Science.gov

Sample records for land cover mapping

  1. Alaska interim land cover mapping program

    USGS Publications Warehouse

    U.S. Geological Survey

    1987-01-01

    In order to meet the requirements of the Alaska National Interest Lands Conservation Act (ANILCA) for comprehensive resource and management plans from all major land management agencies in Alaska, the USGS has begun a program to classify land cover for the entire State using Landsat digital data. Vegetation and land cover classifications, generated in cooperation with other agencies, currently exist for 115 million acres of Alaska. Using these as a base, the USGS has prepared a comprehensive plan for classifying the remaining areas of the State. The development of this program will lead to a complete interim vegetation and land cover classification system for Alaska and allow the dissemination of digital data for those areas classified. At completion, 153 Alaska 1:250,000-scale quadrangles will be published and will include land cover from digital Landsat classifications, statistical summaries of all land cover by township, and computer-compatible tapes. An interagency working group has established an Alaska classification system (table 1) composed of 18 classes modified from "A land use and land cover classification system for use with remote sensor data" (Anderson and others, 1976), and from "Revision of a preliminary classification system for vegetation of Alaska" (Viereck and Dyrness, 1982) for the unique ecoregions which are found in Alaska.

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

    USGS Publications Warehouse

    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.

  3. EVALUATING ECOREGIONS FOR SAMPLING AND MAPPING LAND-COVER PATTERNS

    EPA Science Inventory

    Ecoregional stratification has been proposed for sampling and mapping land- cover composition and pattern over time. Using a wall-to-wall land-cover map of the United States, we evaluated geographic scales of variance for 17 landscape pattern indices, and compared stratification ...

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

    NASA Astrophysics Data System (ADS)

    Giri, C.; Pengra, B.; Long, J.; Loveland, T. R.

    2013-12-01

    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).

  5. Survey methods for assessing land cover map accuracy

    USGS Publications Warehouse

    Nusser, S.M.; Klaas, E.E.

    2003-01-01

    The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.

  6. Global land cover mapping: a review and uncertainty analysis

    USGS Publications Warehouse

    Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu

    2014-01-01

    Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

  7. Trends in Mapping, Measuring, and Monitoring Land Cover Change

    NASA Astrophysics Data System (ADS)

    Loveland, T. R.; Hansen, M.

    2007-12-01

    The necessity for improved maps and statistics documenting the rates and characteristics of land cover change from local to global scales has been driven by the acceptance that land change has pervasive and substantial environmental consequences. The expanding need for accurate land cover change characteristics data over long time periods and large geographic areas has stimulated both methodological and mission advances. Two important methodological advances include the use of the continuous fields approach for quantifying key landscape characteristics including vegetation cover and surface imperviousness, and the increased emphasis on using probability sampling to precisely estimate land cover change rates. We are using Landsat-based sampling within ecoregions to assess 1972-2000 land change in the United States. An additional methodological trend is the use of multi-source remotely sensed data for land cover change assessments. An example that blends these three elements is our use of MODIS and Landsat to map and measure 2000-2005 global deforestation. MODIS forest fraction maps provide an annual source of forest change locations that provides an efficient means to stratify probable change. Automated classification of randomly sampled Landsat scenes provide a means for detecting forest change within forest biomes and generating precise estimates of period deforestation. Studies such as this are enabled by the NASA Earth Observing System program and the NASA-USGS Landsat Data Continuity Mission. Together, these missions extend the global Earth observation record to unprecedented levels and enable new generations of detailed land change assessments.

  8. Land cover mapping of Greater Mesoamerica using MODIS data

    USGS Publications Warehouse

    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.

  9. Effects of different scale land cover maps in watershed modelling

    NASA Astrophysics Data System (ADS)

    Nunes, Antonio; Araújo, Antonio; Alexandridis, Thomas; Chambel, Pedro

    2013-04-01

    Water management is a rather complex process that usually involves multiple stakeholder, multiple data and sources, and complex mathematical modelling. One of the key data sets to understand a particular water system is the characterization of the land cover. Land cover maps are essential for the estimation of environmental variables (e.g. LAI, ETa) related to water quantity. Also, land cover maps are used for modelling the water quality. For instance, watersheds that have intensive agriculture can have poor water quality due to increase of nutrients loading; forest fires have a significant negative impact over the water quality by increasing the sediment loads; forest fires can increase flood risks. The land cover dynamics can as well severely affect the water quantity and quality in watersheds. In the MyWater project we are conducting a study to supply water quantity and quality information services for five study areas in five different countries (Brazil, Greece, Mozambique, Netherlands, and Portugal). In this project several land cover maps were produced both at regional and local scales, based on the exploitation of medium and high resolution satellite images (MERIS and SPOT 4). These maps were produced through semi-automatic supervised classification procedures, using an LCCS based nomenclature of 15 classes. Validation results pointed to global accuracy values greater than 80% for all maps. In this paper we focus on studying the effect of using different scale land cover maps in the watershed modelling and its impact in results. The work presented is part of the FP7-EU project "Merging hydrological models and Earth observation data for reliable information on water - MyWater".

  10. OVERVIEW OF US NATIONAL LAND-COVER MAPPING PROGRAM

    EPA Science Inventory

    Because of escalating costs amid growing needs for large-scale, satellite-based landscape information, a group of US federal agencies agreed to pool resources and operate as a consortium to acquire the necessary data land-cover mapping of the nation . The consortium was initiated...

  11. ASSESSING ACCURACY OF NET CHANGE DERIVED FROM LAND COVER MAPS

    EPA Science Inventory

    Net change derived from land-cover maps provides important descriptive information for environmental monitoring and is often used as an input or explanatory variable in environmental models. The sampling design and analysis for assessing net change accuracy differ from traditio...

  12. Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery

    NASA Technical Reports Server (NTRS)

    Estes, John E.; Gebelein, Jennifer

    1999-01-01

    This report is produced in accordance with the requirements outlined in the NASA Research Grant NAG9-1032 titled "Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery". This grant funds the Remote Sensing Research Unit of the University of California, Santa Barbara. This document summarizes the research progress and accomplishments to date and describes current on-going research activities. Even though this grant has technically expired, in a contractual sense, work continues on this project. Therefore, this summary will include all work done through and 5 May 1999. The principal goal of this effort is to test the accuracy of a sub-regional portion of an AVHRR-based land cover product. Land cover mapped to three different classification systems, in the southwestern United States, have been subjected to two specific accuracy assessments. One assessment utilizing astronaut acquired photography, and a second assessment employing Landsat Thematic Mapper imagery, augmented in some cases, high aerial photography. Validation of these three land cover products has proceeded using a stratified sampling methodology. We believe this research will provide an important initial test of the potential use of imagery acquired from Shuttle and ultimately the International Space Station (ISS) for the operational validation of the Moderate Resolution Imaging Spectrometer (MODIS) land cover products.

  13. Improved land cover mapping using aerial photographs and satellite images

    NASA Astrophysics Data System (ADS)

    Varga, Katalin; Szabó, Szilárd; Szabó, Gergely; Dévai, György; Tóthmérész, Béla

    2014-10-01

    Manual Land Cover Mapping using aerial photographs provides sufficient level of resolution for detailed vegetation or land cover maps. However, in some cases it is not possible to achieve the desired information over large areas, for example from historical data where the quality and amount of available images is definitely lower than from modern data. The use of automated and semiautomated methods offers the means to identify the vegetation cover using remotely sensed data. In this paper automated methods were tested on aerial photographs and satellite images to extract better and more reliable information about vegetation cover. These testswere performed by using automated analysis of LANDSAT7 images (with and without the surface model of the Shuttle Radar Topography Mission (SRTM)) and two temporally similar aerial photographs. The spectral bands were analyzed with supervised (maximum likelihood) methods. In conclusion, the SRTM and the combination of two temporally similar aerial photographs from earlier years were useful in separating the vegetation cover on a floodplain area. In addition the different date of the vegetation season also gave reliable information about the land cover. High quality information about old and present vegetation on a large area is an essential prerequisites ensuring the conservation of ecosystems

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

    NASA Technical Reports Server (NTRS)

    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.

  15. Consistent Global Land Cover Maps For Climate Modelling Communities: Current Achievements Of The ESA' Land Cover CCI

    NASA Astrophysics Data System (ADS)

    Bontemps, S.; Defourny, P.; Radoux, J.; Van Bogaert, E.; Lamarche, C.; Achard, F.; Mayaux, P.; Boettcher, M.; Brockmann, C.; Kirches, G.; Zulkhe, M.; Kalogirou, V.; Seifert, F. M.; Arino, O.

    2013-12-01

    Led by the European Space Agency, the Climate Change Initiative land cover project focuses on the land cover observed as an Essential Climate Variable. Consultation mechanisms were established with the climate modelling community in order to identify its specific needs in terms of satellite-based global land cover products. Key finding was the needs for stable land cover data and a dynamic component in form of time-series. An innovative land cover concept is proposed, along with an new global land cover mapping approach, based on multi-year earth observation datasets. The corresponding products are presented, which consist of three successive and consistent global LC maps centred to the epochs 2000, 2005 and 2010.

  16. Combining satellite data with ancillary data to produce a refined land-use/land-cover map

    USGS Publications Warehouse

    Stewart, J.S.

    1998-01-01

    As part of the U.S. Geological Survey's National Water-Quality Assessment Program in the Western Lake Michigan Drainages Study Unit, a current map of land use and land cover is needed to gain a better understanding of how land use and land cover may influence water quality. Satellite data from the Landsat Thematic Mapper provides a means to map and measure the type and amount of various land-cover types across the Study Unit and can be easily updated as changes occur in the landscape or in water quality. Translating these land cover categories to land use, however, requires the use of other thematic maps or ancillary data layers, such as wetland inventories, population data, or road networks. This report describes a process of (1) using satellite imagery to produce a land-cover map for the Fox/Wolf River basin, a portion of the Western Lake Michigan Drainages NAWQA Study Unit and (2) improving the satellite-derived land-cover map by using other thematic maps. The multiple data layers are processed in a geographic information system (GIS), and the combination provides more information than individual sources alone.

  17. Land Cover Mapping Using SENTINEL-1 SAR Data

    NASA Astrophysics Data System (ADS)

    Abdikan, S.; Sanli, F. B.; Ustuner, M.; Calò, F.

    2016-06-01

    In this paper, the potential of using free-of-charge Sentinel-1 Synthetic Aperture Radar (SAR) imagery for land cover mapping in urban areas is investigated. To this aim, we use dual-pol (VV+VH) Interferometric Wide swath mode (IW) data collected on September 16th 2015 along descending orbit over Istanbul megacity, Turkey. Data have been calibrated, terrain corrected, and filtered by a 5x5 kernel using gamma map approach. During terrain correction by using a 25m resolution SRTM DEM, SAR data has been resampled resulting into a pixel spacing of 20m. Support Vector Machines (SVM) method has been implemented as a supervised pixel based image classification to classify the dataset. During the classification, different scenarios have been applied to find out the performance of Sentinel-1 data. The training and test data have been collected from high resolution image of Google Earth. Different combinations of VV and VH polarizations have been analysed and the resulting classified images have been assessed using overall classification accuracy and Kappa coefficient. Results demonstrate that, combining opportunely dual polarization data, the overall accuracy increases up to 93.28% against 73.85% and 70.74% of using individual polarization VV and VH, respectively. Our preliminary analysis points out that dual polarimetric Sentinel-1SAR data can be effectively exploited for producing accurate land cover maps, with relevant advantages for urban planning and management of large cities.

  18. Floodplain land cover mapping using Thematic Mapper data

    NASA Technical Reports Server (NTRS)

    Kerber, A. G.; Gervin, J. C.; Lu, Y.-C.; Marcell, R.; Edwardo, H. A.

    1986-01-01

    The accuracy of land-cover classifications based on Landsat-4 TM and MSS images (obtained in August 1982) and airborne TMS images (obtained in September 1981) of the New Martinsville, West Virginia area is evaluated by comparison with ground-truth data. TM, TMS, and MSS are found to have overall mapping accuracies 80.1, 78.5, and 75.6 percent; agriculture/grass accuracies 62.0, 29.7, and 46.6 percent; and developed-area accuracies 67.2, 77.8, and 59.4 percent, respectively.

  19. A methodology to generate a synergetic land-cover map by fusion of different land-cover products

    NASA Astrophysics Data System (ADS)

    Pérez-Hoyos, A.; García-Haro, F. J.; San-Miguel-Ayanz, J.

    2012-10-01

    The main goal of this study is to develop a general framework for building a hybrid land-cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions. The proposed approach assesses class-specific accuracies of datasets and establishes affinity between thematic legends using a common land-cover language such as the UN Land-Cover Classification System (LCCS). The approach is illustrated over a large region in Europe using four land-cover datasets (CORINE, GLC2000, MODIS and GlobCover), but it can be applied to any set of existing products. The multi-classification map is expected to improve the performance of individual classifications by reconciling their best characteristics while avoiding their main weaknesses. The intermap comparison reveals improved agreement of the hybrid map with all other land-cover products and therefore indicates the successful exploration of synergies between the different products. The approach offers also estimates for the classification confidence associated with the pixel label and flexibility to shift the balance between commission and omission errors, which are critical in order to obtain a desired reliable map.

  20. Creation of a global land cover and a probability map through a new map integration method

    NASA Astrophysics Data System (ADS)

    Kinoshita, Tsuguki; Iwao, Koki; Yamagata, Yoshiki

    2014-05-01

    Global land cover maps are widely used for assessment and in research of various kinds, and in recent years have also come to be used for socio-economic forecasting. However, existing maps are not very accurate, and differences between maps also contribute to their unreliability. Improving the accuracy of global land cover maps would benefit a number of research fields. In this paper, we propose a methodology for using ground truth data to integrate existing global land cover maps. We checked the accuracy of a map created using this methodology and found that the accuracy of the new map is 74.6%, which is 3% higher than for existing maps. We then created a 0.5-min latitude by 0.5-min longitude probability map. This map indicates the probability of agreement between the category class of the new map and truth data. Using the map, we found that the probabilities of cropland and grassland are relatively low compared with other land cover types. This appears to be because the definitions of cropland differ between maps, so the accuracy may be improved by including pasture and idle plot categories.

  1. MRLC-LAND COVER MAPPING, ACCURACY ASSESSMENT AND APPLICATION RESEARCH

    EPA Science Inventory

    The National Land Cover Database (NLCD), produced by the Multi-Resolution Land Characteristics (MRLC) provides consistently classified land-cover and ancillary data for the United States. These data support many of the modeling and monitoring efforts related to GPRA goals of Cle...

  2. Selawik National Wildlife Refuge land cover mapping project users guide

    USGS Publications Warehouse

    Markon, Carl J.

    1988-01-01

    The U.S. Fish & Wildlife Service (USFWS) has the responsibility for collecting the resource information to address the research, management, development and planning requirements identified in Section 304. Because of the brief period provided by the Act for data collection, habitat mapping, and habitat assessment, the USFWS in cooperation with the U.S. Geological Survey's EROS Field Office, used digital Landsat multispectral scanner (MSS) data and digital terrain data to produce land cover and terrain maps. A computer assisted digital analysis of Landsat MSS data was used because coverage by aerial photographs was incomplete for the refuge and because the level of detail obtained from Landsat data was adequate to meet most USFWS research, management and planning needs. Relative cost and time requirements were also factors in the decision to use the digital analysis approach.

  3. Tetlin National Wildlife Refuge land cover mapping project users guide

    USGS Publications Warehouse

    Markon, Carl J.

    1987-01-01

    The U. S. Fish & Wildlife Service (USFWS) has the responsibility for collecting the resource information to address the research, management, development and planning requirements identified in Section 304. Because of the brief period provided by the Act for data collection, habitat mapping, and habitat assessment, the USFWS in cooperation with the U.S. Geological Survey's EROS Field Office, used digital Landsat multispectral scanner data (MSS) and digital terrain data to produce land cover and terrain maps. A computer assisted digital analysis of Landsat MSS data was used because coverage by aerial photographs was incomplete for much of the refuge and because the level of detail, obtained from the analysis of Landsat data, is adequate to meet most USFWS research, management and planning needs. Relative cost and time requirements were also factors in the decision to use the digital analysis approach.

  4. Mapping urban land cover from space: Some observations for future progress

    NASA Technical Reports Server (NTRS)

    Gaydos, L.

    1982-01-01

    The multilevel classification system adopted by the USGS for operational mapping of land use and land cover at levels 1 and 2 is discussed and the successes and failures of mapping land cover from LANDSAT digital data are reviewed. Techniques used for image interpretation and their relationships to sensor parameters are examined. The requirements for mapping levels 2 and 3 classes are considered.

  5. Collecting Sketch Maps to Understand Property Land Use and Land Cover in Large Surveys

    PubMed Central

    D’ANTONA, ÁLVARO DE OLIVEIRA; CAK, ANTHONY D.; VANWEY, LEAH K.

    2009-01-01

    This article describes a method to collect data on the spatial organization of land use within a rural property as part of a large-scale project examining the linkages between household demographic change and land use and land cover change in the Brazilian Amazon. Previous studies used several different spatial approaches, including maps and satellite images, to improve the information collected in standard survey questionnaires. However, few used sketch maps to obtain information from the point of view of the survey respondent about the spatial organization of land use and infrastructure. We developed a method of creating sketch maps with respondents to describe their properties. These maps then provided a spatially referenced database of the social and land use organization of the properties from the perspective of the respondent. Systematic rules allowed sketches to be used in subsequent spatial analyses in combination with satellite images and Global Positioning System reference points PMID:19789719

  6. The role of change data in a land use and land cover map updating program

    USGS Publications Warehouse

    Milazzo, Valerie A.

    1981-01-01

    An assessment of current land use and a process for identifying and measuring change are needed to evaluate trends and problems associated with the use of our Nation's land resources. The U. S. Geological Survey is designing a program to maintain the currency of its land use and land cover maps and digital data base and to provide data on changes in our Nation's land use and land cover. Ways to produce and use change data in a map updating program are being evaluated. A dual role for change data is suggested. For users whose applications require specific polygon data on land use change, showing the locations of all individual category changes and detailed statistical data on these changes can be provided as byproducts of the map-revision process. Such products can be produced quickly and inexpensively either by conventional mapmaking methods or as specialized output from a computerized geographic information system. Secondly, spatial data on land use change are used directly for updating existing maps and statistical data. By incorporating only selected change data, maps and digital data can be updated in an efficient and timely manner without the need for complete and costly detailed remapping and redigitization of polygon data.

  7. MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY

    EPA Science Inventory

    This paper presents a fuzzy set-based method of mapping spatial accuracy of thematic map and computing several ecological indicators while taking into account spatial variation of accuracy associated with different land cover types and other factors (e.g., slope, soil type, etc.)...

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Assessment of the Thematic Accuracy of Land Cover Maps

    NASA Astrophysics Data System (ADS)

    Höhle, J.

    2015-08-01

    Several land cover maps are generated from aerial imagery and assessed by different approaches. The test site is an urban area in Europe for which six classes (`building', `hedge and bush', `grass', `road and parking lot', `tree', `wall and car port') had to be derived. Two classification methods 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 such as user's and producer's accuracy, and kappa coefficient. In addition, confidence intervals were computed for several accuracy measures. The achieved accuracies and confidence intervals are thoroughly analysed and recommendations are derived from the gained experiences. Reliable reference values are obtained using stereovision, false-colour image pairs, and positioning to the checkpoints with 3D coordinates. The influence of the training areas on the results is studied. Cross validation has been tested with a few reference points in order to derive approximate accuracy measures. The two classification 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 of the confidence interval of six classes was 14% of the user's accuracy.

  10. A review and evaluation of alternatives for updating U.S. Geological Survey land use and land cover maps

    USGS Publications Warehouse

    Milazzo, Valerie A.

    1980-01-01

    Since 1974, the U.S. Geological Survey has been engaged in a nationwide program of baseline mapping of land use and land cover and associated data at a scale of 1:250,000. As l:100,000-scale bases have become available, they have been used for mapping certain areas and for special applications. These two scales are appropriate for mapping land use and land cover data on a nationwide basis within a practical time frame, and with an acceptable degree of standardization, accuracy, and level of detail. An essential requisite to better use of the land is current information on land use and land cover conditions and on the rates and trends of changes with time. Thus, plans are underway to update these maps and data. The major considerations in planning a nationwide program for updating U.S. Geological Survey land use and land cover maps are as follows: (1) How often should maps be updated? (2) What remotely sensed source materials should be used for detecting and compiling changes in land use and land cover? (3) What base maps should be used for presenting data on land use and land cover changes? (4) What maps or portions of a map should be updated? (5) What methods should be used for identifying and mapping changes? (6) What procedures should be followed for updating maps and what formats should be used? These factors must be considered in developing a map update program that portrays an appropriate level of information, relates to and builds upon the existing U.S. Geological Survey land use and land cover digital and statistical data base, is timely, cost-effective and standardized, and meets the varying needs of land use and land cover data users.

  11. Characterizing The Surface Dynamics For Land Cover Mapping: Current Achievements Of The ESA CCI Land Cover

    NASA Astrophysics Data System (ADS)

    Lamarche, Celine; Bontemps, Sophie; Verhegghen, Astrid; Radoux, Jullien; Vanbogaert, Eric; Kalogirou, Vasileios; Seifert, Frank Martin; Arino, Olivier; Defourny, Pierre

    2013-12-01

    Land Cover (LC) was listed as an Essential Climate Variable by the Global Climate Observing System and included the ESA Climate Change Initiative (CCI) that aims at providing global long-term satellite-based products tailored to the need of the climate modelling community. In the framework of the CCI-LC project, the LC concept was revisited in order to reconcile the LC users' divergent needs for both stable/consistent global LC products over time and more dynamic information related to the dynamic processes of the land surface. This paper aims first at describing the three global products generated in response to this need for more dynamic information, namely the condition products. These products characterize globally the green vegetation phenology, the burnt areas and snow occurrences. The main challenge beyond the production of these datasets refers to the spatio/temporal consistency between the stable and dynamic components of the LC. The second objective of this paper is therefore to address the work on-going on the characterization of this consistency.

  12. Building a hybrid land cover map with crowdsourcing and geographically weighted regression

    NASA Astrophysics Data System (ADS)

    See, Linda; Schepaschenko, Dmitry; Lesiv, Myroslava; McCallum, Ian; Fritz, Steffen; Comber, Alexis; Perger, Christoph; Schill, Christian; Zhao, Yuanyuan; Maus, Victor; Siraj, Muhammad Athar; Albrecht, Franziska; Cipriani, Anna; Vakolyuk, Mar'yana; Garcia, Alfredo; Rabia, Ahmed H.; Singha, Kuleswar; Marcarini, Abel Alan; Kattenborn, Teja; Hazarika, Rubul; Schepaschenko, Maria; van der Velde, Marijn; Kraxner, Florian; Obersteiner, Michael

    2015-05-01

    Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products.

  13. Topographic Maps: Rediscovering an Accessible Data Source for Land Cover Change Research

    ERIC Educational Resources Information Center

    McChesney, Ron; McSweeney, Kendra

    2005-01-01

    Given some limitations of satellite imagery for the study of land cover change, we draw attention here to a robust and often overlooked data source for use in student research: USGS topographic maps. Topographic maps offer an inexpensive, rapid, and accessible means for students to analyze land cover change over large areas. We demonstrate our…

  14. Agricultural land cover mapping with the aid of digital soil survey data

    NASA Technical Reports Server (NTRS)

    Stoner, E. R.

