Remote sensing for forest management in semi-arid environments
(1990) Proceedings of the 23rd International Symposium on Remote Sensing of Environment 1. p.573-580- Abstract
The aim of the study is to (i) evaluate a method, based on the normalized difference vegetation index (NDVI) calculated from Landsat TM data, for estimation of tree canopy cover in semi-arid environments and (ii) determine canopy cover changes in a forest area in the Sudan by applying the method on Landsat MSS data from 1972 and 1979. Primarily, the NDVI based method was applied on NDVI values, calculated from corrected Landsat TM scene from 1987. The output of the applied model was a continuous canopy cover map. The result was compared to field data from 1989. The total accuracy is above 70 per cent for the classified areas. To determine canopy cover changes from 1972 to 1987 the Landsat TM scene was complemented by two Landsat MSS... (More)
The aim of the study is to (i) evaluate a method, based on the normalized difference vegetation index (NDVI) calculated from Landsat TM data, for estimation of tree canopy cover in semi-arid environments and (ii) determine canopy cover changes in a forest area in the Sudan by applying the method on Landsat MSS data from 1972 and 1979. Primarily, the NDVI based method was applied on NDVI values, calculated from corrected Landsat TM scene from 1987. The output of the applied model was a continuous canopy cover map. The result was compared to field data from 1989. The total accuracy is above 70 per cent for the classified areas. To determine canopy cover changes from 1972 to 1987 the Landsat TM scene was complemented by two Landsat MSS scenes from 1972 and 1979. The same regression model as in the first part of the study was applied on the scenes. Between 1972 and 1979 an increase, from 60 per cent to 76 per cent, in mean canopy cover was determined. In 1987 the mean canopy cover was only 16 per cent.
(Less)
- author
- Larsson, Helena and Pilesjo, Petter LU
- organization
- publishing date
- 1990
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the International Symposium on Remote Sensing of Environment
- volume
- 1
- pages
- 8 pages
- publisher
- Ann Arbor : Environmental Research Institute of Michigan
- conference name
- Proceedings of the 23rd International Symposium on Remote Sensing of Environment
- conference location
- Bangkok, Thail
- conference dates
- 1990-04-18 - 1990-04-25
- external identifiers
-
- scopus:0025556970
- language
- English
- LU publication?
- yes
- id
- 1604dd48-1399-4445-981c-d2d7ec78eed9
- date added to LUP
- 2022-03-25 13:09:35
- date last changed
- 2022-03-31 16:24:33
@inproceedings{1604dd48-1399-4445-981c-d2d7ec78eed9,
abstract = {{<p>The aim of the study is to (i) evaluate a method, based on the normalized difference vegetation index (NDVI) calculated from Landsat TM data, for estimation of tree canopy cover in semi-arid environments and (ii) determine canopy cover changes in a forest area in the Sudan by applying the method on Landsat MSS data from 1972 and 1979. Primarily, the NDVI based method was applied on NDVI values, calculated from corrected Landsat TM scene from 1987. The output of the applied model was a continuous canopy cover map. The result was compared to field data from 1989. The total accuracy is above 70 per cent for the classified areas. To determine canopy cover changes from 1972 to 1987 the Landsat TM scene was complemented by two Landsat MSS scenes from 1972 and 1979. The same regression model as in the first part of the study was applied on the scenes. Between 1972 and 1979 an increase, from 60 per cent to 76 per cent, in mean canopy cover was determined. In 1987 the mean canopy cover was only 16 per cent.</p>}},
author = {{Larsson, Helena and Pilesjo, Petter}},
booktitle = {{Proceedings of the International Symposium on Remote Sensing of Environment}},
language = {{eng}},
pages = {{573--580}},
publisher = {{Ann Arbor : Environmental Research Institute of Michigan}},
title = {{Remote sensing for forest management in semi-arid environments}},
volume = {{1}},
year = {{1990}},
}