent years. A fuzzy logic would possibly fit well to the types of data analyzed, because the attribute query is overly complex.

  2. Land Use and Land Cover - Volusia County Future Land Use (FLU) 2010

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Volusia County Future Land Use 2010. This is the original land use map for 2010. It was drafted for the comprehensive plan in 1990 and contains adopted amendments.

  3. Sensitivity of selected landscape pattern metrics to land-cover misclassification and differences in land-cover composition

    Science.gov (United States)

    James D. Wickham; Robert V. O' Neill; Kurt H. Riitters; Timothy G. Wade; K. Bruce Jones

    1997-01-01

    Calculation of landscape metrics from land-cover data is becoming increasingly common. Some studies have shown that these measurements are sensitive to differences in land-cover composition, but none are known to have tested also their a sensitivity to land-cover misclassification. An error simulation model was written to test the sensitivity of selected land-scape...

  4. Open Land-Use Map: A Regional Land-Use Mapping Strategy for Incorporating OpenStreetMap with Earth Observations

    Science.gov (United States)

    Yang, D.; Fu, C. S.; Binford, M. W.

    2017-12-01

    The southeastern United States has high landscape heterogeneity, withheavily managed forestlands, highly developed agriculture lands, and multiple metropolitan areas. Human activities are transforming and altering land patterns and structures in both negative and positive manners. A land-use map for at the greater scale is a heavy computation task but is critical to most landowners, researchers, and decision makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating the classification maps at the regional scale: the necessity of large training point sets and the expensive computation cost-in terms of both money and time-in classifier modeling. Volunteered Geographic Information (VGI) opens a new era in mapping and visualizing our world, where the platform is open for collecting valuable georeferenced information by volunteer citizens, and the data is freely available to the public. As one of the most well-known VGI initiatives, OpenStreetMap (OSM) contributes not only road network distribution, but also the potential for using this data to justify land cover and land use classifications. Google Earth Engine (GEE) is a platform designed for cloud-based mapping with a robust and fast computing power. Most large scale and national mapping approaches confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover datasets. Unlike most other large-scale approaches, we distinguish and differentiate land-use from land cover. By focusing our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach is developed by incorporating the OpenstreepMap dataset into Earth observation remote sensing imageries instead of the often-used land cover base maps.

  5. Land use and land cover change based on historical space-time model

    Science.gov (United States)

    Sun, Qiong; Zhang, Chi; Liu, Min; Zhang, Yongjing

    2016-09-01

    Land use and cover change is a leading edge topic in the current research field of global environmental changes and case study of typical areas is an important approach understanding global environmental changes. Taking the Qiantang River (Zhejiang, China) as an example, this study explores automatic classification of land use using remote sensing technology and analyzes historical space-time change by remote sensing monitoring. This study combines spectral angle mapping (SAM) with multi-source information and creates a convenient and efficient high-precision land use computer automatic classification method which meets the application requirements and is suitable for complex landform of the studied area. This work analyzes the histological space-time characteristics of land use and cover change in the Qiantang River basin in 2001, 2007 and 2014, in order to (i) verify the feasibility of studying land use change with remote sensing technology, (ii) accurately understand the change of land use and cover as well as historical space-time evolution trend, (iii) provide a realistic basis for the sustainable development of the Qiantang River basin and (iv) provide a strong information support and new research method for optimizing the Qiantang River land use structure and achieving optimal allocation of land resources and scientific management.

  6. MODELING OF FUTURE LAND COVER LAND USE CHANGE IN NORTH CAROLINA USING MARKOV CHAIN AND CELLULAR AUTOMATA MODEL

    OpenAIRE

    Mohammad Sayemuzzaman; Manoj K. Jha

    2014-01-01

    State wide variant topographic features in North Carolina attract the hydro-climatologist. There is none modeling study found that predict future Land Cover Land Use (LCLU) change for whole North Carolina. In this study, satellite-derived land cover maps of year 1992, 2001 and 2006 of North Carolina were integrated within the framework of the Markov-Cellular Automata (Markov-CA) model which combines the Markov chain and Cellular Automata (CA) techniques. A Multi-Criteria Evaluation (MCE) was ...

  7. 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)

  8. Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter

    CSIR Research Space (South Africa)

    Salmon, BP

    2012-07-01

    Full Text Available - fective way to monitor and evaluate land cover changes. An operator making an image-to-image comparison is still a com- mon method in most organizations when mapping land cover change, which is time consuming and resource intensive. Au- tomated change...

  9. Millennium Ecosystem Assessment: MA Rapid Land Cover Change

    Data.gov (United States)

    National Aeronautics and Space Administration — The Millennium Ecosystem Assessment: MA Rapid Land Cover Change provides data and information on global and regional land cover change in raster format for...

  10. Photogrammetry, Digital mapping and Land Informations Systems

    DEFF Research Database (Denmark)

    Frederiksen, Poul

    1998-01-01

    Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia.......Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia....

  11. CLC2000 land cover database of the Netherlands; monitoring land cover changes between 1986 and 2000

    NARCIS (Netherlands)

    Hazeu, G.W.

    2003-01-01

    The 1986 CORINE land cover database of the Netherlands was revised and updated on basis of Landsat satellite images and ancillary data. Interpretation of satellite images from 1986 and 2000 resulted in the CLC2000, CLC1986rev and CLCchange databases. A standard European legend and production

  12. EnviroAtlas - Milwaukee, WI - Meter-Scale Urban Land Cover Data (MULC) Data (2010)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The EnviroAtlas Milwaukee, WI Meter Urban Land Cover (MULC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green,...

  13. EnviroAtlas - Woodbine, IA - Meter-Scale Urban Land Cover (MULC) Data (2011)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The EnviroAtlas Woodbine, IA Meter-Scale Urban Land Cover (MULC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red,...

  14. EnviroAtlas - Phoenix, AZ - Meter-Scale Urban Land Cover (MULC) Data (2010)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The EnviroAtlas Phoenix, AZ Meter-Scale Urban Land Cover (MULC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red,...

  15. 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

  16. The Improvement of Land Cover Classification by Thermal Remote Sensing

    Directory of Open Access Journals (Sweden)

    Liya Sun

    2015-06-01

    Full Text Available Land cover classification has been widely investigated in remote sensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remote sensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images.

  17. 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.

  18. Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005

    Science.gov (United States)

    Gutman, G.; Byrnes, Raymond A.; Masek, J.; Covington, S.; Justice, C.; Franks, S.; Headley, Rachel

    2008-01-01

    Land cover is a critical component of the Earth system, infl uencing land-atmosphere interactions, greenhouse gas fl uxes, ecosystem health, and availability of food, fi ber, and energy for human populations. The recent Integrated Global Observations of Land (IGOL) report calls for the generation of maps documenting global land cover at resolutions between 10m and 30m at least every fi ve years (Townshend et al., in press). Moreover, despite 35 years of Landsat observations, there has not been a unifi ed global analysis of land-cover trends nor has there been a global assessment of land-cover change at Landsat-like resolution. Since the 1990s, the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) have supported development of data sets based on global Landsat observations (Tucker et al., 2004). These land survey data sets, usually referred to as GeoCover ™, provide global, orthorectifi ed, typically cloud-free Landsat imagery centered on the years 1975, 1990, and 2000, with a preference for leaf-on conditions. Collectively, these data sets provided a consistent set of observations to assess land-cover changes at a decadal scale. These data are freely available via the Internet from the USGS Center for Earth Resources Observation and Science (EROS) (see http://earthexplorer.usgs.gov or http://glovis.usgs.gov). This has resulted in unprecedented downloads of data, which are widely used in scientifi c studies of land-cover change (e.g., Boone et al., 2007; Harris et al., 2005; Hilbert, 2006; Huang et al. 2007; Jantz et al., 2005, Kim et al., 2007; Leimgruber, 2005; Masek et al., 2006). NASA and USGS are continuing to support land-cover change research through the development of GLS2005 - an additional global Landsat assessment circa 20051 . Going beyond the earlier initiatives, this data set will establish a baseline for monitoring changes on a 5-year interval and will pave the way toward continuous global land-cover

  19. Forest Cover Mapping in Iskandar Malaysia Using Satellite Data

    Science.gov (United States)

    Kanniah, K. D.; Mohd Najib, N. E.; Vu, T. T.

    2016-09-01

    Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

  20. Potential Role of Land Use and Land Cover Information in Powerplant Siting: Example of Three Mile Island

    Science.gov (United States)

    Wray, J. R.

    1982-01-01

    Selecting a site for a nuclear powerplant can be helped by digitizing land use and land cover data, population data, and other pertinent data sets, and then placing them in a geographic information system. Such a system begins with a set of standardized maps for location reference and then provides for retrieval and analysis of spatial data keyed to the maps. This makes possible thematic mapping by computer, or interactive visual display for decisionmaking. It also permits correlating land use area measurements with census and other data (such as fallout dosages), and the updating of all data sets. The system is thus a tool for dealing with resource management problems and for analyzing the interaction between people and their environment. An explanation of a computer-plotted map of land use and cover for Three Mile Island and vicinity is given.

  1. 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.

  2. Impact of land cover and population density on land surface temperature: case study in Wuhan, China

    Science.gov (United States)

    Li, Lin; Tan, Yongbin; Ying, Shen; Yu, Zhonghai; Li, Zhen; Lan, Honghao

    2014-01-01

    With the rapid development of urbanization, the standard of living has improved, but changes to the city thermal environment have become more serious. Population urbanization is a driving force of residential expansion, which predominantly influences the land surface temperature (LST). We obtained the land covers and LST maps of Wuhan from Landsat-5 images in 2000, 2002, 2005, and 2009, and discussed the distribution of land use/cover change and LST variation, and we analyzed the correlation between population distribution and LST values in residential regions. The results indicated massive variation of land cover types, which was shown as a reduction in cultivatable land and the expansion of building regions. High-LST regions concentrated on the residential and industrial areas with low vegetation coverage. In the residential region, the population density (PD) had effects on the LST values. Although the area or variation of residential regions was close, lower PD was associated with lower mean LST or LST variation. Thus, decreasing the high-LST regions concentration by reducing the PD may alleviate the urban heat island effect on the residential area. Taken together, these results can provide supports for urban planning projects and studies on city ecological environments.

  3. Assessing the Impact of Land Use and Land Cover Change on Global Water Resources

    Science.gov (United States)

    Batra, N.; Yang, Y. E.; Choi, H. I.; Islam, A.; Charlotte, D. F.; Cai, X.; Kumar, P.

    2007-12-01

    Land use and land cover changes (LULCC) significantly modify the hydrological regime of the watersheds, affecting water resources and environment from regional to global scale. This study seeks to advance and integrate water and energy cycle observation, scientific understanding, and human impacts to assess future water availability. To achieve the research objective, we integrate and interpret past and current space based and in situ observations into a global hydrologic model (GHM). GHM is developed with enhanced spatial and temporal resolution, physical complexity, hydrologic theory and processes to quantify the impact of LULCC on physical variables: surface runoff, subsurface flow, groundwater, infiltration, ET, soil moisture, etc. Coupled with the common land model (CLM), a 3-dimensional volume averaged soil-moisture transport (VAST) model is expanded to incorporate the lateral flow and subgrid heterogeneity. The model consists of 11 soil-hydrology layers to predict lateral as well as vertical moisture flux transport based on Richard's equations. The primary surface boundary conditions (SBCs) include surface elevation and its derivatives, land cover category, sand and clay fraction profiles, bedrock depth and fractional vegetation cover. A consistent global GIS-based dataset is constructed for the SBCs of the model from existing observational datasets comprising of various resolutions, map projections and data formats. Global ECMWF data at 6-hour time steps for the period 1971 through 2000 is processed to get the forcing data which includes incoming longwave and shortwave radiation, precipitation, air temperature, pressure, wind components, boundary layer height and specific humidity. Land use land cover data, generated using IPCC scenarios for every 10 years from 2000 to 2100 is used for future assessment on water resources. Alterations due to LULCC on surface water balance components: ET, groundwater recharge and runoff are then addressed in the study. Land

  4. Land use and land cover dynamics on the campus of Federal University of Lavras from 1964 to 2009

    Directory of Open Access Journals (Sweden)

    Elizabeth Ferreira

    2013-03-01

    Full Text Available This study identified, quantified and analyzed changes in land use and cover on the campus of Federal University of Lavras campus, located in Lavras city (Minas Gerais State. The 2009 QuickBird satellite imagery and 1985, 1979, 1971, 1964 vertical aerial photographs were used to produce a set of land use and land cover maps. The work started with the orthorectification of the QuickBird satellite imagery and vertical aerial photographs. The identification and definition of land cover and land use classes were obtained from field surveys in 2009. First, the land cover and land use maps were made from that information. Finally, the quantification and analysis of changes were performed at the imagery time range. The results showed that in 2009 the "urbanized area class" of the campus reached 65.79 ha and that the most significant increase of this class occurred between the years 1964 (6.24 ha and 1971 (24.4 ha. The smallest area of "forest land class" found on the campus was 38.38 ha in 1971, and from 1979 on this situation has been improved reaching 113.18 ha of "forest land class" in 2009. For the "water class" there was not any dam constructed yet in the campus before 1971. Most of the campus area, previously used for "agricultural land class" had a significant reduction within this category, from 384.19 ha in 1964 to 271.16 ha in 2009.

  5. Carbon emissions from land use and land-cover change

    Directory of Open Access Journals (Sweden)

    R. A. Houghton

    2012-12-01

    Full Text Available The net flux of carbon from land use and land-cover change (LULCC accounted for 12.5% of anthropogenic carbon emissions from 1990 to 2010. This net flux is the most uncertain term in the global carbon budget, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and not all analyses consider the same types of management activities. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. In addition to deforestation, all analyses considered changes in the area of agricultural lands (croplands and pastures. Some considered, also, forest management (wood harvest, shifting cultivation. None included emissions from the degradation of tropical peatlands. Means and standard deviations across the thirteen model estimates of annual emissions for the 1980s and 1990s, respectively, are 1.14 ± 0.23 and 1.12 ± 0.25 Pg C yr−1 (1 Pg = 1015 g carbon. Four studies also considered the period 2000–2009, and the mean and standard deviations across these four for the three decades are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The standard deviations across model means shown here are smaller than previous estimates of uncertainty as they do not account for the errors that result from data uncertainty and from an incomplete understanding of all the processes affecting the net flux of carbon from LULCC. Although these errors have not been systematically evaluated, based on partial analyses available in the literature and expert opinion, they are estimated to be on the order of ± 0.5 Pg C yr−1.

  6. Spatial patterns of land cover in the United States: a technical document supporting the Forest Service 2010 RPA Assessment

    Science.gov (United States)

    Kurt H. Riitters

    2011-01-01

    Land cover patterns inventoried from a national land cover map provide information about the landscape context and fragmentation of the Nation’s forests, grasslands, and shrublands. This inventory is required to quantify, map, and evaluate the capacities of landscapes to provide ecological goods and services sustainably. This report documents the procedures to...

  7. Land cover change in coastal watersheds 1996 to 2010

    Science.gov (United States)

    Nate Herold

    2016-01-01

    Land use and land cover play a significant role as drivers of environmental change. Information on what is changing and where those changes are occurring is essential if we are to improve our understanding of...

  8. National Land Cover Database (NLCD) Percent Developed Imperviousness Collection

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The National Land Cover Database (NLCD) Percent Developed Imperviousness Collection is produced through a cooperative project conducted by the Multi-Resolution Land...

  9. 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.

  10. Land cover in Upper Egypt assessed using regional and global land cover products derived from MODIS imagery.

    Science.gov (United States)

    Fuller, Douglas O; Parenti, Michael S; Gad, Adel M; Beier, John C

    2012-01-01

    Irrigation along the Nile River has resulted in dramatic changes in the biophysical environment of Upper Egypt. In this study we used a combination of MODIS 250 m NDVI data and Landsat imagery to identify areas that changed from 2001-2008 as a result of irrigation and water-level fluctuations in the Nile River and nearby water bodies. We used two different methods of time series analysis -- principal components (PCA) and harmonic decomposition (HD), applied to the MODIS 250 m NDVI images to derive simple three-class land cover maps and then assessed their accuracy using a set of reference polygons derived from 30 m Landsat 5 and 7 imagery. We analyzed our MODIS 250 m maps against a new MODIS global land cover product (MOD12Q1 collection 5) to assess whether regionally specific mapping approaches are superior to a standard global product. Results showed that the accuracy of the PCA-based product was greater than the accuracy of either the HD or MOD12Q1 products for the years 2001, 2003, and 2008. However, the accuracy of the PCA product was only slightly better than the MOD12Q1 for 2001 and 2003. Overall, the results suggest that our PCA-based approach produces a high level of user and producer accuracies, although the MOD12Q1 product also showed consistently high accuracy. Overlay of 2001-2008 PCA-based maps showed a net increase of 12 129 ha of irrigated vegetation, with the largest increase found from 2006-2008 around the Districts of Edfu and Kom Ombo. This result was unexpected in light of ambitious government plans to develop 336 000 ha of irrigated agriculture around the Toshka Lakes.

  11. Nielsen number of a covering map

    Directory of Open Access Journals (Sweden)

    Jerzy Jezierski

    2006-02-01

    Full Text Available We consider a finite regular covering pH:X˜H→X over a compact polyhedron and a map f:X→X admitting a lift f˜:X˜H→X˜H. We show some formulae expressing the Nielsen number N(f as a linear combination of the Nielsen numbers of its lifts.

  12. Central American Vegetation/Land Cover Classification and Conservation Status

    Data.gov (United States)

    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,...

  13. Land use map, Finney County, Kansas

    Science.gov (United States)

    Morain, S. A. (Principal Investigator); Williams, D. L.; Coiner, J. C.

    1973-01-01

    The author has identified the following significant results. Methods for the mapping of land use in agricultural regions are developed and applied to preparation of a land use map of Finney County, Kanas. Six land use categories were identified from an MSS-5 image. These categories are: (1) large field irrigation; (2) small field irrigation; (3) dryland cultivation; (4) rangeland; (5) cultural features; and (6) riverine land. The map is composed of basically homogeneous regions with definable mixtures of the six categories. Each region is bounded by an ocularly evident change in land use.

  14. 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

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

    Directory of Open Access Journals (Sweden)

    Alfredo Fernández-Landa

    2016-07-01

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

  16. Integrated modelling of anthropogenic land-use and land-cover change on the global scale

    Science.gov (United States)

    Schaldach, R.; Koch, J.; Alcamo, J.

    2009-04-01

    In many cases land-use activities go hand in hand with substantial modifications of the physical and biological cover of the Earth's surface, resulting in direct effects on energy and matter fluxes between terrestrial ecosystems and the atmosphere. For instance, the conversion of forest to cropland is changing climate relevant surface parameters (e.g. albedo) as well as evapotranspiration processes and carbon flows. In turn, human land-use decisions are also influenced by environmental processes. Changing temperature and precipitation patterns for example are important determinants for location and intensity of agriculture. Due to these close linkages, processes of land-use and related land-cover change should be considered as important components in the construction of Earth System models. A major challenge in modelling land-use change on the global scale is the integration of socio-economic aspects and human decision making with environmental processes. One of the few global approaches that integrates functional components to represent both anthropogenic and environmental aspects of land-use change, is the LandSHIFT model. It simulates the spatial and temporal dynamics of the human land-use activities settlement, cultivation of food crops and grazing management, which compete for the available land resources. The rational of the model is to regionalize the demands for area intensive commodities (e.g. crop production) and services (e.g. space for housing) from the country-level to a global grid with the spatial resolution of 5 arc-minutes. The modelled land-use decisions within the agricultural sector are influenced by changing climate and the resulting effects on biomass productivity. Currently, this causal chain is modelled by integrating results from the process-based vegetation model LPJmL model for changing crop yields and net primary productivity of grazing land. Model output of LandSHIFT is a time series of grid maps with land-use/land-cover information

  17. Accuracy assessment of seven global land cover datasets over China

    Science.gov (United States)

    Yang, Yongke; Xiao, Pengfeng; Feng, Xuezhi; Li, Haixing

    2017-03-01

    Land cover (LC) is the vital foundation to Earth science. Up to now, several global LC datasets have arisen with efforts of many scientific communities. To provide guidelines for data usage over China, nine LC maps from seven global LC datasets (IGBP DISCover, UMD, GLC, MCD12Q1, GLCNMO, CCI-LC, and GlobeLand30) were evaluated in this study. First, we compared their similarities and discrepancies in both area and spatial patterns, and analysed their inherent relations to data sources and classification schemes and methods. Next, five sets of validation sample units (VSUs) were collected to calculate their accuracy quantitatively. Further, we built a spatial analysis model and depicted their spatial variation in accuracy based on the five sets of VSUs. The results show that, there are evident discrepancies among these LC maps in both area and spatial patterns. For LC maps produced by different institutes, GLC 2000 and CCI-LC 2000 have the highest overall spatial agreement (53.8%). For LC maps produced by same institutes, overall spatial agreement of CCI-LC 2000 and 2010, and MCD12Q1 2001 and 2010 reach up to 99.8% and 73.2%, respectively; while more efforts are still needed if we hope to use these LC maps as time series data for model inputting, since both CCI-LC and MCD12Q1 fail to represent the rapid changing trend of several key LC classes in the early 21st century, in particular urban and built-up, snow and ice, water bodies, and permanent wetlands. With the highest spatial resolution, the overall accuracy of GlobeLand30 2010 is 82.39%. For the other six LC datasets with coarse resolution, CCI-LC 2010/2000 has the highest overall accuracy, and following are MCD12Q1 2010/2001, GLC 2000, GLCNMO 2008, IGBP DISCover, and UMD in turn. Beside that all maps exhibit high accuracy in homogeneous regions; local accuracies in other regions are quite different, particularly in Farming-Pastoral Zone of North China, mountains in Northeast China, and Southeast Hills. Special

  18. ISLSCP II Historical Land Cover and Land Use, 1700-1990

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: The Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set...

  19. The impact of Future Land Use and Land Cover Changes on Atmospheric Chemistry-Climate Interactions

    NARCIS (Netherlands)

    Ganzeveld, L.N.; Bouwman, L.

    2010-01-01

    To demonstrate potential future consequences of land cover and land use changes beyond those for physical climate and the carbon cycle, we present an analysis of large-scale impacts of land cover and land use changes on atmospheric chemistry using the chemistry-climate model EMAC (ECHAM5/MESSy

  20. U.S. landowner behavior, land use and land cover changes, and climate change mitigation.

    Science.gov (United States)

    Ralph J. Alig

    2003-01-01

    Landowner behavior is a major determinant of land use and land cover changes. an important consideration for policy analysts concerned with global change. Study of landowner behavior aids in designing more effective incentives for inducing land use and land cover changes to help mitigate climate change by reducing net greenhouse gas emissions. Afforestation,...

  1. C-CAP Land Cover, Kauai, Hawaii

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...

  2. C-CAP Land Cover, Niihau, Hawaii

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...

  3. Percent Agricultural Land Cover on Steep Slopes

    Data.gov (United States)

    U.S. Environmental Protection Agency — Clearing land for agriculture tends to increase soil erosion. The amount of erosion is related to the steepness of the slope, farming methods used and soil type....

  4. Impact of Land Use Land Cover Change on East Asian monsoon

    Science.gov (United States)

    Chilukoti, N.; Xue, Y.; Liu, Y.; Lee, J.

    2017-12-01

    Humans modify the Earth's terrestrial surface on a continental scale by removing natural vegetation for crops/grazing. The current rates, extents and intensities of Land Use and Land Cover Change (LULCC) are greater than ever in history. The earlier studies of Land-atmosphere interactions used specified land surface conditions without interannual variations. In this study using NCEP CFSv2 coupled with Simplified Simple Biosphere (SSiB) model, biogeophysical impacts of LULCC on climate variability, anomaly, and changes are investigated by using the LULCC map from the Hurtt et al. (2006, 2011), which covered 66 years from 1950-2015 with annual variability. We combined the changes in crop and pasture fractions and consider as LULCC. A methodology had been developed to convert the Hurtt LULCC change map with 1° resolution to the GCM grid points. Since the GCM has only one dominant type, when the crop and pasture frction value at one point was larger than the critical value, that grid was assigned as degraded. Comprehensive evaluation was conducted to ensure the consistence of the trend of land degradation in the Hurtt's map and in the GCM LULCC map. In the degraded point, trees were changed to low vegetation or grasses, and low vegetation to bare soil. A set of surface parameters such as leaf area index, vegetation height, roughness length, and soil parameters, associated with vegetation are changed to show the degradation effects. We integrated the model with the potential vegetation map and the map with LULCC from 1950 to 2015, and the results indicate the LULCC causes precipitation reduction globally, with the strongest signals over monsoon regions. For instance, the degradation in Mexico, West Africa, south and East Asia and South America produced significant precipitation anomalies, some of which are consistent with observed regional precipitation anomalies. Meanwhile, it has also found that the LULCC enhances the surface warming during the summer in monsoon

  5. The managed clearing: An overlooked land-cover type in urbanizing regions?

    Science.gov (United States)

    Madden, Marguerite; Gray, Josh; Meentemeyer, Ross K.

