@Article{SimionatoBertOsak:2021:IdArMi,
               author = "Simionato, Jackson and Bertani, Gabriel and Osako, Liliana 
                         Sayuri",
          affiliation = "{Universidade Federal de Santa Catarina (UFSC)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Santa Catarina (UFSC)}",
                title = "Identification of artisanal mining sites in the Amazon Rainforest 
                         using Geographic Object-Based Image Analysis (GEOBIA) and Data 
                         Mining techniques",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2021",
               volume = "24",
                pages = "e100633",
                month = "Nov.",
             keywords = "Amazon Rainforest, Artisanal mining, Data mining, Decision tree, 
                         Geographic object-based image analysis (GEOBIA), Sentinel-2.",
             abstract = "Par{\'a} is a Brazilian state leader in deforestation and 
                         deserves special attention due to the intense artisanal mining 
                         activity that has caused severe environmental damage to the Amazon 
                         Rainforest. Remote sensing is an important tool for identifying 
                         areas degraded by mining activities. However, the large 
                         territorial extension of the Amazon Rainforest and the equally 
                         large corresponding database make the mapping by 
                         photointerpretation a costly and slow process. This study attempts 
                         to overcome this obstacle by employing the Geographic Object-Based 
                         Image Analysis (GEOBIA) approach together with Data Mining 
                         techniques in the automatic identification of areas degraded by 
                         artisanal mining in the Crepori National Forest (CNF). A NDVI 
                         image and a multiband image derived from Sentinel-2 data were 
                         segmented and the former proved to be more appropriate to the 
                         development of this research. The use of the Correlation-based 
                         Feature Selection (CFS) algorithm in attribute selection led to a 
                         55% database dimensionality reduction. Additionally, the results 
                         obtained in the decision tree construction by the J48 algorithm 
                         showed that the spectral attributes were the most relevant in the 
                         classification of artisanal mining areas, especially the 
                         attributes related to the near infrared (NIR) band. The attributes 
                         of textural and spatial origin also contributed to the model, 
                         whereas the contextual attribute was not relevant to our 
                         classification problem. The results from classification 
                         demonstrated that the Vegetation class is the largest in the 
                         Crepori National Forest, representing 99.50% of the total area, 
                         followed by Areas Degraded by Artisanal Mining and Other 
                         Anthropized Areas, representing 0.17% of the total area, and, 
                         lastly, the Hydrography class totaling 0.16%. Total anthropization 
                         in the CNF decreased between 2014 and 2017, from 2,955 ha to 2,506 
                         ha. It is worth noting that, when compared with the Brazilian 
                         Forest Service's (Servi{\c{c}}o Florestal Brasileiro) data, our 
                         results reveal that more than 50% (679.46 ha) of artisanal mining 
                         areas mapped in 2017 were installed after 2014, majorly in the CNF 
                         southern region. The performance of our classification model is 
                         good, reaching a global accuracy of 88.18% and a Kappa coefficient 
                         of 0.84. In class-by-class indexes, the method presented a minimum 
                         precision of 0.79 and a minimum recall of 0.75, both referring to 
                         the Other Anthropized Areas class.",
                  doi = "10.1016/j.rsase.2021.100633",
                  url = "http://dx.doi.org/10.1016/j.rsase.2021.100633",
                 issn = "2352-9385",
             language = "en",
           targetfile = "simionato_2021.pdf",
        urlaccessdate = "27 abr. 2024"
}