@Article{PrietoLMBRWSS:2023:InOpSA,
               author = "Prieto, Juan Doblas and Lima, Lucas and Mermoz, Stephane and 
                         Bouvet, Alexandre and Reiche, Johannes and Watanabe, Manabu and 
                         Sant'Anna, Sidnei Jo{\~a}o Siqueira and Shimabukuro, Yosio 
                         Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and SIG 
                         Formation, IDGEO and GlobEO and {CNRS/CNES/ IRD/INRAE/UPS} and 
                         {Wageningen University} and {Japan Aerospace Exploration Agency 
                         (JAXA)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Inter-comparison of optical and SAR-based forest disturbance 
                         warning systems in the Amazon shows the potential of combined 
                         SAR-optical monitoring",
              journal = "International Journal of Remote Sensing",
                 year = "2023",
               volume = "44",
               number = "1",
                pages = "59--77",
                month = "Jan.",
             abstract = "More than half a decade after the launch of the Sentinel-1A C-band 
                         SAR satellite, several near real-time forest disturbances 
                         detection systems based on backscattering time series analysis 
                         have been developed and made operational. Every system has its own 
                         particular approach to change detection. Here, we have compared 
                         the performance of the main SAR-based near real-time operational 
                         forest disturbance detection systems produced by research agencies 
                         (INPE, in Brazil, CESBIO, in France, JAXA, in Japan, and 
                         Wageningen University, in the Netherlands), and compared them to 
                         the state-of-the-art optical algorithm, University of Maryland's 
                         GLAD-S2. We implemented an innovative validation protocol, 
                         specially conceived to encompass all the analysed systems, which 
                         measured every system's accuracy and detection speed in four 
                         different areas of the Amazon basin. The results indicated that, 
                         when parametrized equally, all the Sentinel-1 SAR methods 
                         outperformed the reference optical method in terms of sample-count 
                         F1-Score, having comparable results among them. The GLAD-S2 
                         optical method showed superior results in terms of user's accuracy 
                         (UA), issuing no false detections, but had a lower producer 
                         accuracy (PA, 84.88%) when compared to the Sentinel-1 SAR-based 
                         systems (PA,90%). Wageningen University's system, RADD, proved to 
                         be relatively faster, especially in heavily clouded regions, where 
                         RADD warnings were issued 41 days before optical ones, and the one 
                         that better performs on small disturbed patches (< 0.25 ha) with a 
                         UA of 70.11%. Of all the high-resolution SAR methods, CESBIO's had 
                         the best results regarding UA (99.0%). Finally, we tested the 
                         potential of three hypothetical combined optical-SAR systems. The 
                         results show that these combined systems would have excellent 
                         detection capabilities, exceeding largely the producer's accuracy 
                         of all the tested methods at the cost of a slightly diminished 
                         user's accuracy, and constitute a promising and feasible approach 
                         for the forthcoming forest monitoring systems.",
                  doi = "10.1080/01431161.2022.2157684",
                  url = "http://dx.doi.org/10.1080/01431161.2022.2157684",
                 issn = "0143-1161",
             language = "en",
           targetfile = "Inter comparison of optical and SAR based forest disturbance 
                         warning systems in the Amazon shows the potential of combined SAR 
                         optical monitoring.pdf",
        urlaccessdate = "11 maio 2024"
}