    2018-01-01

    Urban ecosystem assessments increasingly rely on widely available map products, such as the U.S. Geological Service (USGS) National Land Cover Database (NLCD), and datasets that use generic classification schemes to detect and model large-scale impacts of land-cover change. However, utilizing existing map products or schemes without identifying relevant urban class types such as semi-natural, yet managed land areas that account for differences in ecological functions due to their pervious surfaces may severely constrain assessments. To address this gap, we introduce the managed clearings land-cover type–semi-natural, vegetated land surfaces with varying degrees of management practices–for urbanizing landscapes. We explore the extent to which managed clearings are common and spatially distributed in three rapidly urbanizing areas of the Charlanta megaregion, USA. We visually interpreted and mapped fine-scale land cover with special attention to managed clearings using 2012 U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) images within 150 randomly selected 1-km2 blocks in the cities of Atlanta, Charlotte, and Raleigh, and compared our maps with National Land Cover Database (NLCD) data. We estimated the abundance of managed clearings relative to other land use and land cover types, and the proportion of land-cover types in the NLCD that are similar to managed clearings. Our study reveals that managed clearings are the most common land cover type in these cities, covering 28% of the total sampled land area– 6.2% higher than the total area of impervious surfaces. Managed clearings, when combined with forest cover, constitutes 69% of pervious surfaces in the sampled region. We observed variability in area estimates of managed clearings between the NAIP-derived and NLCD data. This suggests using high-resolution remote sensing imagery (e.g., NAIP) instead of modifying NLCD data for improved representation of spatial heterogeneity and

  6. Towards realistic Holocene land cover scenarios: integration of archaeological, palynological and geomorphological records and comparison to global land cover scenarios.

    Science.gov (United States)

    De Brue, Hanne; Verstraeten, Gert; Broothaerts, Nils; Notebaert, Bastiaan

    2016-04-01

    Accurate and spatially explicit landscape reconstructions for distinct time periods in human history are essential for the quantification of the effect of anthropogenic land cover changes on, e.g., global biogeochemical cycles, ecology, and geomorphic processes, and to improve our understanding of interaction between humans and the environment in general. A long-term perspective covering Mid and Late Holocene land use changes is recommended in this context, as it provides a baseline to evaluate human impact in more recent periods. Previous efforts to assess the evolution and intensity of agricultural land cover in past centuries or millennia have predominantly focused on palynological records. An increasing number of quantitative techniques has been developed during the last two decades to transfer palynological data to land cover estimates. However, these techniques have to deal with equifinality issues and, furthermore, do not sufficiently allow to reconstruct spatial patterns of past land cover. On the other hand, several continental and global databases of historical anthropogenic land cover changes based on estimates of global population and the required agricultural land per capita have been developed in the past decennium. However, at such long temporal and spatial scales, reconstruction of past anthropogenic land cover intensities and spatial patterns necessarily involves many uncertainties and assumptions as well. Here, we present a novel approach that combines archaeological, palynological and geomorphological data for the Dijle catchment in the central Belgium Loess Belt in order to arrive at more realistic Holocene land cover histories. Multiple land cover scenarios (> 60.000) are constructed using probabilistic rules and used as input into a sediment delivery model (WaTEM/SEDEM). Model outcomes are confronted with a detailed geomorphic dataset on Holocene sediment fluxes and with REVEALS based estimates of vegetation cover using palynological data from

  7. Investigating the Feasibility of Geo-Tagged Photographs as Sources of Land Cover Input Data

    Directory of Open Access Journals (Sweden)

    Vyron Antoniou

    2016-05-01

    Full Text Available Geo-tagged photographs are used increasingly as a source of Volunteered Geographic Information (VGI, which could potentially be used for land use and land cover applications. The purpose of this paper is to analyze the feasibility of using this source of spatial information for three use cases related to land cover: Calibration, validation and verification. We first provide an inventory of the metadata that are collected with geo-tagged photographs and then consider what elements would be essential, desirable, or unnecessary for the aforementioned use cases. Geo-tagged photographs were then extracted from Flickr, Panoramio and Geograph for an area of London, UK, and classified based on their usefulness for land cover mapping including an analysis of the accompanying metadata. Finally, we discuss protocols for geo-tagged photographs for use of VGI in relation to land cover applications.

  8. Development of 2010 national land cover database for the Nepal.

    Science.gov (United States)

    Uddin, Kabir; Shrestha, Him Lal; Murthy, M S R; Bajracharya, Birendra; Shrestha, Basanta; Gilani, Hammad; Pradhan, Sudip; Dangol, Bikash

    2015-01-15

    Land cover and its change analysis across the Hindu Kush Himalayan (HKH) region is realized as an urgent need to support diverse issues of environmental conservation. This study presents the first and most complete national land cover database of Nepal prepared using public domain Landsat TM data of 2010 and replicable methodology. The study estimated that 39.1% of Nepal is covered by forests and 29.83% by agriculture. Patch and edge forests constituting 23.4% of national forest cover revealed proximate biotic interferences over the forests. Core forests constituted 79.3% of forests of Protected areas where as 63% of area was under core forests in the outside protected area. Physiographic regions wise forest fragmentation analysis revealed specific conservation requirements for productive hill and mid mountain regions. Comparative analysis with Landsat TM based global land cover product showed difference of the order of 30-60% among different land cover classes stressing the need for significant improvements for national level adoption. The online web based land cover validation tool is developed for continual improvement of land cover product. The potential use of the data set for national and regional level sustainable land use planning strategies and meeting several global commitments also highlighted. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Modeled impact of anthropogenic land cover change on climate

    Science.gov (United States)

    Findell, K.L.; Shevliakova, E.; Milly, P.C.D.; Stouffer, R.J.

    2007-01-01

    Equilibrium experiments with the Geophysical Fluid Dynamics Laboratory's climate model are used to investigate the impact of anthropogenic land cover change on climate. Regions of altered land cover include large portions of Europe, India, eastern China, and the eastern United States. Smaller areas of change are present in various tropical regions. This study focuses on the impacts of biophysical changes associated with the land cover change (albedo, root and stomatal properties, roughness length), which is almost exclusively a conversion from forest to grassland in the model; the effects of irrigation or other water management practices and the effects of atmospheric carbon dioxide changes associated with land cover conversion are not included in these experiments. The model suggests that observed land cover changes have little or no impact on globally averaged climatic variables (e.g., 2-m air temperature is 0.008 K warmer in a simulation with 1990 land cover compared to a simulation with potential natural vegetation cover). Differences in the annual mean climatic fields analyzed did not exhibit global field significance. Within some of the regions of land cover change, however, there are relatively large changes of many surface climatic variables. These changes are highly significant locally in the annual mean and in most months of the year in eastern Europe and northern India. They can be explained mainly as direct and indirect consequences of model-prescribed increases in surface albedo, decreases in rooting depth, and changes of stomatal control that accompany deforestation. ?? 2007 American Meteorological Society.

  10. 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.

  11. Mapping Forest Inventory and Analysis forest land use: timberland, reserved forest land, and other forest land

    Science.gov (United States)

    Mark D. Nelson; John Vissage

    2007-01-01

    The Forest Inventory and Analysis (FIA) program produces area estimates of forest land use within three subcategories: timberland, reserved forest land, and other forest land. Mapping these subcategories of forest land requires the ability to spatially distinguish productive from unproductive land, and reserved from nonreserved land. FIA field data were spatially...

  12. Characterizing Degradation Gradients through Land Cover Change Analysis in Rural Eastern Cape, South Africa

    Directory of Open Access Journals (Sweden)

    Zahn Münch

    2017-02-01

    Full Text Available Land cover change analysis was performed for three catchments in the rural Eastern Cape, South Africa, for two time steps (2000 and 2014, to characterize landscape conversion trajectories for sustained landscape health. Land cover maps were derived: (1 from existing data (2000; and (2 through object-based image analysis (2014 of Landsat 8 imagery. Land cover change analysis was facilitated using land cover labels developed to identify landscape change trajectories. Land cover labels assigned to each intersection of the land cover maps at the two time steps provide a thematic representation of the spatial distribution of change. While land use patterns are characterized by high persistence (77%, the expansion of urban areas and agriculture has occurred predominantly at the expense of grassland. The persistence and intensification of natural or invaded wooded areas were identified as a degradation gradient within the landscape, which amounted to almost 10% of the study area. The challenge remains to determine significant signals in the landscape that are not artefacts of error in the underlying input data or scale of analysis. Systematic change analysis and accurate uncertainty reporting can potentially address these issues to produce authentic output for further modelling.

  13. Influence of land development on stormwater runoff from a mixed land use and land cover catchment.

    Science.gov (United States)

    Paule-Mercado, M A; Lee, B Y; Memon, S A; Umer, S R; Salim, I; Lee, C-H

    2017-12-01

    Mitigating for the negative impacts of stormwater runoff is becoming a concern due to increased land development. Understanding how land development influences stormwater runoff is essential for sustainably managing water resources. In recent years, aggregate low impact development-best management practices (LID-BMPs) have been implemented to reduce the negative impacts of stormwater runoff on receiving water bodies. This study used an integrated approach to determine the influence of land development and assess the ecological benefits of four aggregate LID-BMPs in stormwater runoff from a mixed land use and land cover (LULC) catchment with ongoing land development. It used data from 2011 to 2015 that monitored 41 storm events and monthly LULC, and a Personalized Computer Storm Water Management Model (PCSWMM). The four aggregate LID-BMPs are: ecological (S1), utilizing pervious covers (S2), and multi-control (S3) and (S4). These LID-BMPs were designed and distributed in the study area based on catchment characteristics, cost, and effectiveness. PCSWMM was used to simulate the monitored storm events from 2014 (calibration: R 2 and NSE>0.5; RMSE 0.5; RMSE runoff data and LULC change patterns (only 2015 for LID-BMPs) were used. Results show that the expansion of bare land and impervious cover, soil alteration, and high amount of precipitation influenced the stormwater runoff variability during different phases of land development. The four aggregate LID-BMPs reduced runoff volume (34%-61%), peak flow (6%-19%), and pollutant concentrations (53%-83%). The results of this study, in addition to supporting local LULC planning and land development activities, also could be applied to input data for empirical modeling, and designing sustainable stormwater management guidelines and monitoring strategies. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Developing a New North American Land Cover Product at 30m Resolution: Methods, Results and Future Plans

    Science.gov (United States)

    Homer, C.; Colditz, R. R.; Latifovic, R.; Llamas, R. M.; Pouliot, D.; Danielson, P.; Meneses, C.; Victoria, A.; Ressl, R.; Richardson, K.; Vulpescu, M.

    2017-12-01

    Land cover and land cover change information at regional and continental scales has become fundamental for studying and understanding the terrestrial environment. With recent advances in computer science and freely available image archives, continental land cover mapping has been advancing to higher spatial resolution products. The North American Land Change Monitoring System (NALCMS) remains the principal provider of seamless land cover maps of North America. Founded in 2006, this collaboration among the governments of Canada, Mexico and the United States has released two previous products based on 250m MODIS images, including a 2005 land cover and a 2005-2010 land cover change product. NALCMS has recently completed the next generation North America land cover product, based upon 30m Landsat images. This product now provides the first ever 30m land cover produced for the North American continent, providing 19 classes of seamless land cover. This presentation provides an overview of country-specific image classification processes, describes the continental map production process, provides results for the North American continent and discusses future plans. NALCMS is coordinated by the Commission for Environmental Cooperation (CEC) and all products can be obtained at their website - www.cec.org.

  15. GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS

    Directory of Open Access Journals (Sweden)

    K. Fukue

    2016-06-01

    Full Text Available The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year SR(Surface Reflectance and NBAR(Nadir BRDF-Adjusted Reflectance products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NBAR products showed similar classification accuracy of 99%.

  16. Chittenden County, Vermont land cover project

    Science.gov (United States)

    Malloy, D. E.

    1981-01-01

    The testing of LANDSAT applicability to urban and agricultural land use analysis at the substate level is described. It is concluded that the LANDSAT system has a place in Vermont and places like it, but that the present operation is inadequate and the need for technology transfer and excellent communication between the producers and users is fundamental to the future of the system and for the realization of benefit from the investment.

  17. 2005 Kansas Land Cover Patterns, Level I, State of Kansas (300m buffer) and Kansas River Watershed (1,000m buffer)

    Data.gov (United States)

    Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly...

  18. The causes of land-use and land-cover change : moving beyond the myths

    NARCIS (Netherlands)

    Lambin, E.F.; Turner, B.L.; Geist, H.J.; Agbola, S.B.; Angelsen, A.; Bruce, J.W.; Coomes, O.T.; Dirzo, R.; Fischer, G.; Folke, C.; George, P.S.; Homewood, K.; Imbernon, J.; Leemans, R.; Xiubin Li,; Moran, E.F.; Mortimore, M.; Ramakrishnan, P.S.; Richards, J.F.; Skanes, H.; Steffen, W.; Stone, G.D.; Svedin, U.; Veldkamp, A.; Vogel, C.; Jianchu Xu,

    2001-01-01

    Common understanding of the causes of land-use and land-cover change is dominated by simplifications which, in turn, underlie many environment-development policies. This article tracks some of the major myths on driving forces of land-cover change and proposes alternative pathways of change that are

  19. Using remote sensing imagery and GIS to identify land cover and land use within Ceahlau Massif (Romania

    Directory of Open Access Journals (Sweden)

    GEORGE CRACU

    2014-11-01

    Full Text Available Using remote sensing imagery and GIS to identify land cover and land use within Ceahlău Massif (Romania. In this study we considerer land cover and land use asessment within Ceahlău Massif (Romania using satellite imagery and GIS . To achieve this goal, we used a Landsat 7 ETM + satellite image, which was processed using specialized software in analyzing satellite images and GIS software in several stages:  Downloading, importing and layer stack of all spectral bands composing satellite image;  Establishment of areas of interest for each category of land cover and land use, which were digitized on - screen and for which spectral signatures characteristics were established;  Supervised image classification using Maximum Likelihood Method;  Importing the resulting m ap (raster in GIS environment and creating the final land cover/land use map for Ceahlău Massif. In the study area we identified nine land cover/land use classes: deciduous forests, mixed forests, coniferous forests, secondary grasslands, subalpine vegeta tion, alpine meadows, agricultural land, lakes and built area. By analizing the spatial distribution of these classes, it was found that forests are the best represented class, occupying an area of 188.4 km² (56.4% of total, followed by secondary grassl and, which occupies an area of 68.2 km² (20.4% of total, lakes (26.6 km² or 7.98% of total and agricultural land (16.1 km² or 4.86%

  20. Vegetation cover and land use of a protected coastal area and its surroundings, southeast Brazil

    OpenAIRE

    Caris,Elisa Araujo Penna; Kurtz,Bruno Coutinho; Cruz,Carla Bernadete Madureira; Scarano,Fabio Rubio

    2013-01-01

    We applied remote sensing techniques on a TM Landsat 5 image (1:50,000) to map land use and vegetation cover of the Restinga de Jurubatiba National Park and surroundings. The thematic map generated from the digital classification of the image allowed us to spatially characterize and quantify the different land uses and soil covers of the area. Thirteen classes were identified. The most representative classes in the park were the Clusia (31.99%) and Ericaceae formations (29.14%). More than 90%...

  1. Impacts of Land Cover Changes on Climate over China

    Science.gov (United States)

    Chen, L.; Frauenfeld, O. W.

    2014-12-01

    Land cover changes can influence regional climate through modifying the surface energy balance and water fluxes, and can also affect climate at large scales via changes in atmospheric general circulation. With rapid population growth and economic development, China has experienced significant land cover changes, such as deforestation, grassland degradation, and farmland expansion. In this study, the Community Earth System Model (CESM) is used to investigate the climate impacts of anthropogenic land cover changes over China. To isolate the climatic effects of land cover change, we focus on the CAM and CLM models, with prescribed climatological sea surface temperature and sea ice cover. Two experiments were performed, one with current vegetation and the other with potential vegetation. Current vegetation conditions were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations, and potential vegetation over China was obtained from Ramankutty and Foley's global potential vegetation dataset. Impacts of land cover changes on surface air temperature and precipitation are assessed based on the difference of the two experiments. Results suggest that land cover changes have a cold-season cooling effect in a large region of China, but a warming effect in summer. These temperature changes can be reconciled with albedo forcing and evapotranspiration. Moreover, impacts on atmospheric circulation and the Asian Monsoon is also discussed.

  2. Assessing land use/cover changes: a nationwide multidate spatial database for Mexico

    Science.gov (United States)

    Mas, Jean-François; Velázquez, Alejandro; Díaz-Gallegos, José Reyes; Mayorga-Saucedo, Rafael; Alcántara, Camilo; Bocco, Gerardo; Castro, Rutilio; Fernández, Tania; Pérez-Vega, Azucena

    2004-10-01

    A nationwide multidate GIS database was generated in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in Mexico. Existing cartography on land use/cover at a 1:250,000 scale was revised to select compatible inputs regarding the scale, the classification scheme and the mapping method. Digital maps from three different dates (the late 1970s, 1993 and 2000) were revised, evaluated, corrected and integrated into a GIS database. In order to improve the reliability of the database, an attempt was made to assess the accuracy of the digitalisation procedure and to detect and correct unlikely changes due to thematic errors in the maps. Digital maps were overlaid in order to generate LUCC maps, transition matrices and to calculate rates of conversion. Based upon this database, rates of deforestation between 1976 and 2000 were evaluated as 0.25 and 0.76% per year for temperate and tropical forests, respectively.

  3. Assessment of Land-Use/Land-Cover Change and Forest Fragmentation in the Garhwal Himalayan Region of India

    Directory of Open Access Journals (Sweden)

    Amit Kumar Batar

    2017-04-01

    Full Text Available The Garhwal Himalaya has experienced extensive deforestation and forest fragmentation, but data and documentation detailing this transformation of the Himalaya are limited. The aim of this study is to analyse the observed changes in land cover and forest fragmentation that occurred between 1976 and 2014 in the Garhwal Himalayan region in India. Three images from Landsat 2 Multispectral Scanner System (MSS, Landsat 5 Thematic Mapper (TM, and Landsat 8 Operational Land Imager (OLI were used to extract the land cover maps. A cross-tabulation detection method in the geographic information system (GIS module was used to detect land cover changes during the 1st period (1976–1998 and 2nd period (1998–2014. The landscape fragmentation tool LFT v2.0 was used to construct a forest fragmentation map and analyse the forest fragmentation pattern and change during the 1st period (1976–1998 and 2nd period (1998–2014. The overall annual rate of change in the forest cover was observed to be 0.22% and 0.27% in the 1st period (1976–1998 and 2nd period (1998–2014, respectively. The forest fragmentation analysis shows that a large core forest has decreased throughout the study period. The total area of forest patches also increased from 1976 to 2014, which are completely degraded forests. The results indicate that anthropogenic activities are the main causes of the loss of forest cover and forest fragmentation, but that natural factors also contributed. An increase in the area of scrub and barren land also contributed to the accumulation of wasteland or non-forest land in this region. Determining the trend and the rate of land cover conversion is necessary for development planners to establish a rational land use policy.

  4. National Land Cover Database (NLCD) Percent Tree Canopy Collection

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The National Land Cover Database (NLCD) Percent Tree Canopy Collection is a product of the U.S. Forest Service (USFS), and is produced through a cooperative project...

  5. Unsupervised land cover change detection: meaningful sequential time series analysis

    CSIR Research Space (South Africa)

    Salmon, BP

    2011-06-01

    Full Text Available An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short...

  6. 2005 C-CAP Land Cover of Oahu, Hawaii

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land cover derived from high resolution imagery according to the Coastal Change Analysis Program (C-CAP) protocol. This data set utilized...

  7. Assessing Wetland Health Using a Newly Developed Land Cover ...

    African Journals Online (AJOL)

    Citizen science combines environmental research with environmental education .... health of the wetland using land cover type impacts. Once the impact is ... to interpret the findings of the quantitative method using the qualitative findings.

  8. 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...

  9. Land cover classification using reformed fuzzy C-means

    Indian Academy of Sciences (India)

    This paper uses segmentation based on unsupervised clustering techniques for classification of land cover. ∗ ... and unsupervised classification can be solved by FCM. ..... They also act as input to the development and monitoring of a range of ...

  10. Monitoring Urban Land Cover/land Use Change in Algiers City Using Landsat Images (1987-2016)

    Science.gov (United States)

    Bouchachi, B.; Zhong, Y.

    2017-09-01

    Monitoring the Urban Land Cover/Land Use change detection is important as one of the main driving forces of environmental change because Urbanization is the biggest changes in form of Land, resulting in a decrease in cultivated areas. Using remote sensing ability to solve land resources problems. The purpose of this research is to map the urban areas at different times to monitor and predict possible urban changes, were studied the annual growth urban land during the last 29 years in Algiers City. Improving the productiveness of long-term training in land mapping, were have developed an approach by the following steps: 1) pre-processing for improvement of image characteristics; 2) extract training sample candidates based on the developed methods; and 3) Derive maps and analyzed of Algiers City on an annual basis from 1987 to 2016 using a Supervised Classifier Support Vector Machine (SVMs). Our result shows that the strategy of urban land followed in the region of Algiers City, developed areas mostly were extended to East, West, and South of Central Regions. The urban growth rate is linked with National Office of Statistics data. Future studies are required to understand the impact of urban rapid lands on social, economy and environmental sustainability, it will also close the gap in data of urbanism available, especially on the lack of reliable data, environmental and urban planning for each municipality in Algiers, develop experimental models to predict future land changes with statistically significant confidence.

  11. IDENTIFICATION OF LAND COVER IN THE PAST USING INFRARED IMAGES AT PRESENT

    Directory of Open Access Journals (Sweden)

    V. Šafář

    2012-07-01

    Full Text Available Czech landscape is an old residential area used by humans since ancient times. People have influenced it since their arrival and various activities in different periods create landscape layers called a palimpsest. Land Cover of one location could have changed several times. The most important reason is meandering and subsequent straightening of rivers, deforestation, relocation and change in soil layers. These changes in the past affected the present management and it is important to identify them. A suitable tool for the determination of different sites is remote sensing in the infrared spectrum, which monitors changes in the vegetation with the support of archival materials. After identifying the different places you can search the archival materials, how the land cover looked in the past. There have been used these archival materials: maps II. and III. military mapping, basic maps and other maps and historical orthophotomap. Czech Republic has a national archive of aerial photographs with aerial photographs from the thirties of the last century maintained by MGHO Dobruska. A comparative analysis of Land Cover shows the increases and decreases in agricultural land, changes in communication line elements, forest losses and increases, comparing the legal and actual status of the forest boundaries and their changes over time, changes in the built areas and links to the surrounding countryside. Land Cover of this study was created primarily with a visual interpretation of each area with their vectorization and assigning attributes to these areas and then comparing each of archival materials.

  12. Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

    Science.gov (United States)

    Clark, M. L.; Kilham, N. E.

    2015-12-01

    Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as

  13. The Interpretation of Urban Land Use Maps

    Science.gov (United States)

    Robinson, Roger J.

    1973-01-01

    Three steps in urban land use analysis, fieldwork mapping, processing of data, and classification and delimitation of zones in an urban area, are described. An appendix presents a classification of buildings by function. (KM)

  14. 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 ...

  15. IMPLEMENTATION STRATEGY FOR PRODUCTION OF NATIONAL LAND-COVER DATA (NLCD) FROM THE LANDSAT 7 THEMATIC MAPPER SATELLITE

    Science.gov (United States)

    As environmental programs within and outside the federal government continue to move away from point-based studies to larger and larger spatial (not cartographic) scale, the need for land-cover and other geographic data have become ineluctable. The national land-cover mapping pr...

  16. Estimating land use / land cover changes in Denmark from 1990 - 2012

    DEFF Research Database (Denmark)

    Levin, Gregor; Kastrup Blemmer, Morten; Gyldenkærne, Steen

    According to the article 3(4) of the Kyoto Protocol, Denmark is obliged to document sequestration and emission of carbon dioxide from land use and land cover and changes in these. This report documents and describes applied data end developed methods aiming at estimating amounts and changes in land...... use and land cover for Denmark for since 1990. Estimation of land use and land cover categories and changes in these is predominantly based on existing categorical (i.e. pre-classified) geographical information. Estimations are elaborated for the period from 1990 to 2005, from 2005 to 2011 and from...... 2011 to 2012. Due to limited availability of historical spatially explicit information, estimations of change in land use and land cover from 1990 up to 2011 do, to some degree, involve decisions based on expert knowledge. Due to a significant increase in the availability of detailed spatially specific...

  17. VT Generalized Land Cover Land Use for Champlain Basin - SAL 1992

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) Circa 1992 land use - land cover (LULC) for the Lake Champlain Basin. This layer was created by performing a retrospective change detection on the...

  18. VT Generalized Land Cover Land Use for Champlain Basin - SAL 2001

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) Circa 2001 land use / land cover (LULC) for the Lake Champlain Basin. The goal in creating this layer was to generate an "improved" version of...

  19. A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

    Directory of Open Access Journals (Sweden)

    Akpona Okujeni

    2014-07-01

    Full Text Available Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR, kernel ridge regression (KRR, artificial neural networks (NN, random forest regression (RFR and partial least squares regression (PLSR. Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN or limited (RFR and PLSR performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.

  20. Global Land Survey Impervious Mapping Project Web Site

    Science.gov (United States)

    DeColstoun, Eric Brown; Phillips, Jacqueline

    2014-01-01

    The Global Land Survey Impervious Mapping Project (GLS-IMP) aims to produce the first global maps of impervious cover at the 30m spatial resolution of Landsat. The project uses Global Land Survey (GLS) Landsat data as its base but incorporates training data generated from very high resolution commercial satellite data and using a Hierarchical segmentation program called Hseg. The web site contains general project information, a high level description of the science, examples of input and output data, as well as links to other relevant projects.