    1982-01-01

    A study is recounted which assessed the effect of stratifying multidate Landsat MSS data on land cover classification accuracy. The study area covered 49,184 ha (121,534 acres) in Gentry County in northwestern Missouri. A pixel-by-pixel comparison of the two land cover classifications with field-verified land cover indicated improvements in identification of all cover types when land areas were stratified by soils. The introduction of soil map information to the land cover mapping process can improve discrimination of land cover types and reduce confusion among crop types that may be caused by soil-specific management practices, soil-induced crop development differences, and background reflectance characteristics.

  15. Mapping land cover from satellite images: A basic, low cost approach

    NASA Technical Reports Server (NTRS)

    Elifrits, C. D.; Barney, T. W.; Barr, D. J.; Johannsen, C. J.

    1978-01-01

    Simple, inexpensive methodologies developed for mapping general land cover and land use categories from LANDSAT images are reported. One methodology, a stepwise, interpretive, direct tracing technique was developed through working with university students from different disciplines with no previous experience in satellite image interpretation. The technique results in maps that are very accurate in relation to actual land cover and relative to the small investment in skill, time, and money needed to produce the products.

  16. Integrating Recent Land Cover Mapping Efforts to Update the National Gap Analysis Program's Species Habitat Map

    NASA Astrophysics Data System (ADS)

    McKerrow, A. J.; Davidson, A.; Earnhardt, T. S.; Benson, A. L.

    2014-11-01

    Over the past decade, great progress has been made to develop national extent land cover mapping products to address natural resource issues. One of the core products of the GAP Program is range-wide species distribution models for nearly 2000 terrestrial vertebrate species in the U.S. We rely on deductive modeling of habitat affinities using these products to create models of habitat availability. That approach requires that we have a thematically rich and ecologically meaningful map legend to support the modeling effort. In this work, we tested the integration of the Multi-Resolution Landscape Characterization Consortium's National Land Cover Database 2011 and LANDFIRE's Disturbance Products to update the 2001 National GAP Vegetation Dataset to reflect 2011 conditions. The revised product can then be used to update the species models. We tested the update approach in three geographic areas (Northeast, Southeast, and Interior Northwest). We used the NLCD product to identify areas where the cover type mapped in 2011 was different from what was in the 2001 land cover map. We used Google Earth and ArcGIS base maps as reference imagery in order to label areas identified as "changed" to the appropriate class from our map legend. Areas mapped as urban or water in the 2011 NLCD map that were mapped differently in the 2001 GAP map were accepted without further validation and recoded to the corresponding GAP class. We used LANDFIRE's Disturbance products to identify changes that are the result of recent disturbance and to inform the reassignment of areas to their updated thematic label. We ran species habitat models for three species including Lewis's Woodpecker (Melanerpes lewis) and the White-tailed Jack Rabbit (Lepus townsendii) and Brown Headed nuthatch (Sitta pusilla). For each of three vertebrate species we found important differences in the amount and location of suitable habitat between the 2001 and 2011 habitat maps. Specifically, Brown headed nuthatch habitat in

  17. Assessment of Digital Land Cover Maps for Hydrological Modeling in the Yampa River Basin, Colorado, USA

    NASA Astrophysics Data System (ADS)

    Repass, J. M.; Fassnacht, S.; Reich, R.

    2004-12-01

    Land cover data are required to parameterize watersheds for hydrological modeling. There is a multitude of different land cover maps, and determining which input data map for the model can be unclear. The goal of this study is to quantify the differences between various publically available land cover maps to determine their relative suitability for hydrological modeling of the Yampa River Basin in northern Colorado. The land cover maps compared in this study are derived from Advanced Very High Resolution Radiometer (AVHRR), Landsat Thematic Mapper (TM), and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. These maps are compared to a 30-m land cover map modeled from ground data and MODIS imagery. This map is regarded as "truth" in this investigation due to its fine resolution and use of recent ground data and imagery, and will be used to rank publicly available AVHRR and MODIS land cover maps. In order to compare the different land cover products, all data must be degraded to the coarsest spatial resolution (1 km) and the coarsest species resolution. Once this is accomplished, the maps are compared on 4 levels. The 4 comparisons are based on: (i) the relative agreement of the total aggregated land class percentages for the 1-km data present after the data has been cross-walked; (ii) pixel accuracy; (iii) scene accuracy; and (iv) cumulative streamflow model output from the US Geological Survey Precipitation-Runoff Modeling System (PRMS) in relation to observed cumulative streamflow. The results determine the best input land cover data for modeling streamflow in the Yampa River Basin, and provide information about the required spatial, spectral, and classification resolution of these maps to optimize results for streamflow modeling.

  18. Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

    NASA Astrophysics Data System (ADS)

    Ban, Yifang; Gong, Peng; Giri, Chandra

    2015-05-01

    Land cover is an important variable for many studies involving the Earth surface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover not only changes with human caused land use changes, but also changes with nature. Therefore, the state of land cover is highly dynamic. In winter snow shields underneath various other land cover types in higher latitudes. Floods may persist for a long period in a year over low land areas in the tropical and subtropical regions. Forest maybe burnt or clear cut in a few days and changes to bare land. Within several months, the coverage of crops may vary from bare land to nearly 100% crops and then back to bare land following harvest. The highly dynamic nature of land cover creates a challenge in mapping and monitoring which remains to be adequately addressed. As economic globalization continues to intensify, there is an increasing trend of land cover/land use change, environmental pollution, land degradation, biodiversity loss at the global scale, timely and reliable information on global land cover and its changes is urgently needed to mitigate the negative impact of global environment change.

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

    USGS Publications Warehouse

    Stibig, H.-J.; Belward, A.S.; Roy, P.S.; Rosalina-Wasrin, U.; Agrawal, S.; Joshi, P.K.; Hildanus; 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-tropical Asia, and it delivers

  20. THE USE OF NTM DATA FOR THE ACCURACY ASSESSMENT OF LANDSAT DERIVED LAND USE/LAND COVER MAPS

    EPA Science Inventory

    National Technical Means (NTM) data were utilized to validate the accuracy of a series of LANDSAT derived Land Use / Land Cover (LU/LC) maps for the time frames mid- I 970s, early- I 990s and mid- I 990s. The area-of-interest for these maps is a 2000 square mile portion of the De...

  1. The potential of Landsat-3 RBV images for thematic mapping. [geomorphological, geological and land cover applications

    NASA Technical Reports Server (NTRS)

    Townshend, J. R. G.; Justice, C. O.

    1980-01-01

    The potential of Return Beam Vidicon (RBV) imagery from Landsat-3 is discussed for thematic mapping. The advantages of the imagery arising from its high spatial resolution are described as well as the restrictions stemming from its limited spectral characteristics. The principal application areas discussed are geomorphological and geological mapping and land cover mapping.

  2. Land cover maps, BVOC emissions, and SOA burden in a global aerosol-climate model

    NASA Astrophysics Data System (ADS)

    Stanelle, Tanja; Henrot, Alexandra; Bey, Isaelle

    2015-04-01

    It has been reported that different land cover representations influence the emission of biogenic volatile organic compounds (BVOC) (e.g. Guenther et al., 2006). But the land cover forcing used in model simulations is quite uncertain (e.g. Jung et al., 2006). As a consequence the simulated emission of BVOCs depends on the applied land cover map. To test the sensitivity of global and regional estimates of BVOC emissions on the applied land cover map we applied 3 different land cover maps into our global aerosol-climate model ECHAM6-HAM2.2. We found a high sensitivity for tropical regions. BVOCs are a very prominent precursor for the production of Secondary Organic Aerosols (SOA). Therefore the sensitivity of BVOC emissions on land cover maps impacts the SOA burden in the atmosphere. With our model system we are able to quantify that impact. References: Guenther et al. (2006), Estimates of global terrestrial isoprene emissions using MEGAN, Atmos. Chem. Phys., 6, 3181-3210, doi:10.5194/acp-6-3181-2006. Jung et al. (2006), Exploiting synergies of global land cover products for carbon cycle modeling, Rem. Sens. Environm., 101, 534-553, doi:10.1016/j.rse.2006.01.020.

  3. Producing Alaska interim land cover maps from Landsat digital and ancillary data

    USGS Publications Warehouse

    Fitzpatrick-Lins, Katherine; Doughty, Eileen Flanagan; Shasby, Mark; Loveland, Thomas R.; Benjamin, Susan

    1987-01-01

    In 1985, the U.S. Geological Survey initiated a research program to produce 1:250,000-scale land cover maps of Alaska using digital Landsat multispectral scanner data and ancillary data and to evaluate the potential of establishing a statewide land cover mapping program using this approach. The geometrically corrected and resampled Landsat pixel data are registered to a Universal Transverse Mercator (UTM) projection, along with arc-second digital elevation model data used as an aid in the final computer classification. Areas summaries of the land cover classes are extracted by merging the Landsat digital classification files with the U.S. Bureau of Land Management's Public Land Survey digital file. Registration of the digital land cover data is verified and control points are identified so that a laser plotter can products screened film separate for printing the classification data at map scale directly from the digital file. The final land cover classification is retained both as a color map at 1:250,000 scale registered to the U.S. Geological Survey base map, with area summaries by township and range on the reverse, and as a digital file where it may be used as a category in a geographic information system.

  4. Object-Based Analysis of Aerial Photogrammetric Point Cloud and Spectral Data for Land Cover Mapping

    NASA Astrophysics Data System (ADS)

    Debella-Gilo, M.; Bjørkelo, K.; Breidenbach, J.; Rahlf, J.

    2013-04-01

    The acquisition of 3D point data with the use of both aerial laser scanning (ALS) and matching of aerial stereo images coupled with advances in image processing algorithms in the past years provide opportunities to map land cover types with better precision than before. The present study applies Object-Based Image Analysis (OBIA) to 3D point cloud data obtained from matching of stereo aerial images together with spectral data to map land cover types of the Nord-Trøndelag county of Norway. The multi-resolution segmentation algorithm of the Definiens eCognition™ software is used to segment the scenes into homogenous objects. The objects are then classified into different land cover types using rules created based on the definitions given for each land cover type by the Norwegian Forest and Landscape Institute. The quality of the land cover map was evaluated using data collected in the field as part of the Norwegian National Forest Inventory. The results show that the classification has an overall accuracy of about 80% and a kappa index of about 0.65. OBIA is found to be a suitable method for utilizing 3D remote sensing data for land cover mapping in an effort to replace manual delineation methods.

  5. Land Cover of Northern Eurasia: Comparison and Assessment of Coarse Resolution Maps

    NASA Astrophysics Data System (ADS)

    Krankina, O. N.; Pflugmacher, D.; Cohen, W.; Kennedy, R.; Nelson, P.; Loboda, T.

    2007-12-01

    Consistent measurements of land cover are critical for addressing a range of important science questions, from quantifying the effects of vegetation on the carbon, energy, and water cycles, to understanding the social and economic causes and consequences of land-use and land-cover change. While multiple moderate and coarse- resolution land-cover products have been developed, they disagree significantly. Resolving discrepancies among maps is particularly challenging for boreal and temperate Northern Eurasia, where validation sites are sparse and processes of ecosystem disturbance and land-cover change are widespread. To identify specific needs and possibilities for improved mapping of land cover across boreal and temperate Northern Eurasia, we compared the performance of three recent land-cover products based on different sensors: MODIS (Global Land Cover Collection 4), AVHRR (DISCover v. 2.0), and SPOT VEGETATION (GLC2000 for Northern Eurasia v. 4.0). First, we examined the level of agreement among these data sets across the entire region. On a qualitative level, the assessment of general patterns indicates the highest degree of disagreement in transitional zones at the northern and southern fringes of boreal forest, in mountainous regions, and in areas of extensive wetlands, agricultural development, and urban land use. The quantitative analysis measured the level of disagreement between land-cover classes aggregated according to dominant type of vegetation (trees, shrubs, herbaceous, bare land, permanent snow/ice). Secondly, validation of these products was performed at two test sites where Landsat-based classifications were developed based on FAO Land Cover Classification System. Fractional land cover was calculated for each 1x1 km pixel and used to construct fractional error matrices. Most errors were associated with "mixed" coarse-resolution pixels (i.e. those having nearly equal percentage of multiple class types), while errors in "pure" (single class) pixels

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

    NASA Astrophysics Data System (ADS)

    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).

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

    NASA Astrophysics Data System (ADS)

    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.

  8. Nowitna National Wildlife Refuge land cover mapping project users guide

    USGS Publications Warehouse

    Markon, Carl J.

    1988-01-01

    Title III of the Alaska National Interest Lands Conservation Act of 1980 (ANILCA 1980) established the Nowitna National Wildlife Refuge (NNWR).  Section 304 of the Act requires the Secretary of Interior to "prepare, and from time to time revise, a comprehensive conservation plan" for the refuge.  

  9. Land cover characterization and mapping of continental southeast Asia using multi-resolution satellite sensor data

    USGS Publications Warehouse

    Giri, Chandra; Defourny, P.; Shrestha, Surendra

    2003-01-01

    Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.

  10. Arctic National Wildlife Refuge land cover mapping project users guide

    USGS Publications Warehouse

    Markon, Carl J.

    1986-01-01

    Section 1002 of the Alaska National Interest Lands Conservation Act of 1980 (ANILCA, 1980) requires the Secretary of Interior to conduct a continuing study of fish, wildlife, and habitats on the coastal plain of the Arctic National Wildlife Refuge (ANWR). Included in this study is a determination of the extent, location, and carrying capacity of fish and wildlife habitats.

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

    NASA Astrophysics Data System (ADS)

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

    2014-04-01

    Detailed data on land use and land cover constitutes 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 an agricultural mosaic catchment Haean, South Korea. We recorded the land cover types with additional information on agricultural practice and make this data available at the public repository Pangaea (doi:10.1594/PANGAEA.823677). In this paper we introduce the data, its collection and the post-processing protocol. During the studied period, a large portion of dry fields was converted to perennial crops. A comparison between our dataset and MODIS Land Cover Type (MCD12Q1) suggested that the MODIS product was restricted in this area since it does not distinguish irrigated fields from general croplands. In addition, linear landscape elements such as water bodies were not detected in the MODIS product due to its coarse spatial resolution. The data presented here can be useful for earth science and ecosystem services research.

  12. Using high-resolution digital aerial imagery to map land cover

    USGS Publications Warehouse

    Dieck, J.J.; Robinson, Larry

    2014-01-01

    The Upper Midwest Environmental Sciences Center (UMESC) has used aerial photography to map land cover/land use on federally owned and managed lands for over 20 years. Until recently, that process used 23- by 23-centimeter (9- by 9-inch) analog aerial photos to classify vegetation along the Upper Mississippi River System, on National Wildlife Refuges, and in National Parks. With digital aerial cameras becoming more common and offering distinct advantages over analog film, UMESC transitioned to an entirely digital mapping process in 2009. Though not without challenges, this method has proven to be much more accurate and efficient when compared to the analog process.

  13. a Methodology for Assessing Openstreetmap Degree of Coverage for Purposes of Land Cover Mapping

    NASA Astrophysics Data System (ADS)

    Ribeiro, A.; Fonte, C. C.

    2015-08-01

    The data available in the collaborative project OpenStreetMap (OSM) is in some locations so detailed and complete that it may provide useful data for Land Cover Map creation and validation. However, this degree of detail is not uniform along space. Therefore, one of the first requirements that needs to be assessed to determine if the creation and validation of Land Cover Maps using data available in OSM may be feasible, is the availability of data to provide a relatively complete coverage of the region of interest. To provide a fast and automatic quantitative assessment of this requirement a methodology is presented and tested in this article. Four study areas are considered, all located in Europe. The results show that the four regions presented very different coverages at the time of data download and its spatial distribution was not uniform. This approach enabled the identification of the most problematic regions for land cover mapping, where low levels of data coverage are available. Since the proposed methodology can be automated, it enables a fast identification of the regions that, in a preliminary analysis, may be considered fit for further analysis to assess fitness for use for Land Cover Map creation and/or validation.

  14. Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

    NASA Astrophysics Data System (ADS)

    Höhle, J.

    2014-09-01

    A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means 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 other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  16. Mapping Land Cover Types in Amazon Basin Using 1km JERS-1 Mosaic

    NASA Technical Reports Server (NTRS)

    Saatchi, Sassan S.; Nelson, Bruce; Podest, Erika; Holt, John

    2000-01-01

    In this paper, the 100 meter JERS-1 Amazon mosaic image was used in a new classifier to generate a I km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages: 1) a supervised maximum a posteriori Bayesian approach to classify the mean backscatter image into 5 general land cover categories of forest, savannah, inundated, white sand, and anthropogenic vegetation classes, and 2) a texture measure decision rule approach to further discriminate subcategory classes based on taxonomic information and biomass levels. Fourteen classes were successfully separated at 1 km scale. The results were verified by examining the accuracy of the approach by comparison with the IBGE and the AVHRR 1 km resolution land cover maps.

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

    NASA Astrophysics Data System (ADS)

    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.

  18. APPLICATION OF A "VITURAL FIELD REFERENCE DATABASE" TO ASSESS LAND-COVER MAP ACCURACIES

    EPA Science Inventory

    An accuracy assessment was performed for the Neuse River Basin, NC land-cover/use
    (LCLU) mapping results using a "Virtual Field Reference Database (VFRDB)". The VFRDB was developed using field measurement and digital imagery (camera) data collected at 1,409 sites over a perio...

  19. Vegetation database for land-cover mapping, Clark and Lincoln Counties, Nevada

    USGS Publications Warehouse

    Charlet, David A.; Damar, Nancy A.; Leary, Patrick J.

    2014-01-01

    Floristic and other vegetation data were collected at 3,175 sample sites to support land-cover mapping projects in Clark and Lincoln Counties, Nevada, from 2007 to 2013. Data were collected at sample sites that were selected to fulfill mapping priorities by one of two different plot sampling approaches. Samples were described at the stand level and classified into the National Vegetation Classification hierarchy at the alliance level and above. The vegetation database is presented in geospatial and tabular formats.

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

    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 typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R2 = 0.96), shrub cover (adj. R2 = 0.83), grass cover (adj. R2 = 0.71), bare soil (adj. R2 = 0.88), stone cover (adj. R2 = 0.83) and litter cover (adj. R2 = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land

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

    NASA Astrophysics Data System (ADS)

    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

  2. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

    USGS Publications Warehouse

    Yang, Limin; Xian, George Z.; Klaver, Jacqueline M.; Deal, Brian

    2003-01-01

    We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

  3. Using Multitemporal Remote Sensing to Map Global Land Cover and Vegetation Dynamics

    NASA Astrophysics Data System (ADS)

    Friedl, M. A.; Zhang, X.; van Dellen, C.

    2004-05-01

    Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra and Aqua spacecraft provides a wealth of information regarding the spatio-temporal dynamics in land surface properties. In this paper, we describe results from efforts to map land surface properties from MODIS, emphasizing land cover and vegetation dynamics. Specifically, we describe algorithms and data sets that are designed to characterize the geographic distribution and phenology of vegetation and land cover types at global scales. Multitemporal data from MODIS is central to these efforts in three regards. First, multitemporal information provides a key source of information that helps to distinguish between vegetation and land cover classes that are otherwise spectrally similar. Second, MODIS data is being used to monitor continental to global scale vegetation phenology, and to identify key intra-annual transition dates such as the onset of greenup and senescence. As part of this effort we are also developing empirical models that characterize and explain the first order sources of spatial variation in these terms (i.e., precipitation and temperature regimes). Third, multitemporal vegetation indices are being used in combination with observations of vegetation phenology to characterize the time-varying fraction of green vegetation at the land surface. This paper will describe how multitemporal data from MODIS is being used to map each of these fields, and in this way, to provide a more realistic representation of time-varying biophysical conditions at the Earth's land surfaces for use in models.

  4. Mapping and measuring land-cover characteristics of New River Basin, Tennessee, using Landsat digital tapes

    USGS Publications Warehouse

    Hollyday, E.F.; Sauer, S.P.

    1976-01-01

    Land-cover information is needed to select subbasins within the New River basin, Tennessee, for the study of hydrologic processes and is also needed to transfer study results to other sites affected by coal mining. This study demonstrates that digital processing of Landsat tapes can produce maps and tables of the areal extent of selected land-cover categories. The relative area of each category within the basin is agriculture, 5 percent; evergreens, 7 percent; bare earth, 6 percent; three categories of hardwoods, 81 percent; and water, rock, and uncategorized areas, each less than 1 percent. (Woodard-USGS)

  5. Land use and land cover digital data from 1:250,000- and 1:100,000- scale maps

    USGS Publications Warehouse

    U.S. Geological Survey

    1990-01-01

    The Earth Science Information Centers (ESIC) distribute digital cartographic/geographic data files produced by the U.S. Geological Survey (USGS) as part of the National Mapping Program. The data files are grouped into four basic types. The first type, called a Digital Line Graph (DLG), is line map information in digital form. These data files include information on planimetric base categories, such as transportation, hydrography, and boundaries. The second type, called a Digital Elevation Model (DEM), consists of a sampled array of elevations for ground positions that are usually at regularly spaced intervals. The third type, Land Use and Land Cover digital data, provide information on nine major classes of land use such as urban, agricultural, or forest as well as associated map data such as political units and Federal land ownership. The fourth type, the Geographic Names Information System, provides primary information for known places, features, and areas in the United States identified by a proper name.

  6. Multi-year global land cover mapping at 300 m and characterization for climate modelling: achievements of the Land Cover component of the ESA Climate Change Initiative

    NASA Astrophysics Data System (ADS)

    Bontemps, S.; Boettcher, M.; Brockmann, C.; Kirches, G.; Lamarche, C.; Radoux, J.; Santoro, M.; Vanbogaert, E.; Wegmuller, U.; Herold, M.; Achard, F.; Ramoino, F.; Arino, O.; Defourny, P.

    2015-04-01

    Essential Climate Variables were listed by the Global Climate Observing System as critical information to further understand the climate system and support climate modelling. The European Space Agency launched its Climate Change Initiative in order to provide an adequate response to the set of requirements for long-term satellite-based products for climate. Within this program, the CCI Land Cover project aims at revisiting all algorithms required for the generation of global land cover products that are stable and consistent over time, while also reflecting the land surface seasonality. To this end, the land cover concept is revisited to deliver a set of three consistent global land cover products corresponding to the 1998-2002, 2003-2007 and 2008-2012 periods, along with climatological 7-day time series representing the average seasonal dynamics of the land surface over the 1998-2012 period. The full Envisat MERIS archive (2003-2012) is used as main Earth Observation dataset to derive the 300-m global land cover maps, complemented with SPOT-Vegetation time series between 1998 and 2012. Finally, a 300-m global map of open permanent water bodies is derived from the 2005-2010 archive of the Envisat Advanced SAR imagery mainly acquired in the 150m Wide Swath Mode.

