@Article{ZhangHagaSilvLiu:2021:FoCaCh,
               author = "Zhang, Huixiang and Hagan, Daniel Fiifi Tawia and Silva, Ricardo 
                         Dal'Agnol da and Liu, Yi",
          affiliation = "{Nanjing University of Information Science and Technology} and 
                         {Nanjing University of Information Science and Technology} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Nanjing 
                         University of Information Science and Technology}",
                title = "Forest canopy changes in the southern amazon during the 2019 fire 
                         season based on passive microwave and optical satellite 
                         observations",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "12",
                pages = "e2238",
                month = "June",
             keywords = "Canopy changes, Fire, Optical indices, The Amazon, Vegetation 
                         optical depth.",
             abstract = "Canopy dynamics associated with fires in tropical forests play a 
                         critical role in the terrestrial carbon cycle and climate 
                         feedbacks. The aim of this study was to characterize forest canopy 
                         dynamics in the southern Amazon during the 2019 fire season 
                         (JulyOctober) using passive microwave-based vegetation optical 
                         depth (VOD) and three optical-based indices. First, we found that 
                         precipitation during JulyOctober 2019 was close to the climatic 
                         means, suggesting that there were no extreme hydrometeorological 
                         events in 2019 and that fire was the dominant factor causing 
                         forest canopy anomalies. Second, based on the active fire product 
                         (MCD14ML), the total number of active fires over each grid cell 
                         was calculated for each month. The number of active fires during 
                         the fire season in 2019 was above average, particularly in August 
                         and September. Third, we compared the anomalies of VOD and 
                         optical-based indices (the normalized difference vegetation index 
                         (NDVI), the enhanced vegetation index (EVI), and the normalized 
                         burn ratio (NBR)) against the spatiotemporal distribution of fires 
                         during JulyOctober 2019. Spatially, the location with a 
                         concentrated distribution of significant negative VOD anomalies 
                         was matched with the grid cells with fire activities, whereas the 
                         concentrated distribution of strong negative anomalies in 
                         optical-based indices were found in both burned and unburned grid 
                         cells. When we focused on the temporal pattern over the grid cells 
                         with fire activity, the VOD and the optical-based indices behaved 
                         similarly from July to October 2019, i.e., the magnitude of 
                         negative anomalies became stronger with increased fire occurrences 
                         and reached the peak of negative anomalies in September before 
                         decreasing in October. A discrepancy was observed in the magnitude 
                         of negative anomalies of the optical-based indices and the VOD; 
                         the magnitude of optical-based indices was larger than the VOD in 
                         AugustSeptember and recovered much faster than the VOD over the 
                         grid cells with relatively low fire activity in October. The most 
                         likely reason for their different responses is that the VOD 
                         represents the dynamics of both photosynthetic (leaf) and 
                         nonphotosynthetic (branches) biomass, whereas optical-based 
                         indices are only sensitive to photosynthetic (leaf) active 
                         biomass, which recovers faster. Our results demonstrate that VOD 
                         can detect the spatiotemporal of canopy dynamics caused by fire 
                         and postfire canopy biomass recovery over high-biomass rainforest, 
                         which enables more comprehensive assessments, together with 
                         classic optical remote sensing approaches.",
                  doi = "10.3390/rs13122238",
                  url = "http://dx.doi.org/10.3390/rs13122238",
                 issn = "2072-4292",
             language = "en",
           targetfile = "zhang_forest.pdf",
        urlaccessdate = "09 maio 2024"
}