  1. Minnesota Land Use and Cover - A 1990's Census of the Land - Tiled

    Data.gov (United States)

    Minnesota Department of Natural Resources — This data set integrates six different source data sets to provide a simplified overall view of Minnesota's land use / cover. The six source data sets covered...

  2. 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.

  3. 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.

  4. Land-Cover Change Analysis and Simulation in Conakry (Guinea, Using Hybrid Cellular-Automata and Markov Model

    Directory of Open Access Journals (Sweden)

    Arafan Traore

    2018-04-01

    Full Text Available In this study, land-cover change in the capital Conakry of Guinea was simulated using the integrated Cellular Automata and Markov model (CA-Markov in the Geographic Information System (GIS and Remote Sensing (RS. Historical land-cover change information was derived from 1986, 2000 and 2016 Landsat data. Using the land-cover change maps of 1986 and 2000, the land-cover change map for 2016 was simulated based on the Markov model in IDRISSI software (Clark University, Worcester, MA, USA. The simulated result was compared with the 2016 land-cover map for validation using the Relative Operating Characteristic (ROC. The ROC result showed a very strong agreement between the two maps. From this result, the land-cover change map for 2025 was simulated using CA-Markov model. The result has indicated that the proportion of the urban area was 49% in 2016, and it is expected to increase to 52% by 2025, while vegetation will decrease from 35% in 2016 to 32% in 2025. This study suggests that the rapid land-cover change has been led by both rapid population growth and extreme poverty in rural areas, which will result in migration into Conakry. The results of this study will provide bases for assessing the sustainability and the management of the urban area and for taking actions to mitigate the degradation of the urban environment.

  5. The Effect Of Land Cover/Land Use On Groundwater Resources In Southern Egypt (Luxor Area): Remote Sensing And Field Studies

    International Nuclear Information System (INIS)

    Faid, A.M.; Hinz, E.A.; Montgomery, H.

    2003-01-01

    The impact of land cover/land use on groundwater can be critical. Land cover / land use maps give an early warning for planners and developers to protect groundwater resources from depletion and preserve its sustain ability. These land cover / land use maps can be used for the planning of groundwater development to prevent the deterioration of the aquifer. The Research Institute for Groundwater of Egypt (RIGW) has carried out hydrogeological studies in 1990 to evaluate the potentiality of groundwater in Luxor area in southern Egypt close to the Nile valley. The region is characterized by a rapid and continuous increase in land reclamation and development on the fringes which surround the already heavily cultivated land within the Nile valley. This presented a need for continuous monitoring and information updating over a vast region in a short time and at a reasonable cost. This study illustrates how remote sensing techniques can be effectively used for monitoring changes in land cover / land use in an effort to aid groundwater management. Landsat Thematic Mapper (TM) data collected in 1984 and 2000 were processed and analyzed over the study area to produce land cover/land use maps. The Normalized Difference Vegetation Index (NDVI) technique is used for Landsat TM images of to quantify areas which are covered by vegetation. Results indicated significant increase in cultivated areas. Remote sensing results are compared with iso-piezo metric maps and iso-salinity maps that were produced in 1984 and 2000. Comparison of these maps indicates groundwater depletion and salinity increase from 1984 to 2000. We relate this to the increase of the area being cultivated

  6. Temporal Land Cover Analysis for Net Ecosystem Improvement

    Energy Technology Data Exchange (ETDEWEB)

    Ke, Yinghai; Coleman, Andre M.; Diefenderfer, Heida L.

    2013-04-09

    We delineated 8 watersheds contributing to previously defined river reaches within the 1,468-km2 historical floodplain of the tidally influenced lower Columbia River and estuary. We assessed land-cover change at the watershed, reach, and restoration site scales by reclassifying remote-sensing data from the National Oceanic and Atmospheric Administration Coastal Change Analysis Program’s land cover/land change product into forest, wetland, and urban categories. The analysis showed a 198.3 km2 loss of forest cover during the first 6 years of the Columbia Estuary Ecosystem Restoration Program, 2001–2006. Total measured urbanization in the contributing watersheds of the estuary during the full 1996-2006 change analysis period was 48.4 km2. Trends in forest gain/loss and urbanization differed between watersheds. Wetland gains and losses were within the margin of error of the satellite imagery analysis. No significant land cover change was measured at restoration sites, although it was visible in aerial imagery, therefore, the 30-m land-cover product may not be appropriate for assessment of early-stage wetland restoration. These findings suggest that floodplain restoration sites in reaches downstream of watersheds with decreasing forest cover will be subject to increased sediment loads, and those downstream of urbanization will experience effects of increased impervious surfaces on hydrologic processes.

  7. Land suitability maps for waste disposal siting

    International Nuclear Information System (INIS)

    Hrasna, M.

    1996-01-01

    The suitability of geoenvironment for waste disposal depends mainly on its stability and on the danger of groundwater pollution. Besides them, on the land suitability maps for the given purpose also those factors of the factors of the geoenvironment and the landscape should be taken into account, which enable another way of the land use, such as mineral resources, water resources, fertile soils, nature reserves, etc. On the base of the relevant factors influence evaluation - suitable, moderately suitable and unsuitable territorial units are delimited on the maps. The different way of various scale maps compilation is applied, taken into account their different representing feasibilities. (authors)

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

    Science.gov (United States)

    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.

  9. MODIS land cover and LAI collection 4 product quality across nine states in the western hemisphere.

    Science.gov (United States)

    Warren B. Cohen; Thomas K. Maiersperger; David P. Turner; William D. Ritts; Dirk Pflugmacher; Robert E. Kennedy; Alan Kirschbaum; Steven W. Running; Marcos Costa; Stith T. Gower

    2006-01-01

    Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS...

  10. EnviroAtlas - Percent Stream Buffer Zone As Natural Land Cover for the Conterminous United States

    Science.gov (United States)

    This EnviroAtlas dataset shows the percentage of land area within a 30 meter buffer zone along the National Hydrography Dataset (NHD) high resolution stream network, and along water bodies such as lakes and ponds that are connected via flow to the streams, that is classified as forest land cover, modified forest land cover, and natural land cover using the 2006 National Land Cover Dataset (NLCD) for each Watershed Boundary Dataset (WBD) 12-digit hydrological unit (HUC) in the conterminous United States. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  11. Land use/cover classification in the Brazilian Amazon using satellite images.

    Science.gov (United States)

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

    2012-09-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

  12. 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.

  13. Recent land cover history and nutrient retention in riparian wetlands

    Science.gov (United States)

    Hogan, D.M.; Walbridge, M.R.

    2009-01-01

    Wetland ecosystems are profoundly affected by altered nutrient and sediment loads received from anthropogenic activity in their surrounding watersheds. Our objective was to compare a gradient of agricultural and urban land cover history during the period from 1949 to 1997, with plant and soil nutrient concentrations in, and sediment deposition to, riparian wetlands in a rapidly urbanizing landscape. We observed that recent agricultural land cover was associated with increases in Nitrogen (N) and Phosphorus (P) concentrations in a native wetland plant species. Conversely, recent urban land cover appeared to alter receiving wetland environmental conditions by increasing the relative availability of P versus N, as reflected in an invasive, but not a native, plant species. In addition, increases in surface soil Fe content suggests recent inputs of terrestrial sediments associated specifically with increasing urban land cover. The observed correlation between urban land cover and riparian wetland plant tissue and surface soil nutrient concentrations and sediment deposition, suggest that urbanization specifically enhances the suitability of riparian wetland habitats for the invasive species Japanese stiltgrass [Microstegium vimenium (Trinius) A. Camus]. ?? 2009 Springer Science+Business Media, LLC.

  14. Land use/Land Cover Changes and Causes of Deforestation in the ...

    African Journals Online (AJOL)

    The objective of this paper is to provide the non-existent data on land use/land cover changes in the Wilberforce Island for the purposes of determining the causes of deforestation and changes in the vegetation cover for a 13 – year period. Accordingly, 125 questionnaires were administered in five communities to determine ...

  15. Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon.

    Science.gov (United States)

    Lu, Dengsheng; Li, Guiying; Moran, Emilio; Hetrick, Scott

    2013-01-01

    This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes - forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.

  16. Integrating Crowdsourced Data with a Land Cover Product: A Bayesian Data Fusion Approach

    Directory of Open Access Journals (Sweden)

    Sarah Gengler

    2016-06-01

    Full Text Available For many environmental applications, an accurate spatial mapping of land cover is a major concern. Currently, land cover products derived from satellite data are expected to offer a fast and inexpensive way of mapping large areas. However, the quality of these products may also largely depend on the area under study. As a result, it is common that various products disagree with each other, and the assessment of their respective quality still relies on ground validation datasets. Recently, crowdsourced data have been suggested as an alternate source of information that might help overcome this problem. However, crowdsourced data still remain largely discarded in scientific studies due to their inherent poor quality assurance. The aim of this paper is to present an efficient methodology that allows the user to code information brought by crowdsourced data even if no prior quality estimation is at hand and possibly to fuse this information with existing land cover products in order to improve their accuracy. It is first suggested that information brought by volunteers can be coded as a set of inequality constraints about the probabilities of the various land use classes at the visited places. This in turn allows estimating optimal probabilities based on a maximum entropy principle and to proceed afterwards with a spatial interpolation of these volunteers’ information. Finally, a Bayesian data fusion approach can be used for fusing multiple volunteers’ contributions with a remotely-sensed land cover product. This methodology is illustrated in this paper by focusing on the mapping of croplands in Ethiopia, where the aim is to improve the mapping of cropland as coming out from a land cover product with mitigated performances. It is shown how crowdsourced information can seriously improve the quality of the final product. The corresponding results also suggest that a prior assessing of remotely-sensed data quality can seriously improve the benefit

  17. LACO-Wiki: A land cover validation tool and a new, innovative teaching resource for remote sensing and the geosciences

    Science.gov (United States)

    See, Linda; Perger, Christoph; Dresel, Christopher; Hofer, Martin; Weichselbaum, Juergen; Mondel, Thomas; Steffen, Fritz

    2016-04-01

    The validation of land cover products is an important step in the workflow of generating a land cover map from remotely-sensed imagery. Many students of remote sensing will be given exercises on classifying a land cover map followed by the validation process. Many algorithms exist for classification, embedded within proprietary image processing software or increasingly as open source tools. However, there is little standardization for land cover validation, nor a set of open tools available for implementing this process. The LACO-Wiki tool was developed as a way of filling this gap, bringing together standardized land cover validation methods and workflows into a single portal. This includes the storage and management of land cover maps and validation data; step-by-step instructions to guide users through the validation process; sound sampling designs; an easy-to-use environment for validation sample interpretation; and the generation of accuracy reports based on the validation process. The tool was developed for a range of users including producers of land cover maps, researchers, teachers and students. The use of such a tool could be embedded within the curriculum of remote sensing courses at a university level but is simple enough for use by students aged 13-18. A beta version of the tool is available for testing at: http://www.laco-wiki.net.

  18. Land use/land cover changes around Rameshwaram Island, east coast of India

    Digital Repository Service at National Institute of Oceanography (India)

    Gowthaman, R.; Dwarakish, G.S.; Sanilkumar, V.

    Land-use/land cover changes are studied using the Indian Remote Sensing satellite (IRS-1C, IRS-6) Linear Image Self-scan Sensor (LISS) III data of 1998 and 2010 Coastal land use categories such as sand, vegetation, coral reef and water have been...

  19. Land management and land-cover change have impacts of similar magnitude on surface temperature

    DEFF Research Database (Denmark)

    Luyssaert, Sebastiaan; Jammet, Mathilde; Stoy, Paul C.

    2014-01-01

    Anthropogenic changes to land cover (LCC) remain common, but continuing land scarcity promotes the widespread intensification of land management changes (LMC) to better satisfy societal demand for food, fibre, fuel and shelter1. The biophysical effects of LCC on surface climate are largely unders...

  20. Impact of land cover changes on the South African climate

    International Nuclear Information System (INIS)

    Ngwana, T I; Demory, M-E; Vidale, P L; Plant, R S; Mbedzi, M P

    2010-01-01

    The Joint UK Land Environmental Simulator (JULES) was run offline to investigate the sensitivity of land surface type changes over South Africa. Sensitivity tests were made in idealised experiments where the actual land surface cover is replaced by a single homogeneous surface type. The vegetation surface types on which some of the experiments were made are static. Experimental tests were evaluated against the control. The model results show among others that the change of the surface cover results in changes of other variables such as soil moisture, albedo, net radiation and etc. These changes are also visible in the spin up process. The model shows different surfaces spinning up at different cycles. Because JULES is the land surface model of Unified Model, the results could be more physically meaningful if it is coupled to the Unified Model.

  1. Land cover and topography affect the land transformation caused by wind facilities.

    Directory of Open Access Journals (Sweden)

    Jay E Diffendorfer

    Full Text Available Land transformation (ha of surface disturbance/MW associated with wind facilities shows wide variation in its reported values. In addition, no studies have attempted to explain the variation across facilities. We digitized land transformation at 39 wind facilities using high resolution aerial imagery. We then modeled the effects of turbine size, configuration, land cover, and topography on the levels of land transformation at three spatial scales. The scales included strings (turbines with intervening roads only, sites (strings with roads connecting them, buried cables and other infrastructure, and entire facilities (sites and the roads or transmission lines connecting them to existing infrastructure. An information theoretic modeling approach indicated land cover and topography were well-supported variables affecting land transformation, but not turbine size or configuration. Tilled landscapes, despite larger distances between turbines, had lower average land transformation, while facilities in forested landscapes generally had the highest land transformation. At site and string scales, flat topographies had the lowest land transformation, while facilities on mesas had the largest. The results indicate the landscape in which the facilities are placed affects the levels of land transformation associated with wind energy. This creates opportunities for optimizing wind energy production while minimizing land cover change. In addition, the results indicate forecasting the impacts of wind energy on land transformation should include the geographic variables affecting land transformation reported here.

  2. Relationship between Organic Carbon Runoff to River and Land Cover

    Science.gov (United States)

    Kim, G. S.; Lee, S. G.; Lim, C. H.; Lee, W.; Yoo, S.; Kim, S. J.; Heo, S.; Lee, W. K.

    2017-12-01

    Carbon is an important unit in understanding the ecosystem and energy circulation. Each ecosystem, land, water, and atmosphere, is interconnected through the exchange of energy and organic carbon. In the rivers, primary producers utilize the organic carbon from the land. Understanding the organic carbon uptake into the river is important for understanding the mechanism of river ecosystems. The main organic carbon source of the river is land. However, it is difficult to observe the amount of organic carbon runoff to the river. Therefore, an indirect method should be used to estimate the amount of organic carbon runoff to the river. The organic carbon inflow is caused by the runoff of organic carbon dissolved in water or the inflow of organic carbon particles by soil loss. Therefore, the hydrological model was used to estimate organic carbon runoff through the flow of water. The land cover correlates with soil respiration, soil loss, and so on, and the organic carbon runoff coefficient will be estimated to the river by land cover. Using the organic carbon concentration from water quality data observed at each point in the river, we estimate the amount of organic carbon released from the land. The reason is that the runoff from the watershed converges into the rivers in the watershed, the watershed simulation is conducted based on the water quality data observation point. This defines a watershed that affects organic carbon observation sites. The flow rate of each watershed is calculated by the SWAT (Soil and Water Assessment Tool), and the total organic carbon runoff is calculated by using flow rate and organic carbon concentration. This is compared with the factors related to the amount of organic carbon such as land cover, soil loss, and soil organic carbon, and spatial analysis is carried out to estimate the organic carbon runoff coefficient per land cover.

  3. Land-cover change and avian diversity in the conterminous United States

    Science.gov (United States)

    Chadwick D. Rittenhouse; Anna M. Pidgeon; Thomas P. Albright; Patrick D. Culbert; Murray K. Clayton; Curtis H. Flather; Jeffrey G. Masek; Volker C. Radeloff

    2012-01-01

    Changes in land use and land cover have affected and will continue to affect biological diversity worldwide. Yet, understanding the spatially extensive effects of land-cover change has been challenging because data that are consistent over space and time are lacking. We used the U.S. National Land Cover Dataset Land Cover Change Retrofit Product and North American...

  4. Simulated impacts of land cover change on summer climate in the Tibetan Plateau

    International Nuclear Information System (INIS)

    Li Qian; Xue Yongkang

    2010-01-01

    The Tibetan Plateau (TP) is a key region of land-atmosphere interactions with severe eco-environment degradation. This study uses an atmospheric general circulation model, NCEP GCM/SSiB, to present the major TP summer climate features for six selected ENSO years and preliminarily assess the possible impact of land cover change on the summer circulation over the TP. Compared to Reanalysis II data, the GCM using satellite derived vegetation properties generally reproduces the main 6-year-mean TP summer circulation features despite some discrepancies in intensity and geographic locations of some climate features. Two existing vegetation maps with very different land cover conditions over the TP, one with bare ground and one with vegetation cover, derived from satellite derived data, are tested and produce clearer climate signals due to land cover change. It shows that land cover change from vegetated land to bare ground decreases the radiation absorbed by the surface and results in weaker surface thermal effects, which lead to lower atmospheric temperature, as well as weaker vertical ascending motion, low-layer cyclonic, upper level anticyclonic, and summer monsoon circulation. These changes in circulation cause a decrease in the precipitation in the southeastern TP.

  5. Land Use and Land Cover Change, Urban Heat Island Phenomenon, and Health Implications: A Remote Sensing Approach

    Science.gov (United States)

    Lo, C. P.; Quattrochi, Dale A.

    2003-01-01

    Land use and land cover maps of Atlanta Metropolitan Area in Georgia were produced from Landsat MSS and TM images for 1973,1979,1983,1987,1992, and 1997, spanning a period of 25 years. Dramatic changes in land use and land cover have occurred with loss of forest and cropland to urban use. In particular, low-density urban use, which includes largely residential use, has increased by over 119% between 1973 and 1997. These land use and land cover changes have drastically altered the land surface characteristics. An analysis of Landsat images revealed an increase in surface temperature and a decline in NDVI from 1973 to 1997. These changes have forced the development of a significant urban heat island effect and an increase in ground level ozone production to such an extent, that Atlanta has violated EPA's ozone level standard in recent years. The urban heat island initiated precipitation events that were identified between 1996 and 2000 tended to occur near high-density urban areas but outside the I-285 loop that traverses around the Central Business District, i.e. not in the inner city area, but some in close proximity to the highways. The health implications were investigated by comparing the spatial patterns of volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions, the two ingredients that form ozone by reacting with sunlight, with those of rates of cardiovascular and chronic lower respiratory diseases. A clear core-periphery pattern was revealed for both VOC and NOx emissions, but the spatial pattern was more random in the cases of rates of cardiovascular and chronic lower respiratory diseases. Clearly, factors other than ozone pollution were involved in explaining the rates of these diseases. Further research is therefore needed to understand the health geography and its relationship to land use and land cover change as well as urban heat island effect. This paper illustrates the usefulness of a remote sensing approach for this purpose.

  6. 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

  7. Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China

    Directory of Open Access Journals (Sweden)

    Huiran Han

    2015-04-01

    Full Text Available Land use and land cover (LULC models are essential for analyzing LULC change and predicting land use requirements and are valuable for guiding reasonable land use planning and management. However, each LULC model has its own advantages and constraints. In this paper, we explore the characteristics of LULC change and simulate future land use demand by combining a CLUE-S model with a Markov model to deal with some shortcomings of existing LULC models. Using Beijing as a case study, we describe the related driving factors from land-adaptive variables, regional spatial variables and socio-economic variables and then simulate future land use scenarios from 2010 to 2020, which include a development scenario (natural development and rapid development and protection scenarios (ecological and cultivated land protection. The results indicate good consistency between predicted results and actual land use situations according to a Kappa statistic. The conversion of cultivated land to urban built-up land will form the primary features of LULC change in the future. The prediction for land use demand shows the differences under different scenarios. At higher elevations, the geographical environment limits the expansion of urban built-up land, but the conversion of cultivated land to built-up land in mountainous areas will be more prevalent by 2020; Beijing, however, still faces the most pressure in terms of ecological and cultivated land protection.

  8. SAR China Land Mapping Project: Development, Production and Potential Applications

    International Nuclear Information System (INIS)

    Zhang, Lu; Guo, Huadong; Liu, Guang; Fu, Wenxue; Yan, Shiyong; Song, Rui; Ji, Peng; Wang, Xinyuan

    2014-01-01

    Large-area, seamless synthetic aperture radar (SAR) mosaics can reflect overall environmental conditions and highlight general trends in observed areas from a macroscopic standpoint, and effectively support research at the global scale, which is in high demand now across scientific fields. The SAR China Land Mapping Project (SCLM), supported by the Digital Earth Science Platform Project initiated and managed by the Center for Earth Observation and Digital Earth, Chinese Academy of Sciences (CEODE), is introduced in this paper. This project produced a large-area SAR mosaic dataset and generated the first complete seamless SAR map covering the entire land area of China using EnviSat-ASAR images. The value of the mosaic map is demonstrated by some potential applications in studies of urban distribution, rivers and lakes, geologic structures, geomorphology and paleoenvironmental change

  9. Modelling land change: the issue of use and cover in wide-scale applications

    NARCIS (Netherlands)

    Bakker, M.M.; Veldkamp, A.

    2008-01-01

    In this article, the underlying causes for the apparent mismatch between land cover and land use in the context of wide-scale land change modelling are explored. A land use-land cover (LU/LC) ratio is proposed as a relevant landscape characteristic. The one-to-one ratio between land use and land

  10. 2005 Kansas Land Cover Patterns, Level IV, State of Kansas (300m buffer) and Kansas River Watershed (1,000m buffer)

    Data.gov (United States)

    Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns (KLCP) Mapping Initiative was a two-phase mapping endeavor that occurred over a three-year period (2007-2009). Note that while...

  11. Land cover change interacts with drought severity to change fire regimes in Western Amazonia.

    Science.gov (United States)

    Gutiérrez-Vélez, Víctor H; Uriarte, María; DeFries, Ruth; Pinedo-Vásquez, Miguel; Fernandes, Katia; Ceccato, Pietro; Baethgen, Walter; Padoch, Christine

    Fire is becoming a pervasive driver of environmental change in Amazonia and is expected to intensify, given projected reductions in precipitation and forest cover. Understanding of the influence of post-deforestation land cover change on fires in Amazonia is limited, even though fires in cleared lands constitute a threat for ecosystems, agriculture, and human health. We used MODIS satellite data to map burned areas annually between 2001 and 2010. We then combined these maps with land cover and climate information to understand the influence of land cover change in cleared lands and dry-season severity on fire occurrence and spread in a focus area in the Peruvian Amazon. Fire occurrence, quantified as the probability of burning of individual 232-m spatial resolution MODIS pixels, was modeled as a function of the area of land cover types within each pixel, drought severity, and distance to roads. Fire spread, quantified as the number of pixels burned in 3 × 3 pixel windows around each focal burned pixel, was modeled as a function of land cover configuration and area, dry-season severity, and distance to roads. We found that vegetation regrowth and oil palm expansion are significantly correlated with fire occurrence, but that the magnitude and sign of the correlation depend on drought severity, successional stage of regrowing vegetation, and oil palm age. Burning probability increased with the area of nondegraded pastures, fallow, and young oil palm and decreased with larger extents of degraded pastures, secondary forests, and adult oil palm plantations. Drought severity had the strongest influence on fire occurrence, overriding the effectiveness of secondary forests, but not of adult plantations, to reduce fire occurrence in severely dry years. Overall, irregular and scattered land cover patches reduced fire spread but irregular and dispersed fallows and secondary forests increased fire spread during dry years. Results underscore the importance of land cover

  12. Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties

    DEFF Research Database (Denmark)

    Pirzamanbein, Behnaz; Lindström, Johan; Poska, Anneli

    2018-01-01

    In this paper, we construct a hierarchical model for spatial compositional data, which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past $6\\,000$ years over Europe. The model...... to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint...... confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map...

  13. Land cover in single-family housing areas and how it correlates with urban form

    DEFF Research Database (Denmark)

    Nielsen, Mette Boye; Jensen, Marina Bergen

    2015-01-01

    Land cover composition is a valuable indicator of the ecological performance of a city. Single-family housing areas constitute a substantial part of most cities and may as such play an important role for sustainable urban development. From aerial photos we performed detailed GIS-based mapping...... of land cover in three detached single-family housing areas in Denmark of different urban form but comparable housing densities (ranging from 10.0 to 11.3 houses per hectare). The findings were subjected to statistical analysis and landscape metrics. Land cover varied with urban form: A traditional...... spatial configuration with rectangular parcels contained significantly more vegetation and less impervious surfaces per parcel than newer Radburn-inspired configurations with more quadratic parcels. Correlation analysis showed size of paved access ways to be positively correlated with distance from road...

  14. Integrating land cover and terrain characteristics to explain plague ...

    African Journals Online (AJOL)

    Literature suggests that higher resolution remote sensing data integrated in Geographic Information System (GIS) can provide greater possibility to refine the analysis of land cover and terrain characteristics for explanation of abundance and distribution of plague hosts and vectors and hence of health risk hazards to ...

