Algorithm for detecting deforestation and forest degradation using vegetation indices

M. Buce Saleh (Bogor Agricultural University)
I Nengah Surati Jaya (Bogor Agricultural University)
Nitya Ade Santi (Bogor Agricultural University)
Dewayany Sutrisno (Geospatial Information Agency)
Ita Carolita (National Aeronautic and Aerospace Agency)
Zhang Yuxing (Academy of Forest Inventory and Planning)
Wang Xuenjun (Academy of Forest Inventory and Planning)
Liu Qian (Academy of Forest Inventory and Planning)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 17, No 5: October 2019

In forestry sector, the remote sensing technology hold a key role on forest inventory and monitoring their changes. This paper describes the algorithm for detecting deforestation and forest degradation using high resolution satellite imageries with knowledge-based approach. The main objective of the study is to develop a practical technique for monitoring deforestation and forest degradation occurred within the mangrove and swamp forest ecosystem.  The SPOT 4, 5, and 6 images acquired in 2007, 2012 and 2014 were transformed into three vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Green-Normalized Difference Vegetation index (GNDVI) and Normalized Green-Red Vegetation index (NRGI).  The study found that deforestation was well detected and identified using the NDVI and GNDVI, however the forest degradation could be well detected using NRGI, better than NDVI and GNDVI.  The study concludes that the strategy for monitoring deforestation, biomass-based forest degradation as well as forest growth could be done by combining the use of NDVI, GNDVI and NRGI respectively.