  7. Interim program for land cover mapping in Alaska utilizing Landsat digital data

    USGS Publications Warehouse

    Shasby, Mark; Carneggie, David; Gaydos, Leonard; Fitzpatrick-Lins, Katherine; Lauer, Donald; Ambrosia, Vincent; Benjamin, Susan

    1985-01-01

    The enactment of the Alaska National Interest Lands Conservation Act (ANILCA) in 1980 imposed mandates on all major land management agencies in Alaska to prepare comprehensive resource and management plans to assess wildlife habitat, oil and gas exploration and development, wild and scenic river, land disposals, timber production, and archaeological and cultural resources, To meet these objective, the U. S. Geological Survey (USGS) has embarked on a plan to classify land cover for the entire State of Alaska using Landsat digital data. the USGS, in cooperation with other agencies, has completely Landsat-derived land use and land cover classification of 115 million acres for the State of Alaska. With this work as a substantial foundation, the USGS has prepared a comprehensive plan for classifying the remaining areas of Alaska. The development of this program will lead to a complete interim land use and land cover classification system for Alaska and provide for the dissemination of map products, statistics, and acreage summaries for all areas of Alaska at 1:250,000 scale. It also allows for the dissemination of Landsat digital data for those areas.

  8. Land cover map of Great Britain. An automated classification of Landsat Thematic Mapper data

    SciTech Connect

    Fuller, R.M.; Groom, G.B.; Jones, A.R.

    1994-05-01

    The Land Cover Map of Great Britain was produced using supervised maximum-likelihood classifications of Landsat Thematic Mapper data. By combining summer and winter data, classification accuracies were substantially improved over single-data analyses. The map, bosed on a 25-m grid, records 25 cover types, consisting of sea and inland water, beaches and bare ground, developed and arable land, and 18 types of semi-natural vegetation. General cover is recorded at a field-by-field scale, while key landscape features, with strong spectral signatures, show patterns down to a minimum mappable unit of 0.125 ha. Comparisons with independent ground reference data showed correspondences which varied between 67 percent and 89 percent depending on the level of detail at which comparisons were made.

  9. Application of Landsat data to map and monitor agricultural land cover

    NASA Astrophysics Data System (ADS)

    Erdenee, B.; Tana, Gegen; Tateishi, Ryutaro

    2010-11-01

    Agriculture is one of the major economic sectors of Mongolia and the country's economy is very much dependent on the development of agricultural production. Being the rural and poorest conditions of Mongolia, 60-90% of its labor force employed in agriculture and agricultural sector has a prominent economic role. Mongolian agriculture has been successful in increasing food grains production in the past, guided by the goals of self-sufficiency in the country. The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. Objective of this study to monitor in the agricultural land cover changes in the Tov aimag, as there is important agricultural producing area in Mongolia. We have developed approaches to map and monitor land cover and land use change across in the Tov aimag using multi-spectral image data. In this study, maximum likelihood supervised classification was applied to Landsat TM and ETM images acquired in 1989 and 2000, respectively, to map cropland area cover changes in the Tov aimag of Mongolia. A supervised classification was carried out on the six reflective bands (bands 1-5 and band 7) for the two images individually with the aid of ground based agricultural monitoring data. Results were then tested using ground check data.

  10. Application of Landsat data to map and monitor agricultural land cover

    NASA Astrophysics Data System (ADS)

    Erdenee, B.; Tana, Gegen; Tateishi, Ryutaro

    2009-09-01

    Agriculture is one of the major economic sectors of Mongolia and the country's economy is very much dependent on the development of agricultural production. Being the rural and poorest conditions of Mongolia, 60-90% of its labor force employed in agriculture and agricultural sector has a prominent economic role. Mongolian agriculture has been successful in increasing food grains production in the past, guided by the goals of self-sufficiency in the country. The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. Objective of this study to monitor in the agricultural land cover changes in the Tov aimag, as there is important agricultural producing area in Mongolia. We have developed approaches to map and monitor land cover and land use change across in the Tov aimag using multi-spectral image data. In this study, maximum likelihood supervised classification was applied to Landsat TM and ETM images acquired in 1989 and 2000, respectively, to map cropland area cover changes in the Tov aimag of Mongolia. A supervised classification was carried out on the six reflective bands (bands 1-5 and band 7) for the two images individually with the aid of ground based agricultural monitoring data. Results were then tested using ground check data.

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

    PubMed Central

    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

  12. Development of Ground Reference GIS for Assessing Land Cover Maps of Northeast Yellowstone National Park

    NASA Technical Reports Server (NTRS)

    Spruce, Joe; Warner, Amanda; Terrie, Greg; Davis, Bruce

    2001-01-01

    GIS technology and ground reference data often play vital roles in assessing land cover maps derived from remotely sensed data. This poster illustrates these roles, using results from a study done in Northeast Yellowstone National Park. This area holds many forest, range, and wetland cover types of interest to park managers. Several recent studies have focused on this locale, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project performed by Yellowstone Ecosystems Studies (YES) on riparian and in-stream habitat mapping. This poster regards a spin-off to the EOCAP project in which YES and NASA's Earth Science Applications Directorate explored the potential for synergistic use of hyperspecral, synthetic aperture radar, and multiband thermal imagery in mapping land cover types. The project included development of a ground reference GIS for site-specific data needed to evaluate maps from remotely sensed imagery. Field survey data included reflectance of plant communities, native and exotic plant species, and forest health conditions. Researchers also collected GPS points, annotated aerial photographs, and took hand held photographs of reference sites. The use of ESRI, ERDAS, and ENVI software enabled reference data entry into a GIS for comparision to georeferenced imagery and thematic maps. The GIS-based ground reference data layers supported development and assessment of multiple maps from remotely sensed data sets acquired over the study area.

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

    PubMed

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

    2015-05-01

    Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8's spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82%, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation. PMID:25910718

  14. Time-Series analysis of MODIS NDVI data along with ancillary data for Land use/Land cover mapping of Uttarakhand

    NASA Astrophysics Data System (ADS)

    Patakamuri, S. K.; Agrawal, S.; Krishnaveni, M.

    2014-12-01

    Land use and land cover plays an important role in biogeochemical cycles, global climate and seasonal changes. Mapping land use and land cover at various spatial and temporal scales is thus required. Reliable and up to date land use/land cover data is of prime importance for Uttarakhand, which houses twelve national parks and wildlife sanctuaries and also has a vast potential in tourism sector. The research is aimed at mapping the land use/land cover for Uttarakhand state of India using Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The study also incorporated smoothening of time-series plots using filtering techniques, which helped in identifying phenological characteristics of various land cover types. Multi temporal Normalized Difference Vegetation Index (NDVI) data for the year 2010 was used for mapping the Land use/land cover at 250m coarse resolution. A total of 23 images covering a single year were layer stacked and 150 clusters were generated using unsupervised classification (ISODATA) on the yearly composite. To identify different types of land cover classes, the temporal pattern (or) phenological information observed from the MODIS (MOD13Q1) NDVI, elevation data from Shuttle Radar Topography Mission (SRTM), MODIS water mask (MOD44W), Nighttime Lights Time Series data from Defense Meteorological Satellite Program (DMSP) and Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS) data were used. Final map product is generated by adopting hybrid classification approach, which resulted in detailed and accurate land use and land cover map.

  15. Object-based approach to national land cover mapping using HJ satellite imagery

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Li, Xiaosong; Yuan, Quanzhi; Liu, Yu

    2014-01-01

    To meet the carbon storage estimate in ecosystems for a national carbon strategy, we introduce a consistent database of China land cover. The Chinese Huan Jing (HJ) satellite is proven efficient in the cloud-free acquisition of seasonal image series in a monsoon region and in vegetation identification for mesoscale land cover mapping. Thirty-eight classes of level II land cover are generated based on the Land Cover Classification System of the United Nations Food and Agriculture Organization that follows a standard and quantitative definition. Twenty-four layers of derivative spectral, environmental, and spatial features compose the classification database. Object-based approach characterizing additional nonspectral features is conducted through mapping, and multiscale segmentations are applied on object boundary match to target real-world conditions. This method sufficiently employs spatial information, in addition to spectral characteristics, to improve classification accuracy. The algorithm of hierarchical classification is employed to follow step-by-step procedures that effectively control classification quality. This algorithm divides the dual structures of universal and local trees. Consistent universal trees suitable to most regions are performed first, followed by local trees that depend on specific features of nine climate stratifications. The independent validation indicates the overall accuracy reaches 86%.

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

    NASA Astrophysics Data System (ADS)

    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.

  17. Mapping of land cover in northern California with simulated hyperspectral satellite imagery

    NASA Astrophysics Data System (ADS)

    Clark, Matthew L.; Kilham, Nina E.

    2016-09-01

    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. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4-21.8% and 3.1-6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9-3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands

  18. Combining accuracy assessment of land-cover maps with environmental monitoring programs

    USGS Publications Warehouse

    Stehman, S.V.; Czaplewski, R.L.; Nusser, S.M.; Yang, L.; Zhu, Z.

    2000-01-01

    A scientifically valid accuracy assessment of a large-area, land-cover map is expensive. Environmental monitoring programs offer a potential source of data to partially defray the cost of accuracy assessment while still maintaining the statistical validity. In this article, three general strategies for combining accuracy assessment and environmental monitoring protocols are described. These strategies range from a fully integrated accuracy assessment and environmental monitoring protocol, to one in which the protocols operate nearly independently. For all three strategies, features critical to using monitoring data for accuracy assessment include compatibility of the land-cover classification schemes, precisely co-registered sample data, and spatial and temporal compatibility of the map and reference data. Two monitoring programs, the National Resources Inventory (NRI) and the Forest Inventory and Monitoring (FIM), are used to illustrate important features for implementing a combined protocol.

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

    NASA Astrophysics Data System (ADS)

    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.

  20. Modeling and mapping regional land use/land cover change in South Central Texas

    NASA Astrophysics Data System (ADS)

    Ranatunga, T.; Messen, D.

    2014-12-01

    Houston-Galveston Area Council (H-GAC) conducted a land use/land cover (LULC) change detection study to generate information about the LULC changes in a 15-county area of South Central Texas. Such information is essential in regional planning, natural resource management, monitoring and modeling of environmental characteristics. The objectives of this study are (1) Identification of regional spatial patterns of each LULC conversion, (2) Estimation of the area coverage of each LULC conversion, and (3) Estimation of the net gain and losses of each LULC classes. To achieve these objectives, ArcGIS Spatial analysis functions and data management tools were employed in python environment. Change detection was estimated from 1992 to 2011 using datasets from NLCD (National Land Cover Database) 1992, NLCD 2001 and NOAA C-CAP (National Oceanic and Atmospheric Administration, Coastal Change Analysis Program) 2011. Through visual analysis and comparisons with aerial imagery, we established that NLCD 1992 and 2001 datasets contained more classification inaccuracies than the NOAA 2011 dataset. The misclassified cells in the 1992 and 2001 NLCD datasets were corrected to be consistent with the 2011 C-CAP dataset. The NLCD 2001 dataset was first corrected using a logical evaluation with 2011 classes in each pixel. Then the NLCD 1992 dataset was corrected using the correct 2001 dataset. After correcting 1992 dataset, a cell by cell comparison was conducted with the NOAA 2011 dataset, and individual changes were recorded.

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

    NASA Astrophysics Data System (ADS)

    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.

  2. Geographic stacking: Decision fusion to increase global land cover map accuracy

    NASA Astrophysics Data System (ADS)

    Clinton, Nicholas; Yu, Le; Gong, Peng

    2015-05-01

    Techniques to combine multiple classifier outputs is an established sub-discipline in data mining, referred to as "stacking," "ensemble classification," or "meta-learning." Here we describe how stacking of geographically allocated classifications can create a map composite of higher accuracy than any of the individual classifiers. We used both voting algorithms and trainable classifiers with a set of validation data to combine individual land cover maps. We describe the generality of this setup in terms of existing algorithms and accuracy assessment procedures. This method has the advantage of not requiring posterior probabilities or level of support for predicted class labels. We demonstrate the technique using Landsat based, 30-meter land cover maps, the highest resolution, globally available product of this kind. We used globally distributed validation samples to composite the maps and compute accuracy. We show that geographic stacking can improve individual map accuracy by up to 6.6%. The voting methods can also achieve higher accuracy than the best of the input classifications. Accuracies from different classifiers, input data, and output type are compared. The results are illustrated on a Landsat scene in California, USA. The compositing technique described here has broad applicability in remote sensing based map production and geographic classification.

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

    USGS Publications Warehouse

    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.

  4. Downscaling of satellite remote sensing data: Application to land cover mapping

    NASA Astrophysics Data System (ADS)

    Boucher, Alexandre

    Many satellite images have a spatial resolution coarser than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. Downscaling, also known as super-resolution or sub-pixel mapping, turns these proportions into a fine resolution map of class labels. Sub-pixel mapping is undetermined, in that many different fine resolution maps can lead to an equally good reproduction of the available coarse fractions. Thus, the unknown fine resolution land cover map is regarded as a realization of a random set. Simulated realizations are generated using the geostatistical paradigm of sequential simulation. At any pixel along a path visiting all fine scale pixels, a class label is simulated from a local probability distribution made conditional to: (i) the coarse class fraction data, (ii) any simulated land cover classes at fine pixels previously visited along that path, and (iii) a prior structural model. Two algorithms using different structural model types are proposed for the sequential simulation. The first method proposed is built on block indicator cokriging which allows evaluating the previous local probability distributions by a form of kriging; the structural model is then a series of class labels indicator variograms. The second method is based on the multiple-point simulation algorithm SNESIM where the local probability distributions are read from a training image; the structural function is then that training image which can be seen as an analog image depicting the patterns deemed present at the fine resolution. Two case studies derived from Landsat TM imagery demonstrates the two approaches proposed. The resulting alternative downscaled class maps all honor the coarse proportion data, any fine scale data available, and exhibit the

  5. Estimation of agricultural pesticide use in drainage basins using land cover maps and county pesticide data

    USGS Publications Warehouse

    Nakagaki, Naomi; Wolock, David M.

    2005-01-01

    A geographic information system (GIS) was used to estimate agricultural pesticide use in the drainage basins of streams that are studied as part of the U.S. Geological Survey?s National Water-Quality Assessment (NAWQA) Program. Drainage basin pesticide use estimates were computed by intersecting digital maps of drainage basin boundaries with an enhanced version of the National Land Cover Data 1992 combined with estimates of 1992 agricultural pesticide use in each United States county. This report presents the methods used to quantify agricultural pesticide use in drainage basins using a GIS and includes the estimates of atrazine use applied to row crops, small-grain crops, and fallow lands in 150 watersheds in the conterminous United States. Basin atrazine use estimates are presented to compare and analyze the results that were derived from 30-meter and 1-kilometer resolution land cover and county pesticide use data, and drainage basin boundaries at various grid cell resolutions. Comparisons of the basin atrazine use estimates derived from watershed boundaries, county pesticide use, and land cover data sets at different resolutions, indicated that overall differences were minor. The largest potential for differences in basin pesticide use estimates between those derived from the 30-meter and 1-kilometer resolution enhanced National Land Cover Data 1992 exists wherever there are abrupt agricultural land cover changes along the basin divide. Despite the limitations of the drainage basin pesticide use data described in this report, the basin estimates provide consistent and comparable indicators of agricultural pesticide application in surface-water drainage basins studied in the NAWQA Program.

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

    NASA Astrophysics Data System (ADS)

    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

  7. Agricultural land cover mapping in the context of a geographically referenced digital information system. [Carroll, Macon, and Gentry Counties, Missouri

    NASA Technical Reports Server (NTRS)

    Stoner, E. R.

    1982-01-01

    The introduction of soil map information to the land cover mapping process can improve discrimination of land cover types and reduce confusion among crop types that may be caused by soil-specific management practices and background reflectance characteristics. Multiple dates of LANDSAT MSS digital were analyzed for three study areas in northern Missouri to produce cover types for major agricultural land cover classes. Digital data bases were then developed by adding ancillary data such as digitized soil and transportation network information to the LANDSAT-derived cover type map. Procedures were developed to manipulate the data base parameters to extract information applicable to user requirements. An agricultural information system combining such data can be used to determine the productive capacity of land to grow crops, fertilizer needs, chemical weed control rates, irrigation suitability, and trafficability of soil for planting.

  8. GlobCorine- A Joint EEA-ESA Project for Operational Land Cover and Land Use Mapping at Pan-European Scale

    NASA Astrophysics Data System (ADS)

    Bontemps, S.; Defourny, P.; Van Bogaert, E.; Weber, J. L.; Arino, O.

    2010-12-01

    Regular and global land cover mapping contributes to evaluating the impact of human activities on the environment. Jointly supported by the European Space Agency and the European Environmental Agency, the GlobCorine project builds on the GlobCover findings and aims at making the full use of the MERIS time series for frequent land cover monitoring. The GlobCover automated classification approach has been tuned to the pan-European continent and adjusted towards a classification compatible with the Corine typology. The GlobCorine 2005 land cover map has been achieved, validated and made available to a broad- level stakeholder community from the ESA website. A first version of the GlobCorine 2009 map has also been produced, demonstrating the possibility for an operational production of frequent and updated global land cover maps.

  9. Low Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

    NASA Technical Reports Server (NTRS)

    Spruce, Joe

    2001-01-01

    Yellowstone National Park (YNP) contains a diversity of land cover. YNP managers need site-specific land cover maps, which may be produced more effectively using high-resolution hyperspectral imagery. ISODATA clustering techniques have aided operational multispectral image classification and may benefit certain hyperspectral data applications if optimally applied. In response, a study was performed for an area in northeast YNP using 11 select bands of low-altitude AVIRIS data calibrated to ground reflectance. These data were subjected to ISODATA clustering and Maximum Likelihood Classification techniques to produce a moderately detailed land cover map. The latter has good apparent overall agreement with field surveys and aerial photo interpretation.

  10. Using phenological metrics and the multiple classifier fusion method to map land cover types

    NASA Astrophysics Data System (ADS)

    Liu, Jianhong; Pan, Yaozhong; Zhu, Xiufang; Zhu, Wenquan

    2014-01-01

    Feature selection and multiple classifier fusion (MCF) are effective approaches to improve land cover classification accuracy. In this study, we combined phenological metrics and the MCF method to map land cover types in Jiangsu province of China during the second crop growing season using moderate resolution imaging spectroradiometer time-series data. Eight phenological metrics were developed and calculated, and a MCF scheme was proposed by combining a simple majority vote and the measurement of posterior probabilities. The four base classifiers (i.e., the maximum likelihood classifier, the Mahalanobis distance classifier, the support vector machine classifier, and the neural networks classifier) and the MCF method were used in classifications using two spectral indices from the original satellite data (direct classification) and the computed metric data (metrics-based classification). Accuracy assessments indicated that the overall accuracies and kappa coefficients of the metrics-based classifications were all higher than those of direct classifications. The average overall accuracy and kappa coefficient of metrics-based classifications were 8.36% and 0.1 higher than that of direct classifications, respectively. Similarly, the overall accuracy and kappa coefficient of MCF generally were close to or exceeded the highest accuracy among all the base classifiers. The highest overall accuracy and kappa coefficient was achieved by classification with the MCF method based on phenological metrics (m-MCF), which were 88% and 0.85, respectively. Our results suggested that combining phenological metrics and MCF in classification is a promising method for land cover mapping in regions where strong phenological signals can be detected.

  11. Yukon Flats National Wildlife Refuge land cover mapping project users guide

    USGS Publications Warehouse

    Markon, Carl J.

    1987-01-01

    The U. S. Fish & Wildlife Service (USFWS) has the responsibility for collecting the resource information to address the research, management, development and planning requirements identified in Section 304. Because of the brief period provided by the Act for data collection, habitat mapping, and habitat assessment, the USFWS in cooperation with the U.S. Geological Survey's EROS Field Office, used digital Landsat multispectral scanner (MSS) data and digital terrain data to produce land cover and terrain maps. A computer assisted digital analysis of Landsat MSS data was used because coverage by aerial photographs was incomplete for much of the refuge and because the level of detail obtained from Landsat data was adequate to meet most USFWS research, management and planning needs. Relative cost and time requirements were also factors in the decision to use the digital analysis approach.

  12. The effect of Thematic Mapper spectral properties on land cover mapping for hydrologic modeling

    NASA Technical Reports Server (NTRS)

    Gervin, J. C.; Lu, Y. C.; Gauthier, R. L.; Miller, J. R.; Irish, R. R.

    1986-01-01

    The accuracy of unsupervised land-cover classification from all seven Landsat TM bands and from six combinations of three or four bands is evaluated using images of the Clinton River Basin, a suburban watershed near Detroit. Data from aerial TMS photography, USGS topographic maps, and ground surveys are employed to determine the classification accuracy. The mapping accuracy of all seven bands is found to be significantly better (6 percent overall, 12 percent for residential areas, and 13 percent for commercial districts) than that with bands 2, 3, and 4; but almost the same accuracy is obtained by including at least one band from each major spectral region (visible, NIR, and mid-IR).

  13. Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery

    PubMed Central

    Sheffield, Kathryn; Morse-McNabb, Elizabeth; Clark, Rob; Robson, Susan; Lewis, Hayden

    2015-01-01

    There is a demand for regularly updated, broad-scale, accurate land cover information in Victoria from multiple stakeholders. This paper documents the methods used to generate an annual dominant land cover (DLC) map for Victoria, Australia from 2009 to 2013. Vegetation phenology parameters derived from an annual time series of the Moderate Resolution Imaging Spectroradiometer Vegetation Indices 16-day 250 m (MOD13Q1) product were used to generate annual DLC maps, using a three-tiered hierarchical classification scheme. Classification accuracy at the broadest (primary) class level was over 91% for all years, while it ranged from 72 to 81% at the secondary class level. The most detailed class level (tertiary) had accuracy levels ranging from 61 to 68%. The approach used was able to accommodate variable climatic conditions, which had substantial impacts on vegetation growth patterns and agricultural production across the state between both regions and years. The production of an annual dataset with complete spatial coverage for Victoria provides a reliable base data set with an accuracy that is fit-for-purpose for many applications. PMID:26602009

  14. Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery.

    PubMed

    Sheffield, Kathryn; Morse-McNabb, Elizabeth; Clark, Rob; Robson, Susan; Lewis, Hayden

    2015-01-01

    There is a demand for regularly updated, broad-scale, accurate land cover information in Victoria from multiple stakeholders. This paper documents the methods used to generate an annual dominant land cover (DLC) map for Victoria, Australia from 2009 to 2013. Vegetation phenology parameters derived from an annual time series of the Moderate Resolution Imaging Spectroradiometer Vegetation Indices 16-day 250 m (MOD13Q1) product were used to generate annual DLC maps, using a three-tiered hierarchical classification scheme. Classification accuracy at the broadest (primary) class level was over 91% for all years, while it ranged from 72 to 81% at the secondary class level. The most detailed class level (tertiary) had accuracy levels ranging from 61 to 68%. The approach used was able to accommodate variable climatic conditions, which had substantial impacts on vegetation growth patterns and agricultural production across the state between both regions and years. The production of an annual dataset with complete spatial coverage for Victoria provides a reliable base data set with an accuracy that is fit-for-purpose for many applications. PMID:26602009

  15. Land cover mapping based on a frequency based contextual classifier from remote sensing data over Penang Island, Malaysia

    NASA Astrophysics Data System (ADS)

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

    2010-11-01

    Remote sensing data have been widely used for land cover mapping using supervised and unsupervised methods. The produced land cover maps are useful for various applications. This paper presents a technique for land use/cover mapping using THEOS data of the Penang Island, Malaysia. The objective is to assess the capability of a THEOS image to provide useful remotely sensed images for land cover mapping. The land cover information was extracted from the visible digital spectral bands using PCI Geomatica 10.3 software package. A frequency based contextual classifier was applied to the imagery to extract the spectral information from the acquired scene. Contextual classification is employed when neighbouring pixels are taken into account. The accuracy of each classification map was assessed using the reference data set consisted of a large number of samples collected per category. The study revealed that the frequency based contextual classifier produced superior result and achieved a high degree of accuracy. The preliminary result indicates that THEOS image can be provided useful data for remote sensing to retrieve land cover information at local scale.

