Algorithm for detecting deforestation and forest degradation using vegetation indices
| Main Authors: | Saleh, M. Buce; Bogor Agricultural University, Jaya, I Nengah Surati; Bogor Agricultural University, Santi, Nitya Ade; Bogor Agricultural University, Sutrisno, Dewayany; Geospatial Information Agency, Carolita, Ita; National Aeronautic and Aerospace Agency, Yuxing, Zhang; Academy of Forest Inventory and Planning, Xuenjun, Wang; Academy of Forest Inventory and Planning, Qian, Liu; Academy of Forest Inventory and Planning |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Ahmad Dahlan
, 2019
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| Subjects: | |
| Online Access: |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/12585 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/12585/6863 |
Daftar Isi:
- 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.