@Article{GanemXRFOCCRDS:2022:MaSoAm,
               author = "Ganem, Khalil Ali and Xue, Yongkang and Rodrigues, Ariane de 
                         Almeida and Franca Rocha, Washington and Oliveira, Marceli Terra 
                         de and Carvalho, Nathalia Silva de and Cayo, Efrain Yury Turpo and 
                         Rosa, Marcos Reis and Dutra, Andeise Cerqueira and Shimabukuro, 
                         Yosio Edemir",
          affiliation = "{University of California} and {University of California} and 
                         {Universidade de Bras{\'{\i}}lia (UnB)} and {Universidade 
                         Estadual de Feira de Santana (UEFS)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidad Nacional Agraria La Molina} and 
                         {Universidade Estadual de Feira de Santana (UEFS)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Mapping South America's Drylands through Remote Sensing-A Review 
                         of the Methodological Trends and Current Challenges",
              journal = "Remote Sensing",
                 year = "2022",
               volume = "14",
               number = "3",
                pages = "e736",
                month = "Feb.",
             keywords = "land use and land cover, aridity, drought, Landsat, MODIS, 
                         savannas, shrublands, grasslands, woodlands.",
             abstract = "The scientific grasp of the distribution and dynamics of land use 
                         and land cover (LULC) changes in South America is still limited. 
                         This is especially true for the continent's hyperarid, arid, 
                         semiarid, and dry subhumid zones, collectively known as drylands, 
                         which are under-represented ecosystems that are highly threatened 
                         by climate change and human activity. Maps of LULC in drylands 
                         are, thus, essential in order to investigate their vulnerability 
                         to both natural and anthropogenic impacts. This paper 
                         comprehensively reviewed existing mapping initiatives of South 
                         America's drylands to discuss the main knowledge gaps, as well as 
                         central methodological trends and challenges, for advancing our 
                         understanding of LULC dynamics in these fragile ecosystems. Our 
                         review centered on five essential aspects of remote-sensing-based 
                         LULC mapping: scale, datasets, classification techniques, number 
                         of classes (legends), and validation protocols. The results 
                         indicated that the Landsat sensor dataset was the most frequently 
                         used, followed by AVHRR and MODIS, and no studies used recently 
                         available high-resolution satellite sensors. Machine learning 
                         algorithms emerged as a broadly employed methodology for land 
                         cover classification in South America. Still, such advancement in 
                         classification methods did not yet reflect in the upsurge of 
                         detailed mapping of dryland vegetation types and functional 
                         groups. Among the 23 mapping initiatives, the number of LULC 
                         classes in their respective legends varied from 6 to 39, with 1 to 
                         14 classes representing drylands. Validation protocols included 
                         fieldwork and automatic processes with sampling strategies ranging 
                         from solely random to stratified approaches. Finally, we discussed 
                         the opportunities and challenges for advancing research on 
                         desertification, climate change, fire mapping, and the resilience 
                         of dryland populations. By and large, multi-level studies for 
                         dryland vegetation mapping are still lacking.",
                  doi = "10.3390/rs14030736",
                  url = "http://dx.doi.org/10.3390/rs14030736",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-14-00736-v2.pdf",
        urlaccessdate = "06 jun. 2024"
}