  16. MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY

    EPA Science Inventory

    The accuracy of thematic map products is not spatially homogenous, but instead variable across most landscapes. Properly analyzing and representing the spatial distribution (pattern) of thematic map accuracy would provide valuable user information for assessing appropriate applic...

  17. Biodiversity Pressure Maps to evaluate the impact of land use and land cover change on Endangered Ecological Communities

    NASA Astrophysics Data System (ADS)

    Chisholm, L. A.; Gill, N.

    2014-12-01

    The dynamics of biodiversity are associated with human activities such as land use and land cover change (LULCC). An integrated spatial approach to identify the effects of LULCC is helpful to determine the impact or pressure of human activities on biodiversity. The concept of creating 'biodiversity pressure maps' includes the use of spatial technologies (remote sensing, GIS) over time on areas of sensitivity, for example, areas classified as endangered ecological communities (EEC). The use of a cross-tabulation matrix often forms the basis of creating pressure maps, yet spatial datasets appropriate as input are not always available. The focus of this study was to investigate and evaluate spatial datasets and cross-tabulation techniques useful for producing biodiversity pressure maps. A method will be presented in the form of a case study for an area in the Shoalhaven Local Government Area on the south coast of NSW, Australia. This area is a focus of investigation of the spatial distribution of invasive plants and landholder management practices.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 ...

  20. Global land cover mapping and characterization: present situation and future research priorities

    USGS Publications Warehouse

    Giri, Chandra

    2005-01-01

    The availability and accessibility of global land cover data sets plays an important role in many global change studies. The importance of such science‐based information is also reflected in a number of international, regional, and national projects and programs. Recent developments in earth observing satellite technology, information technology, computer hardware and software, and infrastructure development have helped developed better quality land cover data sets. As a result, such data sets are increasingly becoming available, the user‐base is ever widening, application areas have been expanding, and the potential of many other applications are enormous. Yet, we are far from producing high quality global land cover data sets. This paper examines the progress in the development of digital global land cover data, their availability, and current applications. Problems and opportunities are also explained. The overview sets the stage for identifying future research priorities needed for operational land cover assessment and monitoring.

  1. USGS Historical, Current, and Projected Future Land Cover Mapping for the Northern Great Plains

    NASA Astrophysics Data System (ADS)

    Sohl, T. L.; Gallant, A.; Sayler, K. L.

    2008-12-01

    Land cover in the Northern Great Plains has changed considerably in the last several decades. While a significant proportion of the landscape has been cultivated for over one hundred years, the intensity of cultivation, crop type, and management practices have changed in response to shifts in government policy, commodity prices, access to water, and technological advances. Changes in land cover impact a wide variety of ecosystem processes and services, including carbon balances, climate, hydrology and water quality, and biodiversity. A consistent record of historical land cover is required to understand relations between land- cover change and these ecological processes, while projections of future land cover are needed for planning and potential mitigation efforts. Several U.S. Geological Survey efforts have been completed or are ongoing in the Northern Great Plains, resulting in the compilation of an unmatched record of historical, current, and future land-cover information for the region. The USGS Land Cover Trends project is using the historical record of Landsat imagery and a robust sampling approach to examine the rates, causes, and consequences of contemporary (1973-2000) land-cover change on an ecoregional basis for the conterminous United States. Results from completed Trends analyses for Great Plains ecoregions revealed changes in the proportion and distribution of grassland/shrubland and agricultural uses during the study period; Some areas exhibited considerable loss in cultivated land after initiation of the Conservation Reserve Program (CRP) in the mid 1980s. In recent years (post-2000), agricultural commodity prices have skyrocketed as food and energy compete for use of agricultural products, which in conjunction with the expiration of many CRP contracts, has led to expansion of cultivated land. In the coming decades, calls for U.S. energy independence and the development of biofuels from cellulosic stock could result in a transformation of the Great

  2. Land use and land cover digital data

    USGS Publications Warehouse

    U.S. Geological Survey

    1994-01-01

    Computer tapes derived from land use and land cover (LULC) data and associated maps at scales of 1 :250,000 and 1: 100,000 are available from the U.S. Geological Survey. This data can be used alone or combined with a base map or other supplemental data for a variety of applications, using commercially available software. You can produce area summary statistics, select specific portions of a map to study or display single classifications, such as bodies of water. LULC and associated digital data offer convenient, accurate, flexible, and cost-effective access to users who are involved in environmental studies, land use planning, land management, or resource planning.

  3. Comparing National Differences in what People Perceive to BE There: Mapping Variations in Crowd Sourced Land Cover

    NASA Astrophysics Data System (ADS)

    Comber, A.; Mooney, P.; Purves, R. S.; Rocchini, D.; Walz, A.

    2015-08-01

    This paper describes a simple comparison of the distributions of land cover features identified from volunteered data contributed by different social groups - in this case comparing two groups of Geo-Wiki campaigns. Understanding the impacts on analyses of citizen science data contributed by different groups is critical to ensure robust scientific outputs and to fully realise the potential benefits to formal scientific research. It is well known that different people, with different backgrounds and subject to different cultural factors, hold varying landscape conceptualisations. This paper analyses volunteered geographical information on land cover to generate land cover maps. It uses a geographically weighted approach to generate land cover mappings. The mappings generated by different groups (in this case a from a specific unnamed country) are compared and the results show how the predicted land cover distributions vary, with large differences in some classes (e.g. Barren land, Shrubland, Wetland) and little difference in others (e.g. Tree cover). This suggests that for some landscape features cultural and national differences matter when it comes to using crowdsourced data in formal scientific analyses and highlights the potential problems of not considering contributor backgrounds in citizen science. This is important because such data re now routinely being used to develop global land cover data, to generate uncertainty estimates of existing global land cover products and to generate global forest inventories. These in turn are being suggested as suitable inputs to such things as global climate models. A number of critical research directions arising from these findings are discussed.

  4. Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia

    NASA Astrophysics Data System (ADS)

    Khvostikov, S.; Venevsky, S.; Bartalev, S.

    2015-12-01

    The dynamic global vegetation model (DGVM) SEVER has been regionally adapted using a remote sensing data-derived land cover map in order to improve the reconstruction conformity of the distribution of vegetation functional types over Russia. The SEVER model was modified to address noticeable divergences between modelling results and the land cover map. The model modification included a light competition method elaboration and the introduction of a tundra class into the model. The rigorous optimisation of key model parameters was performed using a two-step procedure. First, an approximate global optimum was found using the efficient global optimisation (EGO) algorithm, and afterwards a local search in the vicinity of the approximate optimum was performed using the quasi-Newton algorithm BFGS. The regionally adapted model shows a significant improvement of the vegetation distribution reconstruction over Russia with better matching with the satellite-derived land cover map, which was confirmed by both a visual comparison and a formal conformity criterion.

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

    NASA Technical Reports Server (NTRS)

    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.

  6. Low-Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

    NASA Technical Reports Server (NTRS)

    Spruce, Joseph P.

    2001-01-01

    Northeast Yellowstone National Park (YNP) has a diversity of forest, range, and wetland cover types. Several remote sensing studies have recently been done in this area, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project conducted by Yellowstone Ecosystems Studies (YES) on the use of hyperspectral imaging for assessing riparian and in-stream habitats. In 1999, YES and NASA's Commercial Remote Sensing Program Office began collaborative study of this area, assessing the potential of synergistic use of hyperspectral, synthetic aperture radar (SAR), and multiband thermal data for mapping forest, range, and wetland land cover. Since the beginning, a quality 'reference' land cover map has been desired as a tool for developing and validating other land cover maps produced during the project. This paper recounts an effort to produce such a reference land cover map using low-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and unsupervised classification techniques. The main objective of this study is to assess ISODATA classification for mapping land cover in Northeast YNP using select bands of low-altitude AVIRIS data. A secondary, more long-term objective is to assess the potential for improving ISODATA-based classification of land cover through use of principal components analysis and minimum noise fraction (MNF) techniques. This paper will primarily report on work regarding the primary research objective. This study focuses on an AVIRIS cube acquired on July 23, 1999, by the confluence of Soda Butte Creek with the Lamar River. Range and wetland habitats dominate the image with forested habitats being a comparatively minor component of the scene. The scene generally tracks from southwest to northeast. Most of the scene is valley bottom with some lower side slopes occurring on the western portion. Elevations within the AVIRIS scene range from approximately 1998 to 2165 m above sea level, based on US

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

    NASA Astrophysics Data System (ADS)

    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.

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

    USGS Publications Warehouse

    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. ?? 2006 Elsevier B.V. All rights reserved.

  9. Mapping the land cover in coastal Gabes oases using the EO-1 HYPERION hyperspectral sensor

    NASA Astrophysics Data System (ADS)

    Ben-Arfa, Jouda; Bergès, Jean Claude; Beltrando, Gérard; Rim, Katlane; Zargouni, Fouad

    2015-04-01

    Gabes region is characterized by unique maritime oases in Mediterranean basin. Unfortunately these oases are sensitive areas due to a harsh competition for land and water between different user groups (urban, industry, agriculture). An industrial complex is now located in center of this region, cultivation practices have shifted from a traditional multi-layer plant association system and moreover the Gabes city itself is expanding in the very core of oases. The oases of Gabes are transformed into city oases; they undergo multiform interactions whose amplify their environmental dynamic. A proper management of this environment should be based on a fine cartography of land use and remote sensing plays a major role in this issue. However the use of legacy natural resource remote sensing data is disappointing. The crop production strategies rely on a fine scale ground split among various uses and the ground resolution of these satellites is not adequate. Our study relies on hyperspectral images in order to cartography oases boundaries and land use. We tested the potential of Hyperion hyperspectral satellite imagery for mapping dynamics oases covered. We have the opportunity to access EO1/Hyperion data on seven different dates on 2009 and 2010. This dataset allows us to compare various hyperspectral based processing both on the basis on information pertinence and time stability. In this frame some index appear as significantly efficient: cellulose index, vegetation mask, water presence index. On another side spectral unmixing looks as more sensitive to slight ground changes. These results raise the issue of compared interest of enhancing spatial resolution versus spectral resolution. Whereas high resolution ground observation satellites are obviously more appropriate for visual recognition process, reliable information could be extracted from hyperspectral information through a fully automatic process.

  10. Delivering the Copernicus land monitoring service, production of the CORINE Land Cover Map in the UK. A forward looking perspective to the Sentinel-2 mission.

    NASA Astrophysics Data System (ADS)

    Cole, Beth; Balzter, Heiko; Smith, Geoff; Morton, Dan; King, Sophie

    2014-05-01

    The Copernicus land monitoring service became operational in 2012 as the GIO Land (initial operations of the land monitoring service) initiative and builds upon work under FP7 geoland2 project. The Centre for Landscape and Climate Research (CLCR), part of the UK National Reference Centre (NRC) for land cover, is responsible for the production of the UK contribution to the Pan-European component of GIO Land. The CORINE Land Cover (CLC) map is now the most up to date national land cover product for the UK. The national plan for future production of CLC data will incorporate the increased capability of the Copernicus space component, utilising data from the Sentinel missions. Monitoring land cover and change will be assisted by the increased performance and the reduced revisit time interval of the Sentinel-2 satellites. Repeat coverages are essential to remove the effects of vegetation phenology and identify land cover changes. Also, UK data acquisitions opportunities are limited by cloud cover, as has been seen in the GIO-Land monitoring program, therefore more frequent imagining increases the likelihood of suitable data being available. The vegetation classes are the most difficult aspects of the nomenclature in the UK, in particular discrimination between the arable, pasture and the natural grasslands. The spectral capabilities of Sentinel-2 allow the automatic correction of atmospheric effects so that reflectance features in the images can be more easily linked to land cover features on the surface. It is also envisaged that the increased spectral resolution, with 5 bands around the red edge, will benefit the discrimination of difficult vegetation features. Finally the improve calibration of Sentinel-2 will allow the production of biophysical variables which are import for condition assessment and landscape modelling. The methodological shift in land cover mapping in the UK is described here, also incorporating a look forward to overcoming challenges in the

  11. EVOLUTIONARY COMPUTATION AND POST-WILDFIRE LAND-COVER MAPPING WITH MULTISPECTRAL IMAGERY.

    SciTech Connect

    Brumby, Steven P.; Koch, S. W.; Hansen, L. A.

    2001-01-01

    The Cerro Grande Los Alamos wildfire devastated approximately 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos. The need to monitor the continuing impact of the fire on the local environment has led to the application of a number of advanced remote sensing technologies. During and after the fire, remote-sensing data was acquired fiorn a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique io the automated classification of land cover using multispectral imagery. We apply a hybrid gertelic programminghupervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery fiom the Landsat 7 ETM+ instrument fiom before and after the wildfire. Using an existing land cover classification based on a Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, along with clouds and cloud shadows. The details of our evolved classification are compared to the manually produced land-cover classification. Keywords: Feature Extraction, Genetic programming, Supervised classification, Multi-spectral imagery, Land cover, Wildfire.

  12. Land Cover Characterization Program

    USGS Publications Warehouse

    U.S. Geological Survey

    1997-01-01

    (2) identify sources, develop procedures, and organize partners to deliver data and information to meet user requirements. The LCCP builds on the heritage and success of previous USGS land use and land cover programs and projects. It will be compatible with current concepts of government operations, the changing needs of the land use and land cover data users, and the technological tools with which the data are applied.

  13. Multiple-class land-cover mapping at the sub-pixel scale using a Hopfield neural network

    NASA Astrophysics Data System (ADS)

    Tatem, Andrew J.; Lewis, Hugh G.; Atkinson, Peter M.; Nixon, Mark S.

    Land cover class composition of image pixels can be estimated using soft classification techniques. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Robust techniques to provide an improved spatial representation of land cover have yet to be developed. The use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification was investigated in previous papers by Tatem et al. The network converges to a minimum of an energy function defined as a goal and several constraints. The approach involved designing the energy function to produce a 'best guess' prediction of the spatial distribution of class components in each pixel. Tatem et al described the application of the technique to target mapping at the sub-pixel scale, but only for single classes. We now show how this approach can be extended to map multiple classes at the sub-pixel scale, by adding new constraints into the energy formulation. The new technique has been applied to simulated SPOT HRV and Landsat TM agriculture imagery to derive accurate estimates of land cover. The results show that this extension of the neural network now represents a simple efficient tool for mapping land cover and can deliver requisite results for the analysis of practical remotely sensed imagery at the sub pixel scale.

  14. Updating Landsat-derived land-cover maps using change detection and masking techniques

    NASA Technical Reports Server (NTRS)

    Likens, W.; Maw, K.

    1982-01-01

    The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

  15. An Assessment of Citizen Contributed Ground Reference Data for Land Cover Map Accuracy Assessment

    NASA Astrophysics Data System (ADS)

    Foody, G. M.

    2015-08-01

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

  16. Evolutionary computation and post-wildfire land-cover mapping with multispectral imagery

    NASA Astrophysics Data System (ADS)

    Brumby, Steven P.; Koch, Steven; Hansen, Leslie A.

    2002-01-01

    The Cerro Grande/Los Alamos wildfire devastated approximately 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos. The need to monitor the continuing impact of the fire on the local environment has led to the application of a number of advanced remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multispectral imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before and after the wildfire. Using an existing land cover classification based on a Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, along with clouds and cloud shadows. The details of our evolved classification are compared to the manually produced land-cover classification.

  17. Land Cover Trends Project

    USGS Publications Warehouse

    Acevedo, William

    2006-01-01

    The Land Cover Trends Project is designed to document the types, rates, causes, and consequences of land cover change from 1973 to 2000 within each of the 84 U.S. Environmental Protection Agency (EPA) Level III ecoregions that span the conterminous United States. The project's objectives are to: * Develop a comprehensive methodology using probability sampling and change analysis techniques and Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM) data for estimating regional land cover change. * Characterize the spatial and temporal characteristics of conterminous U.S. land cover change for five periods from 1973 to 2000 (nominally 1973, 1980, 1986, 1992, and 2000). * Document the regional driving forces and consequences of change. * Prepare a national synthesis of land cover change.

  18. Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping.

    PubMed

    Qin, Yuchu; Li, Shihua; Vu, Tuong-Thuy; Niu, Zheng; Ban, Yifang

    2015-06-01

    Urban land cover map is essential for urban planning, environmental studies and management. This paper aims to demonstrate the potential of geometric and radiometric features derived from LiDAR waveform and point cloud data in urban land cover mapping with both parametric and non-parametric classification algorithms. Small footprint LiDAR waveform data acquired by RIEGL LMS-Q560 in Zhangye city, China is used in this study. A LiDAR processing chain is applied to perform waveform decomposition, range determination and radiometric characterization. With the synergic utilization of geometric and radiometric features derived from LiDAR data, urban land cover classification is then conducted using the Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and random forest algorithms. The results suggest that the random forest classifier achieved the most accurate result with overall classification accuracy of 91.82% and the kappa coefficient of 0.88. The overall accuracies of MLC and SVM are 84.02, and 88.48, respectively. The study suggest that the synergic utilization of geometric and radiometric features derived from LiDAR data can be efficiently used for urban land cover mapping, the non-parametric random forest classifier is a promising approach for the various features with different physical meanings. PMID:26072748

  19. NALC/MEXICO LAND-COVER MAPPING RESULTS: IMPLICATIONS FOR ASSESSING LANDSCAPE CHANGE

    EPA Science Inventory

    An inventory of land-cover conditions throughout Mexico was performed using North American Landscape Characterization (NALC) Landsat Multi-Spectral Scanner (MSS) 'triplicate' images, corresponding to the 1970s, 1980s, and 1990s epoch periods. The equivalent of 300 image scenes we...

  20. NALC/MEXICO LAND-COVER MAPPING RESULTS: IMPLICATIONS FOR ASSESSING LANDSCAPE CONDITION

    EPA Science Inventory

    An inventory of land-cover conditions throughout Mexico was performed using North American Landscape Characterization (NLAC) Landsat Mult-Spectral Scann (MSS) 'triplicate' images, corresponding to the 1970s, 1980s and 1990s epoch periods. The equivalents of 300 image scenes were...

  1. LAND COVER MAPPING IN AN AGRICULTURAL SETTING USING MULTISEASONAL THEMATIC MAPPER DATA

    EPA Science Inventory

    A multiseasonal Landsat Thematic Mapper (TM) data set consisting of five image dates from a single year was used to characterize agricultural and related land cover in the Willamette River Basin (WRB) of western Oregon. Image registration was accomplished using an automated grou...

  2. LARGE AREA LAND COVER MAPPING THROUGH SCENE-BASED CLASSIFICATION COMPOSITING

    EPA Science Inventory

    Over the past decade, a number of initiatives have been undertaken to create definitive national and global data sets consisting of precision corrected Landsat MSS and TM scenes. One important application of these data is the derivation of large area land cover products spanning ...

  3. NALC/MEXICO LAND-COVER MAPPING RESULTS: IMPLICATIONS FOR ASSESSING LANDSCAPE CONDITION

    EPA Science Inventory

    An inventory of land-cover conditions throughout Mexico was performed using North American Landscap Characterization ( NALC) Landsat Multi-Spectral Scanner (MSS) 'triplicate' images, corresponding to the 1970s, 1980s and1990s epoch periods. The equivalent of 300 image scenes were...

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

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

    NASA Technical Reports Server (NTRS)

    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

  6. Verification of land cover maps from LANDSAT data. [for Alaska, Arizona and Oklahoma

    NASA Technical Reports Server (NTRS)

    Linden, D. S.; Szajgin, J.

    1981-01-01

    The application of cluster sampling to verifying the accuracy of maps derived from digital data is discussed. Variants of the cluster sampling technique were used in large scale assessments for areas in excess of one million hectares. Three types of classification errors are possible: commission errors, omission errors, and overall error classification. Commission errors for a particular cover type occur when pixels are classified as that cover type but are found to be some other cover type when the field is checked. Omission errors occur when pixels of fields already visited and known to be of a particular cover type, are classified as some other cover type. Overall error is the proportion of pixels incorrectly classified, without regard to omission or commission. Since the classified image represents the sampling frame, sampling for accuracy assessment was designed to estimate commission error. However, the sample can also provide useful estimates of omission and overall error. Assessment for Alaska, Arizona, and Oklahoma are presented.

  7. Mapping Land Cover in the Taita Hills, se Kenya, Using Airborne Laser Scanning and Imaging Spectroscopy Data Fusion

    NASA Astrophysics Data System (ADS)

    Piiroinen, R.; Heiskanen, J.; Maeda, E.; Hurskainen, P.; Hietanen, J.; Pellikka, P.

    2015-04-01

    The Taita Hills, located in south-eastern Kenya, is one of the world's biodiversity hotspots. Despite the recognized ecological importance of this region, the landscape has been heavily fragmented due to hundreds of years of human activity. Most of the natural vegetation has been converted for agroforestry, croplands and exotic forest plantations, resulting in a very heterogeneous landscape. Given this complex agro-ecological context, characterizing land cover using traditional remote sensing methods is extremely challenging. The objective of this study was to map land cover in a selected area of the Taita Hills using data fusion of airborne laser scanning (ALS) and imaging spectroscopy (IS) data. Land Cover Classification System (LCCS) was used to derive land cover nomenclature, while the height and percentage cover classifiers were used to create objective definitions for the classes. Simultaneous ALS and IS data were acquired over a 10 km x 10 km area in February 2013 of which 1 km x 8 km test site was selected. The ALS data had mean pulse density of 9.6 pulses/m2, while the IS data had spatial resolution of 1 m and spectral resolution of 4.5-5 nm in the 400-1000 nm spectral range. Both IS and ALS data were geometrically co-registered and IS data processed to at-surface reflectance. While IS data is suitable for determining land cover types based on their spectral properties, the advantage of ALS data is the derivation of vegetation structural parameters, such as tree height and crown cover, which are crucial in the LCCS nomenclature. Geographic object-based image analysis (GEOBIA) was used for segmentation and classification at two scales. The benefits of GEOBIA and ALS/IS data fusion for characterizing heterogeneous landscape were assessed, and ALS and IS data were considered complementary. GEOBIA was found useful in implementing the LCCS based classification, which would be difficult to map using pixel-based methods.

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

    NASA Technical Reports Server (NTRS)

    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.

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

    NASA Astrophysics Data System (ADS)

    Lawawirojwong, Siam

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

  10. Effects of new MODIS land cover map replacement in a regional climate model on surface temperature and humidity

    NASA Astrophysics Data System (ADS)

    Yucel, I.

