ASSESSMENT OF VEGETATION DYNAMICS AND FOREST LOSS USING GOOGLE EARTH ENGINE AND MULTI-TEMPORAL SENTINEL-2 IMAGERY
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Date
2022-04
Journal Title
Journal ISSN
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Publisher
Faculty of Agriculture, University of Nigeria, Nsuka
Abstract
This study evaluated regional vegetation dynamics and changes between 2015 and 2020 using Google earth
engine (GEE) platform and normalized difference vegetation index (NDVI) derived from the multi-petabyte
catalogue of sentinel-2 imageries. Using the computational capability of GEE, yearly mean NDVI from 2015
to 2020 were computed using level C-1 product. Subsequently, each of the NDVI images was classified into
four land cover classes; water bodies, non-vegetated, grassland/cropland/shrubs, and forest using NDVI
threshold values of < 0.01, 0.01-0.20, 0.20-0.30 and > 0.30, respectively. The classified maps allowed for the
assessment of yearly variation in vegetation and changes between 2015 and 2020. Result showed that nonvegetated area increased from 18.53% in 2015 to 42.56% in 2020 (~ 25.00% gain), the forest area reduced to
6.78% in 2020 compared to 23.76% measured in 2015 (~ 17.00% loss in forest); whereas water bodies and
grassland/cropland/shrubs remained relatively constant (0.21 and ~ 50.00%, respectively) across the years
studied. Presently, the forest land was estimated to be about 2, 371.131 km2 (~ 6.70%) of the total land mass,
grassland/cropland/shrubs occupied 17, 770.79 km2 (~ 50.07%), non-vegetated area was slightly less than
half with 15, 274.85 km2 (~ 43.04%) and water bodies occupied 75.68 km2 (~ 0.21%).
Description
Keywords
NDVI, tropical savannah, vegetation dynamics, sentinel-2, desertification, Kwara
Citation
Omar D., Idrees M., Ahmadu H., Yusuf A., Ipadeola O., Babalola A. and Abdulyekeen A. (2022). Assessment of vegetation dynamics and forest loss using Google earth engine and multi-temporal sentinel-2 imagery. Agro-Science, 21 (2), 85-94