@Article{UeharaKörtSoarQuev:2022:TiMeAp,
               author = "Uehara, Tatiana Dias Tardelli and K{\"o}rting, Thales Sehn and 
                         Soares, Anderson dos Reis and Quevedo, Renata Pacheco",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Cognizant Technology 
                         Solutions} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Time-series metrics applied to land use and land cover mapping 
                         with focus on landslide detection",
              journal = "Journal of Applied Remote Sensing",
                 year = "2022",
               volume = "16",
               number = "3",
                pages = "e034518",
                month = "July",
             keywords = "mass movements, image time series, landslide inventory, random 
                         forest, machine learning, remote sensing.",
             abstract = "Landslides are a recurring phenomenon in Brazil and have caused 
                         many socioeconomic losses and casualties. To monitor them, land 
                         use and land cover (LULC) and landslide inventory maps are 
                         essential to identifying high susceptibility areas. In this sense, 
                         the main aim of this study is to produce LULC classification 
                         focused on landslide detection via semi-automatic methods, using 
                         data mining techniques with remote sensing time-series imagery. 
                         For that, different indices, such as the normalized difference 
                         vegetation index, the normalized difference built-up index (NDBI), 
                         and the soil adjusted vegetation index were extracted from 
                         Sentinel-2 imagery. Basic, polar, and fractal metrics were 
                         extracted from the time series. From the Shuttle Radar Topography 
                         Mission digital elevation model, six geomorphometric features were 
                         extracted. Then, classification was performed with random forest 
                         with four different approaches: mono-temporal, bi-temporal, 
                         metrical, and all. In every approach, the NDBI index or metric 
                         derived from it presented the highest importance, and the slope 
                         was ranked among the six first predictors. The all approach showed 
                         the highest overall accuracy (OA) (88.96%), followed by metrical 
                         (87.90%), bi-temporal (82.59%), and mono-temporal (74.95%). 
                         Briefly, the metrical approach presented the most beneficial 
                         result, presenting high OA and low levels of commission and 
                         omission errors.",
                  doi = "10.1117/1.JRS.16.034518",
                  url = "http://dx.doi.org/10.1117/1.JRS.16.034518",
                 issn = "1931-3195",
             language = "en",
           targetfile = "034518_1.pdf",
        urlaccessdate = "01 maio 2024"
}