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Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon
- Shimabukuro, Yosio Edemir, Arai, Egidio, Duarte, Valdete, Jorge, Anderson, Santos, Erone Ghizoni dos, Gasparini, Kaio Allan Cruz, Dutra, Andeise Cerqueira
- International journal of remote sensing 2019 v.40 no.14 pp. 5475-5496
- Landsat, United Nations Framework Convention on Climate Change, canopy, carbon, deforestation, emissions, fires, forest damage, forests, land use, logging, monitoring, remote sensing, soil, statistical models, thematic maps, Amazonia, Brazil
- Deforestation is the replacement of forest by other land use while degradation is a reduction of long-term canopy cover and/or forest stock. Forest degradation in the Brazilian Amazon is mainly due to selective logging of intact/un-managed forests and to uncontrolled fires. The deforestation contribution to carbon emission is already known but determining the contribution of forest degradation remains a challenge. Discrimination of logging from fires, both of which produce different levels of forest damage, is important for the UNFCCC (United Nations Framework Convention on Climate Change) REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program. This work presents a semi-automated procedure for monitoring deforestation and forest degradation in the Brazilian Amazon using fraction images derived from Linear Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper (TM) scene (path/row 226/068) covering part of Mato Grosso State in the Brazilian Amazon, was selected to develop the proposed method. First, the approach consisted of mapping deforested areas and mapping forest degraded by fires using image segmentation. Next, degraded areas due to selective logging activities were mapped using a pixel-based classifier. The results showed that the vegetation, soil, and shade fraction images allowed deforested areas to be mapped and monitored and to separate degraded forest areas caused by selective logging and by fires. The comparison of Landsat Operational Land Imager (OLI) and RapidEye results for the year 2013 showed an overall accuracy of 94%. We concluded that spatial resolution plays an important role for mapping selective logging features due to their characteristics. Therefore, when compared to Landsat data, the current availability of higher spatial and temporal resolution data, such as provided by Sentinel-2, is expected to improve the assessment of deforestation and forest degradation, especially caused by selective logging. This will facilitate the implementation of actions for forest protection.