Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon

Abstract

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.

Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon

“…Therefore, it is possible that the key to more accurately identify disturbed areas does not lie in the lack of "detectability" through the ∆rNBR index, but rather in the capacity to separate real forest disturbances from noise. Notwithstanding, the range of all accuracies found here are well within the range of previous works, or exceed them [30,31,34,38,50] [43,45,46,[74][75][76]. However, more information is needed regarding the differing capabilities of the two sensors for land cover mapping and the consequences of these differences in the context of forest cover monitoring and forest cover change assessment.…”

“…Landsat 8 data increased the estimated area of selective logging by 36.9%, compared to Sentinel-2 ( Table 3). Logging infrastructure, such as log landings and logging roads, are generally smaller than the Landsat spatial resolution [38]. In consequence, the possible area overestimation can be attributed to the high response values of mixed pixels in Landsat 8, that show up as a set of "pure" pixels in Sentinel-2, for the same corresponding area.…”

Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks

“…To create our ground truth masks, we used the Brazilian Institute of Space Research's Project for Deforestation Mapping (INPE's PRODES) data [7] for the years of 2018 and 2019 as a visual guide and then refined it by remapping the deforestation polygons on a smaller scale. PRODES data are commonly used for deforestation reports and studies have used it before when modeling and studying deforestation dynamics [85,86]. The changes were mapped using digitizing tools from the QGIS software [87] at 1:30,000 scale and subsequently transformed into binary raster files with 0 and 1 as absence-presence codes, respectively.…”

Análise Estatística Espacial De Métricas Da Paisagem Utilizando O Patch Analyst

“…Além disso, as imagens fração podem ser usadas para mapear florestas degradadas devido às seguintes características: a) imagens fração vegetação destacam as condições da cobertura florestal e permitem diferenciar entre áreas florestais e não florestais da mesma maneira que os índices de vegetação existentes (como por exemplo, Índice de Vegetação de Diferença Normalizada -NDVI e Índice de Vegetação Melhorada -EVI); b) imagens fração sombra destacam áreas com baixos valores de reflexão, como água, sombra e áreas queimadas e, consequentemente, permitem identificar a degradação florestal causada por queimadas; e c) imagens fração solo destacam áreas com altos valores de reflexão, como solo exposto e, também, destacam áreas menores do que o tamanho do pixel (como por exemplo, pátio de estoque e carreadores de atividades de exploração seletiva de madeira), permitindo, consequentemente, identificar as áreas de floresta degradada causada pela exploração seletiva de madeira. Devido ao desafio de estabelecer um método operacional e eficiente para detecção e monitoramento da degradação florestal, muitos esforços têm sido relatados usando o Modelo Linear de Mistura Espectral para mapear o corte seletivo (ASNER et al, 2005; ANWAR e STEIN, 2012), áreas queimadas (QUINTANO et al, 2006;ANDERSON et al, 2005;SHIMABUKURO et al, 2009), degradação florestal (SOUZA et al, 2005, SHIMABUKURO et al 2019, mudanças na cobertura da floresta associadas ao desmatamento e degradação florestal (SOUZA et al, 2013).…”