@Article{DalagnolWGBOSBPSFSA:2023:MaTrFo,
               author = "Dalagnol, Ricardo and Wagner, Fabien Hubert and Galv{\~a}o, 
                         L{\^e}nio Soares and Braga, Daniel and Osborn, Fiona and Sagang, 
                         Le Bienfaiteur and Bispo, Polyanna da Concei{\c{c}}{\~a}o and 
                         Payne, Matthew and Silva Junior, Celso and Favrichon, Samuel and 
                         Silgueiro, Vinicius and Anderson, Liana O.",
          affiliation = "{University of California} and {University of California} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and CTrees and {University 
                         of California} and {University of Manchester} and {University of 
                         Manchester} and {University of California} and {NASA-Jet 
                         Propulsion Laboratory} and {Instituto Centro de Vida (ICV)} and 
                         {Centro Nacional de Monitoramento e Alertas de Desastres Naturais 
                         (CEMADEN)}",
                title = "Mapping tropical forest degradation with deep learning and Planet 
                         NICFI data",
              journal = "Remote Sensing of Environment",
                 year = "2023",
               volume = "298",
                pages = "e113798",
                month = "Dec.",
             keywords = "Amazon, Fire, Forest degradation, Logging, U-net.",
             abstract = "Tropical rainforests from the Brazilian Amazon are frequently 
                         degraded by logging, fire, edge effects and minor unpaved roads. 
                         However, mapping the extent of degradation remains challenging 
                         because of the lack of frequent high-spatial resolution satellite 
                         observations, occlusion of understory disturbances, quick recovery 
                         of leafy vegetation, and limitations of conventional 
                         reflectance-based remote sensing techniques. Here, we introduce a 
                         new approach to map forest degradation caused by logging, fire, 
                         and road construction based on deep learning (DL), henceforth 
                         called DL-DEGRAD, using very high spatial (4.77 m) and bi-annual 
                         to monthly temporal resolution of the Planet NICFI imagery. We 
                         applied DL-DEGRAD model over forests of the state of Mato Grosso 
                         in Brazil to map forest degradation with attributions from 2016 to 
                         2021 at six-month intervals. A total of 73,744 images (256 × 256 
                         pixels in size) were visually interpreted and manually labeled 
                         with three semantic classes (logging, fire, and roads) to 
                         train/validate a U-Net model. We predicted the three classes over 
                         the study area for all dates, producing accumulated degradation 
                         maps biannually. Estimates of accuracy and areas of degradation 
                         were performed using a probability design-based stratified random 
                         sampling approach (n = 2678 samples) and compared it with existing 
                         operational data products at the state level. DL-DEGRAD performed 
                         significantly better than all other data products in mapping 
                         logging activities (F1-score = 68.9) and forest fire (F1-score = 
                         75.6) when compared with the Brazil's national maps (SIMEX, DETER, 
                         MapBiomas Fire) and global products (UMD-GFC, TMF, FireCCI, 
                         FireGFL, GABAM, MCD64). Pixel-based spatial comparison of 
                         degradation areas showed the highest agreement with DETER and 
                         SIMEX as Brazil official data products derived from visual 
                         interpretation of Landsat imagery. The U-Net model applied to 
                         NICFI data performed as closely to a trained human delineation of 
                         logged and burned forests, suggesting the methodology can readily 
                         scale up the mapping and monitoring of degraded forests at 
                         national to regional scales. Over the state of Mato Grosso, the 
                         combined effects of logging and fire are degrading the remaining 
                         intact forests at an average rate of 8443 km2 year\−1 from 
                         2017 to 2021. In 2020, a record degradation area of 13,294 km2 was 
                         estimated from DL-DEGRAD, which was two times the areas of 
                         deforestation.",
                  doi = "10.1016/j.rse.2023.113798",
                  url = "http://dx.doi.org/10.1016/j.rse.2023.113798",
                 issn = "0034-4257",
             language = "en",
           targetfile = "1-s2.0-S0034425723003498-main.pdf",
        urlaccessdate = "12 maio 2024"
}