    This study investigates the extent to which utilizing 1-km new the Moderate-resolution Imaging-Spectroradiometer (MODIS) land use data in the Pennsylvania State University/NCAR's MM5 coupled with Oregon State University (OSU) provides an improved regional diagnosis of near-surface atmospheric state variables as well as characteristics of the planetary boundary layer (PBL). Those variables are strongly influenced by the energy, matter and momentum exchange between the land surface and the atmosphere. MODIS data provides not only a detailed spatial distribution of vegetation, but also a delineation between water bodies and land surface for MM5 high-resolution applications. Advances in remote sensing technology allow MODIS to collect higher-quality data than previous sensors, yielding the most detailed land cover classification maps to date. The new maps are better because the quality of MODIS data is much higher than the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR). The default 25-category United States Geological Survey (USGS) land cover classification in MM5 was produced using data acquired in from 1992-1993 by AVHRR. Parameter sets of 17-category MODIS land use dataset are determined by making close match between MODIS, USGS and SIB categories to use in OSU land-surface model. 1-km Land-Water Mask (LWM) data is also derived from this new data as an input to MM5. When the MM5 horizontal grid increment is larger than 1-km (4-km and 12-km in current study), the dominant vegetation type in each grid box is selected to represent the ``grid level'' vegetation characteristics. The MODIS data consider the influence of detailed picture of the distribution of Earth's ecosystems in the surface energy and water budget and hence the evolution of the boundary layer. The impact on the near-surface temperature and humidity is given by making comparison between model and observations at selected land surface types.

  11. Airborne Multispectral LIDAR Data for Land-Cover Classification and Land/water Mapping Using Different Spectral Indexes

    NASA Astrophysics Data System (ADS)

    Morsy, S.; Shaker, A.; El-Rabbany, A.; LaRocque, P. E.

    2016-06-01

    Airborne Light Detection And Ranging (LiDAR) data is widely used in remote sensing applications, such as topographic and landwater mapping. Recently, airborne multispectral LiDAR sensors, which acquire data at different wavelengths, are available, thus allows recording a diversity of intensity values from different land features. In this study, three normalized difference feature indexes (NDFI), for vegetation, water, and built-up area mapping, were evaluated. The NDFIs namely, NDFIG-NIR, NDFIG-MIR, and NDFINIR-MIR were calculated using data collected at three wavelengths; green: 532 nm, near-infrared (NIR): 1064 nm, and mid-infrared (MIR): 1550 nm by the world's first airborne multispectral LiDAR sensor "Optech Titan". The Jenks natural breaks optimization method was used to determine the threshold values for each NDFI, in order to cluster the 3D point data into two classes (water and land or vegetation and built-up area). Two sites at Scarborough, Ontario, Canada were tested to evaluate the performance of the NDFIs for land-water, vegetation, and built-up area mapping. The use of the three NDFIs succeeded to discriminate vegetation from built-up areas with an overall accuracy of 92.51%. Based on the classification results, it is suggested to use NDFIG-MIR and NDFINIR-MIR for vegetation and built-up areas extraction, respectively. The clustering results show that the direct use of NDFIs for land-water mapping has low performance. Therefore, the clustered classes, based on the NDFIs, are constrained by the recorded number of returns from different wavelengths, thus the overall accuracy is improved to 96.98%.

  12. Evaluating the Synergistic Use of Low-Altitude AVIRIS and AIRSAR Data for Land Cover Mapping in Northeast Yellowstone National Park

    NASA Technical Reports Server (NTRS)

    Berglund, Judith; Spruce, Joseph

    2001-01-01

    Current land cover maps are needed by Yellowstone National Park (YNP) managers to assist them in protecting and preserving native flora and fauna. Synergistic use of hyperspectral and radar imagery offers great promise for mapping habitat in terms of cover type composition and structure. In response, a study was conducted to assess the utility of combining low-altitude AVIRIS and AIRSAR data for mapping land cover in a portion of northeast YNP. Land cover maps were produced from individual AVIRIS and AIRSAR data sets, as well as from a hybrid data stack of selected AVIRIS and AIRSAR data bands. The three resulting classifications were compared to field survey data and aerial photography to assess apparent benefits of hyperspectral/SAR data fusion for land cover mapping. Preliminary results will be presented.

  13. Mapping Soil Depth with Topographic and Land Cover Attributes from Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Chen, Cheng-Ru; Chen, Chi-Farn; Son, Nguyen-Thanh; Lau, Va-Khin

    2016-04-01

    Soil depth is an important parameter for identification of the overused slope land in Taiwan. The retrieval of high resolution soil depth at a large scale is costly and time-consuming. The main objective of this study is to develop an approach to estimate soil depths using satellite data with the aid of field survey data in Taiwan. The data were processed using the soil-landscape regression kriging model. The predictor variables, including elevation, slope, aspect, curvature, topographic wetness, spectral indices, and land use, derived from remotely sensed data were used as model inputs for the soil depth estimation. In this study, topographic attributes were derived from an 5-m resolution digital elevation model, and the land-use map and spectral indices were obtained through interpretation of Landsat-8 data. The absolute mean and root mean-square errors were used to access the reliability of the prediction, indicating a goodness-of-fit of the estimation model. The results of soil depth estimation compared with the field survey data indicated close relationship between these two datasets. The results obtained from this study could spatially provide quantitative information of soil depths, which is an important indicator for assessing the overused slope land. The methods were thus proposed for retrieval of soil depths in Taiwan.

  14. Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping

    NASA Astrophysics Data System (ADS)

    Lucas, Richard; Rowlands, Aled; Brown, Alan; Keyworth, Steve; Bunting, Peter

    AimTo evaluate the use of time-series of Landsat sensor data acquired over an annual cycle for mapping semi-natural habitats and agricultural land cover. LocationBerwyn Mountains, North Wales, United Kingdom. MethodsUsing eCognition Expert, segmentation of the Landsat sensor data was undertaken for actively managed agricultural land based on Integrated Administration and Control System (IACS) land parcel boundaries, whilst a per-pixel level segmentation was undertaken for all remaining areas. Numerical decision rules based on fuzzy logic that coupled knowledge of ecology and the information content of single and multi-date remotely sensed data and derived products (e.g., vegetation indices) were developed to discriminate vegetation types based primarily on inferred differences in phenology, structure, wetness and productivity. ResultsThe rule-based classification gave a good representation of the distribution of habitats and agricultural land. The more extensive, contiguous and homogeneous habitats could be mapped with accuracies exceeding 80%, although accuracies were lower for more complex environments (e.g., upland mosaics) or those with broad definition (e.g., semi-improved grasslands). Main conclusionsThe application of a rule-based classification to temporal imagery acquired over selected periods within an annual cycle provides a viable approach for mapping and monitoring of habitats and agricultural land in the United Kingdom that could be employed operationally.

  15. LAND USE LAND COVER (LULC) - US GEOLOGICAL SURVEY

    EPA Science Inventory

    The National Mapping Program, a component of the U. S. Geological Survey (USGS), produces and distributes land use and land cover maps and digitized data for the conterminous U.S. and Hawaii. Land use refers to the human activities that are directly related to the land. The int...

  16. Korean coastal water depth/sediment and land cover mapping (1:25,000) by computer analysis of LANDSAT imagery

    NASA Technical Reports Server (NTRS)

    Park, K. Y.; Miller, L. D.

    1978-01-01

    Computer analysis was applied to single date LANDSAT MSS imagery of a sample coastal area near Seoul, Korea equivalent to a 1:50,000 topographic map. Supervised image processing yielded a test classification map from this sample image containing 12 classes: 5 water depth/sediment classes, 2 shoreline/tidal classes, and 5 coastal land cover classes at a scale of 1:25,000 and with a training set accuracy of 76%. Unsupervised image classification was applied to a subportion of the site analyzed and produced classification maps comparable in results in a spatial sense. The results of this test indicated that it is feasible to produce such quantitative maps for detailed study of dynamic coastal processes given a LANDSAT image data base at sufficiently frequent time intervals.

  17. Improved land cover mapping using high resolution multiangle 8-band WorldView-2 satellite remote sensing data

    NASA Astrophysics Data System (ADS)

    Jawak, Shridhar D.; Luis, Alvarinho J.

    2013-01-01

    Here, we discuss the improvements in urban classification that were made using the spatial-spectral-angular information from a WorldView-2 (WV-2) multiangle image sequence. In this study, we evaluate the use of multiangle high resolution WV-2 panchromatic (PAN) and multispectral image (MSI) data for extracting urban geospatial information. Current multiangular WV-2 data were classified into misclassification-prone surfaces, such as vegetation, water bodies, and man-made features, using a cluster of normalized difference spectral index ratios (SIR). A novel multifold methodology protocol was designed to estimate the consequences of multiangularity and germane PAN-sharpening algorithms on the spectral characteristics (distortions) of satellite data and on the resulting land use/land cover (LU/LC) mapping using an array of SIRs. Eight existing PAN-sharpening algorithms were used for data fusion, followed by estimation of multiple SIRs to mitigate spectral distortions arising from the multiangularity of the data. This research highlights the benefits of using traditional PAN-sharpening techniques with a specific set of SIRs on land cover mapping based on five available tiles of satellite data. The research provides a method to overcome the atmospherically triggered spectral distortions of multiangular acquisitions, which will facilitate better mapping and understanding of the earth's surface.

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

    NASA Astrophysics Data System (ADS)

    Sah, Shagan

    An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the

  19. Korean coastal water depth/sediment and land cover mapping /1:25,000/ by computer analysis of Landsat imagery

    NASA Technical Reports Server (NTRS)

    Park, K. Y.; Miller, L. D.

    1980-01-01

    Computer analysis was applied to single data Landsat MSS imagery of a coastal area near Seoul, Korea equivalent to a 1:50,000 topographic map, and featuring large dynamic sediment transport processes. Supervised image processing yielded a test classification map containing five water depth/sediment classes, two shoreline/tidal classes and five coastal land cover classes at a scale of 1:25,000 and with a training set accuracy of 76%; the training sets were selected by direct examination of the digitally displayed imagery. The unsupervised ISOCLAS (Senkus, 1976) clustering analysis was performed to assess the relative value of this approach to image classification in areas of sparse or nonexistent ground control. Results indicate that it is feasible to produce quantitative maps for detailed study of dynamic coastal processes given a Landsat image data base at sufficiently frequent time intervals.

  20. Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

    NASA Astrophysics Data System (ADS)

    Bassa, Zaakirah; Bob, Urmilla; Szantoi, Zoltan; Ismail, Riyad

    2016-01-01

    In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the "one-against-one" and "one-against-all" combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (>80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) >80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

  1. Land Cover Mapping for the Development of Green House Gas (GHG) Inventories in the Eastern and Southern Africa Region

    NASA Astrophysics Data System (ADS)

    Wakhayanga, J. A.; Oduor, P.; Korme, T.; Farah, H.; Limaye, A. S.; Irwin, D.; Artis, G.

    2014-12-01

    Anthropogenic activities are responsible for the largest share of green house gas (GHG) emissions. Research has shown that greenhouse gases cause radioactive forcing in the stratosphere, leading to ozone depletion. Different land cover types act as sources or sinks of carbon dioxide (CO2), the most dominant GHG.Under the oversight of the United Nations Framework Convention on Climate Change (UNFCCC) the Eastern and Southern Africa (ESA) region countries are developing Sustainable National GHG Inventory Management Systems. While the countries in the ESA region are making substantial progress in setting up GHG inventories, there remains significant constraints in the development of quality and sustainable National GHG Inventory Systems. For instance, there are fundamental challenges in capacity building and technology transfer, which can affect timely and consistent reporting on the land use, land-use change and forestry (LULUCF) component of the GHG inventory development. SERVIR Eastern and Southern Africa is a partnership project between the National Aeronautics and Space Administration (NASA) and the Regional Center for Mapping of Resources for Development (RCMRD), an intergovernmental organization in Africa, with 21 member states in the ESA region. With support from the United States Agency for International Development (USAID), SERVIR ESA is implementing the GHG Project in 9 countries. The main deliverables of the project are land cover maps for the years 2000 and 2010 (also 1990 for Malawi and Rwanda), and related technical reports, as well as technical training in land cover mapping using replicable methodologies. Landsat imagery which is freely available forms the main component of earth observation input data, in addition to ancillary data collected from each country. Supervised classification using maximum likelihood algorithm is applied to the Landsat images. The work is completed for the initial 6 countries (Malawi, Zambia, Rwanda, Tanzania, Botswana, and

  2. Mapping socio-economic scenarios of land cover change: a GIS method to enable ecosystem service modelling.

    PubMed

    Swetnam, R D; Fisher, B; Mbilinyi, B P; Munishi, P K T; Willcock, S; Ricketts, T; Mwakalila, S; Balmford, A; Burgess, N D; Marshall, A R; Lewis, S L

    2011-03-01

    We present a GIS method to interpret qualitatively expressed socio-economic scenarios in quantitative map-based terms. (i) We built scenarios using local stakeholders and experts to define how major land cover classes may change under different sets of drivers; (ii) we formalized these as spatially explicit rules, for example agriculture can only occur on certain soil types; (iii) we created a future land cover map which can then be used to model ecosystem services. We illustrate this for carbon storage in the Eastern Arc Mountains of Tanzania using two scenarios: the first based on sustainable development, the second based on 'business as usual' with continued forest-woodland degradation and poor protection of existing forest reserves. Between 2000 and 2025 4% of carbon stocks were lost under the first scenario compared to a loss of 41% of carbon stocks under the second scenario. Quantifying the impacts of differing future scenarios using the method we document here will be important if payments for ecosystem services are to be used to change policy in order to maintain critical ecosystem services. PMID:20932636

  3. Phreatophytic land-cover map of the northern and central Great Basin Ecoregion: California, Idaho, Nevada, Utah, Oregon, and Wyoming

    USGS Publications Warehouse

    Mathie, Amy M.; Welborn, Toby L.; Susong, David D.; Tumbusch, Mary L.

    2011-01-01

    Increasing water use and changing climate in the Great Basin of the western United States are likely affecting the distribution of phreatophytic vegetation in the region. Phreatophytic plant communities that depend on groundwater are susceptible to natural and anthropogenic changes to hydrologic flow systems. The purpose of this report is to document the methods used to create the accompanying map that delineates areas of the Great Basin that have the greatest potential to support phreatophytic vegetation. Several data sets were used to develop the data displayed on the map, including Shrub Map (a land-cover data set derived from the Regional Gap Analysis Program) and Gap Analysis Program (GAP) data sets for California and Wyoming. In addition, the analysis used the surface landforms from the U.S. Geological Survey (USGS) Global Ecosystems Mapping Project data to delineate regions of the study area based on topographic relief that are most favorable to support phreatophytic vegetation. Using spatial analysis techniques in a GIS, phreatophytic vegetation classes identified within Shrub Map and GAP were selected and compared to the spatial distribution of selected landforms in the study area to delineate areas of phreatophyte vegetation. Results were compared to more detailed studies conducted in selected areas. A general qualitative description of the data and the limitations of the base data determined that these results provide a regional overview but are not intended for localized studies or as a substitute for detailed field analysis. The map is intended as a decision-support aide for land managers to better understand, anticipate, and respond to ecosystem changes in the Great Basin.

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

    NASA Technical Reports Server (NTRS)

    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.

  5. A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

    NASA Astrophysics Data System (ADS)

    Li, Xiaodong; Ling, Feng; Du, Yun; Feng, Qi; Zhang, Yihang

    2014-07-01

    The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial-temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the

  6. Automatic cloud cover mapping.

    NASA Technical Reports Server (NTRS)

    Strong, J. P., III; Rosenfeld, A.

    1971-01-01

    A method of converting a picture into a 'cartoon' or 'map' whose regions correspond to differently textured regions is described. Texture edges in the picture are detected, and solid regions surrounded by these (usually broken) edges are 'colored in' using a propagation process. The resulting map is cleaned by comparing the region colors with the textures of the corresponding regions in the picture, and also by merging some regions with others according to criteria based on topology and size. The method has been applied to the construction of cloud cover maps from cloud cover pictures obtained by satellites.

  7. Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha forest reserve, Nigeria

    NASA Astrophysics Data System (ADS)

    Olokeogun, O. S.; Iyiola, K.; Iyiola, O. F.

    2014-11-01

    Mapping of LULC and change detection using remote sensing and GIS techniques is a cost effective method of obtaining a clear understanding of the land cover alteration processes due to land use change and their consequences. This research focused on assessing landscape transformation in Shasha Forest Reserve, over an 18 year period. LANDSAT Satellite imageries (of 30 m resolution) covering the area at two epochs were characterized into five classes (Water Body, Forest Reserve, Built up Area, Vegetation, and Farmland) and classification performs with maximum likelihood algorithm, which resulted in the classes of each land use. The result of the comparison of the two classified images showed that vegetation (degraded forest) has increased by 30.96 %, farmland cover increased by 22.82 % and built up area by 3.09 %. Forest reserve however, has decreased significantly by 46.12 % during the period. This research highlights the increasing rate of modification of forest ecosystem by anthropogebic activities and the need to apprehend the situation to ensure sustainable forest management.

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Object Based Agricultural Land Cover Classification Map of Shadowed Areas from Aerial Image and LIDAR Data Using Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Alberto, R. T.; Serrano, S. C.; Damian, G. B.; Camaso, E. E.; Celestino, A. B.; Hernando, P. J. C.; Isip, M. F.; Orge, K. M.; Quinto, M. J. C.; Tagaca, R. C.

    2016-06-01

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

  10. Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa

    NASA Astrophysics Data System (ADS)

    Vaglio Laurin, Gaia; Liesenberg, Veraldo; Chen, Qi; Guerriero, Leila; Del Frate, Fabio; Bartolini, Antonio; Coomes, David; Wilebore, Beccy; Lindsell, Jeremy; Valentini, Riccardo

    2013-04-01

    The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset.

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

    NASA Astrophysics Data System (ADS)

    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.

  12. Mapping Land Cover and Land Use Changes in the Congo Basin Forests with Optical Satellite Remote Sensing: a Pilot Project Exploring Methodologies that Improve Spatial Resolution and Map Accuracy

    NASA Astrophysics Data System (ADS)

    Molinario, G.; Baraldi, A.; Altstatt, A. L.; Nackoney, J.

    2011-12-01

    The University of Maryland has been a USAID Central Africa Rregional Program for the Environment (CARPE) cross-cutting partner for many years, providing remote sensing derived information on forest cover and forest cover changes in support of CARPE's objectives of diminishing forest degradation, loss and biodiversity loss as a result of poor or inexistent land use planning strategies. Together with South Dakota State University, Congo Basin-wide maps have been provided that map forest cover loss at a maximum of 60m resolution, using Landsat imagery and higher resolution imagery for algorithm training and validation. However, to better meet the needs within the CARPE Landscapes, which call for higher resolution, more accurate land cover change maps, UMD has been exploring the use of the SIAM automatic spectral -rule classifier together with pan-sharpened Landsat data (15m resolution) and Very High Resolution imagery from various sources. The pilot project is being developed in collaboration with the African Wildlife Foundation in the Maringa Lopori Wamba CARPE Landscape. If successful in the future this methodology will make the creation of high resolution change maps faster and easier, making it accessible to other entities in the Congo Basin that need accurate land cover and land use change maps in order, for example, to create sustainable land use plans, conserve biodiversity and resources and prepare Reducing Emissions from forest Degradation and Deforestation (REDD) Measurement, Reporting and Verification (MRV) projects. The paper describes the need for higher resolution land cover change maps that focus on forest change dynamics such as the cycling between primary forests, secondary forest, agriculture and other expanding and intensifying land uses in the Maringa Lopori Wamba CARPE Landscape in the Equateur Province of the Democratic Republic of Congo. The Methodology uses the SIAM remote sensing imagery automatic spectral rule classifier, together with pan

  13. Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

    NASA Astrophysics Data System (ADS)

    Poursanidis, Dimitris; Chrysoulakis, Nektarios; Mitraka, Zina

    2015-03-01

    An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.

  14. Land cover/use mapping using multi-band imageries captured by Cropcam Unmanned Aerial Vehicle Autopilot (UAV) over Penang Island, Malaysia

    NASA Astrophysics Data System (ADS)

    Fuyi, Tan; Boon Chun, Beh; Mat Jafri, Mohd Zubir; Hwee San, Lim; Abdullah, Khiruddin; Mohammad Tahrin, Norhaslinda

    2012-11-01

    The problem of difficulty in obtaining cloud-free scene at the Equatorial region from satellite platforms can be overcome by using airborne imagery. Airborne digital imagery has proved to be an effective tool for land cover studies. Airborne digital camera imageries were selected in this present study because of the airborne digital image provides higher spatial resolution data for mapping a small study area. The main objective of this study is to classify the RGB bands imageries taken from a low-altitude Cropcam UAV for land cover/use mapping over USM campus, penang Island, Malaysia. A conventional digital camera was used to capture images from an elevation of 320 meter on board on an UAV autopilot. This technique was cheaper and economical compared with other airborne studies. The artificial neural network (NN) and maximum likelihood classifier (MLC) were used to classify the digital imageries captured by using Cropcam UAV over USM campus, Penang Islands, Malaysia. The supervised classifier was chosen based on the highest overall accuracy (<80%) and Kappa statistic (<0.8). The classified land cover map was geometrically corrected to provide a geocoded map. The results produced by this study indicated that land cover features could be clearly identified and classified into a land cover map. This study indicates the use of a conventional digital camera as a sensor on board on an UAV autopilot can provide useful information for planning and development of a small area of coverage.

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

    NASA Astrophysics Data System (ADS)

    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.

  16. Land Use and Land Cover Change

    SciTech Connect

    Brown, Daniel; Polsky, Colin; Bolstad, Paul V.; Brody, Samuel D.; Hulse, David; Kroh, Roger; Loveland, Thomas; Thomson, Allison M.

    2014-05-01

    A contribution to the 3rd National Climate Assessment report, discussing the following key messages: 1. Choices about land-use and land-cover patterns have affected and will continue to affect how vulnerable or resilient human communities and ecosystems are to the effects of climate change. 2. Land-use and land-cover changes affect local, regional, and global climate processes. 3. Individuals, organizations, and governments have the capacity to make land-use decisions to adapt to the effects of climate change. 4. Choices about land use and land management provide a means of reducing atmospheric greenhouse gas levels.

  17. Land cover trends dataset, 1973-2000

    USGS Publications Warehouse

    Soulard, Christopher E.; Acevedo, William; Auch, Roger F.; Sohl, Terry L.; Drummond, Mark A.; Sleeter, Benjamin M.; Sorenson, Daniel G.; Kambly, Steven; Wilson, Tamara S.; Taylor, Janis L.; Sayler, Kristi L.; Stier, Michael P.; Barnes, Christopher A.; Methven, Steven C.; Loveland, Thomas R.; Headley, Rachel; Brooks, Mark S.

