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Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine

1
Department of Civil and Environmental Engineering (DICA), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
2
General Troops Faculty, Vasil Levski National Military University, 5000 Veliko Tarnovo, Bulgaria
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 580; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100580
Received: 11 August 2020 / Revised: 6 September 2020 / Accepted: 28 September 2020 / Published: 1 October 2020
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. View Full-Text
Keywords: deforestation; forest change detection; land cover dynamics; Google Earth Engine; landsat; Sentinel-2; supervised classification deforestation; forest change detection; land cover dynamics; Google Earth Engine; landsat; Sentinel-2; supervised classification
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MDPI and ACS Style

Brovelli, M.A.; Sun, Y.; Yordanov, V. Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine. ISPRS Int. J. Geo-Inf. 2020, 9, 580. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100580

AMA Style

Brovelli MA, Sun Y, Yordanov V. Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine. ISPRS International Journal of Geo-Information. 2020; 9(10):580. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100580

Chicago/Turabian Style

Brovelli, Maria A., Yaru Sun, and Vasil Yordanov. 2020. "Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine" ISPRS International Journal of Geo-Information 9, no. 10: 580. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100580

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