@Article{AraújoGalvDala:2023:EvChVe,
               author = "Ara{\'u}jo, Juliana de Abreu and Galv{\~a}o, L{\^e}nio Soares 
                         and Dalagnol, Ricardo",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         California Los Angeles (UCLA)}",
                title = "Evaluating changes with vegetation cover in PRISMA's spectral, 
                         spatial, and temporal attributes and their performance for 
                         classifying savannahs in Brazil",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2023",
               volume = "32",
                pages = "e101074",
                month = "Nov.",
             keywords = "Absorption bands, Bras{\'{\i}}lia National Park, Cerrado, 
                         Hyperspectral remote sensing, Reflectance, Vegetation indices.",
             abstract = "The recent advent of hyperspectral satellites with larger swath 
                         width than that of previous sampling missions brings new 
                         perspectives for mapping savannahs in Brazil. Here, we evaluated 
                         changes with vegetation cover in different spectral, spatial, and 
                         temporal attributes, derived from the PRecursore IperSpettrale 
                         della Missione Applicativa (PRISMA), and their performance for 
                         Random Forest (RF) classification of savannah physiognomies at the 
                         Bras{\'{\i}}lia National Park (BNP). To obtain the spectral 
                         attributes, we selected a PRISMA image acquired during the local 
                         dry season (August 17, 2020). We evaluated the classification 
                         performance of the reflectance of 166 bands, 22 vegetation indices 
                         (VIs), and four endmember fractions derived from a linear spectral 
                         mixture model (SMA). In addition, 24 parameters describing the 
                         depth, area, width, and asymmetry of the absorption bands centred 
                         at 680 nm (chlorophyll), 980 nm and 1200 nm (leaf water), and 1750 
                         nm, 2100 nm and 2300 nm (lignin-cellulose) were also considered in 
                         the analysis. For the spatial attributes, we tested the 
                         performance of 8 Gray Level Co-occurrence Matrix (GLCM) metrics of 
                         image texture associated with the 864-nm near-infrared (NIR) band. 
                         In order to determine the temporal attributes, we considered other 
                         three PRISMA images obtained in 2020 (11 May, 4 September, and 3 
                         October). Using these images, we calculated the rate of changes 
                         for each of the 22 VIs in the browning and greening periods of the 
                         savannah environment. A feature selection procedure was applied to 
                         the datasets. The results showed that the vegetation gradient from 
                         savannah grassland to woodland areas controlled the behavior of 
                         most attributes. For instance, the reflectance of the PRISMA NIR 
                         bands and the depth of the chlorophyll (680 nm) and leaf water 
                         (980 nm and 1200 nm) absorption bands increased with increasing 
                         vegetation cover. On the other hand, the reflectance of the 
                         visible and shortwave infrared (SWIR) bands and the depth of 
                         spectral features associated with non-photosynthetic vegetation 
                         followed the opposite pattern. Except for the metrics of image 
                         texture, the other spectral (reflectance, VIs, endmember 
                         fractions, and absorption band parameters) and temporal (browning 
                         and greening rates of vegetation changes) attributes had close 
                         classification performance before or after feature selection. When 
                         combined into a single dataset, gains of 15% in overall 
                         classification accuracy were observed when compared to the 
                         individual use of reflectance data in the analysis. From the seven 
                         savannah classes tested for classification, areas of woodland 
                         savannah, savannah grassland, and riparian forest were adequately 
                         mapped using this approach (F1-scores between 0.72 and 0.91). In 
                         contrast, areas of wooded savannah, with and without Trembleias 
                         species, had low F1-scores (0.28 and 0.20, respectively). Our 
                         findings reinforce the need of considering different hyperspectral 
                         attributes in classification approaches of the savannahs in 
                         Brazil.",
                  doi = "10.1016/j.rsase.2023.101074",
                  url = "http://dx.doi.org/10.1016/j.rsase.2023.101074",
                 issn = "2352-9385",
             language = "en",
           targetfile = "1-s2.0-S2352938523001568-main.pdf",
        urlaccessdate = "21 maio 2024"
}