  15. 1 Integrating land cover and terrain characteristics to explain plague ...

    African Journals Online (AJOL)

    influence of land cover and terrain factors on the abundance and spatial distribution ... factors operating at diverse scales, including climate (Debien et al., 2009; Ben Ari .... A cloud free three-band SPOT 5 image captured on 27 February 2007, ...

  16. Trends in Coastal Development and Land Cover Change: The Case ...

    African Journals Online (AJOL)

    KwaZulu-Natal. Abstract—Current land cover and development in the coastal zone of KwaZulu- ... 75% by 2025 in some regions (Hinrichsen, 1995). .... Functional divisions and case study areas on the KZN coast. .... Extent of development and access via major road networks along the KZN North and South Coasts. 198.

  17. 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 ...

  18. AN ACCURACY ASSESSMENT OF 1997 LANDSAT THEMATIC MAPPER DERIVED LAND COVER FOR THE UPPER SAN PEDRO WATERSHED (U.S./MEXICO)

    Science.gov (United States)

    High-Resolution airborne color video data were used to evaluate the accuracy of a land cover map of the upper San Pedro River watershed, derived from June 1997 Landsat Thematic Mapper data. The land cover map was interpreted and generated by Instituto del Medio Ambiente y el Bes...

  19. Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA

    Directory of Open Access Journals (Sweden)

    Amir Reza Shahtahmassebi

    2017-07-01

    Full Text Available Survey data describing land cover information such as type and diversity over several decades are scarce. Therefore, our capacity to reconstruct historical land cover using field data and archived remotely sensed data over large areas and long periods of time is somewhat limited. This study explores the relationship between CORONA texture—a surrogate for actual land cover type and complexity—with spectral vegetation indices and texture variables derived from Landsat MSS under the Spectral Variation Hypothesis (SVH such as to reconstruct historical continuous land cover type and complexity. Image texture of CORONA was calculated using a mean occurrence measure while image textures of Landsat MSS were calculated by occurrence and co-occurrence measures. The relationship between these variables was evaluated using correlation and regression techniques. The reconstruction procedure was undertaken through regression kriging. The results showed that, as expected, texture based on the visible bands and corresponding indices indicated larger correlation with CORONA texture, a surrogate of land cover (correlation >0.65. In terms of prediction, the combination of the first-order mean of band green, second-order measure of tasseled cap brightness, second-order mean of Normalized Visible Index (NVI and second-order entropy of NIR yielded the best model with respect to Akaike’s Information Criterion (AIC, r-square, and variance inflation factors (VIF. The regression model was then used in regression kriging to map historical continuous land cover. The resultant maps indicated the type and degree of complexity in land cover. Moreover, the proposed methodology minimized the impacts of topographic shadow in the region. The performance of this approach was compared with two conventional classification methods: hard classifiers and continuous classifiers. In contrast to conventional techniques, the technique could clearly quantify land cover complexity and

  20. A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

    Science.gov (United States)

    Sohl, Terry L.; Sleeter, Benjamin M.; Zhu, Zhi-Liang; Sayler, Kristi L.; Bennett, Stacie; Bouchard, Michelle; Reker, Ryan R.; Hawbaker, Todd; Wein, Anne; Liu, Shu-Guang; Kanengieter, Ronald; Acevedo, William

    2012-01-01

    Changes in land use, land cover, disturbance regimes, and land management have considerable influence on carbon and greenhouse gas (GHG) fluxes within ecosystems. Through targeted land-use and land-management activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of other GHGs. National-scale, comprehensive analyses of carbon sequestration potential by ecosystem are needed, with a consistent, nationally applicable land-use and land-cover (LULC) modeling framework a key component of such analyses. The U.S. Geological Survey has initiated a project to analyze current and projected future GHG fluxes by ecosystem and quantify potential mitigation strategies. We have developed a unique LULC modeling framework to support this work. Downscaled scenarios consistent with IPCC Special Report on Emissions Scenarios (SRES) were constructed for U.S. ecoregions, and the FORE-SCE model was used to spatially map the scenarios. Results for a prototype demonstrate our ability to model LULC change and inform a biogeochemical modeling framework for analysis of subsequent GHG fluxes. The methodology was then successfully used to model LULC change for four IPCC SRES scenarios for an ecoregion in the Great Plains. The scenario-based LULC projections are now being used to analyze potential GHG impacts of LULC change across the U.S.

  1. Effect of land cover change on runoff curve number estimation in Iowa, 1832-2001

    Science.gov (United States)

    Wehmeyer, Loren L.; Weirich, Frank H.; Cuffney, Thomas F.

    2011-01-01

    Within the first few decades of European-descended settlers arriving in Iowa, much of the land cover across the state was transformed from prairie and forest to farmland, patches of forest, and urbanized areas. Land cover change over the subsequent 126 years was minor in comparison. Between 1832 and 1859, the General Land Office conducted a survey of the State of Iowa to aid in the disbursement of land. In 1875, an illustrated atlas of the State of Iowa was published, and in 2001, the US Geological Survey National Land Cover Dataset was compiled. Using these three data resources for classifying land cover, the hydrologic impact of the land cover change at three points in time over a period of 132+ years is presented in terms of the effect on the area-weighted average curve number, a term commonly used to predict peak runoff from rainstorms. In the four watersheds studied, the area-weighted average curve number associated with the first 30 years of settlement increased from 61·4 to 77·8. State-wide mapped forest area over this same period decreased 19%. Over the next 126 years, the area-weighted average curve number decreased to 76·7, despite an additional forest area reduction of 60%. This suggests that degradation of aquatic resources (plants, fish, invertebrates, and habitat) arising from hydrologic alteration was likely to have been much higher during the 30 years of initial settlement than in the subsequent period of 126 years in which land cover changes resulted primarily from deforestation and urbanization. 

  2. Improving snow cover mapping in forests through the use of a canopy reflectance model

    International Nuclear Information System (INIS)

    Klein, A.G.; Hall, D.K.; Riggs, G.A.

    1998-01-01

    MODIS, the moderate resolution imaging spectro radiometer, will be launched in 1998 as part of the first earth observing system (EOS) platform. Global maps of land surface properties, including snow cover, will be created from MODIS imagery. The MODIS snow-cover mapping algorithm that will be used to produce daily maps of global snow cover extent at 500 m resolution is currently under development. With the exception of cloud cover, the largest limitation to producing a global daily snow cover product using MODIS is the presence of a forest canopy. A Landsat Thematic Mapper (TM) time-series of the southern Boreal Ecosystem–Atmosphere Study (BOREAS) study area in Prince Albert National Park, Saskatchewan, was used to evaluate the performance of the current MODIS snow-cover mapping algorithm in varying forest types. A snow reflectance model was used in conjunction with a canopy reflectance model (GeoSAIL) to model the reflectance of a snow-covered forest stand. Using these coupled models, the effects of varying forest type, canopy density, snow grain size and solar illumination geometry on the performance of the MODIS snow-cover mapping algorithm were investigated. Using both the TM images and the reflectance models, two changes to the current MODIS snow-cover mapping algorithm are proposed that will improve the algorithm's classification accuracy in forested areas. The improvements include using the normalized difference snow index and normalized difference vegetation index in combination to discriminate better between snow-covered and snow-free forests. A minimum albedo threshold of 10% in the visible wavelengths is also proposed. This will prevent dense forests with very low visible albedos from being classified incorrectly as snow. These two changes increase the amount of snow mapped in forests on snow-covered TM scenes, and decrease the area incorrectly identified as snow on non-snow-covered TM scenes. (author)

  3. Land-cover effects on soil organic carbon stocks in a European city.

    Science.gov (United States)

    Edmondson, Jill L; Davies, Zoe G; McCormack, Sarah A; Gaston, Kevin J; Leake, Jonathan R

    2014-02-15

    Soil is the vital foundation of terrestrial ecosystems storing water, nutrients, and almost three-quarters of the organic carbon stocks of the Earth's biomes. Soil organic carbon (SOC) stocks vary with land-cover and land-use change, with significant losses occurring through disturbance and cultivation. Although urbanisation is a growing contributor to land-use change globally, the effects of urban land-cover types on SOC stocks have not been studied for densely built cities. Additionally, there is a need to resolve the direction and extent to which greenspace management such as tree planting impacts on SOC concentrations. Here, we analyse the effect of land-cover (herbaceous, shrub or tree cover), on SOC stocks in domestic gardens and non-domestic greenspaces across a typical mid-sized U.K. city (Leicester, 73 km(2), 56% greenspace), and map citywide distribution of this ecosystem service. SOC was measured in topsoil and compared to surrounding extra-urban agricultural land. Average SOC storage in the city's greenspace was 9.9 kg m(-2), to 21 cm depth. SOC concentrations under trees and shrubs in domestic gardens were greater than all other land-covers, with total median storage of 13.5 kg m(-2) to 21 cm depth, more than 3 kg m(-2) greater than any other land-cover class in domestic and non-domestic greenspace and 5 kg m(-2) greater than in arable land. Land-cover did not significantly affect SOC concentrations in non-domestic greenspace, but values beneath trees were higher than under both pasture and arable land, whereas concentrations under shrub and herbaceous land-covers were only higher than arable fields. We conclude that although differences in greenspace management affect SOC stocks, trees only marginally increase these stocks in non-domestic greenspaces, but may enhance them in domestic gardens, and greenspace topsoils hold substantial SOC stores that require protection from further expansion of artificial surfaces e.g. patios and driveways. Copyright

  4. Landspotting: collecting essential land cover information via an attractive internet game

    Science.gov (United States)

    Fritz, Steffen; McCallum, Ian; Perger, Christoph; Christian, Schill; Florian, Kraxner; Erik, Lindquist; Michael, Obersteiner

    2010-05-01

    Based on the geo-wiki.org concept of collecting land cover information via crowdsourcing, we present a novel approach on how to get the crowd involved. Internet games as well as social networks are becoming increasingly popular and the full potential is yet to be exploited. However, thus far, few if any games provide anything other than entertainment. Can an attractive philanthropic game be created which uses the crowd to collect essential information needed to help to acquire better data to improve the understanding of the earth system? Since accurate and up to date information on global land cover plays a very important role in a number of different research fields such as climate change, monitoring of tropical deforestation, land use monitoring and land-use modelling, but still shows high levels of disagreement, the game will focus on how this essential land cover calibration and validation data can be collected in areas where uncertainty is currently highest. In the current version of the land spotting game, we combine uncertainty hotspot information from three global land cover datasets (GLC, MODIS and GlobCover). With an ever increasing amount of high resolution images available on Google Earth, it is becoming increasingly possible to distinguish land cover features with a high degree of accuracy. We first direct the landspotting game community to certain hotspots of land cover uncertainty and then ask them to enter/record the type of land cover they see (for this they will be able to acquire a certain number of points), possibly uploading pictures at that location (additional points will be received). Even though the development of the game "landspotting.org" is still underway, we illustrate what the functionality will be and what features are envisaged for the near future. Landspotting.org will be designed in such a way as to challenge users to help map out the remaining areas of confusion over the globe - possibly in the form of an adventure game. Users

  5. 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.

  6. Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

    Directory of Open Access Journals (Sweden)

    Asamaporn Sitthi

    2016-09-01

    Full Text Available Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery containing an immense amount of information. In this paper, geotagged Flickr images are used for automatic extraction of low-level land use/land cover (LULC features. The proposed method uses a naive Bayes classifier with color, shape, and color index descriptors. The classified images are mapped using a majority filtering approach. The classifier performance in overall accuracy, kappa coefficient, precision, recall, and f-measure was 87.94%, 82.89%, 88.20%, 87.90%, and 88%, respectively. Labeled-crowdsourced images were filtered into a spatial tile of a 30 m × 30 m resolution using the majority voting method to reduce geolocation uncertainty from the crowdsourced data. These tile datasets were used as training and validation samples to classify Landsat TM5 images. The supervised maximum likelihood method was used for the LULC classification. The results show that the geotagged Flickr images can classify LULC types with reasonable accuracy and that the proposed approach improves LULC classification efficiency if a sufficient spatial distribution of crowdsourced data exists.

  7. Simulation of boreal Summer Monsoon Rainfall using CFSV2_SSiB model: sensitivity to Land Use Land Cover (LULC)

    Science.gov (United States)

    Chilukoti, N.; Xue, Y.

    2016-12-01

    The land surface play a vital role in determining the surface energy budget, accurate representation of land use and land cover (LULC) is necessary to improve forecast. In this study, we have investigated the influence of surface vegetation maps with different LULC on simulating the boreal summer monsoon rainfall. Using a National Centres for Environmental Prediction (NCEP) Coupled Forecast System version 2(CFSv2) model coupled with Simplified Simple Biosphere (SSiB) model, two experiments were conducted: one with old vegetation map and one with new vegetation map. The significant differences between new and old vegetation map were in semi-arid and arid areas. For example, in old map Tibetan plateau classified as desert, which is not appropriate, while in new map it was classified as grasslands or shrubs with bare soil. Old map classified the Sahara desert as a bare soil and shrubs with bare soil, whereas in new map it was classified as bare ground. In addition to central Asia and the Sahara desert, in new vegetation map, Europe had more cropped area and India's vegetation cover was changed from crops and forests to wooded grassland and small areas of grassland and shrubs. The simulated surface air temperature with new map shows a significant improvement over Asia, South Africa, and northern America by some 1 to 2ºC and 2 to 3ºC over north east China and these are consistent with the reduced rainfall biases over Africa, near Somali coast, north east India, Bangladesh, east China sea, eastern Pacific and northern USA. Over Indian continent and bay of Bengal dry rainfall anomalies that is the only area showing large dry rainfall bias, however, they were unchanged with new map simulation. Overall the CFSv2(coupled with SSiB) model with new vegetation map show a promising result in improving the monsoon forecast by improving the Land -Atmosphere interactions. To compare with the LULC forcing, experiment was conducted using the Global Forecast System (GFS) simulations

  8. Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

    Directory of Open Access Journals (Sweden)

    Courage Kamusoko

    2014-06-01

    Full Text Available Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT, random forests (RF, and support vector machines (SVM in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.

  9. Geospatial Analysis of Land Use and Land Cover Transitions from 1986–2014 in a Peri-Urban Ghana

    Directory of Open Access Journals (Sweden)

    Divine Odame Appiah

    2017-12-01

    Full Text Available Recently, peri-urbanisation has led to the transformation of the rural landscape, changing rural land uses into peri-urban land uses, under varying driving factors. This paper analyzes the dynamic transitions among identified land use and land cover (LULC types in the Bosomtwe district of Ghana, from 1986 to 2014. An integrated approach of geo-information tools of satellite remote sensing in Earth Resource Data Analysis System (ERDAS Imagine 13 and ArcMap 10.2 Geographic Information System (GIS, with Markov chain analytical techniques were used to examine the combined forest land cover transitions, relative to build-up, recent fallows and grasslands and projected possible factors influencing the transitions under business as usual and unusual situations. Statistical analyses of the classified Landsat TM, ETM+ and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIS indicated that over the period of 24 years, the Bosomtwe district has undergone a series of land use conversions with remarkable forest losses especially between 2002 and 2010. In 2010 dense forest cover was degraded to low forest by 4040 ha indicating 0.40% transition probability in the future. There was a remarkable increase of built-up/bare and concrete area with a 380% increment in the 1986–2002 transition periods. The application of the Markov futuristic land use dynamics by the years 2018 and 2028, projected from the 2014 LULC indicated a future steady decline in the area coverage of the dense forest to low forest category. This is currently being driven (as at the 2017 LULC trends, by the combined effects of increasing build up bare and concrete surface land uses as well as the expanding recent fallows and grassland. The paper concluded that the health of the ecosystem and biodiversity of the lake Bosomtwe need to be sustainably managed by the Bosomtwe district assembly.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data

    Science.gov (United States)

    Sanford, Ward E.; Selnick, David L.

    2013-01-01

    Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water-balance method was combined with a climate and land-cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971-2000 across the U.S. to obtain long-term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land-cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land-cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land-cover data can also improve those predictions. Using the climate and land-cover data at an 800-m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land-cover data are plentiful.

  12. Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal

    Directory of Open Access Journals (Sweden)

    Yikalo H. Araya

    2010-06-01

    Full Text Available The expansion of cities entails the abandonment of forest and agricultural lands, and these lands’ conversion into urban areas, which results in substantial impacts on ecosystems. Monitoring these changes and planning urban development can be successfully achieved using multitemporal remotely sensed data, spatial metrics, and modeling. In this paper, urban land use change analysis and modeling was carried out for the Concelhos of Setúbal and Sesimbra in Portugal. An existing land cover map for the year 1990, together with two derived land cover maps from multispectral satellite images for the years 2000 and 2006, were utilized using an object-oriented classification approach. Classification accuracy assessment revealed satisfactory results that fulfilled minimum standard accuracy levels. Urban land use dynamics, in terms of both patterns and quantities, were studied using selected landscape metrics and the Shannon Entropy index. Results show that urban areas increased by 91.11% between 1990 and 2006. In contrast, the change was only 6.34% between 2000 and 2006. The entropy value was 0.73 for both municipalities in 1990, indicating a high rate of urban sprawl in the area. In 2006, this value, for both Sesimbra and Setúbal, reached almost 0.90. This is demonstrative of a tendency toward intensive urban sprawl. Urban land use change for the year 2020 was modeled using a Cellular Automata based approach. The predictive power of the model was successfully validated using Kappa variations. Projected land cover changes show a growing tendency in urban land use, which might threaten areas that are currently reserved for natural parks and agricultural lands.

  13. Clifton, AZ 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  14. Tularosa, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  15. Gallup, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  16. Clifton, AZ 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  17. Brownfield, TX 1:250,000 Quad USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  18. Dalhart, TX 1:250,000 Quad USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  19. Hobbs, NM 1:250,000 Quad USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  20. Albuquerque, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  1. Douglas, AZ 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  2. Gallup, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  3. Roswell, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  4. Socorro, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  5. Clovis, NM 1:250,000 Quad USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  6. Douglas, AZ 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  7. Roswell, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  8. Shiprock, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  9. Aztec, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  10. Aztec, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  11. Socorro, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  12. Carlsbad, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  13. Raton, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  14. Shiprock, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  15. Tucumcari, NM 1:250,000 Quad USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  16. Albuquerque, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  17. Raton, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  18. Carlsbad, NM 1:250,000 Quad East Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  19. Tularosa, NM 1:250,000 Quad West Half USGS Land Use/Land Cover, 2000

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This land cover data set was produced as part of a cooperative project between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA)...

  20. Land-cover mapping of Red Rock Canyon National Conservation Area and Coyote Springs, Piute-Eldorado Valley, and Mormon Mesa Areas of Critical Environmental Concern, Clark County, Nevada

    Science.gov (United States)

    Smith, J. LaRue; Damar, Nancy A.; Charlet, David A.; Westenburg, Craig L.

    2014-01-01

    DigitalGlobe’s QuickBird satellite high-resolution multispectral imagery was classified by using Visual Learning Systems’ Feature Analyst feature extraction software to produce land-cover data sets for the Red Rock Canyon National Conservation Area and the Coyote Springs, Piute-Eldorado Valley, and Mormon Mesa Areas of Critical Environmental Concern in Clark County, Nevada. Over 1,000 vegetation field samples were collected at the stand level. The field samples were classified to the National Vegetation Classification Standard, Version 2 hierarchy at the alliance level and above. Feature extraction models were developed for vegetation on the basis of the spectral and spatial characteristics of selected field samples by using the Feature Analyst hierarchical learning process. Individual model results were merged to create one data set for the Red Rock Canyon National Conservation Area and one for each of the Areas of Critical Environmental Concern. Field sample points and photographs were used to validate and update the data set after model results were merged. Non-vegetation data layers, such as roads and disturbed areas, were delineated from the imagery and added to the final data sets. The resulting land-cover data sets are significantly more detailed than previously were available, both in resolution and in vegetation classes.

  1. A web-based system for supporting global land cover data production

    Science.gov (United States)

    Han, Gang; Chen, Jun; He, Chaoying; Li, Songnian; Wu, Hao; Liao, Anping; Peng, Shu

    2015-05-01

    Global land cover (GLC) data production and verification process is very complicated, time consuming and labor intensive, requiring huge amount of imagery data and ancillary data and involving many people, often from different geographic locations. The efficient integration of various kinds of ancillary data and effective collaborative classification in large area land cover mapping requires advanced supporting tools. This paper presents the design and development of a web-based system for supporting 30-m resolution GLC data production by combining geo-spatial web-service and Computer Support Collaborative Work (CSCW) technology. Based on the analysis of the functional and non-functional requirements from GLC mapping, a three tiers system model is proposed with four major parts, i.e., multisource data resources, data and function services, interactive mapping and production management. The prototyping and implementation of the system have been realised by a combination of Open Source Software (OSS) and commercially available off-the-shelf system. This web-based system not only facilitates the integration of heterogeneous data and services required by GLC data production, but also provides online access, visualization and analysis of the images, ancillary data and interim 30 m global land-cover maps. The system further supports online collaborative quality check and verification workflows. It has been successfully applied to China's 30-m resolution GLC mapping project, and has improved significantly the efficiency of GLC data production and verification. The concepts developed through this study should also benefit other GLC or regional land-cover data production efforts.

  2. Hydrograph sensitivity to estimates of map impervious cover: a WinHSPF BASINS case study

    Science.gov (United States)

    Endreny, Theodore A.; Somerlot, Christopher; Hassett, James M.

    2003-04-01

    The BASINS geographic information system hydrologic toolkit was designed to compute total maximum daily loads, which are often derived by combining water quantity estimates with pollutant concentration estimates. In this paper the BASINS toolkit PLOAD and WinHSPF sub-models are briefly described, and then a 0·45 km2 headwater watershed in the New York Croton River area is used for a case study illustrating a full WinHSPF implementation. The goal of the Croton study was to determine the sensitivity of WinHSPF hydrographs to changes in land cover map inputs. This scenario occurs when scaling the WinHSPF model from the smaller 0·45 km2 watershed to the larger 1000 km2 management basin of the entire Croton area. Methods used to test model sensitivity include first calibrating the WinHSPF hydrograph using research-monitored precipitation and discharge data together with high spatial resolution and accuracy land cover data of impervious and pervious areas, and then swapping three separate land cover files, known as GIRAS, MRLC, and DOQQ data, into the calibrated model. Research results indicated that the WinHSPF land cover swapping had peak flow sensitivity in December 2001 hydrographs between 35% underestimation and 20% overestimation, and that errors in land-cover-derived runoff ratios for storm totals and peak flows tracked with the land cover data estimates of impervious area.

  3. Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

    Directory of Open Access Journals (Sweden)

    Ruci Wang

    2018-06-01

    Full Text Available Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region.

  4. A reconstruction of global agricultural areas and land cover for the last millennium

    Science.gov (United States)

    Pongratz, J.; Reick, C.; Raddatz, T.; Claussen, M.

    2008-09-01

    Humans have substantially modified the Earth's land cover, especially by transforming natural ecosystems to agricultural areas. In preindustrial times, the expansion of agriculture was probably the dominant process by which humankind altered the Earth system, but little is known about its extent, timing, and spatial pattern. This study presents an approach to reconstruct spatially explicit changes in global agricultural areas (cropland and pasture) and the resulting changes in land cover over the last millennium. The reconstruction is based on published maps of agricultural areas for the last three centuries. For earlier times, a country-based method is developed that uses population data as a proxy for agricultural activity. With this approach, the extent of cropland and pasture is consistently estimated since AD 800. The resulting reconstruction of agricultural areas is combined with a map of potential vegetation to estimate the resulting historical changes in land cover. Uncertainties associated with this approach, in particular owing to technological progress in agriculture and uncertainties in population estimates, are quantified. About 5 million km2 of natural vegetation are found to be transformed to agriculture between AD 800 and 1700, slightly more to cropland (mainly at the expense of forested area) than to pasture (mainly at the expense of natural grasslands). Historical events such as the Black Death in Europe led to considerable dynamics in land cover change on a regional scale. The reconstruction can be used with global climate and ecosystem models to assess the impact of human activities on the Earth system in preindustrial times.

  5. 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

  6. Assessment of land use/land cover dynamics of Tso Moriri Lake, a Ramsar site in India.

    Science.gov (United States)

    Gupta, Sharad Kumar; Shukla, Dericks Praise

    2016-12-01

    Wetlands accounts for 6% area of the Earth's land cover and nearly 17% of the Hindu Kush Himalayan region. They are of utmost importance to climate dynamics and are critical links between terrestrial and aquatic ecosystems. Despite the need of high attention towards conserving and managing wetland resources, mapping them is a least practiced activity. This study shows the temporal change in land use and land cover pattern of Tso Moriri Lake, the highest altitude lake in India and designated as Ramsar site in year 2002, using multi-sensor and multi-date imagery. Due to change in hydro-meteorological conditions of the region, this lake area has been reduced. Since the lake recharge is dependent on snowmelt, hence change in climatic conditions (less snowfall in winters), to a certain extent, is also responsible for the decrease in water level and water spread of the lake. The result shows that the lake area has reduced approximately 2 km 2 in the last 15 years, and also, agriculture, grasslands, and vegetation cover have increased to a significant extent. Agricultural land and grasslands have doubled while the vegetation cover has increased more than six times, showing the coupled effect of climate change and anthropogenic activities. Trend of temperature and precipitation corroborates the effects of climate change in this region.

