Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-2 LAND USE AND LAND COVER]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: DIGITAL IMAGE PROCESSING AND GIS APPLICATIONS FOR NATURAL RESOURCES MONITORING AND EVALUATION IN NORTHEASTERN BRAZIL. H. S. Teotia, W. C. Kennard, D. L. Civco and K. A. Ulbricht

Document type:: Multivolume work

Structure type:: Chapter

DIGITAL IMAGE PROCESSING AND GIS APPLICATIONS FOR NATURAL RESOURCES MONITORING AND EVALUATION IN NORTHEASTERN BRAZIL H.S. Teotia 1,5 , W.C. Kennard 2 , D.L. Civco 3 and K.A. Ulbricht 4,5 Laboratory for Remote Sensing, Department of Naturai Resources Management and Engineering, The University of Connecticut, Storrs, CT 06269-4067 USA ABSTRACT A portion of a SPOT HRV image data acquired on 5 May 1987 centered around Santa Luzia and its environs in Paraiba, Brazil, was processed to extract earth resources information. Digital analysis supplemented with selective ground truth data gathered by field surveys in 1988 was applied to the SPOT multispectral data to derive land cover and other natural resources information. The data were processed with ERDAS (Earth Resources Data Analysis System) software operating on a high performance microcomputer. Training areas were selected for 15 categories of land use and land cover. A supervised classification was conducted and the digital land use and land cover map was corrected geometrically and georeferenced to Universal Trans verse Mercator coordinates. Accuracy was assessed by digitizing ground truth test polygons and performing a pixel-by-pixel comparison. Percentage agreement with ground truth data as well as errors of omission and commission were recorded and analyzed. By recoding the land use and land cover classes, six geometrically corrected maps as follows were prepared: 1) land use and land cover, 2) soil associations, 3) land capability, 4) slope classes, 5) land irrigability, and 6) agro-technical limitations. The first four resource information layers (maps) were weighted for their relative importance to land development and irrigation potential and combined in a logical model to produce a land suitability classification map. It was found that water, low density urban and barren rocky land, mixed cultivated and pasture, alluvial cultivated and eroded land, and forest classes produced the best results in terms of high percentage agreement with ground truth data (80.0 to 100.0%) and relatively low percentage commissions (zero to 13.5%). These classes were easily discriminated from other classes with a high overall classification accuracy. Other classes such as cotton sparse caatinga forest and rocky land, cotton cultivated and fallow alluvial land with dense shrubs/trees could also be distinguished but with 1,. Professor, Federal University of Paraiba, Campina Grande, Paraiba, Brasil ' 2 and 3. Director and Vice Director, respectively, Laboratory for Remote Sensing 4, Staff Scientist, German Aerospace Research Establishment, DLR - Optoelektronik , 8031 Oberpfaffenhofen, FRG 5. Visiting Professor at the University of Connecticut when this paper was prepared. 347

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