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Use of Landsat ETM+ data for detection of potential areas for afforestation

Use of Landsat ETM+ data for detection of potential areas for afforestation The estimation of potential areas for afforestation (PAAs) provides the information base for planning, management and monitoring of environmental issues. A remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) dataset was used for detection of PAAs. The existing forest densities in the study area were classified using the Normalized Difference Vegetation Index (NDVI), whereas soil moisture was based on distribution of the Soil Wetness Index (SWI). A combined map of NDVI and SWI was produced. Areas showing adequate soil moisture with inadequate/thin forest density on the combined map were considered as PAAs. Computation revealed that approximately 13% of the area under review had potential for dense afforestation, 27% for medium to dense afforestation and 53% for grasses. The methodology formulated in the present study can be used as a rapid assessment tool prior to afforestation planning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Remote Sensing Taylor & Francis

Use of Landsat ETM+ data for detection of potential areas for afforestation

International Journal of Remote Sensing , Volume 30 (10): 11 – May 1, 2009
11 pages

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References (25)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1366-5901
DOI
10.1080/01431160802552793
Publisher site
See Article on Publisher Site

Abstract

The estimation of potential areas for afforestation (PAAs) provides the information base for planning, management and monitoring of environmental issues. A remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) dataset was used for detection of PAAs. The existing forest densities in the study area were classified using the Normalized Difference Vegetation Index (NDVI), whereas soil moisture was based on distribution of the Soil Wetness Index (SWI). A combined map of NDVI and SWI was produced. Areas showing adequate soil moisture with inadequate/thin forest density on the combined map were considered as PAAs. Computation revealed that approximately 13% of the area under review had potential for dense afforestation, 27% for medium to dense afforestation and 53% for grasses. The methodology formulated in the present study can be used as a rapid assessment tool prior to afforestation planning.

Journal

International Journal of Remote SensingTaylor & Francis

Published: May 1, 2009

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