  7. an assessment of the land use and land cover changes in shurugwi

    African Journals Online (AJOL)

    Dr Osondu

    Zimbabwe's fast-track land reform programme and other economic activities have caused ... Geographic Information System and remote sensing techniques. ... 1990 and 2009 Landsat images of the district were downloaded from the Global Land cover Facility as well ... Information System (GIS) are now providing new.

  8. Land use and land cover dynamics in the Brazilian Amazon: an overview

    Science.gov (United States)

    Robert Walker; Alfredo Kingo Oyama Homma

    1996-01-01

    This paper presents a theoretical discussion of processes linking land use decisions and land cover outcomes at household level, with an emphasis on small proceduers. Evidence from the literature substantiating the existence of domestic cycle phenomena is brought forward and interpreted for the Brazilian case. Also considered are the relative disposition of production...

  9. A proposed periodic national inventory of land use land cover change

    Science.gov (United States)

    Hans T. Schreuder; Paul W. Snook; Raymond L. Czaplewski; Glenn P. Catts

    1986-01-01

    Three alternatives using digital thematic mapper (TM), analog TM, and a combination of either digital or analog TM data with low altitude photography are discussed for level I and level II land use/land cover classes for a proposed national inventory. Digital TM data should prove satisfactory for estimating acreage in level I classes, although estimates of precision...

  10. Santa Fe, NM 1:250,000 Quad USGS Land Use/Land Cover, 1986

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This dataset contains boundaries for land use and land cover polygons in New Mexico at a scale of 1:250,000. It is in a vector digital data structure. The source...

  11. Projecting land-use and land cover change in a subtropical urban watershed

    Science.gov (United States)

    John J. Lagrosa IV; Wayne C. Zipperer; Michael G. Andreu

    2018-01-01

    Urban landscapes are heterogeneous mosaics that develop via significant land-use and land cover (LULC) change. Current LULC models project future landscape patterns, but generally avoid urban landscapes due to heterogeneity. To project LULC change for an urban landscape, we parameterize an established LULC model (Dyna-CLUE) under baseline conditions (continued current...

  12. Image-based change estimation for land cover and land use monitoring

    Science.gov (United States)

    Jeremy Webb; C. Kenneth Brewer; Nicholas Daniels; Chris Maderia; Randy Hamilton; Mark Finco; Kevin A. Megown; Andrew J. Lister

    2012-01-01

    The Image-based Change Estimation (ICE) project resulted from the need to provide estimates and information for land cover and land use change over large areas. The procedure uses Forest Inventory and Analysis (FIA) plot locations interpreted using two different dates of imagery from the National Agriculture Imagery Program (NAIP). In order to determine a suitable...

  13. Gallup, NM AZ 1:250,000 Quad USGS Land Use/Land Cover, 1986

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This dataset contains boundaries for land use and land cover polygons in New Mexico at a scale of 1:250,000. It is in a vector digital data structure. The source...

  14. El Paso, TX NM 1:250,000 Quad USGS Land Use/Land Cover, 1986

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This dataset contains boundaries for land use and land cover polygons in New Mexico at a scale of 1:250,000. It is in a vector digital data structure. The source...

  15. Tularosa, NM 1:250,000 Quad USGS Land Use/Land Cover, 1986

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This dataset contains boundaries for land use and land cover polygons in New Mexico at a scale of 1:250,000. It is in a vector digital data structure. The source...

  16. Las Cruces, NM TX 1:250,000 Quad USGS Land Use/Land Cover, 1986

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This dataset contains boundaries for land use and land cover polygons in New Mexico at a scale of 1:250,000. It is in a vector digital data structure. The source...

  17. Internal Migration and Land Use and Land Cover Changes in the Middle Mountains of Nepal

    Directory of Open Access Journals (Sweden)

    Bhawana KC

    2017-11-01

    Full Text Available The movement of rural households from remote uplands to valley floors and to semiurban and urban areas (internal migration is a common phenomenon in the middle mountain districts of Nepal. Understanding the causes and effects of internal migration is critical to the development and implementation of policies that promote land use planning and sustainable resource management. Using geospatial information technologies and social research methods, we investigated the causes and effects of internal migration on land use and land cover patterns in a western mountain district of Nepal between 1998 and 2013. The results show a decreasing number of households at high elevations (above 1400 m, where an increase in forest cover has been observed with a consequent decrease in agricultural land and shrub- or grassland. At lower elevations (below 1400 m, forest cover has remained constant over the last 25 years, and the agricultural land area has increased but has become geometrically complex to meet the diverse needs and living requirements of the growing population. Our findings indicate that internal migration plays an important role in shaping land use and land cover change in the middle mountains of Nepal and largely determines the resource management, utilization, and distribution patterns within a small geographic unit. Therefore, land use planning must take an integrated and interdisciplinary approach rather than considering social, environmental, and demographic information in isolation.

  18. Saint Johns, AZ NM 1:250,000 Quad USGS Land Use/Land Cover, 1986

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This dataset contains boundaries for land use and land cover polygons in New Mexico at a scale of 1:250,000. It is in a vector digital data structure. The source...

  19. Drivers and Implications of Land Use and Land Cover Change in the ...

    African Journals Online (AJOL)

    This study explores the major drivers of Land-use/Land-cover (LULC) dynamics and the observed environmental degradation as a response to these changes in the Modjo watershed, central Ethiopia. Data for this study were generated through household survey and supplemented with remotely sensed image interpretation ...

  20. Historical Land-Cover Change and Land-Use Conversions Global Dataset

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A set of three estimates of land-cover types and annual transformations of land use are provided on a global 0.5 x0.5 degree lat/lon grid at annual time steps. The...

  1. Silver City, NM AZ 1:250,000 Quad USGS Land Use/Land Cover, 1986

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — This dataset contains boundaries for land use and land cover polygons in New Mexico at a scale of 1:250,000. It is in a vector digital data structure. The source...

  2. Land use, population dynamics, and land-cover change in Eastern Puerto Rico

    Science.gov (United States)

    W.A. Gould; S. Martinuzzi; I.K. Páres-Ramos

    2012-01-01

    We assessed current and historic land use and land cover in the Luquillo Mountains and surrounding area in eastern Puerto Rico, including four small subwatersheds that are study watersheds of the U.S. Geological Survey’s Water, Energy, and Biogeochemical Budgets (WEBB) program. This region occupies an area of 1,616 square kilometers, about 18 percent of the total land...

  3. Remote Sensing Based Two-Stage Sampling for Accuracy Assessment and Area Estimation of Land Cover Changes

    Directory of Open Access Journals (Sweden)

    Heinz Gallaun

    2015-09-01

    Full Text Available Land cover change processes are accelerating at the regional to global level. The remote sensing community has developed reliable and robust methods for wall-to-wall mapping of land cover changes; however, land cover changes often occur at rates below the mapping errors. In the current publication, we propose a cost-effective approach to complement wall-to-wall land cover change maps with a sampling approach, which is used for accuracy assessment and accurate estimation of areas undergoing land cover changes, including provision of confidence intervals. We propose a two-stage sampling approach in order to keep accuracy, efficiency, and effort of the estimations in balance. Stratification is applied in both stages in order to gain control over the sample size allocated to rare land cover change classes on the one hand and the cost constraints for very high resolution reference imagery on the other. Bootstrapping is used to complement the accuracy measures and the area estimates with confidence intervals. The area estimates and verification estimations rely on a high quality visual interpretation of the sampling units based on time series of satellite imagery. To demonstrate the cost-effective operational applicability of the approach we applied it for assessment of deforestation in an area characterized by frequent cloud cover and very low change rate in the Republic of Congo, which makes accurate deforestation monitoring particularly challenging.

  4. 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

  5. Measuring land-use and land-cover change using the U.S. department of agriculture's cropland data layer: Cautions and recommendations

    Science.gov (United States)

    Lark, Tyler J.; Mueller, Richard M.; Johnson, David M.; Gibbs, Holly K.

    2017-10-01

    Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.

  6. Land use/land cover and land capability data for evaluating land utilization and official land use planning in Indramayu Regency, West Java, Indonesia

    Science.gov (United States)

    Ambarwulan, W.; Widiatmaka; Nahib, I.

    2018-05-01

    Land utilization in Indonesia is regulated in an official spatial land use planning (OSLUP), stipulated by government regulations. However in fact, land utilizations are often develops inconsistent with regulations. OSLUP itself is also not usually compatible with sustainable land utilizations. This study aims to evaluate current land utilizations and OSLUP in Indramayu Regency, West Java. The methodology used is the integrated analysis using land use and land cover (LU/LC) data, land capability data and spatial pattern in OSLUP. Actual LU/LC are interpreted using SPOT-6 imagery of 2014. The spatial data of land capabilities are derived from land capability classification using field data and laboratory analysis. The confrontation between these spatial data is interpreted in terms of future direction for sustainable land use planning. The results shows that Indramayu regency consists of 8 types of LU/LC. Land capability in research area range from class II to VIII. Only a small portion of the land in Indramayu has been used in accordance with land capability, but most of the land is used exceeding its land capability.

  7. 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

  8. The impact of land use/land cover changes on land degradation dynamics: a Mediterranean case study.

    Science.gov (United States)

    Bajocco, S; De Angelis, A; Perini, L; Ferrara, A; Salvati, L

    2012-05-01

    In the last decades, due to climate changes, soil deterioration, and Land Use/Land Cover Changes (LULCCs), land degradation risk has become one of the most important ecological issues at the global level. Land degradation involves two interlocking systems: the natural ecosystem and the socio-economic system. The complexity of land degradation processes should be addressed using a multidisciplinary approach. Therefore, the aim of this work is to assess diachronically land degradation dynamics under changing land covers. This paper analyzes LULCCs and the parallel increase in the level of land sensitivity to degradation along the coastal belt of Sardinia (Italy), a typical Mediterranean region where human pressure affects the landscape characteristics through fires, intensive agricultural practices, land abandonment, urban sprawl, and tourism concentration. Results reveal that two factors mainly affect the level of land sensitivity to degradation in the study area: (i) land abandonment and (ii) unsustainable use of rural and peri-urban areas. Taken together, these factors represent the primary cause of the LULCCs observed in coastal Sardinia. By linking the structural features of the Mediterranean landscape with its functional land degradation dynamics over time, these results contribute to orienting policies for sustainable land management in Mediterranean coastal areas.

  9. The Impact of Land Use/Land Cover Changes on Land Degradation Dynamics: A Mediterranean Case Study

    Science.gov (United States)

    Bajocco, S.; De Angelis, A.; Perini, L.; Ferrara, A.; Salvati, L.

    2012-05-01

    In the last decades, due to climate changes, soil deterioration, and Land Use/Land Cover Changes (LULCCs), land degradation risk has become one of the most important ecological issues at the global level. Land degradation involves two interlocking systems: the natural ecosystem and the socio-economic system. The complexity of land degradation processes should be addressed using a multidisciplinary approach. Therefore, the aim of this work is to assess diachronically land degradation dynamics under changing land covers. This paper analyzes LULCCs and the parallel increase in the level of land sensitivity to degradation along the coastal belt of Sardinia (Italy), a typical Mediterranean region where human pressure affects the landscape characteristics through fires, intensive agricultural practices, land abandonment, urban sprawl, and tourism concentration. Results reveal that two factors mainly affect the level of land sensitivity to degradation in the study area: (i) land abandonment and (ii) unsustainable use of rural and peri-urban areas. Taken together, these factors represent the primary cause of the LULCCs observed in coastal Sardinia. By linking the structural features of the Mediterranean landscape with its functional land degradation dynamics over time, these results contribute to orienting policies for sustainable land management in Mediterranean coastal areas.

  10. Land Use and Land Cover - LAND_COVER_PRESETTLEMENT_IDNR_IN: Generalized Presettlement Vegetation Types of Indiana, Circa 1820 (Indiana Department of Natural Resources, Polygon Shapefile)

    Data.gov (United States)

    NSGIC State | GIS Inventory — LAND_COVER_PRESETTLEMENT_IDNR_IN.SHP is a polygon shapefile showing generalized presettlement vegetation types of Indiana, circa 1820. The work was based on original...

  11. Earth observation data for assessment of nationwide land cover and long-term deforestation in Afghanistan

    Science.gov (United States)

    Sudhakar Reddy, C.; Saranya, K. R. L.

    2017-08-01

    This study has generated a national level spatial database of land cover and changes in forest cover of Afghanistan for the 1975-1990, 1990-2005 and 2005-2014 periods. Using these results we have analysed the annual deforestation rates, spatial changes in forests, forest types and fragmentation classes over a period of 1975 to 2014 in Afghanistan. The land cover map of 2014 provides distribution of forest (dry evergreen, moist temperate, dry temperate, pine, sub alpine) and non-forest (grassland, scrub, agriculture, wetlands, barren land, snow and settlements) in Afghanistan. The largest land cover, barren land, contributes to 56% of geographical area of country. Forest is distributed mostly in eastern Afghanistan and constitutes an area of 1.02% of geographical area in 2014. The annual deforestation rate in Afghanistan's forests for the period from 1975 to 1990 estimated as 0.06% which was declined significantly from 2005 to 2014. The predominant forest type in Afghanistan is moist temperate which shows loss of 80 km2 of area during the last four decades of the study period. At national level, the percentage of large core forest area was calculated as 52.20% in 2014.

  12. Carbon dioxide emissions from forestry and peat land using land-use/land-cover changes in North Sumatra, Indonesia

    Science.gov (United States)

    Basyuni, M.; Sulistyono, N.; Slamet, B.; Wati, R.

    2018-03-01

    Forestry and peat land including land-based is one of the critical sectors in the inventory of CO2 emissions and mitigation efforts of climate change. The present study analyzed the land-use and land-cover changes between 2006 and 2012 in North Sumatra, Indonesia with emphasis to CO2 emissions. The land-use/land-cover consists of twenty-one classes. Redd Abacus software version 1.1.7 was used to measure carbon emission source as well as the predicted 2carbon dioxide emissions from 2006-2024. Results showed that historical emission (2006-2012) in this province, significant increases in the intensive land use namely dry land agriculture (109.65%), paddy field (16.23%) and estate plantation (15.11%). On the other hand, land-cover for forest decreased significantly: secondary dry land forest (7.60%), secondary mangrove forest (9.03%), secondary swamp forest (33.98%), and the largest one in the mixed dry land agriculture (79.96%). The results indicated that North Sumatra province is still a CO2 emitter, and the most important driver of emissions mostly derived from agricultural lands that contributed 2carbon dioxide emissions by 48.8%, changing from forest areas into degraded lands (classified as barren land and shrub) shared 30.6% and estate plantation of 22.4%. Mitigation actions to reduce carbon emissions was proposed such as strengthening the forest land, rehabilitation of degraded area, development and plantation forest, forest protection and forest fire control, and reforestation and conservation activity. These mitigation actions have been simulated to reduce 15% for forestry and 18% for peat land, respectively. This data is likely to contribute to the low emission development in North Sumatra.

  13. Globalland30 Mapping Capacity of Land Surface Water in Thessaly, Greece

    Directory of Open Access Journals (Sweden)

    Ioannis Manakos

    2014-12-01

    Full Text Available The National Geomatics Center of China (NGCC produced Global Land Cover (GlobalLand30 maps with 30 m spatial resolution for the years 2000 and 2009–2010, responding to the need for harmonized, accurate, and high-resolution global land cover data. This study aims to assess the mapping accuracy of the land surface water layer of GlobalLand30 for 2009–2010. A representative Mediterranean region, situated in Greece, is considered as the case study area, with 2009 as the reference year. The assessment is realized through an object-based comparison of the GlobalLand30 water layer with the ground truth and visually interpreted data from the Hellenic Cadastre fine spatial resolution (0.5 m orthophoto map layer. GlobCover 2009, GlobCorine 2009, and GLCNMO 2008 corresponding thematic layers are utilized to show and quantify the progress brought along with the increment of the spatial resolution, from 500 m to 300 m and finally to 30 m with the newly produced GlobalLand30 maps. GlobalLand30 detected land surface water areas show a 91.9% overlap with the reference data, while the coarser resolution products are restricted to lower accuracies. Validation is extended to the drainage network elements, i.e., rivers and streams, where GlobalLand30 outperforms the other global map products, as well.

  14. Land survey map of air pollutants

    International Nuclear Information System (INIS)

    Hadzi-Mishev, Dimitar

    1996-01-01

    The first step toward finding a solution to the problems with air pollution is the realization of a land survey map of polluters and a constant acquisition of data from periodical controls of emission of harmful materials, which will be carried out with a determined dynamic. Such a land survey map is not a project which should be finished within a strict time limit, but is intended to create all conditions for a periodical monitoring of emission of harmful materials from registered polluters in order to make a periodical, exact picture of the quantity of harmful materials, which are conveyed by polluters in certain city, a part of the state or the whole country. (author). 4 ills

  15. Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts

    Directory of Open Access Journals (Sweden)

    Dongyang Hou

    2015-05-01

    Full Text Available Sample data plays an important role in land cover (LC map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they contain valuable information for LC map validation. However, this kind of special textual data has seldom been analyzed and used for supporting LC map validation. This paper examines the potential of geo-tagged web texts as a new cost-free sample data source to assist LC map validation and proposes an active data collection approach. The proposed approach uses a customized deep web crawler to search for geo-tagged web texts based on land cover-related keywords and string-based rules matching. A data transformation based on buffer analysis is then performed to convert the collected web texts into LC sample data. Using three provinces and three municipalities directly under the Central Government in China as study areas, geo-tagged web texts were collected to validate artificial surface class of China’s 30-meter global land cover datasets (GlobeLand30-2010. A total of 6283 geo-tagged web texts were collected at a speed of 0.58 texts per second. The collected texts about built-up areas were transformed into sample data. User’s accuracy of 82.2% was achieved, which is close to that derived from formal expert validation. The preliminary results show that geo-tagged web texts are valuable ancillary data for LC map validation and the proposed approach can improve the efficiency of sample data collection.

  16. Theorizing Land Cover and Land Use Changes: The Case of Tropical Deforestation

    Science.gov (United States)

    Walker, Robert

    2004-01-01

    This article addresses land-cover and land-use dynamics from the perspective of regional science and economic geography. It first provides an account of the so-called spatially explicit model, which has emerged in recent years as a key empirical approach to the issue. The article uses this discussion as a springboard to evaluate the potential utility of von Thuenen to the discourse on land-cover and land-use change. After identifying shortcomings of current theoretical approaches to land use in mainly urban models, the article filters a discussion of deforestation through the lens of bid-rent and assesses its effectiveness in helping us comprehend the destruction of tropical forest in the Amazon basin. The article considers the adjustments that would have to be made to existing theory to make it more useful to the empirical issues.

  17. "Land-Cover Conversion in Amazonia, The Role of ENV" Ironment and Substrate composition in Modifying SOI

    Science.gov (United States)

    Roberts, Dar A.; Chadwick, Oliver A.; Batista, Getulio T.

    2003-01-01

    LBA research from the first phase of LBA focused on three broad categories: 1) mapping land cover and quantifying rates of change, persistence of pasture, and area of recovering forest; 2) evaluating the role of environmental factors and land-use history on soil biogeochemistry; and 3) quantifying the natural and human controls on stream nutrient concentrations. The focus of the research was regional, concentrating primarily in the state of RondBnia, but also included land-cover mapping in the vicinity of Maraba, Para, and Manaus, Amazonas. Remote sensing analysis utilized Landsat Thematic Mapper (TM) and Multispectral Scanner (MS S) data to map historical patterns of land-cover change. Specific questions addressed by the remote sensing component of the research included: 1) what is the areal extent of dominant land-cover classes? 2) what are the rates of change of dominant land cover through processes of deforestation, disturbance and regeneration? and 3) what are the dynamic properties of each class that characterize temporal variability, duration, and frequency of repeat disturbance? Biogeochemical analysis focused on natural variability and impacts of land-use/land-cover changes on soil and stream biogeochemical properties at the regional scale. An emphasis was given to specific soil properties considered to be primary limiting factors regionally, including phosphorus, nitrogen, base cations and cation-exchange properties. Stream sampling emphasized the relative effects of the rates and timing of land-cover change on stream nutrients, demonstrating that vegetation conversion alone does not impact nutrients as much as subsequent land use and urbanization.

  18. Interrelationships between soil cover and plant cover depending on land use

    Directory of Open Access Journals (Sweden)

    Tiina Köster

    2013-05-01

    Full Text Available Interrelationships between soil cover and plant cover of normally developed (or postlithogenic mineral soils are analysed on the basis of four sampling soil groups. The four-link pedo-ecological sequence of analysed soils, rendzinas → brown soils → pseudopodzolic soils → gley-podzols, forms a representative cross section in relation to the normal mineral soils of Estonia. All groups differ substantially from each other in terms of soil properties (calcareousness, acidity, nutrition conditions, profile fabric and humus cover. The primary tasks of the research were (1 to elucidate the main pedo-ecological characteristics of the four soil groups and their suitability for plant cover, (2 to evaluate comparatively soils in terms of productivity, sustainability, biodiversity and environmental protection ability and (3 to analyse possibilities for ecologically sound matching of soil cover with suitable plant cover. On the basis of the same material, the influence of land-use change on humus cover (epipedon fabric, properties of the entire soil cover and soil–plant interrelationship were also analysed. An ecosystem approach enables us to observe particularities caused by specific properties of a soil type (species, variety in biological turnover and in the formation of biodiversity.

  19. Land Cover Change Monitoring of Typical Functional Communities of Sichuan Province Based on ZY-3 Data

    Science.gov (United States)

    Li, G. M.; Li, S.; Ying, G. W.; Wu, X. P.

    2018-04-01

    According to the function, land space types are divided into key development areas, restricted development areas and forbidden development areas in Sichuan Province. This paper monitors and analyses the changes of land cover in different typical functional areas from 2010 to 2017, which based on ZY-3 high-score images data and combined with statistical yearbook and thematic data of Sichuan Province. The results show that: The land cover types of typical key development zones are mainly composed of cultivated land, forest land, garden land, and housing construction land, which accounts for the total area of land cover 87 %. The land cover types of typical restricted development zone mainly consists of forest land and grassland, which occupy 97.71 % of the total area of the surface coverage. The land cover types of the typical prohibition development zone mainly consist of forest land, grassland, desert and bared earth, which accounts for the total area of land cover 99.31 %.

  20. A comprehensive change detection method for updating the National Land Cover Database to circa 2011

    Science.gov (United States)

    Jin, Suming; Yang, Limin; Danielson, Patrick; Homer, Collin G.; Fry, Joyce; Xian, George

    2013-01-01

    The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and

  1. TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

    Directory of Open Access Journals (Sweden)

    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

  2. Land-Use and Land-Cover Change around Mobile Bay, Alabama from 1974-2008

    Science.gov (United States)

    Ellis, Jean; Spruce, Joseph P.; Swann, Roberta; Smooth, James C.

    2009-01-01

    This document summarizes the major findings of a Gulf of Mexico Application Pilot project led by NASA Stennis Space Center (SSC) in conjunction with a regional collaboration network of the Gulf of Mexico Alliance (GOMA). NASA researchers processed and analyzed multi-temporal Landsat data to assess land-use and land-cover (LULC) changes in the coastal counties of Mobile and Baldwin, AL between 1974 and 2008. Our goal was to create satellite-based LULC data products using methods that could be transferable to other coastal areas of concern within the Gulf of Mexico. The Mobile Bay National Estuary Program (MBNEP) is the primary end-user, however, several other state and local groups may benefit from the project s data products that will be available through NOAA-NCDDC s Regional Ecosystem Data Management program. Mobile Bay is a critical ecologic and economic region in the Gulf of Mexico and to the entire country. Mobile Bay was designated as an estuary of national significance in 1996. This estuary receives the fourth largest freshwater inflow in the United States. It provides vital nursery habitat for commercially and recreationally important fish species. It has exceptional aquatic and terrestrial bio-diversity, however, its estuary health is influenced by changing LULC patterns, such as urbanization. Mobile and Baldwin counties have experienced a population growth of 1.1% and 20.5% from 2000-2006. Urban expansion and population growth are likely to accelerate with the construction and operation of the ThyssenKrupp steel mill in the northeast portion of Mobile County. Land-use and land-cover change can negatively impact Gulf coast water quality and ecological resources. The conversion of forest to urban cover types impacts the carbon cycle and increases the freshwater and sediment in coastal waters. Increased freshwater runoff decreases salinity and increases the turbidity of coastal waters, thus impacting the growth potential of submerged aquatic vegetation (SAV

  3. Assessing changes in the value of ecosystem services in response to land-use/land-cover dynamics in Nigeria.

    Science.gov (United States)

    Arowolo, Aisha Olushola; Deng, Xiangzheng; Olatunji, Olusanya Abiodun; Obayelu, Abiodun Elijah

    2018-09-15

    Increasing human activities worldwide have significantly altered the natural ecosystems and consequently, the services they provide. This is no exception in Nigeria, where land-use/land-cover has undergone a series of dramatic changes over the years mainly due to the ever-growing large population. However, estimating the impact of such changes on a wide range of ecosystem services is seldom attempted. Thus, on the basis of GlobeLand30 land-cover maps for 2000 and 2010 and using the value transfer methodology, we evaluated changes in the value of ecosystem services in response to land-use/land-cover dynamics in Nigeria. The results showed that over the 10-year period, cultivated land sprawl over the forests and savannahs was predominant, and occurred mainly in the northern region of the country. During this period, we calculated an increase in the total ecosystem services value (ESV) in Nigeria from 665.93 billion (2007 US$) in 2000 to 667.44 billion (2007 US$) in 2010, 97.38% of which was contributed by cultivated land. The value of provisioning services increased while regulation, support, recreation and culture services decreased, amongst which, water regulation (-11.01%), gas regulation (-7.13%), cultural (-4.84%) and climate regulation (-4.3%) ecosystem functions are estimated as the most impacted. The increase in the total ESV in Nigeria associated with the huge increase in ecosystem services due to cultivated land expansion may make land-use changes (i.e. the ever-increasing agricultural expansion in Nigeria) appear economically profitable. However, continuous loss of services such as climate and water regulation that are largely provided by the natural ecosystems can result in huge economic losses that may exceed the apparent gains from cultivated land development. Therefore, we advocate that the conservation of the natural ecosystem should be a priority in future land-use management in Nigeria, a country highly vulnerable to climate change and incessantly

  4. Assessment of land use and land cover change in Tecolutla River Basin, Veracruz, Mexico; during the period 1994-2010

    Directory of Open Access Journals (Sweden)

    Ana Karen Osuna-Osuna

    2015-04-01

    Full Text Available Studies related to changes in vegetation and land use cover have gained importance in environmental research, as they allow for the assessment of time-space trends in deforestation and environmental degradation processes, especially as caused by human activity. In this context, the main goal of this study was to evaluate the impact of human activity in the basin of the Tecolutla River, in Veracruz, Mexico during a 16-year period. Landsat satellite images were used for the years 1994 and 2010, distinguishing nine land use coverage classes: rainforest, forest, agricultural land, water, disturbed vegetation, urban settlements, grasslands, citrus crops and shrubs. Thematic maps were validated, yielding overall accuracies greater than 92% and Kappa coefficients of 0.89 and 0.91 for the 1994 and 2010 classifications, respectively. Analysis of the transition matrix revealed a trend of increasing areas related to human activity (agriculture and urban use showing percentage changes of 28% and 67% within 16 years, respectively. Consequently, a decrease (-1.1% per year in areas with natural cover, specifically forest and jungle, was observed. Similar findings were reported in works done at national and state levels, where the transition of natural cover by the increasing of anthropogenic activities has been proven. The results of this study are useful for future environmental development planning, land management planning and planning strategies for the conservation of the natural resources in the basin.

