Abstract
Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.
Highlights
Deforestation means a long-term reduction in trees due to natural or anthropogenic activities [1]
These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies
The output classification maps were validated through photointerpretation of high-resolution images, the variation in forest loss/gain trends was accurately computed and compared to the results obtained from similar studies
Summary
Deforestation means a long-term reduction in trees due to natural or anthropogenic activities [1]. It occurs worldwide as a result from complex socio-economic processes including population and inhabitation growth, agricultural expansion and wood extraction in developing countries [2,3]. Many severe problems are caused by deforestation, including biodiversity loss, soil erosion, water cycle changes, and potential global effects [4]. Rapid global change urges the understanding of temporal ecosystem dynamics [14], which—with the use of remote sensing techniques—helps to control and prevent further deterioration of forest clearing [15,16]
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