author = "Dalagnol, Ricardo and Phillips, Oliver L. and Gloor, Emanuel and 
                         Galv{\~a}o, L{\^e}nio Soares and Wagner, Fabien Hubert and 
                         Locks, Charton J. and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz 
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Leeds} and {University of Leeds} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Servi{\c{c}}o Florestal Brasileiro} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Quantifying canopy tree loss and gap recovery in tropical forests 
                         under low-intensity logging using VHR satellite imagery and 
                         airborne LiDAR",
              journal = "Remote Sensing",
                 year = "2019",
               volume = "11",
               number = "7",
                month = "Apr.",
             keywords = "remote sensing, forest management, disturbance monitoring, forest 
                         dynamics, multi-temporal analysis, WorldView-2, GeoEye-1, random 
                         forest, Amazon, Jamari National Forest.",
             abstract = "Logging, including selective and illegal activities, is 
                         widespread, affecting the carbon cycle and the biodiversity of 
                         tropical forests. However, automated approaches using very high 
                         resolution (VHR) satellite data (\≤ 1 m spatial resolution) 
                         to accurately track these small-scale human disturbances over 
                         large and remote areas are not readily available. The main 
                         constraint for performing this type of analysis is the lack of 
                         spatially accurate tree-scale validation data. In this study, we 
                         assessed the potential of VHR satellite imagery to detect canopy 
                         tree loss related to selective logging in closed-canopy tropical 
                         forests. To do this, we compared the tree loss detection 
                         capability of WorldView-2 and GeoEye-1 satellites with airborne 
                         LiDAR, which acquired pre- and post-logging data at the Jamari 
                         National Forest in the Brazilian Amazon. We found that logging 
                         drove changes in canopy height ranging from -5.6 to -42.2 m, with 
                         a mean reduction of -23.5 m. A simple LiDAR height difference 
                         threshold of -10 m was enough to map 97% of the logged trees. 
                         Compared to LiDAR, tree losses can be detected using VHR satellite 
                         imagery and a random forest (RF) model with an average precision 
                         of 64%, while mapping 60% of the total tree loss. Tree losses 
                         associated with large gap openings or tall trees were more 
                         successfully detected. In general, the most important remote 
                         sensing metrics for the RF model were standard deviation 
                         statistics, especially those extracted from the reflectance of the 
                         visible bands (R, G, B), and the shadow fraction. While most small 
                         canopy gaps closed within \∼2 years, larger gaps could 
                         still be observed over a longer time. Nevertheless, the use of 
                         annual imagery is advised to reach acceptable detectability. Our 
                         study shows that VHR satellite imagery has the potential for 
                         monitoring the logging in tropical forests and detecting hotspots 
                         of natural disturbance with a low cost at the regional scale.",
                  doi = "10.3390/rs11070817",
                  url = "http://dx.doi.org/10.3390/rs11070817",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-11-00817-v2.pdf",
        urlaccessdate = "27 nov. 2020"