  5. A remote sensing evaluation for agronomic land use mapping in ...

    African Journals Online (AJOL)

    It has become essential to prepare land use maps because these are recognized as necessary tools for the preparation of land capability, land classification and land suitability maps which in turn provide guidelines for the regional planning, development and future orientation of agriculture in the regional system.

  6. Historical changes in caribou distribution and land cover in and around Prince Albert National Park: land management implications

    Directory of Open Access Journals (Sweden)

    Maria L. Arlt

    2011-09-01

    Full Text Available In central Saskatchewan, boreal woodland caribou population declines have been documented in the 1940s and again in the 1980s. Although both declines led to a ban in sport hunting, a recovery was only seen in the 1950s and was attributed to wolf control and hunting closure. Recent studies suggest that this time, the population may not be increasing. In order to contribute to the conservation efforts, historical changes in caribou distribution and land cover types in the Prince Albert Greater Ecosystem (PAGE, Saskatchewan, were documented for the period of 1960s to the present. To examine changes in caribou distribution, survey observations, incidental sightings and telemetry data were collated. To quantify landscape changes, land cover maps were created for 1966 and 2006 using current and historic forest resources inventories, fire, logging, and roads data. Results indicate that woodland caribou are still found throughout the study area although their distribution has changed and their use of the National Park is greatly limited. Results of transition prob¬abilities and landscape composition analyses on the 1966 and 2006 land cover maps revealed an aging landscape for both the National Park and provincial crown land portions of the PAGE. In addition, increased logging and the development of extensive road and trail networks on provincial crown land produced significant landscape fragmentation for woodland caribou and reduced functional attributes of habitat patches. Understanding historical landscape changes will assist with ongoing provincial and federal recovery efforts for boreal caribou, forest management planning activities, and landscape restoration efforts within and beyond the Park boundaries.

  7. Effects of Land Cover / Land Use, Soil Texture, and Vegetation on the Water Balance of Lake Chad Basin

    Science.gov (United States)

    Babamaaji, R. A.; Lee, J.

    2013-12-01

    Lake Chad Basin (LCB) has experienced drastic changes of land cover and poor water management practices during the last 50 years. The successive droughts in the 1970s and 1980s resulted in the shortage of surface water and groundwater resources. This problem of drought has a devastating implication on the natural resources of the Basin with great consequence on food security, poverty reduction and quality of life of the inhabitants in the LCB. Therefore, understanding the effects of land use / land cover must be a first step to find how they disturb cycle especially the groundwater in the LCB. The abundance of groundwater is affected by the climate change through the interaction with surface water, such as lakes and rivers, and disuse recharge through an infiltration process. Quantifying the impact of climate change on the groundwater resource requires reliable forecasting of changes in the major climatic variables and other spatial variations including the land use/land cover, soil texture, topographic slope, and vegetation. In this study, we employed a spatially distributed water balance model WetSpass to simulate a long-term average change of groundwater recharge in the LCB of Africa. WetSpass is a water balance-based model to estimate seasonal and spatial distribution of surface runoff, interception, evapotranspiration, and groundwater recharge. The model is especially suitable for studying the effect of land use/land cover change on the water regime in the LCB. The present study describes the concept of the model and its application to the development of recharge map of the LCB. The study shows that major role in the water balance of LCB. The mean yearly actual evapotranspiration (ET) from the basin range from 60mm - 400 mm, which is 90 % (69mm - 430) of the annual precipitation from 2003 - 2010. It is striking that about 50 - 60 % of the total runoff is produced on build-up (impervious surfaces), while much smaller contributions are obtained from vegetated

  8. Anthropogenic Influences in Land Use/Land Cover Changes in Mediterranean Forest Landscapes in Sicily

    Directory of Open Access Journals (Sweden)

    Donato S. La Mela Veca

    2016-01-01

    Full Text Available This paper analyzes and quantifies the land use/land cover changes of the main forest and semi-natural landscape types in Sicily between 1955 and 2012. We analyzed seven representative forest and shrubland landscapes in Sicily. These study areas were chosen for their importance in the Sicilian forest panorama. We carried out a diachronic survey on historical and current aerial photos; all the aerial images used to survey the land use/land cover changes were digitalized and georeferenced in the UTM WGS84 system. In order to classify land use, the Regional Forest Inventory 2010 legend was adopted for the more recent images, and the CORINE Land Cover III level used for the older, lower resolution images. This study quantifies forest landscape dynamics; our results show for almost all study areas an increase of forest cover and expansion, whereas a regressive dynamic is found in rural areas due to intensive agricultural and pasturage uses. Understanding the dynamics of forest landscapes could enhance the role of forestry policy as a tool for landscape management and regional planning.

  9. Determining Topographic and Some Physical Characteristics of the Land in Artvin City and Investigating Relationship between These Characteristics with Land Cover

    Directory of Open Access Journals (Sweden)

    Ayşe Yavuz Özalp

    2013-11-01

    Full Text Available In this study, the aim was to determine topographic (elevation, slope, and aspect and some physical (Great Soil Groups (GSG and Land Use Capability Classes (LUCC characteristics of the land in Artvin and to reveal relations between these characteristics and land cover of the city that lies along the northeast border of Turkey and due to its natural resources, consists of several protected areas as well as many development projects -both planned and ongoing. Within this scope, areal and percentile distributions in respect to slope, aspect, elevation, GSG, LUCC and land cover were determined for eight towns of Artvin using digitized 1/25000 topographic and soil maps along with CORINE 2006 land cover map with the help of Geographical Information System (GIS. Then, distributions of chosen land use types (forest, agriculture, grassland/meadow were investigated according to the determined-ranges for the parameters of slope, aspect, elevation, GSG, and LUCC. The results showed that about 48.22% of Artvin’s whole land is between an elevation ranges of 1000 – 2000 m while 31.07% of the land lies above 2000 m. Moreover, average elevation of all the towns, except for Hopa, is over the country’s mean elevation of 1132 m and 81.25% of the city’s land consists of more than 30% slope, meaning that topography of the land in Artvin

  10. 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

  11. LAND COVER ASSESSMENT OF INDIGENOUS COMMUNITIES IN THE BOSAWAS REGION OF NICARAGUA

    Science.gov (United States)

    Data derived from remotely sensed images were utilized to conduct land cover assessments of three indigenous communities in northern Nicaragua. Historical land use, present land cover and land cover change processes were all identified through the use of a geographic informat...

  12. Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960-2005.

    Science.gov (United States)

    Dewan, Ashraf M; Yamaguchi, Yasushi

    2009-03-01

    This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city's rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.

  13. Land and Forest Management by Land Use/ Land Cover Analysis and Change Detection Using Remote Sensing and GIS

    Directory of Open Access Journals (Sweden)

    Ankana

    2016-01-01

    Full Text Available Remote sensing and Geographical Information System (GIS are the most effective tools in spatial data analysis. Natural resources like land, forest and water, these techniques have proved a valuable source of information generation as well as in the management and planning purposes. This study aims to suggest possible land and forest management strategies in Chakia tahsil based on land use and land cover analysis and the changing pattern observed during the last ten years. The population of Chakia tahsil is mainly rural in nature. The study has revealed that the northern part of the region, which offers for the settlement and all the agricultural practices constitutes nearly 23.48% and is a dead level plain, whereas the southern part, which constitute nearly 76.6% of the region is characterized by plateau and is covered with forest. The southern plateau rises abruptly from the northern alluvial plain with a number of escarpments. The contour line of 100 m mainly demarcates the boundary between plateau and plain. The plateau zone is deeply dissected and highly rugged terrain. The resultant topography comprises of a number of mesas and isolated hillocks showing elevation differences from 150 m to 385 m above mean sea level. Being rugged terrain in the southern part, nowadays human encroachment are taking place for more land for the cultivation. The changes were well observed in the land use and land cover in the study region. A large part of fallow land and open forest were converted into cultivated land.

  14. Sustaining forest landscape connectivity under different land cover change scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Rubio, L.; Rodriguez-Freire, M.; Mateo-Sanchez, M. C.; Estreguil, C.; Saura, S.

    2012-11-01

    Managing forest landscapes to sustain functional connectivity is considered one of the key strategies to counteract the negative effects of climate and human-induced changes in forest species pools. With this objective, we evaluated whether a robust network of forest connecting elements can be identified so that it remains efficient when facing different types of potential land cover changes that may affect forest habitat networks and ecological fluxes. For this purpose we considered changes both in the forested areas and in the non-forest intervening landscape matrix. We combined some of the most recent developments in graph theory with models of land cover permeability and least-cost analysis through the forest landscape. We focused on a case of study covering the habitat of a forest dwelling bird (nuthatch, Sitta europaea) in the region of Galicia (NW Spain). Seven land-use change scenarios were analysed for their effects on connecting forest elements (patches and links): one was the simplest case in which the landscape is represented as a binary forest/non-forest pattern (and where matrix heterogeneity is disregarded), four scenarios in which forest lands were converted to other cover types (to scrubland due to wildfires, to extensive and intensive agriculture, and to urban areas), and two scenarios that only involved changes in the non-forested matrix (re naturalization and intensification). Our results show that while the network of connecting elements for the species was very robust to the conversion of the forest habitat patches to different cover types, the different change scenarios in the landscape matrix could more significantly weaken its long-term validity and effectiveness. This is particularly the case when most of the key connectivity providers for the nuthatch are located outside the protected areas or public forests in Galicia, where biodiversity-friendly measures might be more easily implemented. We discuss how the methodology can be applied to

  15. ASSESSING LAND COVER CHANGES CAUSED BY GRANITE QUARRYING USING REMOTE SENSING

    Directory of Open Access Journals (Sweden)

    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.

  16. Evaluating Anthropogenic Risk of Grassland and Forest Habitat Degradation using Land-Cover Data

    Directory of Open Access Journals (Sweden)

    Kurt Riitters

    2009-09-01

    Full Text Available The effects of landscape context on habitat quality are receiving increased attention in conservation biology. The objective of this research is to demonstrate a landscape-level approach to mapping and evaluating the anthropogenic risks of grassland and forest habitat degradation by examining habitat context as defined by intensive anthropogenic land uses at multiple spatial scales. A landscape mosaic model classifies a given location according to the amounts of intensive agriculture and intensive development in its surrounding landscape, providing measures of anthropogenic risks attributable to habitat isolation and edge effects at that location. The model is implemented using a land-cover map (0.09 ha/pixel of the conterminous United States and six landscape sizes (4.4, 15.2, 65.6, 591, 5300, and 47800 ha to evaluate the spatial scales of anthropogenic risk. Statistics for grassland and forest habitat are extracted by geographic overlays of the maps of land-cover and landscape mosaics. Depending on landscape size, 81 to 94 percent of all grassland and forest habitat occurs in landscapes that are dominated by natural land-cover including habitat itself. Within those natural-dominated landscapes, 50 percent of grassland and 59 percent of forest is within 590 m of intensive agriculture and/or intensive developed land which is typically a minor component of total landscape area. The conclusion is that anthropogenic risk attributable to habitat patch isolation affects a small proportion of the total grassland or forest habitat area, while the majority of habitat area is exposed to edge effects.

  17. Analysis of potential flooding in the education Jatinangor based approach morphology, land cover, and geology

    Science.gov (United States)

    Rifai, Achmad; Hadian, Sapari Dwi; Mufti, Iqbal Jabbari; Fathoni, Azmi Rizqi; Azy, Fikri Noor; Jihadi, Lutfan Harisan

    2017-07-01

    Jatinangor formerly an agricultural area dominated by rice field. Water in Jatinangor comes from a spring located in north Jatinangor or proximal region of Manglayang mountain to flow to the south and southwest Jatinangor up to Citarum River. Jatinangor plain that was once almost all the rice fields, but now become a land settlement that grew very rapidly since its founding colleges. Flow and puddle were originally be used for agricultural land, but now turned into a disaster risks for humans. The research method using qualitative methods with the weighing factor, scoring, and overlay maps. The cause of the flood is distinguished into two: the first is the natural factors such as the condition of landform, lithology, river flow patterns, and annual rainfall. The second is non-natural factors such as land cover of settlement, irrigation, and land use. The amount of flood risks using probability Gilbert White frequency, magnitude and duration of existing events then correlated with these factors. Based on the results of the study, were divided into 3 zones Jatinangor disaster-prone (high, medium, and safe). High flood zone is located in the South Jatinangor which covers an area Cikeruh Village, Sayang Village, Cipacing village, Mekargalih village, Cintamulya village, west of Jatimukti village, and South Hegarmanah village, has a dominant causative factor is the use of solid land, poor drainage, lithology lacustrine conditions with low permeability, and flat topography. Medium flood zone was located in the central and western regions covering Cibeusi village, Cileles village, south of Cilayung village, Hegarmanah village and Padjadjaran Region, has a dominant causative factor is rather dense land use, lithology breccias and Tuffaceous Sand with moderate permeability, topography is moderately steep. Safe flood zone is located in the east Jatinangor covering Jatiroke village, Cisepur village, east Hegarmanah village, has a dominant factor in the form of a rather steep

  18. Historical Image Registration and Land-Use Land-Cover Change Analysis

    Directory of Open Access Journals (Sweden)

    Fang-Ju Jao

    2014-12-01

    Full Text Available Historical aerial images are important to retain past ground surface information. The land-use land-cover change in the past can be identified using historical aerial images. Automatic historical image registration and stitching is essential because the historical image pose information was usually lost. In this study, the Scale Invariant Feature Transform (SIFT algorithm was used for feature extraction. Subsequently, the present study used the automatic affine transformation algorithm for historical image registration, based on SIFT features and control points. This study automatically determined image affine parameters and simultaneously transformed from an image coordinate system to a ground coordinate system. After historical aerial image registration, the land-use land-cover change was analyzed between two different years (1947 and 1975 at the Tseng Wen River estuary. Results show that sandbars and water zones were transformed into a large number of fish ponds between 1947 and 1975.

  19. Monitoring land use/land cover changes using CORINE land cover data: a case study of Silivri coastal zone in Metropolitan Istanbul.

    Science.gov (United States)

    Yilmaz, Rüya

    2010-06-01

    The objective of the present study was to assess changes in land use/land cover patterns in the coastal town of Silivri, a part of greater Istanbul administratively. In the assessment, remotely sensed data, in the form of satellite images, and geographic information systems were used. Types of land use/land cover were designated as the percentage of the total area studied. Results calculated from the satellite data for land cover classification were compared successfully with the database Coordination of Information on the Environment (CORINE). This served as a reference to appraise the reliability of the study presented here. The CORINE Program was established by the European Commission to create a harmonized Geographical Information System on the state of the environment in the European Community. Unplanned urbanization is causing land use changes mainly in developing countries such as Turkey. This situation in Turkey is frequently observed in the city of Istanbul. There are only a few studies of land use-land cover changes which provide an integrated assessment of the biophysical and societal causes and consequences of environmental degradation in Istanbul. The research area comprised greater Silivri Town which is situated by the coast of Marmara Sea, and it is located approximately 60 km west of Istanbul. The city of Istanbul is one of the largest metropolises in Europe with ca. 15 million inhabitants. Additionally, greater Silivri is located near the terminal point of the state highway connecting Istanbul with Europe. Measuring of changes occurring in land use would help control future planning of settlements; hence, it is of importance for the Greater Silivri and Silivri Town. Following our evaluations, coastal zone of Silivri was classified into the land use groups of artificial surfaces agricultural areas and forests and seminatural areas with 47.1%, 12.66%, and 22.62%, respectively.

  20. A prototype for automation of land-cover products from Landsat Surface Reflectance Data Records

    Science.gov (United States)

    Rover, J.; Goldhaber, M. B.; Steinwand, D.; Nelson, K.; Coan, M.; Wylie, B. K.; Dahal, D.; Wika, S.; Quenzer, R.

    2014-12-01

    Landsat data records of surface reflectance provide a three-decade history of land surface processes. Due to the vast number of these archived records, development of innovative approaches for automated data mining and information retrieval were necessary. Recently, we created a prototype utilizing open source software libraries for automatically generating annual Anderson Level 1 land cover maps and information products from data acquired by the Landsat Mission for the years 1984 to 2013. The automated prototype was applied to two target areas in northwestern and east-central North Dakota, USA. The approach required the National Land Cover Database (NLCD) and two user-input target acquisition year-days. The Landsat archive was mined for scenes acquired within a 100-day window surrounding these target dates, and then cloud-free pixels where chosen closest to the specified target acquisition dates. The selected pixels were then composited before completing an unsupervised classification using the NLCD. Pixels unchanged in pairs of the NLCD were used for training decision tree models in an iterative process refined with model confidence measures. The decision tree models were applied to the Landsat composites to generate a yearly land cover map and related information products. Results for the target areas captured changes associated with the recent expansion of oil shale production and agriculture driven by economics and policy, such as the increase in biofuel production and reduction in Conservation Reserve Program. Changes in agriculture, grasslands, and surface water reflect the local hydrological conditions that occurred during the 29-year span. Future enhancements considered for this prototype include a web-based client, ancillary spatial datasets, trends and clustering algorithms, and the forecasting of future land cover.

  1. Assessment of the effect of land use /land cover changes on total runoff from Ofu River catchment in Nigeria

    Directory of Open Access Journals (Sweden)

    Meshach Ileanwa Alfa

    2018-04-01

    Full Text Available The total runoff from a catchment is dependednt on both the soil characteristics and the land use/land cover (LULC type. This study was conducted to examine the effect of changes in land cover on the total runoff from Ofu River Catchment in Nigeria. Classified Landsat imageries of 1987, 2001 and 2016 in combination with the soil map extracted from the Digital Soil Map of the World was used to estimate the runoff curve number for 1987, 2001 and 2016. The runoff depth for 35 years daily rainfall data was estimated using Natura Resource Conservation Services Curve Number (NRCS-CN method. The runoff depths obtained for the respective years were subjected to a one-way analysis of variance at 95% level of significance. P-value < 0.05 was taken as statistically significant. Runoff curve numbers obtained for 1987, 2001 and 2016 were 61.83, 63.26 and 62.79 respectively. The effects of the changes in LULC for 1987-2001, 2001-2016 and 1987-2016 were statistically significant (P<0.001 at 95% confident interval.  The average change in runoff depths were 79.81%, -11.10% and 48.09% respectively for 1987-2001, 2001-2016 and 1987-2016. The study concluded that the changes in LULC of the catchment had significant effect on the runoff from the catchment.

  2. Development of deforestation and land cover database for Bhutan (1930-2014).

    Science.gov (United States)

    Reddy, C Sudhakar; Satish, K V; Jha, C S; Diwakar, P G; Murthy, Y V N Krishna; Dadhwal, V K

    2016-12-01

    Bhutan is a mountainous country located in the Himalayan biodiversity hotspot. This study has quantified the total area under land cover types, estimated the rate of forest cover change, analyzed the changes across forest types, and modeled forest cover change hotpots in Bhutan. The topographical maps and satellite remote sensing images were analyzed to get the spatial patterns of forest and associated land cover changes over the past eight decades (1930-1977-1987-1995-2005-2014). Forest is the largest land cover in Bhutan and constitutes 68.3% of the total geographical area in 2014. Subtropical broad leaved hill forest is predominant type occupies 34.1% of forest area in Bhutan, followed by montane dry temperate (20.9%), montane wet temperate (18.9%), Himalayan moist temperate (10%), and tropical moist sal (8.1%) in 2014. The major forest cover loss is observed in subtropical broad leaved hill forest (64.5 km 2 ) and moist sal forest (9.9 km 2 ) from 1977 to 2014. The deforested areas have mainly been converted into agriculture and contributed for 60.9% of forest loss from 1930 to 2014. In spite of major decline of forest cover in time interval of 1930-1977, there is no net rate of deforestation is recorded in Bhutan since 1995. Forest cover change analysis has been carried out to evaluate the conservation effectiveness in "Protected Areas" of Bhutan. Hotspots that have undergone high transformation in forest cover for afforestation and deforestation were highlighted in the study for conservation prioritisation. Forest conservation policies in Bhutan are highly effective in controlling deforestation as compared to neighboring Asian countries and such service would help in mitigating climate change.

  3. Impact of land cover and land use change on runoff characteristics.

    Science.gov (United States)

    Sajikumar, N; Remya, R S

    2015-09-15

    Change in Land Cover and Land Use (LCLU) influences the runoff characteristics of a drainage basin to a large extent, which in turn, affects the surface and groundwater availability of the area, and hence leads to further change in LCLU. This forms a vicious circle. Hence it becomes essential to assess the effect of change in LCLU on the runoff characteristics of a region in general and of small watershed levels (sub-basin levels) in particular. Such an analysis can effectively be carried out by using watershed simulation models with integrated GIS frame work. SWAT (Soil and Water Analysis Tool) model, being one of the versatile watershed simulation models, is found to be suitable for this purpose as many GIS integration modules are available for this model (e.g. ArcSWAT, MWSWAT). Watershed simulation using SWAT requires the land use and land cover data, soil data and many other features. With the availability of repository of satellite imageries, both from Indian and foreign sources, it becomes possible to use the concurrent local land use and land cover data, thereby enabling more accurate modelling of small watersheds. Such availability will also enable us to assess the effect of LCLU on runoff characteristics and their reverse impact. The current study assesses the effect of land use and land cover on the runoff characteristics of two watersheds in Kerala, India. It also assesses how the change in land use and land cover in the last few decades affected the runoff characteristics of these watersheds. It is seen that the reduction in the forest area amounts to 60% and 32% in the analysed watersheds. However, the changes in the surface runoff for these watersheds are not comparable with the changes in the forest area but are within 20%. Similarly the maximum (peak) value of runoff has increased by an amount of 15% only. The lesser (aforementioned) effect than expected might be due to the fact that forest has been converted to agricultural purpose with major

  4. Forest cover of North America in the 1970s mapped using Landsat MSS data

    Science.gov (United States)

    Feng, M.; Sexton, J. O.; Channan, S.; Townshend, J. R.

    2015-12-01

    The distribution and changes in Earth's forests impact hydrological, biogeochemical, and energy fluxes, as well as ecosystems' capacity to support biodiversity and human economies. Long-term records of forest cover are needed across a broad range of investigation, including climate and carbon-cycle modeling, hydrological studies, habitat analyzes, biological conservation, and land-use planning. Satellite-based observations enable mapping and monitoring of forests at ecologically and economically relevant resolutions and continental or even global extents. Following early forest-mapping efforts using coarser resolution remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS), forests have been mapped regionally at developed by the Global Land Cover Facility (GLCF) as reference, we developed an automated approach to detect forests using MSS data by leveraging the multispectral and phenological characteristics of forests observed in MSS time-series. The forest-cover map is produced with layers representing the year of observation, detection of forest-cover change relative to 1990, and the uncertainty of forest-cover and -change layers. The approach has been implemented with open-source libraries to facilitate processing large volumes of Landsat MSS images on high-performance computing machines. As the first result of our global mapping effort, we present the forest cover for North America. More than 25,000 Landsat MSS scenes were processed to provide a 120-meter resolution forest cover for North America, which will be made publicly available on the GLCF website (http://www.landcover.org).

