@InProceedings{SantosShiDuaJorGas:2017:MuApEs,
               author = "Santos, Erone Ghizoni and Shimabukuro, Yosio Edemir and Duarte, 
                         Valdete and Jorge, Anderson and Gasparini, Kaio Allan Cruz",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Multi-stage approach to estimate forest biomass in degraded area 
                         by fire and selective logging",
            booktitle = "Proceedings...",
                 year = "2017",
         organization = "AGU Fall Meeting",
             abstract = "The Amazon forest has been the target of several threats 
                         throughout the years. Anthropogenic disturbances in the region can 
                         significantly alter this environment, affecting directly the 
                         dynamics and structure of tropical forests. Monitoring these 
                         threats of forest degradation across the Amazon is of paramount to 
                         understand the impacts of disturbances in the tropics. With the 
                         advance of new technologies such as Light Detection and Ranging 
                         (LiDAR) the quantification and development of methodologies to 
                         monitor forest degradation in the Amazon is possible and may bring 
                         considerable contributions to this topic. The objective of this 
                         study was to use remote sensing data to assess and estimate the 
                         aboveground biomass (AGB) across different levels of degradation 
                         (fire and selective logging) using multi-stage approach between 
                         airborne LiDAR and orbital image. The study area is in the 
                         northern part of the state of Mato Grosso, Brazil. It is 
                         predominantly characterized by agricultural land and remnants of 
                         the Amazon Forest intact and degraded by either anthropic or 
                         natural reasons (selective logging and/or fire). More 
                         specifically, the study area corresponds to path/row 226/69 of 
                         OLI/Landsat 8 image. With a forest mask generated from the 
                         multi-resolution segmentation, agriculture and forest areas, 
                         forest biomass was calculated from LiDAR data and correlated with 
                         texture images, vegetation indices and fraction images by Linear 
                         Spectral Unmixing of OLI/Landsat 8 image and extrapolated to the 
                         entire scene 226/69 and validated with field inventories. The 
                         results showed that there is a moderate to strong correlation 
                         between forest biomass and texture data, vegetation indices and 
                         fraction images. With that, it is possible to extract biomass 
                         information and create maps using optical data, specifically by 
                         combining vegetation indices, which contain forest greening 
                         information with texture data that contains forest structure 
                         information. Then it was possible to extrapolate the biomass to 
                         the entire scene (226/69) from the optical data and to obtain an 
                         overview of the biomass distribution throughout the area.",
  conference-location = "New Orleans",
      conference-year = "11-15 Dec.",
             language = "en",
           targetfile = "santos_mult.pdf",
        urlaccessdate = "28 abr. 2024"
}