@Article{LechlerPiSoSaChVe:2020:ExNaHa,
               author = "Lechler, S. and Picoli, Michelle Cristina Ara{\'u}jo and Soares, 
                         Anderson Reis and Sanchez Ipia, Alber Hamersson and Chaves, Michel 
                         Eust{\'a}quio Dantas and Vertegen, J.",
          affiliation = "{University of Munster} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Munster}",
                title = "Exploring nasa's harmonized landsat and sentinel-2 (HLS) dataset 
                         to monitor deforestation in the amazon rainforest",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2020",
               volume = "43",
               number = "B3",
                pages = "705--711",
                month = "Aug.",
                 note = "2020 24th ISPRS Congress - Technical Commission III; Nice, 
                         Virtual; France; 31 August 2020 through 2 September 2020",
             keywords = "HLS dataset, BFAST monitor, Random Forest, Brazilian Legal Amazon, 
                         Deforestation.",
             abstract = "Deforestation is a threat to biodiversity and the worlds climate. 
                         As agriculture and mining areas grow, forest loss becomes 
                         unbearable for the environment. Consequently, monitoring 
                         deforestation is crucial for decision makers to create polices. 
                         The most reliable deforestation data about the Amazon forest is 
                         generated by the Brazils National Institute for Space Research 
                         (INPE) through its PRODES project. This effort is labor and time 
                         intensive because it depends on visual interpretation from 
                         experts. Additionally, frequent Amazons atmospheric phenomena, 
                         such as clouds, difficult image analysis which induces alternative 
                         approaches such as time series analysis. One way to increase the 
                         number of images of an area consists of using images from 
                         different satellites. NASA provides the Harmonized Landsat and 
                         Sentinel-2 (HLS) dataset solving spectral dissimilarities of 
                         satellite sensors. In this paper, the possibilities of HLS for 
                         forest monitoring are explored by applying two deforestation 
                         detection methods, Break Detection for Additive Season and Trend 
                         (BFAST) monitor and Random Forest, over four different vegetation 
                         indices, NDVI, EVI, GEMI and SAVI. The SAVI index used as input 
                         for BFAST monitor performed the best in this data setup with 
                         95.23% for deforested pixel, 53.69% for non-deforested pixels. 
                         Although the HLS data is described as analysis ready, further 
                         pre-processing can enhance the outcome of the analysis. 
                         Especially, since the cloud and cirrus cover in the Amazon causes 
                         gaps in the dataset, a best pixel method is recommended to create 
                         patched images and thus a continuous time series as input for any 
                         land cover and land use classification.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2020-705-2020",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-705-2020",
                 issn = "0256-1840",
             language = "en",
           targetfile = "lechler_exploring.pdf",
        urlaccessdate = "27 abr. 2024"
}