  5. Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

    Science.gov (United States)

    Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

    2018-04-01

    Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation

  6. Spatial and temporal analysis of the land cover in riparian buffer zones (Areas for Permanent Preservation in Sorocaba City, SP, Brazil

    Directory of Open Access Journals (Sweden)

    Sergio Henrique Alves

    2009-08-01

    Full Text Available Considering the fundamental role that the riparian vegetation plays in relation to maintenance of the environmental health of a watershed and the necessity of restoring sectors of the buffer zone without natural vegetation, in this paper we investigated what land cover classes occur along the riparian buffer stripes considered Area for Permanent Preservation (APP in the Sorocaba municipality, SP in three periods: 1988, 1995 and 2003. Based on GIS technology and using the drainage network map, the APP stripes (riparian buffer zones map was generated, and this map was overlaid to the land cover map (1988, 1995 and 2003 to provide a land cover map specifically of the riparian buffer zones. The results show that 58.43% of the APPs have no land cover of native vegetation and therefore, need to be reforested, representing 5,400 hectares to be restored.

  7. Land-cover change research at the U.S. Geological Survey-assessing our nation's dynamic land surface

    Science.gov (United States)

    Wilson, Tamara S.

    2011-01-01

    The U.S. Geological Survey (USGS) recently completed an unprecedented, 27-year assessment of land-use and land-cover change for the conterminous United States. For the period 1973 to 2000, scientists generated estimates of change in major types of land use and land cover, such as development, mining, agriculture, forest, grasslands, and wetlands. To help provide the insight that our Nation will need to make land-use decisions in coming decades, the historical trends data is now being used by the USGS to help model potential future land use/land cover under different scenarios, including climate, environmental, economic, population, public policy, and technological change.

  8. 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.

  9. 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.

  10. Evaluation of historical land cover, land use, and land-use change emissions in the GCAM integrated assessment model

    Science.gov (United States)

    Calvin, K. V.; Wise, M.; Kyle, P.; Janetos, A. C.; Zhou, Y.

    2012-12-01

    Integrated Assessment Models (IAMs) are often used as science-based decision-support tools for evaluating the consequences of climate and energy policies, and their use in this framework is likely to increase in the future. However, quantitative evaluation of these models has been somewhat limited for a variety of reasons, including data availability, data quality, and the inherent challenges in projections of societal values and decision-making. In this analysis, we identify and confront methodological challenges involved in evaluating the agriculture and land use component of the Global Change Assessment Model (GCAM). GCAM is a global integrated assessment model, linking submodules of the regionally disaggregated global economy, energy system, agriculture and land-use, terrestrial carbon cycle, oceans and climate. GCAM simulates supply, demand, and prices for energy and agricultural goods from 2005 to 2100 in 5-year increments. In each time period, the model computes the allocation of land across a variety of land cover types in 151 different regions, assuming that farmers maximize profits and that food demand is relatively inelastic. GCAM then calculates both emissions from land-use practices, and long-term changes in carbon stocks in different land uses, thus providing simulation information that can be compared to observed historical data. In this work, we compare GCAM results, both in recent historic and future time periods, to historical data sets. We focus on land use, land cover, land-use change emissions, and albedo.

  11. Automatic crown cover mapping to improve forest inventory

    Science.gov (United States)

    Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

    2009-01-01

    To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

  12. Mapping of Landscape Cover Using Remote Sensing and GIS in ...

    African Journals Online (AJOL)

    Humankind to fulfill its needs has put natural resources of the earth to a severe pressure. The rate of degradation and depletion of earth resources has accelerated tremendously in view of the overincreasing demographic pressure. Therefore, mapping of landscape cover types to evaluate it has been a great concern for ...

  13. Management Effectiveness and Land Cover Change in Dynamic Cultural Landscapes - Assessing a Central European Biosphere Reserve

    Directory of Open Access Journals (Sweden)

    Bettina Ohnesorge

    2013-12-01

    Full Text Available Protected areas are a central pillar of efforts to safeguard biodiversity and ecosystem services, but their contribution to the conservation and management of European cultural landscapes that have complex spatial-temporal dynamics is unclear. The conservation strategy of biosphere reserves aims at integrating biodiversity and ecosystem service conservation with economic development by designating zones of differing protection and use intensities. It is applied worldwide to protect and manage valuable cultural landscapes. Using the example of a German biosphere reserve, we developed a framework to assess the effectiveness of Central European reserves in meeting their land cover related management goals. Based on digital biotope maps, we defined and assessed land cover change processes that were relevant to the reserve management's goals over a period of 13 years. We then compared these changes in the reserve's core, buffer, and transition zones and in a surrounding reference area by means of a geographical information system. (Un-desirable key processes related to management aims were defined and compared for the various zones. We found that - despite an overall land cover persistence of approximately 85% across all zones - differences in land cover changes can be more prominent across zones inside the reserve than between the areas inside and outside of it. The reserve as a whole performed better than the surrounding reference area when using land cover related management goals as a benchmark. However, some highly desirable targets, such as the conversion of coniferous plantations into seminatural forests or the gain of valuable biotope types, affected larger areas in the nonprotected reference area than in the transition zone.

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

    Science.gov (United States)

    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.

  15. Land Use Land Cover Change in the fringe of eThekwini ...

    African Journals Online (AJOL)

    Concerns on urban environmental quality, increasing knowledge on impacts of climate change and pursuit for sustainable development have increased the need for past, current and future knowledge on the transformation of remnant urban fringe green ecosystems. Using land-cover change modeler and a Markov chain ...

  16. 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 ...

  17. Generating a National Land Cover Dataset for Mexico at 30m Spatial Resolution in the Framework of the NALCMS Project.

    Science.gov (United States)

    Llamas, R. M.; Colditz, R. R.; Ressl, R.; Jurado Cruz, D. A.; Argumedo, J.; Victoria, A.; Meneses, C.

    2017-12-01

    The North American Land Change Monitoring System (NALCMS) is a tri-national initiative for mapping land cover across Mexico, United States and Canada, integrating efforts of institutions from the three countries. At the continental scale the group released land cover and change maps derived from MODIS image mosaics at 250m spatial resolution for 2005 and 2010. Current efforts are based on 30m Landsat images for 2010 ± 1 year. Each country uses its own mapping approach and sources for ancillary data, while ensuring that maps are produced in a coherent fashion across the continent. This paper presents the methodology and final land cover map of Mexico for the year 2010 that was later integrated into a continental map. The principal input for Mexico was the Monitoring Activity Data for Mexico (MAD-MEX) land cover map (version 4.3), derived from all available mostly cloud-free images for the year 2010. A total of 35 classes were regrouped to 15 classes of the NALCMS legend present in Mexico. Next, various issues of the automatically generated MAD-MEX land cover mosaic were corrected, such as: filling areas of no data due no cloud-free observation or gaps in Landsat 7 ETM+ images, filling inland water bodies which were left unclassified due to masking issues, relabeling isolated unclassified of falsely classified pixels, structural mislabeling due to data gaps, reclassifying areas of adjacent scenes with significant class disagreements and correcting obvious misclassifications, mostly of water and urban areas. In a second step minor missing areas and rare class snow and ice were digitized and a road network was added. A product such as NALCMS land cover map at 30m for North America is an unprecedented effort and will be without doubt an important source of information for many users around the world who need coherent land cover data over a continental domain as an input for a wide variety of environmental studies. The product release to the general public is expected

  18. Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

    Science.gov (United States)

    Di Vittorio, A. V.; Mao, J.; Shi, X.; Chini, L.; Hurtt, G.; Collins, W. D.

    2018-01-01

    Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850-2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

  19. Land Use and Land Cover Change in Sagarmatha National Park, a World Heritage Site in the Himalayas of Eastern Nepal

    Directory of Open Access Journals (Sweden)

    Rodney Garrard

    2016-08-01

    Full Text Available Land use and land cover (LULC changes that occurred during 1992–2011 in Sagarmatha National Park, a United Nations Educational, Scientific, and Cultural Organization World Heritage Site in the Himalayas of eastern Nepal, were evaluated using multitemporal satellite imagery in combination with land use data and sociological information gathered from semistructured interviews and workshops. We asked study participants about LULC changes, the causes of each change, and the likely duration of its effects, and used this information to produce high-resolution maps of local perceptions of LULC change. Satellite image analysis revealed that above 6000 m there has been a decrease in the area covered by snow and ice and a consequent expansion of glacial lakes and areas covered by rock and soil. Between 3000 and 6000 m, forest and farmland are decreasing, and areas under grazing, settlement, and shrubland are increasing. Such LULC changes within the protected area clearly indicate the prevailing danger of land degradation. Results from the interviews and workshops suggest that people tended to detect LULC change that was acute and direct, but were less aware of slower changes that could be identified by satellite imagery analysis. Most study participants said that land use changes were a result of rapid economic development and the consequent pressure on natural resources, especially in the tourism industry and especially below 6000 m elevation, as well as limitations to protected area management and a period of civil war. Human influence coupled with climate change may explain the changes at higher elevations, whereas anthropogenic activities are solely responsible in lower areas. Although global factors cannot be mitigated locally, many of the local drivers of LULC change could be addressed with improved management practices that aid local conservation and development in this high mountain ecosystem. A broader interdisciplinary approach to LULC change

  20. Land cover change detection in West Jilin using ETM+ images

    Institute of Scientific and Technical Information of China (English)

    Edward M.Osei,Jr.; ZHOU Yun-xuan

    2004-01-01

    In order to assess the information content and accuracy ofLandsat ETM+ digital images in land cover change detection,change-detection techniques of image differencing,normalized difference vegetation index,principal components analysis and tasseled-cap transformation were applied to yield 13 images. These images were thresholded into change and no change areas. The thresholded images were then checked in terms of various accuracies. The experiment results show that kappa coefficients of the 13 images range from 48.05 ~78.09. Different images do detect different types of changes. Images associated with changes in the near-infrared-reflectance or greenness detects crop-type changes and changes between vegetative and non-vegetative features. A unique means of using only Landsat imagery without reference data for the assessment of change in arid land are presented. Images of 12th June, 2000 and 2nd June, 2002 are used to validate the means. Analyses of standard accuracy and spatial agreement are performed to compare the new images (hereafter called "change images" ) representing the change between the two dates. Spatial agreement evaluates the conformity in the classified "change pixels" and "no-change pixels" at the same location on different change images and comprehensively examines the different techniques. This method would enable authorities to monitor land degradation efficiently and accurately.

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

    Data.gov (United States)

    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...

  2. ISLSCP II IGBP DISCover and SiB Land Cover, 1992-1993

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set describes the geographic distributions of 17 classes of land cover based on the International Geosphere-Biosphere DISCover land cover legend (Loveland...

  3. C-CAP Santa Cruz 2001 era High Resolution Land Cover Metadata

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset represents land cover for the San Lorenzo River basin in Santa Cruz County, California derived from high resolution imagery. The land cover features in...

  4. EnviroAtlas - Paterson, NJ - Meter-Scale Urban Land Cover (MULC) Data (2010)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Paterson, New Jersey EnviroAtlas Meter-Scale Urban Land Cover (MULC) data comprises approximately 66 km2 around the city of Paterson. The land cover data were...

  5. EnviroAtlas - Des Moines, IA - Meter-Scale Urban Land Cover (MULC) Data (2010)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Des Moines, IA EnviroAtlas Meter-Scale Urban Land Cover (MULC) Data were generated from the High Resolution Land Cover (HRLC) product created by the Iowa...

  6. NLCD - MODIS land cover- albedo dataset for the continental United States

    Data.gov (United States)

    U.S. Environmental Protection Agency — The NLCD-MODIS land cover-albedo database integrates high-quality MODIS albedo observations with areas of homogeneous land cover from NLCD. The spatial resolution...

  7. Pairing FLUXNET sites to validate model representations of land-use/land-cover change

    Science.gov (United States)

    Chen, Liang; Dirmeyer, Paul A.; Guo, Zhichang; Schultz, Natalie M.

    2018-01-01

    Land surface energy and water fluxes play an important role in land-atmosphere interactions, especially for the climatic feedback effects driven by land-use/land-cover change (LULCC). These have long been documented in model-based studies, but the performance of land surface models in representing LULCC-induced responses has not been investigated well. In this study, measurements from proximate paired (open versus forest) flux tower sites are used to represent observed deforestation-induced changes in surface fluxes, which are compared with simulations from the Community Land Model (CLM) and the Noah Multi-Parameterization (Noah-MP) land model. Point-scale simulations suggest the CLM can represent the observed diurnal and seasonal changes in net radiation (Rnet) and ground heat flux (G), but difficulties remain in the energy partitioning between latent (LE) and sensible (H) heat flux. The CLM does not capture the observed decreased daytime LE, and overestimates the increased H during summer. These deficiencies are mainly associated with models' greater biases over forest land-cover types and the parameterization of soil evaporation. Global gridded simulations with the CLM show uncertainties in the estimation of LE and H at the grid level for regional and global simulations. Noah-MP exhibits a similar ability to simulate the surface flux changes, but with larger biases in H, G, and Rnet change during late winter and early spring, which are related to a deficiency in estimating albedo. Differences in meteorological conditions between paired sites is not a factor in these results. Attention needs to be devoted to improving the representation of surface heat flux processes in land models to increase confidence in LULCC simulations.

  8. Forest Aboveground Biomass Mapping and Canopy Cover Estimation from Simulated ICESat-2 Data

    Science.gov (United States)

    Narine, L.; Popescu, S. C.; Neuenschwander, A. L.

    2017-12-01

    The assessment of forest aboveground biomass (AGB) can contribute to reducing uncertainties associated with the amount and distribution of terrestrial carbon. With a planned launch date of July 2018, the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) will provide data which will offer the possibility of mapping AGB at global scales. In this study, we develop approaches for utilizing vegetation data that will be delivered in ICESat-2's land-vegetation along track product (ATL08). The specific objectives are to: (1) simulate ICESat-2 photon-counting lidar (PCL) data using airborne lidar data, (2) utilize simulated PCL data to estimate forest canopy cover and AGB and, (3) upscale AGB predictions to create a wall-to-wall AGB map at 30-m spatial resolution. Using existing airborne lidar data for Sam Houston National Forest (SHNF) located in southeastern Texas and known ICESat-2 beam locations, PCL data are simulated from discrete return lidar points. We use multiple linear regression models to relate simulated PCL metrics for 100 m segments along the ICESat-2 ground tracks to AGB from a biomass map developed using airborne lidar data and canopy cover calculated from the same. Random Forest is then used to create an AGB map from predicted estimates and explanatory data consisting of spectral metrics derived from Landsat TM imagery and land cover data from the National Land Cover Database (NLCD). Findings from this study will demonstrate how data that will be acquired by ICESat-2 can be used to estimate forest structure and characterize the spatial distribution of AGB.

  9. Multisource Data Fusion Framework for Land Use/Land Cover Classification Using Machine Vision

    Directory of Open Access Journals (Sweden)

    Salman Qadri

    2017-01-01

    Full Text Available Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to individual source. This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing. A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly. Multispectral data is obtained using a multispectral radiometer, while digital camera is used for image dataset. It has been observed that each image contained 229 texture features, while 30 optimized texture features data for each image has been obtained by joining together three features selection techniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information. This 30-optimized-texture-feature dataset is merged with five-spectral-feature dataset to build the fused dataset. A comparison is performed among texture, multispectral, and fused dataset using machine vision classifiers. It has been observed that fused dataset outperformed individually both datasets. The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was 96.67%, 97.60%, and 99.60%, respectively.

  10. Land cover/land use change in semi-arid Inner Mongolia: 1992-2004

    Energy Technology Data Exchange (ETDEWEB)

    John, Ranjeet; Chen Jiquan; Lu Nan; Wilske, Burkhard, E-mail: ranjeet.john@utoledo.ed [Department of Environmental Sciences, University of Toledo, Toledo, OH 43606 (United States)

    2009-10-15

    The semi-arid grasslands in Inner Mongolia (IM) are under increasing stress owing to climate change and rapid socio-economic development in the recent past. We investigated changes in land cover/land use and landscape structure between 1992 and 2004 through the analysis of AVHRR and MODIS derived land cover data. The scale of analysis included the regional level (i.e. the whole of IM) as well as the level of the dominant biomes (i.e. the grassland and desert). We quantified proportional change, rate of change and the changes in class-level landscape metrics using the landscape structure analysis program FRAGSTATS. The dominant land cover types, grassland and barren, 0.47 and 0.27 million km{sup 2}, respectively, have increased proportionally. Cropland and urban land use also increased to 0.15 million km{sup 2} and 2197 km{sup 2}, respectively. However, the results further indicated increases in both the homogeneity and fragmentation of the landscape. Increasing homogeneity was mainly related to the reduction in minority cover types such as savanna, forests and permanent wetlands and increasing cohesion, aggregation index and clumpy indices. Conversely, increased fragmentation of the landscape was based on the increase in patch density and the interspersion/juxtaposition index (IJI). It is important to note the socio-economic growth in this fragile ecosystem, manifested by an increasing proportion of agricultural and urban land use not just at the regional level but also at the biome level in the context of regional climate change and increasing water stress.

  11. Land cover/land use change in semi-arid Inner Mongolia: 1992-2004

    International Nuclear Information System (INIS)

    John, Ranjeet; Chen Jiquan; Lu Nan; Wilske, Burkhard

    2009-01-01

    The semi-arid grasslands in Inner Mongolia (IM) are under increasing stress owing to climate change and rapid socio-economic development in the recent past. We investigated changes in land cover/land use and landscape structure between 1992 and 2004 through the analysis of AVHRR and MODIS derived land cover data. The scale of analysis included the regional level (i.e. the whole of IM) as well as the level of the dominant biomes (i.e. the grassland and desert). We quantified proportional change, rate of change and the changes in class-level landscape metrics using the landscape structure analysis program FRAGSTATS. The dominant land cover types, grassland and barren, 0.47 and 0.27 million km 2 , respectively, have increased proportionally. Cropland and urban land use also increased to 0.15 million km 2 and 2197 km 2 , respectively. However, the results further indicated increases in both the homogeneity and fragmentation of the landscape. Increasing homogeneity was mainly related to the reduction in minority cover types such as savanna, forests and permanent wetlands and increasing cohesion, aggregation index and clumpy indices. Conversely, increased fragmentation of the landscape was based on the increase in patch density and the interspersion/juxtaposition index (IJI). It is important to note the socio-economic growth in this fragile ecosystem, manifested by an increasing proportion of agricultural and urban land use not just at the regional level but also at the biome level in the context of regional climate change and increasing water stress.

  12. A globally complete map of supraglacial debris cover and a new toolkit for debris cover research

    Science.gov (United States)

    Herreid, Sam; Pellicciotti, Francesca

    2017-04-01

    A growing canon of literature is focused on resolving the processes and implications of debris cover on glaciers. However, this work is often confined to a handful of glaciers that were likely selected based on criteria optimizing their suitability to test a specific hypothesis or logistical ease. The role of debris cover in a glacier system is likely to not go overlooked in forthcoming research, yet the magnitude of this role at a global scale has not yet been fully described. Here, we present a map of debris cover for all glacierized regions on Earth including the Greenland Ice Sheet using 30 m Landsat data. This dataset will begin to open a wider context to the high quality, localized findings from the debris-covered glacier research community and help inform large-scale modeling efforts. A global map of debris cover also facilitates analysis attempting to isolate first order geomorphological and climate controls of supraglacial debris production. Furthering the objective of expanding the inclusion of debris cover in forthcoming research, we also present an under development suite of open-source, Python based tools. Requiring minimal and often freely available input data, we have automated the mapping of: i) debris cover, ii) ice cliffs, iii) debris cover evolution over the Landsat era and iv) glacier flow instabilities from altered debris structures. At the present time, debris extent is the only globally complete quantity but with the expanding repository of high quality global datasets and further tool development minimizing manual tasks and computational cost, we foresee all of these tools being applied globally in the near future.

  13. Hydrological response to land cover changes and human activities in arid regions using a geographic information system and remote sensing.

    Directory of Open Access Journals (Sweden)

    Shereif H Mahmoud

    Full Text Available The hydrological response to land cover changes induced by human activities in arid regions has attracted increased research interest in recent decades. The study reported herein assessed the spatial and quantitative changes in surface runoff resulting from land cover change in the Al-Baha region of Saudi Arabia between 1990 and 2000 using an ArcGIS-surface runoff model and predicted land cover and surface runoff depth in 2030 using Markov chain analysis. Land cover maps for 1990 and 2000 were derived from satellite images using ArcGIS 10.1. The findings reveal a 26% decrease in forest and shrubland area, 28% increase in irrigated cropland, 1.5% increase in sparsely vegetated land and 0.5% increase in bare soil between 1990 and 2000. Overall, land cover changes resulted in a significant decrease in runoff depth values in most of the region. The decrease in surface runoff depth ranged from 25-106 mm/year in a 7020-km2 area, whereas the increase in such depth reached only 10 mm/year in a 243-km2 area. A maximum increase of 73 mm/year was seen in a limited area. The surface runoff depth decreased to the greatest extent in the central region of the study area due to the huge transition in land cover classes associated with the construction of 25 rainwater harvesting dams. The land cover prediction revealed a greater than twofold increase in irrigated cropland during the 2000-2030 period, whereas forest and shrubland are anticipated to occupy just 225 km2 of land area by 2030, a significant decrease from the 747 km2 they occupied in 2000. Overall, changes in land cover are predicted to result in an annual increase in irrigated cropland and dramatic decline in forest area in the study area over the next few decades. The increase in surface runoff depth is likely to have significant implications for irrigation activities.

  14. Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

    OpenAIRE

    Moran, Emilio Federico.

    2010-01-01

    High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervi...

  15. Quantification of land cover and land use within the rural complex of the Democratic Republic of Congo

    Science.gov (United States)

    Molinario, G.; Hansen, M. C.; Potapov, P. V.; Tyukavina, A.; Stehman, S.; Barker, B.; Humber, M.

    2017-10-01

    The rural complex is the inhabited agricultural land cover mosaic found along the network of rivers and roads in the forest of the Democratic Republic of Congo. It is a product of traditional small-holder shifting cultivation. To date, thanks to its distinction from primary forest, this area has been mapped as relatively homogenous, leaving the proportions of land cover heterogeneity within it unknown. However, the success of strategies for sustainable development, including land use planning and payment for ecosystem services, such as Reduced Emissions from Deforestation and Degradation, depends on the accurate characterization of the impacts of land use on natural resources, including within the rural complex. We photo-interpreted a simple random sample of 1000 points in the established rural complex, using 3106 high resolution satellite images obtained from the National Geospatial-Intelligence Agency, together with 406 images from Google Earth, spanning the period 2008-2016. Results indicate that nationally the established rural complex includes 5% clearings, 10% active fields, 26% fallows, 34% secondary forest, 2% wetland forest, 11% primary forest, 6% grasslands, 3% roads and settlements and 2% commercial plantations. Only a small proportion of sample points were plantations, while other commercial dynamics, such as logging and mining, were not detected in the sample. The area of current shifting cultivation accounts for 76% of the established rural complex. Added to primary forest (11%), this means that 87% of the rural complex is available for shifting cultivation. At the current clearing rate, it would take ~18 years for a complete rotation of the rural complex to occur. Additional pressure on land results in either the cultivation of non-preferred land types within the rural complex (such as wetland forest), or expansion of agriculture into nearby primary forests, with attendant impacts on emissions, habitat loss and other ecosystems services.

  16. 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.

  17. CARETS: A prototype regional environmental information system. Volume 9: Shore zone land use and land cover; Central Atlantic Regional Ecological Test Site

    Science.gov (United States)

    Alexander, R. H. (Principal Investigator); Dolan, R.; Hayden, B. P.; Vincent, C. L.

    1975-01-01

    The author has identified the following significant results. Analysis of the land use and land cover maps provides a stratification of the CARETS shore area into regions which have a similar environmental organization. Different elements of the landscape are altered less frequently moving inland. Near the beach, higher frequency of monitoring is needed than is needed in the inland areas, including the marsh and estuarine areas.

  18. Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

    Science.gov (United States)

    Zhang, Wen-Yan; Lin, Chao-Yuan

    2017-04-01

    The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

  19. Digital Bedrock Compilation: A Geodatabase Covering Forest Service Lands in California

    Science.gov (United States)

    Elder, D.; de La Fuente, J. A.; Reichert, M.

    2010-12-01

    This digital database contains bedrock geologic mapping for Forest Service lands within California. This compilation began in 2004 and the first version was completed in 2005. Second publication of this geodatabase was completed in 2010 and filled major gaps in the southern Sierra Nevada and Modoc/Medicine Lake/Warner Mountains areas. This digital map database was compiled from previously published and unpublished geologic mapping, with source mapping and review from California Geological Survey, the U.S. Geological Survey and others. Much of the source data was itself compilation mapping. This geodatabase is huge, containing ~107,000 polygons and ~ 280,000 arcs. Mapping was compiled from more than one thousand individual sources and covers over 41,000,000 acres (~166,000 km2). It was compiled from source maps at various scales - from ~ 1:4,000 to 1:250,000 and represents the best available geologic mapping at largest scale possible. An estimated 70-80% of the source information was digitized from geologic mapping at 1:62,500 scale or better. Forest Service ACT2 Enterprise Team compiled the bedrock mapping and developed a geodatabase to store this information. This geodatabase supports feature classes for polygons (e.g, map units), lines (e.g., contacts, boundaries, faults and structural lines) and points (e.g., orientation data, structural symbology). Lookup tables provide detailed information for feature class items. Lookup/type tables contain legal values and hierarchical groupings for geologic ages and lithologies. Type tables link coded values with descriptions for line and point attributes, such as line type, line location and point type. This digital mapping is at the core of many quantitative analyses and derivative map products. Queries of the database are used to produce maps and to quantify rock types of interest. These include the following: (1) ultramafic rocks - where hazards from naturally occurring asbestos are high, (2) granitic rocks - increased

  20. 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.

