@Article{JaconGalvSilvSant:2021:ExHy,
               author = "Jacon, Aline Daniele and Galv{\~a}o, L{\^e}nio Soares and Silva, 
                         Ricardo Dal'Agnol da and Santos, Jo{\~a}o Roberto dos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         Manchester} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Aboveground biomass estimates over Brazilian savannas using 
                         hyperspectral metrics and machine learning models: experiences 
                         with Hyperion/EO-1",
              journal = "Giscience and Remote Sensing",
                 year = "2021",
               volume = "58",
               number = "7",
                pages = "1112--1129",
                month = "Oct.",
             keywords = "Hyperspectral remote sensing, aboveground biomass (AGB), savannas, 
                         Cerrado, machine learning, Hyperion/EO-1.",
             abstract = "We investigated the potential of hyperspectral remote sensing to 
                         estimate aboveground biomass (AGB) over the Brazilian savannas 
                         (Cerrado), the second-largest source of carbon emissions in 
                         Brazil. For this purpose, a Hyperion/Earth Observing-1 (EO-1) 
                         image was collected in the dry season at the Ecological Station of 
                         {\'A}guas Emendadas (ESAE). In order to estimate the AGB, we 
                         evaluated the performance of five machine learning models 
                         (Classification and Regression Trees CART; Cubist CB, Partial 
                         Least Squares Regression PLS; Random Forest RF; and Support Vector 
                         Machine SVM) and four sets of metrics (reflectance, narrowband 
                         vegetation indices VIs; absorption band parameters; and the 
                         combination of these attributes). The lowest root mean square 
                         error (RMSE) was obtained for RF using VIs (29%) and a combination 
                         of metrics (28%). For VIs, RF differed from CUB, PLS and SVM at 5% 
                         significance level. From cross-validation results, the RMSE was 
                         26.36% for grasslands, 35.04% for open savannas, and 24.85% for 
                         dense savannas. The RF model with VIs had the most stable 
                         predictive performance across the models, as indicated by small 
                         variations in RMSE from CART to SVM. The five most important 
                         ranked VIs in the RF model were the Normalized Difference 
                         Vegetation Index (NDVI), Pigment Specific Simple Ratio (PSSR), 
                         Enhanced Vegetation Index (EVI), Red Edge Normalized Difference 
                         Vegetation Index (RENDVI) and Structure Insensitive Pigment Index 
                         (SIPI). Most of their relationships with AGB were non-linear. The 
                         resultant AGB estimates showed consistent results with a 
                         vegetation cover map of the ESAE. Areas of the ESAE with AGB lower 
                         than 10 Mg.ha\−1 were coincident with the occurrence of 
                         grassland physiognomies (savanna grasslands and shrub savannas), 
                         while areas with AGB higher than 25 Mg.ha\−1 matched the 
                         occurrence of dense savanna physiognomies (woodland savanna and 
                         dense woodland savanna). Grassland areas showed larger values of 
                         coefficient of variation (CV) than areas of dense savannas. These 
                         first-hand results set a baseline of models and metrics for AGB 
                         modeling of savannas during the future transition from current 
                         sampling-type hyperspectral missions (< 10 km of swath) to 
                         large-coverage hyperspectral satellites (> 100 km of swath).",
                  doi = "10.1080/15481603.2021.1969630",
                  url = "http://dx.doi.org/10.1080/15481603.2021.1969630",
                 issn = "1548-1603",
             language = "en",
           targetfile = "jacon_2021_aboveground.pdf",
        urlaccessdate = "05 maio 2024"
}