    2014-01-01

    The U.S. Geological Survey Land Cover Trends Project is releasing a 1973–2000 time-series land-use/land-cover dataset for the conterminous United States. The dataset contains 5 dates of land-use/land-cover data for 2,688 sample blocks randomly selected within 84 ecological regions. The nominal dates of the land-use/land-cover maps are 1973, 1980, 1986, 1992, and 2000. The land-use/land-cover maps were classified manually from Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus imagery using a modified Anderson Level I classification scheme. The resulting land-use/land-cover data has a 60-meter resolution and the projection is set to Albers Equal-Area Conic, North American Datum of 1983. The files are labeled using a standard file naming convention that contains the number of the ecoregion, sample block, and Landsat year. The downloadable files are organized by ecoregion, and are available in the ERDAS IMAGINETM (.img) raster file format.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Technical Reports Server (NTRS)

    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. Forum on land use and land Cover: Summary report

    USGS Publications Warehouse

    U.S. Environmental Protection Agency; U.S. Geological Survey

    1992-01-01

    This report includes the agenda and abstracts of presentations from the Forum on Land Use and Land Cover Data, cohosted by the U. S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA), February 25-27,1992 at the USGS National Center in Reston, Virginia. The Forum was conducted under the auspices of the Federal Geographic Data Committee (FGDC) and was attended by Federal and State managers of programs that produce and use land use and land cover maps and data in support of environmental analysis, monitoring, and policy development. The goal was to improve opportunities for Federal and State coordination, information exchange, data sharing, and work sharing in land use and land cover mapping.

  1. Mapping the invasive species, Chinese tallow, with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

    USGS Publications Warehouse

    Ramsey, Elijah W., III; Rangoonwala, A.; Nelson, G.; Ehrlich, R.

    2005-01-01

    Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress-tupelo forest, senescing Chinese tallow with red leaves ('red tallow'), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress-tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non-active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs. ?? 2005 US Government.

  2. How Scientists Differentiate Between Land Cover Types

    NASA Technical Reports Server (NTRS)

    2002-01-01

    Before scientists can transform raw satellite image data into land cover maps, they must decide on what categories of land cover they would like to use. Categories are simply the types of landscape that the scientists are trying to map and can vary greatly from map to map. For flood maps, there may be only two categories-dry land and wet land-while a standard global land cover map may have seventeen categories including closed shrub lands, savannas, evergreen needle leaf forest, urban areas, and ice/snow. The only requirement for any land cover category is that it have a distinct spectral signature that a satellite can record. As can be seen through a prism, many different colors (wavelengths) make up the spectra of sunlight. When sunlight strikes objects, certain wavelengths are absorbed and others are reflected or emitted. The unique way in which a given type of land cover reflects and absorbs light is known as its spectral signature. Anyone who has flown over the midwestern United States has seen evidence of this phenomenon. From an airplane window, the ground appears as a patchwork of different colors formed by the fields of crops planted there. The varying pigments of the leaves, the amount of foliage per square foot, the age of the plants, and many other factors create this tapestry. Most imaging satellites are sensitive to specific wavelengths of light, including infrared wavelengths that cannot be seen with the naked eye. Passive satellite remote sensors-such as those flown on Landsat 5, Landsat 7, and Terra-have a number of light detectors (photoreceptors) on board that measure the energy reflected or emitted by the Earth. One light detector records only the blue part of the spectrum coming off the Earth. Another observes all the yellow-green light and still another picks up on all the near-infrared light. The detectors scan the Earth's surface as the satellite travels in a circular orbit very nearly from pole-to-pole. To differentiate between types of

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

    NASA Astrophysics Data System (ADS)

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

    1997-12-01

    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

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

    USGS Publications Warehouse

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

  5. The Land Surface Temperature Impact to Land Cover Types

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  6. Land Use and Land Cover Analysis in Indian Context

    NASA Astrophysics Data System (ADS)

    Roy, P. S.; Giriraj, A.

    Information on land use/land cover in the form of maps and statistical data is very vital for spatial planning, management and utilization of land. Land-Use and Land-Cover (LULC) scenario in India has undergone a radical change since the onset of economic revolution in early 1990s. These changes involve a series of complex interaction between biophysical and socioeconomic variables. LULC follows a set of scientific themes which includes detection and monitoring, carbon and biogeochemical cycle, ecosystems and biodiversity, water and energy cycle, predictive land use modeling and climate variability and change. With the changing times and increasing demand on the availability of information on land use/land cover, it becomes necessary to have a standard classification system, precise definition of land use/land cover and its categories, uniform procedures of data collection and mapping on different scales over Indian region. The current review thus attempts to focus on development of a national goal towards changes in LULC as a necessary step for an interdisciplinary research program involving climate, ecological and socioeconomic drives, the processes of change and the responses and consequences of change.

  7. Accuracy levels of land cover classified maps derived from mid and high spatial resolution remote sensing data

    NASA Astrophysics Data System (ADS)

    Brown, Bonnie J.

    This dissertation compares the accuracy of results of classifying data from mid-level to very high spatial resolutions (Landsat ETM+, SPOT 4, ASTER, SPOT 5, and QuickBird). Data from all of these sensors were classified for both urban and rural settings. The dissertation also examines accuracy levels between spectral and radiometric resolutions. Finally, it investigates the role that shadow plays in affecting accuracy levels from higher spatial resolution satellites. To compare as to whether there were significant differences in the accuracy levels between different sensors, each map's accuracy percentages were analyzed using Z-scores and kappa as described in the methodology section. QuickBird, with the highest spatial resolution, performed significantly more poorly in terms of providing accurate classification than any other sensor with respect to the rural environment. It also was significantly worse than Landsat ETM+ in providing accurate classification in the urban environment. In order to control for radiometric resolution, the 11-bit QuickBird data were converted to 8-bit data since QuickBird is the only sensor that does not have the same radiometric resolution. The resulting classification accuracy percentages were no better than that of random chance. When testing for accuracy in classification using only the three bands common to all sensors (green, red, and near-infrared) the result was there was essentially no difference between any of the sensors. This outcome supports the hypothesis that spectral resolution plays an important role in land cover accuracy. Using simple linear regression, the relationship between the percentage of shadow pixels and spatial resolution is examined. There is a moderate relationship between the spatial resolution of sensors and the percentages of shadow pixels where sensors with higher spatial resolution have a higher percentage of shadow pixels. These results agreed with literature from other studies in similar environments.

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

    USGS Publications Warehouse

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

    2011-01-01

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

  9. The National Land Cover Database

    USGS Publications Warehouse

    Homer, Collin H.; 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.

  10. Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the West African Sahel

    NASA Technical Reports Server (NTRS)

    Marsh, S. E.; Walsh, J. L.; Lee, C. T.; Beck, L. R.; Hutchinson, C. F.

    1992-01-01

    Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land-cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.

  11. Classifying Land Cover Using Spectral Signature

    NASA Astrophysics Data System (ADS)

    Alawiye, F. S.

    2012-12-01

    Studying land cover has become increasingly important as countries try to overcome the destruction of wetlands; its impact on local climate due to seasonal variation, radiation balance, and deteriorating environmental quality. In this investigation, we have been studying the spectral signatures of the Jamaica Bay wetland area based on remotely sensed satellite input data from LANDSAT TM and ASTER. We applied various remote sensing techniques to generate classified land cover output maps. Our classifiers relied on input from both the remote sensing and in-situ spectral field data. Based upon spectral separability and data collected in the field, a supervised and unsupervised classification was carried out. First results suggest good agreement between the land cover units mapped and those observed in the field.

  12. A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps

    USGS Publications Warehouse

    Wickham, J.D.; Stehman, S.V.; Smith, J.H.; Wade, T.G.; Yang, L.

    2004-01-01

    Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, within-cluster correlation may reduce the precision of the accuracy estimates. The detailed population information to quantify a priori the effect of within-cluster correlation on precision is typically unavailable. Consequently, a convenient, practical approach to evaluate the likely performance of a two-stage cluster sample is needed. We describe such an a priori evaluation protocol focusing on the spatial distribution of the sample by land-cover class across different cluster sizes and costs of different sampling options, including options not imposing clustering. This protocol also assesses the two-stage design's adequacy for estimating the precision of accuracy estimates for rare land-cover classes. We illustrate the approach using two large-area, regional accuracy assessments from the National Land-Cover Data (NLCD), and describe how the a priorievaluation was used as a decision-making tool when implementing the NLCD design.

  13. Urban land use/land cover mapping with high-resolution SAR imagery by integrating support vector machines into object-based analysis

    NASA Astrophysics Data System (ADS)

    Hu, Hongtao; Ban, Yifang

    2008-10-01

    This paper investigates the capability of high-resolution SAR data for urban landuse/land-cover mapping by integrating support vector machines (SVMs) into object-based analysis. Five-date RADARSAT fine-beam C-HH SAR images with a pixel spacing of 6.25 meter were acquired over the rural-urban fringe of the Great Toronto Area (GTA) during May to August in 2002. First, the SAR images were segmented using multi-resolution segmentation algorithm and two segmentation levels were created. Next, a range of spectral, shape and texture features were selected and calculated for all image objects on both levels. The objects on the lower level then inherited features of their super objects. In this way, the objects on the lower level received detailed descriptions about their neighbours and contexts. Finally, SVM classifiers were used to classify the image objects on the lower level based on the selected features. For training the SVM, sample image objects on the lower level were used. One-against-one approach was chosen to apply SVM to multiclass classification of SAR images in this research. The results show that the proposed method can achieve a high accuracy for the classification of high-resolution SAR images over urban areas.

  14. Land cover: national inventory of vegetation and land use

    USGS Publications Warehouse

    Gergely, Kevin J.; McKerrow, Alexa

    2013-01-01

    The Gap Analysis Program (GAP) produces data and tools that help meet critical national challenges such as biodiversity conservation, renewable energy development, climate change adaptation, and infrastructure investment. The GAP national land cover includes data on the vegetation and land-use patterns of the United States, including Alaska, Hawaii, and Puerto Rico. This national dataset combines land cover data generated by regional GAP projects with Landscape Fire and Resource Management Planning Tools (LANDFIRE) data. LANDFIRE is an interagency vegetation, fire, and fuel characteristics mapping program, sponsored by the U.S. Department of the Interior and the U.S. Department of Agriculture Forest Service.

  15. Land-cover change detection

    USGS Publications Warehouse

    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.

  16. Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

    NASA Astrophysics Data System (ADS)

    Mücher, C. A.; Roupioz, L.; Kramer, H.; Bogers, M. M. B.; Jongman, R. H. G.; Lucas, R. M.; Kosmidou, V. E.; Petrou, Z.; Manakos, I.; Padoa-Schioppa, E.; Adamo, M.; Blonda, P.

    2015-05-01

    A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of

  17. Changes in Land Use and Land Cover

    NASA Astrophysics Data System (ADS)

    Meyer, William B.; Turner, B. L., II

    1994-10-01

    This book deals with the relationship between land use and land cover: between human activities and the transformation of the Earth's surface. It describes the recent changes in the world's farmland, forests, grasslands and settlements, and the impacts of these changes on soil, water resources and the atmosphere. It explores what is known about the importance of various underlying human sources of land transformation: population growth, technological change, political-economic institutions, political structure, and attitudes and beliefs. Three working group reports outline important avenues for future research: the construction of a global land model, the division of the world into regional situations of land transformation, and a wiring diagram to structure the division of research among fields of study.

  18. Challenges in Global Land Use/Land Cover Change Modeling

    NASA Astrophysics Data System (ADS)

    Clarke, K. C.

    2011-12-01

    For the purposes of projecting and anticipating human-induced land use change at the global scale, much work remains in the systematic mapping and modeling of world-wide land uses and their related dynamics. In particular, research has focused on tropical deforestation, loss of prime agricultural land, loss of wild land and open space, and the spread of urbanization. Fifteen years of experience in modeling land use and land cover change at the regional and city level with the cellular automata model SLEUTH, including cross city and regional comparisons, has led to an ability to comment on the challenges and constraints that apply to global level land use change modeling. Some issues are common to other modeling domains, such as scaling, earth geometry, and model coupling. Others relate to geographical scaling of human activity, while some are issues of data fusion and international interoperability. Grid computing now offers the prospect of global land use change simulation. This presentation summarizes what barriers face global scale land use modeling, but also highlights the benefits of such modeling activity on global change research. An approach to converting land use maps and forecasts into environmental impact measurements is proposed. Using such an approach means that multitemporal mapping, often using remotely sensed sources, and forecasting can also yield results showing the overall and disaggregated status of the environment.

  19. Land Cover Analysis of Temperate Asia

    NASA Technical Reports Server (NTRS)

    Justice, Chris

    1998-01-01

    Satellite data from the advanced very high resolution radiometer (AVHRR) instrument were used to produce a general land cover distribution of temperate Asia (referred to hence as Central Asia) from 1982, starting with the NOAA-7 satellite, and continuing through 1991, ending with the NOAA-11 satellite. Emphasis was placed upon delineating the and and semi-arid zones of Central Asia (largely Mongolia and adjacent areas), mapping broad categories of aggregated land cover, and upon studying photosynthetic capacity increases in Central Asia from 1982 to 1991.

  20. The 1980 land cover for the Puget Sound region

    NASA Technical Reports Server (NTRS)

    Shinn, R. D.; Westerlund, F. V.; Eby, J. R.

    1982-01-01

    Both LANDSAT imagery and the video information communications and retrieval software were used to develop a land cover classifiction of the Puget Sound of Washington. Planning agencies within the region were provided with a highly accurate land cover map registered to the 1980 census tracts which could subsequently be incorporated as one data layer in a multi-layer data base. Many historical activities related to previous land cover mapping studies conducted in the Puget Sound region are summarized. Valuable insight into conducting a project with a large community of users and in establishing user confidence in a multi-purpose land cover map derived from LANDSAT is provided.

  1. Accuracy Assessment for the U.S. Geological Survey Regional Land-Cover Mapping Program: New York and New Jersey Region

    USGS Publications Warehouse

    Zhu, Zhi-Liang; Yang, Limin; Stehman, Stephen V.; Czaplewski, Raymond L.

    2000-01-01

    The U.S. Geological Survey, in cooperation with other government and private organizations, is producing a conterminous U.S. land-cover map using Landsat Thematic Mapper 30-meter data for the Federal regions designated by the U.S. Environmental Protection Agency. Accuracy assessment is to be conducted for each Federal region to estimate overall and class-specific accuracies. In Region 2, consisting of New York and New Jersey, the accuracy assessment was completed for 15 land-cover and land-use classes, using interpreted 1:40,000-scale aerial photographs as reference data. The methodology used for Region 2 features a two-stage, geographically stratified approach, with a general sample of all classes (1,033 sample sites), and a separate sample for rare classes (294 sample sites). A confidence index was recorded for each land-cover interpretation on the 1:40,000-scale aerial photography The estimated overall accuracy for Region 2 was 63 percent (standard error 1.4 percent) using all sample sites, and 75.2 percent (standard error 1.5 percent) using only reference sites with a high-confidence index. User's and producer's accuracies for the general sample and user's accuracy for the sample of rare classes, as well as variance for the estimated accuracy parameters, were also reported. Narrowly defined land-use classes and heterogeneous conditions of land cover are the major causes of misclassification errors. Recommendations for modifying the accuracy assessment methodology for use in the other nine Federal regions are provided.

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

    NASA Astrophysics Data System (ADS)

    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

  3. SOUTHWEST REGIONAL GAP LAND COVER

    EPA Science Inventory

    The Gap Analysis Program is a national inter-agency program that maps the distribution

    of plant communities and selected animal species and compares these distributions with land

    stewardship to identify gaps in biodiversity protection. GAP uses remote satellite imag...

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

    NASA Technical Reports Server (NTRS)

    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.

  5. Digital classification of Landsat data for vegetation and land-cover mapping in the Blackfoot River watershed, southeastern Idaho

    USGS Publications Warehouse

    Pettinger, L.R.

    1982-01-01

    This paper documents the procedures, results, and final products of a digital analysis of Landsat data used to produce a vegetation and landcover map of the Blackfoot River watershed in southeastern Idaho. Resource classes were identified at two levels of detail: generalized Level I classes (for example, forest land and wetland) and detailed Levels II and III classes (for example, conifer forest, aspen, wet meadow, and riparian hardwoods). Training set statistics were derived using a modified clustering approach. Environmental stratification that separated uplands from lowlands improved discrimination between resource classes having similar spectral signatures. Digital classification was performed using a maximum likelihood algorithm. Classification accuracy was determined on a single-pixel basis from a random sample of 25-pixel blocks. These blocks were transferred to small-scale color-infrared aerial photographs, and the image area corresponding to each pixel was interpreted. Classification accuracy, expressed as percent agreement of digital classification and photo-interpretation results, was 83.0:t 2.1 percent (0.95 probability level) for generalized (Level I) classes and 52.2:t 2.8 percent (0.95 probability level) for detailed (Levels II and III) classes. After the classified images were geometrically corrected, two types of maps were produced of Level I and Levels II and III resource classes: color-coded maps at a 1:250,000 scale, and flatbed-plotter overlays at a 1:24,000 scale. The overlays are more useful because of their larger scale, familiar format to users, and compatibility with other types of topographic and thematic maps of the same scale.

  6. Holocene land-cover reconstructions for studies on land cover-climate feedbacks

    NASA Astrophysics Data System (ADS)

    Gaillard, M.-J.; Sugita, S.; Mazier, F.; Kaplan, J. O.; Trondman, A.-K.; Broström, A.; Hickler, T.; Kjellström, E.; Kuneš, P.; Lemmen, C.; Olofsson, J.; Smith, B.; Strandberg, G.

    2010-03-01

    The major objectives of this paper are: (1) to review the pros and cons of the scenarios of past anthropogenic land cover change (ALCC) developed during the last ten years, (2) to discuss issues related to pollen-based reconstruction of the past land-cover and introduce a new method, REVEALS (Regional Estimates of VEgetation Abundance from Large Sites), to infer long-term records of past land-cover from pollen data, (3) to present a new project (LANDCLIM: LAND cover - CLIMate interactions in NW Europe during the Holocene) currently underway, and show preliminary results of REVEALS reconstructions of the regional land-cover in the Czech Republic for five selected time windows of the Holocene, and (4) to discuss the implications and future directions in climate and vegetation/land-cover modeling, and in the assessment of the effects of human-induced changes in land-cover on the regional climate through altered feedbacks. The existing ALCC scenarios show large discrepancies between them, and few cover time periods older than AD 800. When these scenarios are used to assess the impact of human land-use on climate, contrasting results are obtained. It emphasizes the need of REVEALS model-based land-cover reconstructions. They might help to fine-tune descriptions of past land-cover and lead to a better understanding of how long-term changes in ALCC might have influenced climate. The REVEALS model is proved to provide better estimates of the regional vegetation/land-cover changes than the traditional use of pollen percentages. Thus, the application of REVEALS opens up the possibility of achieving a more robust assessment of land cover at regional- to continental-spatial scale throughout the Holocene. We present maps of REVEALS estimates for the percentage cover of 10 plant functional types (PFTs) at 200 BP and 6000 BP, and of the two open-land PFTs "grassland" and "agricultural land" at five time-windows from 6000 BP to recent time. The LANDCLIM results are expected to

  7. Holocene land-cover reconstructions for studies on land cover-climate feedbacks

    NASA Astrophysics Data System (ADS)

    Gaillard, M.-J.; Sugita, S.; Mazier, F.; Trondman, A.-K.; Broström, A.; Hickler, T.; Kaplan, J. O.; Kjellström, E.; Kokfelt, U.; Kuneš, P.; Lemmen, C.; Miller, P.; Olofsson, J.; Poska, A.; Rundgren, M.; Smith, B.; Strandberg, G.; Fyfe, R.; Nielsen, A. B.; Alenius, T.; Balakauskas, L.; Barnekow, L.; Birks, H. J. B.; Bjune, A.; Björkman, L.; Giesecke, T.; Hjelle, K.; Kalnina, L.; Kangur, M.; van der Knaap, W. O.; Koff, T.; Lagerâs, P.; Latałowa, M.; Leydet, M.; Lechterbeck, J.; Lindbladh, M.; Odgaard, B.; Peglar, S.; Segerström, U.; von Stedingk, H.; Seppä, H.

    2010-07-01

    The major objectives of this paper are: (1) to review the pros and cons of the scenarios of past anthropogenic land cover change (ALCC) developed during the last ten years, (2) to discuss issues related to pollen-based reconstruction of the past land-cover and introduce a new method, REVEALS (Regional Estimates of VEgetation Abundance from Large Sites), to infer long-term records of past land-cover from pollen data, (3) to present a new project (LANDCLIM: LAND cover - CLIMate interactions in NW Europe during the Holocene) currently underway, and show preliminary results of REVEALS reconstructions of the regional land-cover in the Czech Republic for five selected time windows of the Holocene, and (4) to discuss the implications and future directions in climate and vegetation/land-cover modeling, and in the assessment of the effects of human-induced changes in land-cover on the regional climate through altered feedbacks. The existing ALCC scenarios show large discrepancies between them, and few cover time periods older than AD 800. When these scenarios are used to assess the impact of human land-use on climate, contrasting results are obtained. It emphasizes the need for methods such as the REVEALS model-based land-cover reconstructions. They might help to fine-tune descriptions of past land-cover and lead to a better understanding of how long-term changes in ALCC might have influenced climate. The REVEALS model is demonstrated to provide better estimates of the regional vegetation/land-cover changes than the traditional use of pollen percentages. This will achieve a robust assessment of land cover at regional- to continental-spatial scale throughout the Holocene. We present maps of REVEALS estimates for the percentage cover of 10 plant functional types (PFTs) at 200 BP and 6000 BP, and of the two open-land PFTs "grassland" and "agricultural land" at five time-windows from 6000 BP to recent time. The LANDCLIM results are expected to provide crucial data to reassess

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

    EPA Science Inventory

    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...

  9. Utilizing NASA Earth Observations to Assess Estuary Health and Enhance Management of Water Resources in Coastal Texas through Land Cover and Precipitation Mapping

    NASA Astrophysics Data System (ADS)

    Crepps, G.; Gonsoroski, E.; Lynn, T.; Schick, R.; Pereira da Silva, R.

    2015-12-01

    This project partnered with the National Park Service (NPS) to help analyze the correlation between mesquite trees and the salinity of the Laguna Madre of Padre Island National Seashore. The lagoon is a hypersaline estuary; however, there is historical evidence that this was not always the case. It is hypothesized that the increase in the number of honey mesquite trees (Prosopis grandulosa var. glandulosa) in the area has contributed to the Laguna Madre's increased salinity by decreasing the groundwater inflow to the lagoon. These mesquite trees have long taproots capable of extracting significant amounts of groundwater. This project utilized Earth observation data in ERDAS IMAGINE and ArcGIS software to create map time series and analyze the data. Landsat 5, 7, and 8 data were used to create land use/land cover (LULC) maps in order to analyze the change in the occurrence of mesquite trees over time. Thermal maps of the lagoon were generated using Landsat 5, 7, and 8 data to understand changes in groundwater inflow. In addition, TRMM and GRACE derived changes in root zone soil moisture content data were compared over the study period. By investigating the suspected positive correlation between the mesquite trees and the salinity of the Laguna Madre, the NPS can improve future land management practices.

  10. Land use/land cover mapping (1:25000) of Taiwan, Republic of China by automated multispectral interpretation of LANDSAT imagery

    NASA Technical Reports Server (NTRS)

    Sung, Q. C.; Miller, L. D.

