%0 Journal Article
%4 sid.inpe.br/mtc-m21d/2022/09.16.12.13
%2 sid.inpe.br/mtc-m21d/2022/09.16.12.13.10
%@doi 10.3390/rs14153658
%@issn 2072-4292
%T DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis
%D 2022
%8 Aug.
%9 journal article
%A Doblas Prieto, Juan,
%A Reis, Mariane Souza,
%A Belluzzo, Amanda Pinoti,
%A Quadros, Camila Barata,
%A Moraes, Douglas Rafael Vidal de,
%A Almeida, Claudio Aparecido de,
%A Maurano, Luís Eduardo Pinheiro,
%A Carvalho, André Fernando Araújo de,
%A Sant'Anna, Sidnei João Siqueira,
%A Shimabukuro, Yosio Edemir,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress juandb@gmail.com
%@electronicmailaddress rin.reis@gmail.com
%@electronicmailaddress amanda.belluzzo@gmail.com
%@electronicmailaddress camilbquadros@gmail.com
%@electronicmailaddress moraesdrv@gmail.com
%@electronicmailaddress almeidaclaudio68@gmail.com
%@electronicmailaddress luis.maurano@inpe.br
%@electronicmailaddress afacarvalho@yahoo.com.br
%@electronicmailaddress sjssantanna@gmail.com
%@electronicmailaddress edemirshima@gmail.com
%B Remote Sensing
%V 14
%N 15
%P e3658
%K forest monitoring, SAR, Sentinel-1, time series analysis.
%X Continuous monitoring of forest disturbance on tropical forests is a fundamental tool to support proactive preservation actions and to stop further destruction of native vegetation. Currently most of the monitoring systems in operation are based on optical imagery, and thus are flaw-prone on areas with frequent cloud cover. As this, several Synthetic Aperture Radar (SAR)-based systems have been developed recently, aiming all-weather disturbance detection. This article presents the main aspects and the results of the first year of operation of the SAR based Near Real-Time Deforestation Detection System (DETER-R), an automated deforestation detection system focused on the Brazilian Amazon. DETER-R uses the Google Earth Engine platform to preprocess and analyze Sentinel-1 SAR time series. New images are treated and analyzed daily. After the automated analysis, the system vectorizes clusters of deforested pixels and sends the corresponding polygons to the environmental enforcement agency. After 12 months of operational life, the system has produced 88,572 forest disturbance warnings. Human validation of the warning polygons showed a extremely low rate of misdetections, with less than 0.2% of the detected area corresponding to false positives. During the first year of operation, DETER-R provided 33,234 warnings of interest to national monitoring agencies which were not detected by its optical counterpart DETER in the same period, corresponding to an area of 105,238.5 ha, or approximately 5% of the total detections. During the rainy season, the rate of additional detections increased as expected, reaching 8.1%.
%@language en
%3 remotesensing-14-03658-v2_compressed.pdf
%O Prêmio CAPES Elsevier 2023 - ODS 15: Vida terrestre