  1. Soil chemical and physical properties that differentiate urban land-use and cover types

    Science.gov (United States)

    R.V. Pouyat; I.D. Yesilonis; J. Russell-Anelli; N.K. Neerchal

    2007-01-01

    We investigated the effects of land use and cover and surface geology on soil properties in Baltimore, MD, with the objectives to: (i) measure the physical and chemical properties of surface soils (0?10 cm) by land use and cover; and (ii) ascertain whether land use and cover explain differences in these properties relative to surface geology. Mean and median values of...

  2. Does estuarine health relate to catchment land-cover in the East ...

    African Journals Online (AJOL)

    Possible links between catchment and buffer zone land-cover class composition and the health of the East Kleinemonde Estuary were explored. There was a relationship between catchment land-cover and estuarine health within all assessed catchment delineations. Natural land-cover was determined to be the best ...

  3. Challenges and opportunities in mapping land use intensity globally

    DEFF Research Database (Denmark)

    Kuemmerle, Tobias; Erb, Karlheinz; Meyfroidt, Patrick

    2013-01-01

    Future increases in land-based production will need to focus more on sustainably intensifying existing production systems. Unfortunately, our understanding of the global patterns of land use intensity is weak, partly because land use intensity is a complex, multidimensional term, and partly becau...... challenges and opportunities for mapping land use intensity for cropland, grazing, and forestry systems, and identify key issues for future research....... we lack appropriate datasets to assess land use intensity across broad geographic extents. Here, we review the state of the art regarding approaches for mapping land use intensity and provide a comprehensive overview of available global-scale datasets on land use intensity. We also outline major...

  4. 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...

  5. Effect of land-use/land-cover change on the future of rainfed agriculture in the Jenin Governorate, Palestine

    NARCIS (Netherlands)

    Thawaba, Salem; Abu-Madi, Maher; Özerol, Gül

    2017-01-01

    Land cover has been changed by humans throughout history. At the global level, population growth and socio-economic development have a significant impact on land resources. Recently, scholars added climate change as one of the major factors affecting land-cover transformation. In the West Bank of

  6. The application of remote sensing in detail land use mapping of RDE site, Puspiptek Serpong

    International Nuclear Information System (INIS)

    Heni Susiati; Habib Subagio

    2016-01-01

    Land cover mapping and its development has been performed in a detail scale (1:5000) within the radius of 5 km from the center of Experimental Power Reactor (RDE) site at Kawasan Nuklir Serpong (KNS), PUSPIPTEK Serpong. The objective of this study is to establish land cover database in a detail scale 1:5000 as part of the preparation toward RDE development plan and also to complete the land cover map of a scale 1:10.000. The research method is accomplished in several stages, namely data collection and processing of high-resolution satellite imagery and aerial photographs, field surveys, land use analysis within the radius of 300 - 500 m, 1 km, 2 km, 3 km, 4 km and 5 km from the RDE site as well as analysis of land use change by 2014-2015. Satellite image processing is carried out at Center for Land Mapping and Atlas, Badan Informasi Geospasial (BIG). Data processing is done by using ArcGis and Er Mapper software, whereas the satellite image analysis is executed by using Image Analysis as one of tool in ArcGis software. The result shows that KNS land cover outside the radius of 3 km is a dense residential in many places. Analysis of land use change by year 2014- 2015 shows that vast development of residential has occur which demonstrated by the increase of residential area in North-East of PUSPIPTEK. (author)

  7. Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

    Science.gov (United States)

    Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

    2017-11-01

    Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

  8. NASA Land Cover and Land Use Change (LCLUC): an interdisciplinary research program.

    Science.gov (United States)

    Justice, Chris; Gutman, Garik; Vadrevu, Krishna Prasad

    2015-01-15

    Understanding Land Cover/Land Use Change (LCLUC) in diverse regions of the world and at varied spatial scales is one of the important challenges in global change research. In this article, we provide a brief overview of the NASA LCLUC program, its focus areas, and the importance of satellite remote sensing observations in LCLUC research including future directions. The LCLUC Program was designed to be a cross-cutting theme within NASA's Earth Science program. The program aims to develop and use remote sensing technologies to improve understanding of human interactions with the environment. Since 1997, the NASA LCLUC program has supported nearly 280 research projects on diverse topics such as forest loss and carbon, urban expansion, land abandonment, wetland loss, agricultural land use change and land use change in mountain systems. The NASA LCLUC program emphasizes studies where land-use changes are rapid or where there are significant regional or global LCLUC implications. Over a period of years, the LCLUC program has contributed to large regional science programs such as Land Biosphere-Atmosphere (LBA), the Northern Eurasia Earth Science Partnership Initiative (NEESPI), and the Monsoon Area Integrated Regional Study (MAIRS). The primary emphasis of the program will remain on using remote sensing datasets for LCLUC research. The program will continue to emphasize integration of physical and social sciences to address regional to global scale issues of LCLUC for the benefit of society. Copyright © 2014. Published by Elsevier Ltd.

  9. ANALYSING THE EFFECTS OF DIFFERENT LAND COVER TYPES ON LAND SURFACE TEMPERATURE USING SATELLITE DATA

    Directory of Open Access Journals (Sweden)

    A. Şekertekin

    2015-12-01

    Full Text Available Monitoring Land Surface Temperature (LST via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

  10. Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

    Science.gov (United States)

    Şekertekin, A.; Kutoglu, Ş. H.; Kaya, S.; Marangoz, A. M.

    2015-12-01

    Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

  11. 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

  12. Mapping the global land surface using 1 km AVHRR data

    Science.gov (United States)

    Lauer, D.T.; Eidenshink, J.C.

    1998-01-01

    The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

  13. Integration of land use and land cover inventories for landscape management and planning in Italy.

    Science.gov (United States)

    Sallustio, Lorenzo; Munafò, Michele; Riitano, Nicola; Lasserre, Bruno; Fattorini, Lorenzo; Marchetti, Marco

    2016-01-01

    There are both semantic and technical differences between land use (LU) and land cover (LC) measurements. In cartographic approaches, these differences are often neglected, giving rise to a hybrid classification. The aim of this paper is to provide a better understanding and characterization of the two classification schemes using a comparison that allows maximization of the informative power of both. The analysis was carried out in the Molise region (Central Italy) using sample information from the Italian Land Use Inventory (IUTI). The sampling points were classified with a visual interpretation of aerial photographs for both LU and LC in order to estimate surfaces and assess the changes that occurred between 2000 and 2012. The results underscore the polarization of land use and land cover changes resulting from the following: (a) recolonization of natural surfaces, (b) strong dynamisms between the LC classes in the natural and semi-natural domain and (c) urban sprawl on the lower hills and plains. Most of the observed transitions are attributable to decreases in croplands, natural grasslands and pastures, owing to agricultural abandonment. The results demonstrate that a comparison between LU and LC estimates and their changes provides an understanding of the causes of misalignment between the two criteria. Such information may be useful for planning policies in both natural and semi-natural contexts as well as in urban areas.

  14. 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.

  15. Ecology of Land Cover Change in Glaciated Tropical Mountains

    Directory of Open Access Journals (Sweden)

    Kenneth R. Young

    2014-12-01

    Full Text Available Tropical mountains contain unique biological diversity, and are subject to many consequences of global climate change, exasperated by concurrent socioeconomic shifts. Glaciers are in a negative mass balance, exposing substrates to primary succession and altering downslope wetlands and streams. A review of recent trends and future predictions suggests a likely reduction in areas of open habitat for species of high mountains due to greater woody plant cover, accompanied by land use shifts by farmers and pastoralists along the environmental gradients of tropical mountains. Research is needed on the biodiversity and ecosystem consequences of successional change, including the direct effects of retreating glaciers and the indirect consequences of combined social and ecological drivers in lower elevations. Areas in the high mountains that are protected for nature conservation or managed collectively by local communities represent opportunities for integrated research and development approaches that may provide ecological spaces for future species range shifts.

  16. Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems.

    Science.gov (United States)

    Kimball, Heather L; Selmants, Paul C; Moreno, Alvaro; Running, Steve W; Giardina, Christian P

    2017-01-01

    Gross primary production (GPP) is the Earth's largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

  17. Urban land use and land cover change analysis and modeling a case study area Malatya, Turkey

    OpenAIRE

    Baysal, Gülendam

    2013-01-01

    Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies. This research was conducted to analyze the land use and land cover changes and to model the changes for the case study area Malatya, Turkey. The first step of the study was acquisition of multi temporal data in order to detect the changes over the time. For this purpose satellite images (Landsat 1990-2000-2010) have been used. In order to acquire data from satel...

  18. Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells

    International Nuclear Information System (INIS)

    Bagan, Hasi; Yamagata, Yoshiki

    2014-01-01

    Global urban expansion has created incentives to convert green spaces to urban/built-up area. Therefore, understanding the distribution and dynamics of the land-cover changes in cities is essential for better understanding of the cities’ fundamental characteristics and processes, and of the impact of changing land-cover on potential carbon storage. We present a grid square approach using multi-temporal Landsat data from around 1985–2010 to monitor the spatio-temporal land-cover dynamics of 50 global cities. The maximum-likelihood classification method is applied to Landsat data to define the cities’ urbanized areas at different points in time. Subsequently, 1 km 2 grid squares with unique cell IDs are designed to link among land-cover maps for spatio-temporal land-cover change analysis. Then, we calculate land-cover category proportions for each map in 1 km 2 grid cells. Statistical comparison of the land-cover changes in grid square cells shows that urban area expansion in 50 global cities was strongly negatively correlated with forest, cropland and grassland changes. The generated land-cover proportions in 1 km 2 grid cells and the spatial relationships between the changes of land-cover classes are critical for understanding past patterns and the consequences of urban development so as to inform future urban planning, risk management and conservation strategies. (letters)

  19. Spatio-temporal Characteristics of Land Use Land Cover Change Driven by Large Scale Land Transactions in Cambodia

    Science.gov (United States)

    Ghosh, A.; Smith, J. C.; Hijmans, R. J.

    2017-12-01

    Since mid-1990s, the Cambodian government granted nearly 300 `Economic Land Concessions' (ELCs), occupying approximately 2.3 million ha to foreign and domestic organizations (primarily agribusinesses). The majority of Cambodian ELC deals have been issued in areas of both relatively low population density and low agricultural productivity, dominated by smallholder production. These regions often contain highly biodiverse areas, thereby increasing the ecological cost associated with land clearing for extractive purposes. These large-scale land transactions have also resulted in substantial and rapid changes in land-use patterns and agriculture practices by smallholder farmers. In this study, we investigated the spatio-temporal characteristics of land use change associated with large-scale land transactions across Cambodia using multi-temporal multi-reolution remote sensing data. We identified major regions of deforestation during the last two decades using Landsat archive, global forest change data (2000-2014) and georeferenced database of ELC deals. We then mapped the deforestation and land clearing within ELC boundaries as well as areas bordering or near ELCs to quantify the impact of ELCs on local communities. Using time-series from MODIS Vegetation Indices products for the study period, we also estimated the time period over which any particular ELC deal initiated its proposed activity. We found evidence of similar patterns of land use change outside the boundaries of ELC deals which may be associated with i) illegal land encroachments by ELCs and/or ii) new agricultural practices adopted by local farmers near ELC boundaries. We also detected significant time gaps between ELC deal granting dates and initiation of land clearing for ELC purposes. Interestingly, we also found that not all designated areas for ELCs were put into effect indicating the possible proliferation of speculative land deals. This study demonstrates the potential of remote sensing techniques

  20. Multitemporal analysis of Landsat images to detect land use land cover changes for monitoring soil sealing in the Nola area (Naples, Italy)

    Science.gov (United States)

    De Giglio, Michaela; Allocca, Maria; Franci, Francesca

    2016-10-01

    Land Use Land Cover Changes (LULCC) data provide objective information to support environmental policy, urban planning purposes and sustainable land development. Understanding of past land use/cover practices and current landscape patterns is critical to assess the effects of LULCC on the Earth system. Within the framework of soil sealing in Italy, the present study aims to assess the LULCC of the Nola area (Naples metropolitan area, Italy), relating to a thirty year period from 1984 to 2015. The urban sprawl affects this area causing the impervious surface increase, the loss in rural areas and landscape fragmentation. Located near Vesuvio volcano and crossed by artificial filled rivers, the study area is subject to landslide, hydraulic and volcanic risks. Landsat time series has been processed by means of the supervised per-pixel classification in order to produce multitemporal Land Use Land Cover maps. Then, post-classification comparison approach has been applied to quantify the changes occurring between 1984 and 2015, also analyzing the intermediate variations in 1999, namely every fifteen years. The results confirm the urban sprawl. The increase of the built-up areas mainly causes the habitat fragmentation and the agricultural land conversion of the Nola area that is already damaged by unauthorized disposal of urban waste. Moreover, considering the local risk maps, it was verified that some of the new urban areas were built over known hazardous sites. In order to limit the soil sealing, urgent measures and sustainable urban planning are required.

  1. Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling

    Directory of Open Access Journals (Sweden)

    Marina Alberti

    2009-12-01

    Full Text Available Urbanization and the resulting changes in land cover have myriad impacts on ecological systems. Monitoring these changes across large spatial extents and long time spans requires synoptic remotely sensed data with an appropriate temporal sequence. We developed a multi-temporal land cover dataset for a six-county area surrounding the Seattle, Washington State, USA, metropolitan region. Land cover maps for 1986, 1991, 1995, 1999, and 2002 were developed from Landsat TM images through a combination of spectral unmixing, image segmentation, multi-season imagery, and supervised classification approaches to differentiate an initial nine land cover classes. We then used ancillary GIS layers and temporal information to define trajectories of land cover change through multiple updating and backdating rules and refined our land cover classification for each date into 14 classes. We compared the accuracy of the initial approach with the landscape trajectory modifications and determined that the use of landscape trajectory rules increased our ability to differentiate several classes including bare soil (separated into cleared for development, agriculture, and clearcut forest and three intensities of urban. Using the temporal dataset, we found that between 1986 and 2002, urban land cover increased from 8 to 18% of our study area, while lowland deciduous and mixed forests decreased from 21 to 14%, and grass and agriculture decreased from 11 to 8%. The intensity of urban land cover increased with 252 km2 in Heavy Urban in 1986 increasing to 629 km2 by 2002. The ecological systems that are present in this region were likely significantly altered by these changes in land cover. Our results suggest that multi-temporal (i.e., multiple years and multiple seasons within years Landsat data are an economical means to quantify land cover and land cover change across large and highly heterogeneous urbanizing landscapes. Our data, and similar temporal land cover change

  2. 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.

  3. Land Use-Land Cover dynamics of Huluka watershed, Central Rift Valley, Ethiopia

    Directory of Open Access Journals (Sweden)

    Hagos Gebreslassie

    2014-12-01

    Full Text Available Land Use-Land Cover (LULC dynamic has of human kind age and is one of the phenomenons which interweave the socio economic and environmental issues in Ethiopia. Huluka watershed is one of the watersheds in Central Rift Valley of Ethiopia which drains to Lake Langano. Few decades ago the stated watershed was covered with dense acacia forest. But, nowadays like other part of Ethiopia, it is experiencing complex dynamics of LULC. The aim of this research was thus to evaluate the LULC dynamics seen in between 1973–2009. This was achieved through collecting qualitative and quantitative data using Geographic Information System (GIS and Remote Sensing (RS technique. Field observations, discussion with elders were also employed to validate results from remotely sensed data. Based on the result, eight major dynamic LULC classes were identified from the watershed. Of these LULC classes, only cultivated and open lands had shown continuous and progressive expansion mainly at the expense of grass, shrub and forest lands. The 25% and 0% of cultivated and open land of the watershed in 1973 expanded to 84% and 4% in 2009 respectively while the 29%, 18% and 22% of grass, shrub and forest land of the watershed in 1973 degraded to 3.5%, 4% and 1.5% in 2009 respectively. As a result, land units which had been used for pastoralist before 1973 were identified under mixed agricultural system after 2000. In the end, this study came with a recommendation of an intervention of concerned body to stop the rapid degradation of vegetation on the watershed.

  4. Assessment of land cover changes in Lampedusa Island (Italy) using Landsat TM and OLI data

    Science.gov (United States)

    Mei, Alessandro; Manzo, Ciro; Fontinovo, Giuliano; Bassani, Cristiana; Allegrini, Alessia; Petracchini, Francesco

    2016-10-01

    The Lampedusa Island displays important socio-economic criticalities related to an intensive touristic activity, which implies an increase in electricity consumption and waste production. An adequate island conversion to a more environmental, sustainable community needs to be faced by the local Management Plans establishment. For this purpose, several thematic datasets have to be produced and evaluated. Socio-economic and bio-ecological components as well as land cover/use assessment are some of the main topics to be managed within the Decision Support Systems. Considering the lack of Land Cover (LC) and vegetation change detection maps in Lampedusa Island (Italy), this paper focuses on the retrieval of these topics by remote sensing techniques. The analysis was carried out by Landsat 5 TM and Landsat 8 OLI multispectral images from 1984 to 2014 in order to obtain spatial and temporal information of changes occurred in the island. Firstly, imagery was co-registered and atmospherically corrected; secondly, it was then classified for land cover and vegetation distribution analysis with the use of QGIS and Saga GIS open source softwares. The Maximum Likelihood Classifier (MLC) was used for LC maps production, while the Normalized Difference Vegetation Index (NDVI) was used for vegetation examination and distribution. Topographic maps, historical aerial photos, ortophotos and field data are merged in the GIS for accuracy assessment. Finally, change detection of MLC and NDVI are provided respectively by Post-Classification Comparison (PCC) and Image Differencing (ID). The provided information, combined with local socio-economic parameters, is essential for the improvement of environmental sustainability of anthropogenic activities in Lampedusa.

  5. Digital Mapping and Land Information Systems - Volume 6

    DEFF Research Database (Denmark)

    Frederiksen, Poul

    1998-01-01

    Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ. Develop......Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ...

  6. Unravelling long-term vegetation change patterns in a binational watershed using multitemporal land cover data and historical photography

    Science.gov (United States)

    Villarreal, Miguel L.; Norman, Laura M.; Webb, Robert H.; Boyer, Diane E.; Turner, Raymond M.

    2011-01-01

    A significant amount of research conducted in the Sonoran Desert of North America has documented, both anecdotally and empirically, major vegetation changes over the past century due to human land use activities. However, many studies lack coincidental landscape-scale data characterizing the spatial and temporal manifestation of these changes. Vegetation changes in a binational (USA and Mexico) watershed were documented using a series of four land cover maps (1979-2009) derived from multispectral satellite imagery. Cover changes are compared to georeferenced, repeat oblique photographs dating from the late 19th century to present. Results indicate the expansion of grassland over the past 20 years following nearly a century of decline. Historical repeat photography documents early-mid 20th century mesquite invasions, but recent land cover data and rephotography demonstrate declines in xeroriparian/riparian mesquite communities in recent decades. These vegetation changes are variable over the landscape and influenced by topography and land management.

  7. Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

    Science.gov (United States)

    Ganguly, Sangram; Kalia, Subodh; Li, Shuang; Michaelis, Andrew; Nemani, Ramakrishna R.; Saatchi, Sassan A

    2017-01-01

    Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

  8. Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

    Science.gov (United States)

    Ganguly, S.; Kalia, S.; Li, S.; Michaelis, A.; Nemani, R. R.; Saatchi, S.

    2017-12-01

    Uncertainties in input land cover estimates contribute to a significant bias in modeled above gound biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition/ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree/non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial/satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

  9. LBA-ECO LC-01 Landsat TM Land Use/Land Cover, Northern Ecuadorian Amazon: 1986-1999

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes:...

  10. LBA-ECO LC-01 Landsat TM Land Use/Land Cover, Northern Ecuadorian Amazon: 1986-1999

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest,...

  11. EnviroAtlas - Percent Land Cover with Potentially Restorable Wetlands on Agricultural Land per 12-Digit HUC - Contiguous United States

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset shows the percent land cover with potentially restorable wetlands on agricultural land for each 12-digit Hydrologic Unit (HUC) watershed in...

  12. Land Cover Change Detection using Neural Network and Grid Cells Techniques

    Science.gov (United States)

    Bagan, H.; Li, Z.; Tangud, T.; Yamagata, Y.

    2017-12-01

    In recent years, many advanced neural network methods have been applied in land cover classification, each of which has both strengths and limitations. In which, the self-organizing map (SOM) neural network method have been used to solve remote sensing data classification problems and have shown potential for efficient classification of remote sensing data. In SOM, both the distribution and the topology of features of the input layer are identified by using an unsupervised, competitive, neighborhood learning method. The high-dimensional data are then projected onto a low-dimensional map (competitive layer), usually as a two-dimensional map. The neurons (nodes) in the competitive layer are arranged by topological order in the input space. Spatio-temporal analyses of land cover change based on grid cells have demonstrated that gridded data are useful for obtaining spatial and temporal information about areas that are smaller than municipal scale and are uniform in size. Analysis based on grid cells has many advantages: grid cells all have the same size allowing for easy comparison; grids integrate easily with other scientific data; grids are stable over time and thus facilitate the modelling and analysis of very large multivariate spatial data sets. This study chose time-series MODIS and Landsat images as data sources, applied SOM neural network method to identify the land utilization in Inner Mongolia Autonomous Region of China. Then the results were integrated into grid cell to get the dynamic change maps. Land cover change using MODIS data in Inner Mongolia showed that urban area increased more than fivefold in recent 15 years, along with the growth of mining area. In terms of geographical distribution, the most obvious place of urban expansion is Ordos in southwest Inner Mongolia. The results using Landsat images from 1986 to 2014 in northeastern part of the Inner Mongolia show degradation in grassland from 1986 to 2014. Grid-cell-based spatial correlation

  13. 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.

  14. Understanding Land Use and Land Cover Dynamics from 1976 to 2014 in Yellow River Delta

    Directory of Open Access Journals (Sweden)

    Baolei Zhang

    2017-03-01

    Full Text Available Long-term intensive land use/cover changes (LUCCs of the Yellow River Delta (YRD have been happening since the 1960s. The land use patterns of the LUCCs are crucial for bio-diversity conservation and/or sustainable development. This study quantified patterns of the LUCCs, explored the systematic transitions, and identified wetland change trajectory for the period 1976–2014 in the YRD. Landsat imageries of 1976, 1984, 1995, 2006, and 2014 were used to derive nine land use classes. Post classification change detection analysis based on enhanced transition matrix was applied to identify land use dynamics and trajectory of wetland change. The five cartographic outputs for changes in land use underlined major decreases in natural wetland areas and increases in artificial wetland and non-wetland, especially aquafarms, salt pans and construction lands. The systematic transitions in the YRD were wetland degradation, wetland artificialization, and urbanization. Wetland change trajectory results demonstrated that the main wetland changes were wetland degradation and wetland artificialization. Coastline change is the subordinate reason for natural wetland degradation in comparison with human activities. The results of this study allowed for an improvement in the understanding of the LUCC processes and enabled researchers and planners to focus on the most important signals of systematic landscape transitions while also allowing for a better understanding of the proximate causes of changes.

  15. High spatial resolution decade-time scale land cover change at multiple locations in the Beringian Arctic (1948–2000s)

    International Nuclear Information System (INIS)

    Lin, D H; Johnson, D R; Tweedie, C E; Andresen, C

    2012-01-01

    Analysis of time series imagery from satellite and aircraft platforms is useful for detecting land cover change at plot to regional scales. In this study, we created multi-temporal high spatial resolution land cover maps for seven locations in the Beringian Arctic and assessed the change in land cover over time. Land cover classifications were site specific and mostly aligned with a soil moisture gradient. Time series varied between 60 and 21 years. Four of the five landscapes studied in Alaska underwent an expansion of drier land cover classes while the two landscapes studies in Chukotka, Russia showed an expansion of wetter land cover types. While a range of land cover types was present across the landscapes studied, the extent of shrubs (in Chukotka) and open water (in Alaska) increased in all landscapes where these land cover types were present. The results support trends documented for regional change in NDVI (a measure of vegetation greenness and productivity) as well as a host of other long term, experimental and modeling studies. Using historic change trends for each land cover type at each landscape, we use a simple probabilistic vegetation model to establish hypotheses of future change trajectories for different land cover types at each of the landscapes investigated. This study is a contribution to the International Polar Year Back to the Future project (IPY-BTF). (letter)

  16. A New Fusion Technique of Remote Sensing Images for Land Use/Cover

    Institute of Scientific and Technical Information of China (English)

    WU Lian-Xi; SUN Bo; ZHOU Sheng-Lu; HUANG Shu-E; ZHAO Qi-Guo

    2004-01-01

    In China,accelerating industrialization and urbanization following high-speed economic development and population increases have greatly impacted land use/cover changes,making it imperative to obtain accurate and up to date information