    1977-01-01

    Three methods were tested for collection of the training sets needed to establish the spectral signatures of the land uses/land covers sought due to the difficulties of retrospective collection of representative ground control data. Computer preprocessing techniques applied to the digital images to improve the final classification results were geometric corrections, spectral band or image ratioing and statistical cleaning of the representative training sets. A minimal level of statistical verification was made based upon the comparisons between the airphoto estimates and the classification results. The verifications provided a further support to the selection of MSS band 5 and 7. It also indicated that the maximum likelihood ratioing technique can achieve more agreeable classification results with the airphoto estimates than the stepwise discriminant analysis.

  11. Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression

    PubMed Central

    Mallinis, Georgios; Koutsias, Nikos

    2008-01-01

    Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral classification method of a LANDSAT-5 TM imagery that uses several binomial logistic regression models was developed, evaluated and compared to the familiar parametric maximum likelihood algorithm. The classification approach based on logistic regression modelling was extended to a contextual one by using autocovariates to consider spatial dependencies of every pixel with its neighbours. Finally, the maximum likelihood algorithm was upgraded to contextual by considering typicality, a measure which indicates the strength of class membership. The use of logistic regression for broad-scale land cover classification presented higher overall accuracy (75.61%), although not statistically significant, than the maximum likelihood algorithm (64.23%), even when the latter was refined following a spatial approach based on Mahalanobis distance (66.67%). However, the consideration of the spatial autocovariate in the logistic models significantly improved the fit of the models and increased the overall accuracy from 75.61% to 80.49%.

  12. Land Cover Indicators for U.S. National Climate Assessments

    NASA Astrophysics Data System (ADS)

    Channan, S.; Thomson, A. M.; Collins, K. M.; Sexton, J. O.; Torrens, P.; Emanuel, W. R.

    2014-12-01

    Land is a critical resource for human habitat and for the vast majority of human activities. Many natural resources are derived from terrestrial ecosystems or otherwise extracted from the landscape. Terrestrial biodiversity depends on land attributes as do people's perceptions of the value of land, including its value for recreation or tourism. Furthermore, land surface properties and processes affect weather and climate, and land cover change and land management affect emissions of greenhouse gases. Thus, land cover with its close association with climate is so pervasive that a land cover indicator is of fundamental importance to U.S. national climate assessments and related research. Moderate resolution remote sensing products (MODIS) were used to provide systematic data on annual distributions of land cover over the period 2001-2012. Selected Landsat observations and data products further characterize land cover at higher resolution. Here we will present the prototype for a suite of land cover indicators including land cover maps as well as charts depicting attributes such as composition by land cover class, statistical indicators of landscape characteristics, and tabular data summaries indispensable for communicating the status and trends of U.S. land cover at national, regional and state levels.

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

    NASA Astrophysics Data System (ADS)

    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

  14. Estimating Landscape Pattern Metrics from a Sample of Land Cover

    EPA Science Inventory

    Although landscape pattern metrics can be computed directly from wall-to-wall land-cover maps, statistical sampling offers a practical alternative when complete coverage land-cover information is unavailable. Partitioning a region into spatial units (“blocks”) to create a samplin...

  15. 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.

    NASA Astrophysics Data System (ADS)

    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

  16. AVHRR channel selection for land cover classification

    USGS Publications Warehouse

    Maxwell, S.K.; Hoffer, R.M.; Chapman, P.L.

    2002-01-01

    Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized inter-annual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more

  17. A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

    USGS Publications Warehouse

    Giri, C.; Zhu, Z.; Reed, B.

    2005-01-01

    Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced

  18. Seasat SAR identification of dry climate urban land cover

    NASA Technical Reports Server (NTRS)

    Henderson, F. M.; Wharton, S. W.

    1980-01-01

    Digitally processed Seasat synthetic aperture radar (SAR) imagery of the Denver, Colorado area was examined to assess its potential for mapping urban land cover and the compatibility of SAR derived classes with those described in the U.S. Geological Survey classification system. The entire scene was interpreted to generate a small-scale land cover map. In addition, six subscene enlargements representative of urban land cover categories extant in the area were used as test sites for detailed analysis of land cover types. Two distinct approaches were employed and compared in examining the imagery - a visual interpretation of black-and-white positive transparencies and an automated-machine/visual interpretation. The latter used the Image 100 interactive image analysis system to generate land cover classes by density level slicing of the image frequency histogram.

  19. Seasonal land-cover regions of the US

    USGS Publications Warehouse

    Loveland, Thomas R.; Merchant, James W.; Brown, Jesslyn F.; Ohlen, Donald O.; Reed, Bradley C.; Olson, Paul; Hutchinson, John

    1995-01-01

    Global-change investigations have been hindered by deficiencies in the availability and quality of land-cover data. The US Geological Survey and the University of Nebraska-Lincoln have collaborated on the development of a new approach to land-cover characterization that attempts to address requirements of the global-change research community and others interested in regional patterns of land cover. An experimental 1-km-resolution database of land-cover characteristics for the coterminous US has been prepared to test and evaluate the approach. Using multidate Advanced Very High Resolution Radiometer (AVHRR) satellite data complemented by elevation, climate, ecoregions, and other digital spatial datasets, the authors define 15?? seasonal land-cover regions. Data are used in the construction of an illustrative 1:7500 000-scale map of the seasonal land-cover regions as well as of smaller-scale maps portraying general land cover and seasonality. The seasonal land-cover characteristics database can also be tailored to provide a broad range of other landscape parameters useful in national and global-scale environmental modeling and assessment. -from Authors

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

    PubMed

    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. PMID:21229303

  1. A combined segmentation and pixel-based classification approach of QuickBird imagery for land cover mapping

    NASA Astrophysics Data System (ADS)

    Wang, Jianmei; Li, Deren; Qin, Wenzhong

    2005-10-01

    Recent advances in remote-sensing technology suggest that satellite-based earth observation (EO) has great potential for providing and updating spatial information in a timely and cost-effective manner. However, with the improvement of the spatial resolution of satellite image, the detail of the image has become more complicated. Even though texture features included for multi-spectral high-resolution satellite imagery, conventional methods for pixel-based classification have limited success. In order to take better advantage of spatial information of high-resolution satellite imagery, a combined segmentation and pixel-based classification approach is presented in this paper. Firstly, pixel-based multi-spectral maximum-likelihood classification approach obtains initial classification result. Secondly, image segmentation is created by watershed transform and region merging. Finally, based on the proportions of each class present in each segment obtain final classification map. A QuickBird imagery of the suburban area of Shanghai in China is used to validate the proposed method. Experiment proves that classification map produced by the combined approach, is visual noise-free, has clean borders, and has better classification accuracy than that by pixel-based classification approach.

  2. Sample design for estimating change in land use and land cover ( Pennsylvania).

    USGS Publications Warehouse

    Rosenfield, G.H.

    1982-01-01

    The methodology of sample design which is applied to estimating change in land use and land cover is general and extendable to determination of change in any type of thematic mapping that is time variant. Land-use maps of the State of Pennsylvania at a scale of 1:250,000 were compiled circa 1958 with land use classified into six categories. The more detailed land-use and land-cover mapping of the State of Pennsylvania at a scale of 1:250,000 was completed by the U.S. Geological Survey circa 1977. With some rearrangement of these categories, the recent maps are very nearly compatible with a combination of five categories of the earlier maps. -from Author

  3. Monitoring urban land cover change by updating the national land cover database impervious surface products

    USGS Publications Warehouse

    Xian, G.; Homer, C.

    2009-01-01

    The U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 is widely used as a baseline for national land cover and impervious conditions. To ensure timely and relevant data, it is important to update this base to a more recent time period. A prototype method was developed to update the land cover and impervious surface by 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 from both 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, impervious surface was estimated for areas of change by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain a variety of metropolitan areas. Results from the five study areas show that the vast majority of impervious surface changes associated with urban developments were accurately captured and updated. The approach optimizes mapping efficiency and can provide users a flexible method to generate updated impervious surface at national and regional scales. ?? 2009 IEEE.

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

    NASA Technical Reports Server (NTRS)

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

    2000-01-01

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

  5. Spatial relationship between landslide occurrence and land cover

    NASA Astrophysics Data System (ADS)

    Lu, P.

    2013-12-01

    Landslide represents a major type of natural hazards. It may leave great threat to human lives as well as infrastructures. In this study, we tried to understand the spatial relationship between landslide occurrences and land cover types through spatial statistics. The approach was based on the bivariate K-functions which can be used to analyze whether there is spatial clustering, repelling or randomness for landslide occurring in areas within different land covers. The Arno River basin in central Italy was chosen as the study area because the landslide inventory is complete with acquired records of more than 27,000 events. According to the inventory, we divided landslides into four classifications according to their types: slides, sofluctions, falls and flows. The land cover data was derived from the CORINE Land Cover map. The land cover types of artificial lands, natural and forest areas, and agriculture lands were focused on. The results indicate that landslides tend to occur in a clustering way within both three land covers. The difference is from the clustering level and spatial dependence distance. Therefore, no evidence can be found that the spatial pattern of landslide occurrence is dependent on changes of land covers.

  6. Spatial assessment of land surface temperature and land use/land cover in Langkawi Island

    NASA Astrophysics Data System (ADS)

    Abu Bakar, Suzana Binti; Pradhan, Biswajeet; Salihu Lay, Usman; Abdullahi, Saleh

    2016-06-01

    This study investigates the relationship between Land Surface Temperature and Land Use/Land Cover in Langkawi Island by using Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-Up Index (NDBI) and Modified Normalized Difference Water Index (MNDWI) qualitatively by using Landsat 7 ETM+ and Landsat 8 (OLI/TIRS) over the period 2002 and 2015. Pixel-based classifiers Maximum Likelihood (MLC) and Support Vector Machine (SVM), has been performed to prepare the Land Use/ Land Cover map (LU/LC) and the result shows that Support Vector Machine (SVM) achieved maximum accuracy with 90% and 90.46% compared to Maximum Likelihood (MLC) classifier with 86.62% and 86.98% respectively. The result revealed that as the impervious surface (built-up /roads) increases, the surface temperature of the area increased. However, land surface temperature decreased in the vegetated areas. Based from the linear regression between LST and NDVI, NDBI and MNDWI, these indices can be used as an indicator to monitor the impact of Land Use/Land Cover on Land Surface Temperature.

  7. Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection

    NASA Astrophysics Data System (ADS)

    Gao, Jay; Liu, Yansui

    2010-02-01

    Tongyu County in Northeast China is highly prone to land degradation due to its fragile physical settings characterized by a flat topography, a semi-arid climate, and a shallow groundwater table. This study aims to determine the causes of land degradation through detecting the long-term trend of land cover changes. Degraded lands were mapped from satellite images recorded in 1992 and 2002. These land cover maps revealed that the area subject to land degradation in the form of soil salinization, waterlogging and desertification increased from 2400 to 4214 km 2, in sharp contrast to most severely degraded land that decreased by 122.5 km 2. Newly degraded land stems from productive farmland (263 km 2), harvested farmland (551 km 2), and grassland (468 km 2). Therefore, the worsened degradation situation is attributed to excessive reclamation of grassland for farming, over cultivation, overgrazing, and deforestation. Mechanical, biological, ecological and engineering means should be adopted to rehabilitate the degraded land.

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Using Landsat Thematic Mapper records to map land cover change and the impacts of reforestation programmes in the borderlands of southeast Yunnan, China: 1990-2010

    NASA Astrophysics Data System (ADS)

    Zhang, Jialong; Pham, Thi-Thanh-Hiên; Kalacska, Margaret; Turner, Sarah

    2014-09-01

    At the beginning of the new millennium, after a severe drought and destructive floods along the Yangtze River, the Chinese government implemented two large ecological rehabilitation and reforestation projects: the Natural Forest Protection Programme and the Sloping Land Conversion Programme. Using Landsat data from a decade before, during and after the inception of these programmes, we analyze their impacts along with other policies on land use, land cover change (LULCC) in southwest China. Our goal is to quantify the predominant land cover changes in four borderland counties, home to tens of thousands of ethnic minority individuals. We do this in three time stages (1990, 2000 and 2010). We use support vector machines as well as a transition matrix to monitor the land cover changes. The land cover classifications resulted in an overall accuracy and Kappa coefficient for forested area and cropland of respectively 91% (2% confidence interval) and 0.87. Our results suggest that the total forested area observed increased 3% over this 20-year period, while cropland decreased slightly (0.1%). However, these changes varied over specific time periods: forested area decreased between 1990 and 2000 and then increased between 2000 and 2010. In contrast, cropland increased and then decreased. These results suggest the important impacts of reforestation programmes that have accelerated a land cover transition in this region. We also found large changes in LULC occurring around fast growing urban areas, with changes in these peri-urban zones occurring faster to the east than west. This suggests that differences in socioeconomic conditions and specific local and regional policies have influenced the rates of forest, cropland and urban net changes, disturbances and net transitions. While it appears that a combination of economic growth and forest protection in this region over the past 20 years has been fairly successful, threats like drought, other extreme weather events and land

  10. Consequences of land use and land cover change

    USGS Publications Warehouse

    Slonecker, E. Terrence; Barnes, Christopher; Karstensen, Krista; Milheim, Lesley E.; Roig-Silva, Coral M.

    2013-01-01

    The U.S. Geological Survey (USGS) Climate and Land Use Change Mission Area is one of seven USGS mission areas that focuses on making substantial scientific "...contributions to understanding how Earth systems interact, respond to, and cause global change". Using satellite and other remotely sensed data, USGS scientists monitor patterns of land cover change over space and time at regional, national, and global scales. These data are analyzed to understand the causes and consequences of changing land cover, such as economic impacts, effects on water quality and availability, the spread of invasive species, habitats and biodiversity, carbon fluctuations, and climate variability. USGS scientists are among the leaders in the study of land cover, which is a term that generally refers to the vegetation and artificial structures that cover the land surface. Examples of land cover include forests, grasslands, wetlands, water, crops, and buildings. Land use involves human activities that take place on the land. For example, "grass" is a land cover, whereas pasture and recreational parks are land uses that produce a cover of grass.

  11. Urban land cover classification using hyperspectral data

    NASA Astrophysics Data System (ADS)

    Hegde, G.; Ahamed, J. Mohammed; Hebbar, R.; Raj, U.

    2014-11-01

    Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers viz., Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.

  12. A preliminary evaluation of land use mapping and change detection capabilities using an ERTS image covering a portion of the CARETS region

    NASA Technical Reports Server (NTRS)

    Fitzpatrick, K. A.; Lins, H. F., Jr.

    1972-01-01

    The author has identified the following significant results. A preliminary study on the capabilities of ERTS data in land use mapping and change detection was carried out in the area around Frederick County, Maryland, which lies in the northwest corner of the Central Atlantic Regional Ecological Test Site. The investigation has revealed that Level 1 (of the Anderson classification system) land use mapping can be performed and that, in some cases, land undergoing change can be identified. Results to date suggest that more work should be done in areas where land use changes are known to exist, in order to establish some form of base for recognizing the spectral signature indicative of change areas.

  13. Intercomparison of Satellite-Derived Snow-Cover Maps

    NASA Technical Reports Server (NTRS)

    Hall, Dorothy K.; Tait, Andrew B.; Foster, James L.; Chang, Alfred T. C.; Allen, Milan

    1999-01-01

    In anticipation of the launch of the Earth Observing System (EOS) Terra, and the PM-1 spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study area acquired from different sensors using different snow- mapping algorithms. Four locations are studied: 1) southern Saskatchewan; 2) a part of New England (New Hampshire, Vermont and Massachusetts) and eastern New York; 3) central Idaho and western Montana; and 4) parts of North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm used on Landsat Thematic Mapper (TM) scenes of each study area at 30-m and when the TM data were degraded to 1 -km resolution. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 -km resolution snow maps were also used, as were snow maps derived from 1/2 deg. x 1/2 deg. resolution Special Sensor Microwave Imager (SSM/1) data. A land-cover map derived from the International Geosphere-Biosphere Program (IGBP) land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g., tree canopies, branches and trunks) are seen in the TM data, but not in the coarser-resolution maps. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30-m resolution TM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Comparisons show that the percent change in amount of snow cover relative to the 3 0-m resolution TM maps is lowest using the TM I -km resolution maps, ranging from 0 to 40

  14. Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

    NASA Astrophysics Data System (ADS)

    Zabel, F.; Hank, T. B.; Mauser, W.

    2010-10-01

    Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI) measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC) 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI) data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

  15. A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape

    NASA Astrophysics Data System (ADS)

    Parent, Jason R.; Volin, John C.; Civco, Daniel L.

    2015-06-01

    Information on land cover is essential for guiding land management decisions and supporting landscape-level ecological research. In recent years, airborne light detection and ranging (LiDAR) and high resolution aerial imagery have become more readily available in many areas. These data have great potential to enable the generation of land cover at a fine scale and across large areas by leveraging 3-dimensional structure and multispectral information. LiDAR and other high resolution datasets must be processed in relatively small subsets due to their large volumes; however, conventional classification techniques cannot be fully automated and thus are unlikely to be feasible options when processing large high-resolution datasets. In this paper, we propose a fully automated rule-based algorithm to develop a 1 m resolution land cover classification from LiDAR data and multispectral imagery. The algorithm we propose uses a series of pixel- and object-based rules to identify eight vegetated and non-vegetated land cover features (deciduous and coniferous tall vegetation, medium vegetation, low vegetation, water, riparian wetlands, buildings, low impervious cover). The rules leverage both structural and spectral properties including height, LiDAR return characteristics, brightness in visible and near-infrared wavelengths, and normalized difference vegetation index (NDVI). Pixel-based properties were used initially to classify each land cover class while minimizing omission error; a series of object-based tests were then used to remove errors of commission. These tests used conservative thresholds, based on diverse test areas, to help avoid over-fitting the algorithm to the test areas. The accuracy assessment of the classification results included a stratified random sample of 3198 validation points distributed across 30 1 × 1 km tiles in eastern Connecticut, USA. The sample tiles were selected in a stratified random manner from locations representing the full range of

  16. Global land cover products tailored to the needs of the climate modeling community - Land Cover project of the ESA Climate Change Initiative

    NASA Astrophysics Data System (ADS)

    Bontemps, S.; Defourny, P.; Radoux, J.; Kalogirou, V.; Arino, O.

    2012-04-01

    Improving the systematic observation of land cover, as an Essential Climate Variable, will support the United Framework Convention on Climate Change effort to reduce the uncertainties in our understanding of the climate system and to better cope with climate change. The Land Cover project of the ESA Climate Change Initiative aims at contributing to this effort by providing new global land cover products tailored to the expectations of the climate modeling community. During the first three months of the project, consultation mechanisms were established with this community to identify its specific requirements in terms of satellite-based global land cover products. This assessment highlighted specific needs in terms of land cover characterization, accuracy of products, as well as stability and consistency, needs that are currently not met or even addressed. Based on this outcome, the project revisits the current land cover representation and mapping approaches. First, the stable and dynamic components of land cover are distinguished. The stable component refers to the set of land surface features that remains stable over time and thus defines the land cover independently of any sources of temporary or natural variability. Conversely, the dynamic component is directly related to this temporary or natural variability that can induce some variation in land observation over time but without changing the land cover state in its essence (e.g. flood, snow on forest, etc.). Second, the project focuses on the possibility to generate such stable global land cover maps. Previous projects, like GlobCover and MODIS Land Cover, have indeed shown that products' stability is a key issue. In delivering successive global products derived from the same sensor, they highlighted the existence of spurious year-to-year variability in land cover labels, which were not associated with land cover change but with phenology, disturbances or landscape heterogeneity. An innovative land cover

  17. Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

    NASA Astrophysics Data System (ADS)

    Zabel, F.; Hank, T. B.; Mauser, W.

    2010-07-01

    Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist, but generally they do not resolve arable land into different crop types. However, the characteristic phenological behaviour of different crops affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for Central Europe based on CORINE Land Cover 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for Central Europe using statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types, taking a multiseasonal series of MERIS Normalized Difference Vegetation Index (NDVI) into account. The satellite data were used for the separation of spring and summer crops. The hydrological impact of the improved land cover map was modelled exemplarily for the Upper Danube catchment.

  18. Chesapeake bay watershed land cover data series

    USGS Publications Warehouse

    Irani, Frederick M.; Claggett, Peter R.

    2010-01-01

    To better understand how the land is changing and to relate those changes to water quality trends, the USGS EGSC funded the production of a Chesapeake Bay Watershed Land Cover Data Series (CBLCD) representing four dates: 1984, 1992, 2001, and 2006. EGSC will publish land change forecasts based on observed trends in the CBLCD over the coming year. They are in the process of interpreting and publishing statistics on the extent, type and patterns of land cover change for 1984-2006 in the Bay watershed, major tributaries and counties.

  19. Effects of landscape characteristics on land-cover class accuracy

    USGS Publications Warehouse

    Smith, Jonathan H.; Stehman, Stephen V.; Wickham, James D.; Yang, Limin

    2003-01-01

    The effects of patch size and land-cover heterogeneity on classification accuracy were evaluated using reference data collected for the National Land-Cover Data (NLCD) set accuracy assessment. Logistic regression models quantified the relationship between classification accuracy and these landscape variables for each land-cover class at both the Anderson Levels I and II classification schemes employed in the NLCD. The general relationships were consistent, with the odds of correctly classifying a pixel increasing as patch size increased and decreasing as heterogeneity increased. Specific characteristics of these relationships, however, showed considerable diversity among the various classes. Odds ratios are reported to document these relationships. Interaction between the two landscape variables was not a significant influence on classification accuracy, indicating that the effect of heterogeneity was not impacted by the sample being in a small or large patch. Landscape variables remained significant predictors of class-specific accuracy even when adjusted for regional differences in the mapping and assessment processes or landscape characteristics. The land-cover class-specific analyses provide insight into sources of classification error and a capacity for predicting error based on a pixel's mapped land-cover class, patch size and surrounding land-cover heterogeneity.

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

    USGS Publications Warehouse

    Xian, G.; Homer, C.; Fry, J.

    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. ?? 2009 Elsevier Inc.

  1. THEMATIC ACCURACY OF MRLC LAND COVER FOR THE EASTERN UNITED STATES

    EPA Science Inventory



    One objective of the MultiResolution Land Characteristics (MRLC) consortium is to map general land-cover categories for the conterminous United States using Landsat Thematic Mapper (TM) data. Land-cover mapping and classification accuracy assessment are complete for the e...

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

    NASA Astrophysics Data System (ADS)

    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

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

    EPA Science Inventory

    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), ...

  4. Predicting land cover using GIS, Bayesian and evolutionary algorithm methods.

    PubMed

    Aitkenhead, M J; Aalders, I H

    2009-01-01

    Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables. PMID:18079039

  5. FINDINGS ON THE USE OF LANDSAT-3 RETURN BEAM VIDICON IMAGERY FOR DETECTING LAND USE AND LAND COVER CHANGES.

    USGS Publications Warehouse

    Milazzo, Valerie A.

    1983-01-01

    The spatial resolution of imagery from the return beam vidicon (RBV) camera aboard the Landsat-3 satellite suggested that such data might prove useful in inspecting land use and land cover maps. In this study, a 1972 land use and land cover map derived from aerial photographs is compared with a 1978 Landsat RBV image to delineate areas of change. Findings indicate RBV imagery useful in establishing the fact of change and in identifying gross category changes.

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

    USGS Publications Warehouse

    Fry, J.A.; Coan, M.J.; Homer, C.G.; Meyer, D.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.

  7. Ecoregions and land cover trends in Senegal

    USGS Publications Warehouse

    Tappan, G. Gray; Sall, M.; Wood, E.C.; Cushing, M.

    2004-01-01

    This study examines long-term changes in Senegal's natural resources. We monitor and quantify land use and land cover changes occurring across Senegal using nearly 40 years of satellite imagery, aerial surveys, and fieldwork. We stratify Senegal into ecological regions and present land use and land cover trends for each region, followed by a national summary. Results aggregated to the national level show moderate change, with a modest decrease in savannas from 74 to 70 percent from 1965 to 2000, and an expansion of cropland from 17 to 21 percent. However, at the ecoregion scale, we observed rapid change in some and relative stability in others. One particular concern is the decline in Senegal's biodiverse forests. However, in the year 2000, Senegal's savannas, woodlands, and forests still cover more than two-thirds of the country, and the rate of agricultural expansion has slowed.

  8. Polarization in the land distribution, land use and land cover change in the Amazon

    PubMed Central

    D'ANTONA, Alvaro; VANWEY, Leah; LUDEWIGS, Thomas

    2013-01-01

    The objective of this article is to present Polarization of Agrarian Structure as a single, more complete representation than models emphasizing rural exodus and consolidation of land into large agropastoral enterprises of the dynamics of changing land distribution, land use / cover, and thus the rural milieu of Amazonia. Data were collected in 2003 using social surveys on a sample of 587 lots randomly selected from among 5,086 lots on a cadastral map produced in the 1970s. Georeferencing of current property boundaries in the location of these previously demarcated lots allows us to relate sociodemographic and biophysical variables of the surveyed properties to the changes in boundaries that have occurred since the 1970s. As have other authors in other Amazonian regions, we found concentration of land ownership into larger properties. The approach we took, however, showed that changes in the distribution of land ownership is not limited to the appearance of larger properties, those with 200 ha or more; there also exists substantial division of earlier lots into properties with fewer than five hectares, many without any agropastoral use. These two trends are juxtaposed against the decline in establishments with between five and 200 ha. The variation across groups in land use / land cover and population distribution shows the necessity of developing conceptual models, whether from socioeconomic, demographic or environmental perspectives, look beyond a single group of people or properties. PMID:24639597

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

    PubMed Central

    Yüksel, Alaaddin; Akay, Abdullah E.; Gundogan, Recep

    2008-01-01

    The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery has been widely used in classification process of land cover. However, atmospheric corrections have to be made by preprocessing satellite sensor imagery since the electromagnetic radiation signals received by the satellite sensors 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 of INformation on the Environment (CORINE) land cover nomenclature, for an area representing the heterogonous characteristics of eastern Mediterranean regions in Kahramanmaras, Turkey. The results indicated that using the surface reflectance data of ASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of 83.2% and 0.79, respectively) and low-cost cover mapping as a part of inventory for CORINE Land Cover Project.

  10. Digital elevation data as an aid to land use and land cover classification

    USGS Publications Warehouse

    Colvocoresses, Alden P.

    1981-01-01

    In relatively well mapped areas such as the United States and Europe, digital data can be developed from topographic maps or from the stereo aerial photographic movie. For poorer mapped areas (which involved most of the world's land areas), a satellite designed to obtain stereo data offers the best hope for a digital elevation database. Such a satellite, known as Mapsat, has been defined by the U.S. Geological Survey. Utilizing modern solid state technology, there is no reason why such stereo data cannot be acquired simultaneously with the multispectral response, thus simplifying the overall problem of land use and land cover classification.

  11. Assessing Landslide Risk Areas Using Statistical Models and Land Cover

    NASA Astrophysics Data System (ADS)

    Kim, H. G.; Lee, D. K.; Park, C.; Ahn, Y.; Sung, S.; Park, J. H.

    2015-12-01

    Recently, damages due to landslides have increased in Republic of Korea. Extreme weathers like typhoon, heavy rainfall related to climate change are the main factor of the damages. Especially, Inje-gun, Gangwon-do had severe landslide damages in 2006 and 2007. In Inje-gun, 91% areas are forest, therefore, many land covers related to human activities were adjacent to forest land. Thus, establishment of adaptation plans to landslides was urgently needed. Landslide risk assessment can serve as a good information to policy makers. The objective of this study was assessing landslide risk areas to support establishment of adaptation plans to reduce landslide damages. Statistical distribution models (SDMs) were used to evaluate probability of landslide occurrence. Various SDMs were used to make landslide probability maps considering uncertainty of SDMs. The types of land cover were classified into 5 grades considering vulnerable level to landslide. The landslide probability maps were overlaid with land cover map to calculate landslide risk. As a result of overlay analysis, landslide risk areas were derived. Especially agricultural areas and transportation areas showed high risk and large areas in the risk map. In conclusion, policy makers in Inje-gun must consider the landslide risk map to establish adaptation plans effectively.

  12. LAND USE/LAND COVER, NEUSE RIVER WATERSHED (BUFFERED)

    EPA Science Inventory

    EOSAT and the North Carolina State University Computer Graphics Center, in cooperation with the NC Center for Geographic Information and Analysis, developed the Land Use/Land Cover digital data to enhance planning, siting and impact analysis in the Albemarle-Pamlico Estuarine Stu...

  13. Climate impacts of Australian land cover change

    NASA Astrophysics Data System (ADS)

    Lawrence, P. J.

    2004-05-01

    Australian land cover has been dramatically altered since European settlement primarily for agricultural utilization, with native vegetation widely replaced or modified for cropping and intensive animal production. While there have been numerous investigations into the regional and near surface climate impacts of Australian land cover change, these investigation have not included the climate impacts of larger-scale changes in atmospheric circulation and their associated feedbacks, or the impacts of longer-term soil moisture feedbacks. In this research the CSIRO General Circulation Model (GCM) was used to investigate the climate impacts of Australian land cover change, with larger-scale and longer-term feedbacks. To avoid the common problem of overstating the magnitude and spatial extent of changes in land surface conditions prescribed in land cover change experiments, the current Australian land surface properties were described from finer-scale, satellite derived land cover datasets, with land surface conditions extrapolating from remnant native vegetation to pre-clearing extents to recreate the pre-clearing land surface properties. Aggregation rules were applied to the fine-scale data to generate the land surface parameters of the GCM, ensuring the equivalent sub-grid heterogeneity and land surface biogeophysics were captured in both the current and pre-clearing land surface parameters. The differences in climate simulated in the pre-clearing and current experiments were analyzed for changes in Australian continental and regional climate to assess the modeled climate impacts of Australian land cover change. The changes in modeled climate were compared to observed changes in Australian precipitation over the last 50 and 100 years to assess whether modeled results could be detected in the historical record. The differences in climate simulation also were analyzed at the global scale to assess the impacts of local changes on larger scale circulation and climate at

  14. Modelling land cover dynamics: integration of fine-scale land cover data with landscape attributes

    NASA Astrophysics Data System (ADS)

    Mertens, Benoît; Lambin, Eric

    Land cover change detection based on remote sensing data allows the identification of major processes of change and, by inference, the characterization of land use dynamics. Empirical diagnostic models of land use/cover change can be developed from these observations. To grasp the complexity of landscape mosaics and changes in land use, fine-scale land cover and socio-economic data are required. Case studies need to be representative of conditions at a broader scale, and selected where sufficient knowledge on social and ecological processes leading to land use changes exists. For this reason, collaboration between remote sensing specialists and human ecologists conducting long-term field-based land use studies is extremely productive. Continental-scale analysis of Africa was conducted to detect land cover change "hot spots". Fine-scale analyses were performed for validation purposes and to understand better the land cover change processes. Spatial statistical models of land cover change can be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to support the implementation of appropriate policy responses to, for example, land degradation that would lead to the depletion of essential resources. Results of a spatial model of deforestation in southern Cameroon are discussed.

  15. Commentary: A cautionary tale regarding use of the National Land Cover Dataset 1992

    USGS Publications Warehouse

    Thogmartin, Wayne E.; Gallant, Alisa L.; Knutson, Melinda G.; Fox, Timothy J.; Suarez, Manuel J.

    2004-01-01

    Digital land-cover data are among the most popular data sources used in ecological research and natural resource management. However, processes for accurate land-cover classification over large regions are still evolving. We identified inconsistencies in the National Land Cover Dataset 1992, the most current and available representation of land cover for the conterminous United States. We also report means to address these inconsistencies in a bird-habitat model. We used a Geographic Information System (GIS) to position a regular grid (or lattice) over the upper midwestern United States and summarized the proportion of individual land covers in each cell within the lattice. These proportions were then mapped back onto the lattice, and the resultant lattice was compared to satellite paths, state borders, and regional map classification units. We observed mapping inconsistencies at the borders between mapping regions, states, and Thematic Mapper (TM) mapping paths in the upper midwestern United States, particularly related to grass I and-herbaceous, emergent-herbaceous wetland, and small-grain land covers. We attributed these discrepancies to differences in image dates between mapping regions, suboptimal image dates for distinguishing certain land-cover types, lack of suitable ancillary data for improving discrimination for rare land covers, and possibly differences among image interpreters. To overcome these inconsistencies for the purpose of modeling regional populations of birds, we combined grassland-herbaceous and pasture-hay land-cover classes and excluded the use of emergent-herbaceous and small-grain land covers. We recommend that users of digital land-cover data conduct similar assessments for other regions before using these data for habitat evaluation. Further, caution is advised in using these data in the analysis of regional land-cover change because it is not likely that future digital land-cover maps will repeat the same problems, thus resulting in

  16. Dependence of Polarimetric Scattering Mechanisms on Land Cover

    NASA Astrophysics Data System (ADS)

    Atwood, D. K.; Meyer, F.

    2011-03-01

    A method for statistically representing the polarimetric SAR scattering mechanisms of individual land cover classes is introduced and applied to ALOS PALSAR L-band quad-pol data. PALSAR scattering signatures are correlated with land cover classification maps to determine typical scattering mechanisms. The approach utilizes two free, open-source software tools, ESA's PolSARpro and the Alaska Satellite Facility's MapReady Remote Sensing Toolbox as well as Geographic Information System (GIS) tools, to compute the probability density functions of normalized decomposition components for each land cover class.The proposed method provides the ability to compare polarimetric decompositions, investigate scattering mechanisms, detect change in land cover classification, and discover inhomogeneities in the spectral characteristics of individual classes. The approach is first employed to compare the Freeman and Van Zyl three-component decomposition techniques, where the former is shown to introduce many pixels with 100% volume saturation.Ideally, the method yields distinctive scattering peaks for each land cover class with minimal variance in the individual scattering components. However, in some instances, bimodal peaks are found. These are shown to either represent changes between the original land classification and the SAR acquisitions, or the existence of spectral subclasses that were not differentiated in the original classification. Last, the method is used to determine the impact of Polarimetric Orientation Angle (POA) correction on the scattering signatures of urban land cover classes. POA compensation is shown to bring about a significant reduction in the volume scattering component.A method for statistically representing the polarimetric SAR scattering mechanisms of individual land cover classes is introduced and applied to ALOS PALSAR L-band quad-pol data. PALSAR scattering signatures are correlated with land cover classification maps to determine typical

  17. Impacts of Land-Use and Land-Cover Change over South America: a modeling study

    NASA Astrophysics Data System (ADS)

    Nascimento, M. G. D.; Herdies, D. L.; Souza, D. O. D.

    2014-12-01

    Changes in patterns of land use and land cover have great influence on hydrology, climate and biogeochemical cycles. In this work the influences caused by changes in patterns of land cover and land use in Brazil on the behavior of the water balance over South America were evaluated. To fulfill this objective numerical experiments were carried out with the regional model ETA for the period between 1979 and 2008, in which three different conditions of land use and land cover in Brazil was used: 1) Potential Experiment, which are not included the anthropogenic changes in vegetation cover; 2) Control Experiment, in which the map of land use and land cover used the conditions of the 90s; 3) New Experiment, which represents the current conditions of land use and land cover. The results show clearly that the constant changes in patterns of land cover and land use in Brazil cause an increase in precipitation and moisture convergence, and reduced evapotranspiration over the Amazon Region. In other words, it can be stated that with the advance of changes in patterns of land use and land cover, Amazon further intensified their behavior as a sink of moisture, mainly due to increased precipitation and significant reduction in evapotranspiration, noting also that reduction of moisture available in the atmosphere was not offset by increased moisture convergence. The results on the La Plata Basin shows that initially (CONTROL) there is an increase in precipitation and evapotranspiration over the region and reduction in moisture convergence, which is later (NEW) modified to a pattern of reduction in precipitation and evapotranspiration followed by an increase in moisture convergence. These changes in the patterns of land use and land cover of the 90s make the area potentially source of moisture to the atmosphere, even with the reduction in moisture convergence, but reversing their behavior to sink moisture by inserting current vegetation cover modifications, mainly due to reduced

  18. Landsat continuity: Issues and opportunities for land cover monitoring

    USGS Publications Warehouse

    Wulder, M.A.; White, Joanne C.; Goward, S.N.; Masek, J.G.; Irons, J.R.; Herold, M.; Cohen, W.B.; Loveland, T.R.; Woodcock, C.E.

    2008-01-01

    Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35??years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures

  19. Remote sensing. [land use mapping

    NASA Technical Reports Server (NTRS)

    Jinich, A.

    1979-01-01

    Various imaging techniques are outlined for use in mapping, land use, and land management in Mexico. Among the techniques discussed are pattern recognition and photographic processing. The utilization of information from remote sensing devices on satellites are studied. Multispectral band scanners are examined and software, hardware, and other program requirements are surveyed.

  20. US LAND-COVER MONITORING AND DETECTION OF CHANGES IN SCALE AND CONTEXT OF FOREST

    EPA Science Inventory

    Disparate land-cover mapping programs, previously focused solely on mission-oriented goals, have organized themselves as the Multi-Resolution Land Characteristics (MRLC) Consortium with a unified goal of producing land-cover nationwide at routine intervals. Under MRLC, United Sta...

  1. EFFECTS OF LANDSCAPE CHARACTERISTICS ON LAND-COVER CLASS ACCURACY

    EPA Science Inventory



    Utilizing land-cover data gathered as part of the National Land-Cover Data (NLCD) set accuracy assessment, several logistic regression models were formulated to analyze the effects of patch size and land-cover heterogeneity on classification accuracy. Specific land-cover ...

  2. Climate Effects of Global Land Cover Change

    SciTech Connect

    Gibbard, S G; Caldeira, K; Bala, G; Phillips, T; Wickett, M

    2005-08-24

    There are two competing effects of global land cover change on climate: an albedo effect which leads to heating when changing from grass/croplands to forest, and an evapotranspiration effect which tends to produce cooling. It is not clear which effect would dominate in a global land cover change scenario. We have performed coupled land/ocean/atmosphere simulations of global land cover change using the NCAR CAM3 atmospheric general circulation model. We find that replacement of current vegetation by trees on a global basis would lead to a global annual mean warming of 1.6 C, nearly 75% of the warming produced under a doubled CO{sub 2} concentration, while global replacement by grasslands would result in a cooling of 0.4 C. These results suggest that more research is necessary before forest carbon storage should be deployed as a mitigation strategy for global warming. In particular, high latitude forests probably have a net warming effect on the Earth's climate.

  3. Remote Sensing for optimum road network development by using Land use Land cover classification

    NASA Astrophysics Data System (ADS)

    More, Snehal; Bhuvana Chandra, mr.; Hebbar, R.

    2012-07-01

    Rural development plays a major role in overall development of any country. Remote Sensing may be helpful in areas like infrastructure development, agricultural development. This paper focuses on implementation of Remote Sensing methods for solving problems in laying new roads and efficient transport in undulating terrain regions. It gives an approach towards economical and ecofriendly rural development. The aim was to suggest a road network with optimum transportation path considering the major factors as slope, road length, least intervention to the natural vegetation, least transportation cost. Area of interest was chosen from Agali-Thuvaipathy area in Palakkad, Kerala. The methodology involves generation of Digital Elevation Model, slope map, land use land cover map for the area of interest. DEM was generated using Cartosat-1 stereo pairs, slope map was generated using Arc Map and land use land cover map was generated by digitizing different feature classes like cropland, vegetation, barren land, water body and town from the LISS 4 data. Weighted overlay analysis was performed for identification of an optimum path by applying required limitations on land use type and maximum slope value. The favorable area for road creation between the two given points in the image was obtained.

  4. Decadal land cover change dynamics in Bhutan.

    PubMed

    Gilani, Hammad; Shrestha, Him Lal; Murthy, M S R; Phuntso, Phuntso; Pradhan, Sudip; Bajracharya, Birendra; Shrestha, Basanta

    2015-01-15

    Land cover (LC) is one of the most important and easily detectable indicators of change in ecosystem services and livelihood support systems. This paper describes the decadal dynamics in LC changes at national and sub-national level in Bhutan derived by applying object-based image analysis (OBIA) techniques to 1990, 2000, and 2010 Landsat (30 m spatial resolution) data. Ten LC classes were defined in order to give a harmonized legend land cover classification system (LCCS). An accuracy of 83% was achieved for LC-2010 as determined from spot analysis using very high resolution satellite data from Google Earth Pro and limited field verification. At the national level, overall forest increased from 25,558 to 26,732 km(2) between 1990 and 2010, equivalent to an average annual growth rate of 59 km(2)/year (0.22%). There was an overall reduction in grassland, shrubland, and barren area, but the observations were highly dependent on time of acquisition of the satellite data and climatic conditions. The greatest change from non-forest to forest (277 km(2)) was in Bumthang district, followed by Wangdue Phodrang and Trashigang, with the least (1 km(2)) in Tsirang. Forest and scrub forest covers close to 75% of the land area of Bhutan, and just over half of the total area (51%) has some form of conservation status. This study indicates that numerous applications and analyses can be carried out to support improved land cover and land use (LCLU) management. It will be possible to replicate this study in the future as comparable new satellite data is scheduled to become available. PMID:24680540

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2012-01-01

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

  7. A multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa Cruz Watershed

    USGS Publications Warehouse

    2011-01-01

    Trends derived from multitemporal land-cover data can be used to make informed land management decisions and to help managers model future change scenarios. We developed a multitemporal land-use/land-cover dataset for the binational Santa Cruz watershed of southern Arizona, United States, and northern Sonora, Mexico by creating a series of land-cover maps at decadal intervals (1979, 1989, 1999, and 2009) using Landsat Multispectral Scanner and Thematic Mapper data and a classification and regression tree classifier. The classification model exploited phenological changes of different land-cover spectral signatures through the use of biseasonal imagery collected during the (dry) early summer and (wet) late summer following rains from the North American monsoon. Landsat images were corrected to remove atmospheric influences, and the data were converted from raw digital numbers to surface reflectance values. The 14-class land-cover classification scheme is based on the 2001 National Land Cover Database with a focus on "Developed" land-use classes and riverine "Forest" and "Wetlands" cover classes required for specific watershed models. The classification procedure included the creation of several image-derived and topographic variables, including digital elevation model derivatives, image variance, and multitemporal Kauth-Thomas transformations. The accuracy of the land-cover maps was assessed using a random-stratified sampling design, reference aerial photography, and digital imagery. This showed high accuracy results, with kappa values (the statistical measure of agreement between map and reference data) ranging from 0.80 to 0.85.

  8. Relation of land use/land cover to resource demands

    NASA Technical Reports Server (NTRS)

    Clayton, C.

    1981-01-01

    Predictive models for forecasting residential energy demand are investigated. The models are examined in the context of implementation through manipulation of geographic information systems containing land use/cover information. Remotely sensed data is examined as a possible component in this process.

  9. Mapping and improving frequency, accuracy, and interpretation of land cover change: Classifying coastal Louisiana with 1990, 1993, 1996, and 1999 Landsat Thematic Mapper image data

    USGS Publications Warehouse

    Nelson, G.; Ramsey, Elijah W., III; Rangoonwala, A.

    2005-01-01

    Landsat Thematic Mapper images and collateral data sources were used to classify the land cover of the Mermentau River Basin within the chenier coastal plain and the adjacent uplands of Louisiana, USA. Landcover classes followed that of the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; however, classification methods needed to be developed to meet these national standards. Our first classification was limited to the Mermentau River Basin (MRB) in southcentral Louisiana, and the years of 1990, 1993, and 1996. To overcome problems due to class spectral inseparable, spatial and spectra continuums, mixed landcovers, and abnormal transitions, we separated the coastal area into regions of commonality and applying masks to specific land mixtures. Over the three years and 14 landcover classes (aggregating the cultivated land and grassland, and water and floating vegetation classes), overall accuracies ranged from 82% to 90%. To enhance landcover change interpretation, three indicators were introduced as Location Stability, Residence stability, and Turnover. Implementing methods substantiated in the multiple date MRB classification, we spatially extended the classification to the entire Louisiana coast and temporally extended the original 1990, 1993, 1996 classifications to 1999 (Figure 1). We also advanced the operational functionality of the classification and increased the credibility of change detection results. Increased operational functionality that resulted in diminished user input was for the most part gained by implementing a classification logic based on forbidden transitions. The logic detected and corrected misclassifications and mostly alleviated the necessity of subregion separation prior to the classification. The new methods provided an improved ability for more timely detection and response to landcover impact. ?? 2005 IEEE.

  10. Impact of climate and land cover changes on snow cover in a small Pyrenean catchment

    NASA Astrophysics Data System (ADS)

    Szczypta, C.; Gascoin, S.; Houet, T.; Hagolle, O.; Dejoux, J.-F.; Vigneau, C.; Fanise, P.

    2015-02-01

    The seasonal snow in the Pyrenees Mountains is an essential source of runoff for hydropower production and crop irrigation in Spain and France. The Pyrenees are expected to undergo strong environmental perturbations over the 21st century because of climate change (rising temperatures) and the abandonment of agro-pastoral areas (reforestation). Both changes are happening at similar timescales and are expected to have an impact on snow cover. The effect of climate change on snow in the Pyrenees is well understood, but the effect of land cover changes is much less documented. Here, we analyze the response of snow cover to a combination of climate and land cover change scenarios in a small Pyrenean catchment (Bassiès, 14.5 km2, elevation range 940-2651 m a.s.l.) using a distributed snowpack evolution model. Climate scenarios were constructed from the output of regional climate model projections, whereas land cover scenarios were generated based on past observed changes and an inductive pattern-based model. The model was validated over a snow season using in situ snow depth measurements and high-resolution snow cover maps derived from SPOT (Satellite Pour l'Observation de la Terre - Earth Observation Satellite) satellite images. Model projections indicate that both climate and land cover changes reduce the mean snow depth. However, the impact on the snow cover duration is moderated in reforested areas by the shading effect of trees on the snow surface radiation balance. Most of the significant changes are expected to occur in the transition zone between 1500 m a.s.l. and 2000 m a.s.l. where (i) the projected increase in air temperatures decreases the snow fraction of the precipitation and (ii) the land cover changes are concentrated. However, the consequences on the runoff are limited because most of the meltwater originates from high-elevation areas of the catchment, which are less affected by climate change and reforestation.