@Article{SilveiraEAGWBMSDS:2019:ReEfVe,
               author = "Silveira, Eduarda Martiniano de Oliveira and Esp{\'{\i}}rito 
                         Santo, Fernando Del Bon and Acerbi J{\'u}nior, Fausto Weimar and 
                         Galv{\~a}o, L{\^e}nio Soares and Withey, Kieran Daniel and 
                         Blackburn, George Alan and Mello, Jos{\'e} M{\'a}rcio de and 
                         Shimabukuro, Yosio Edemir and Domingues, Tomas and Scolforo, 
                         Jos{\'e} Roberto Soares",
          affiliation = "{Universidade Federal de Lavras (UFLA)} and {University of 
                         Leicester} and {Universidade Federal de Lavras (UFLA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Lancaster 
                         University} and {Lancaster University} and {Universidade Federal 
                         de Lavras (UFLA)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Universidade de S{\~a}o Paulo (USP)} and 
                         {Universidade Federal de Lavras (UFLA)}",
                title = "Reducing the effects of vegetation phenology on change detection 
                         in tropical seasonal biomes",
              journal = "GIScience and Remote Sensing",
                 year = "2019",
               volume = "56",
               number = "5",
                pages = "699--717",
                month = "July",
             keywords = "remote sensing, geostatistics, seasonality, LULCC.",
             abstract = "Tropical seasonal biomes (TSBs), such as the savannas (Cerrado) 
                         and semi-arid woodlands (Caatinga) of Brazil, are vulnerable 
                         ecosystems to human-induced disturbances. Remote sensing can 
                         detect disturbances such as deforestation and fires, but the 
                         analysis of change detection in TSBs is affected by seasonal 
                         modifications in vegetation indices due to phenology. To reduce 
                         the effects of vegetation phenology on changes caused by 
                         deforestation and fires, we developed a novel object-based change 
                         detection method. The approach combines both the spatial and 
                         spectral domains of the normalized difference vegetation index 
                         (NDVI), using a pair of Operational Land Imager (OLI)/Landsat-8 
                         images acquired in 2015 and 2016. We used semivariogram indices 
                         (SIs) as spatial features and descriptive statistics as spectral 
                         features (SFs). We tested the performance of the method using 
                         three machine-learning algorithms: support vector machine (SVM), 
                         artificial neural network (ANN) and random forest (RF). The 
                         results showed that the combination of spatial and spectral 
                         information improved change detection by correctly classifying 
                         areas with seasonal changes in NDVI caused by vegetation phenology 
                         and areas with NDVI changes caused by human-induced disturbances. 
                         The use of semivariogram indices reduced the effects of vegetation 
                         phenology on change detection. The performance of the classifiers 
                         was generally comparable, but the SVM presented the highest 
                         overall classification accuracy (92.27%) when using the hybrid set 
                         of NDVI-derived spectral-spatial features. From the vegetated 
                         areas, 18.71% of changes were caused by human-induced disturbances 
                         between 2015 and 2016. The method is particularly useful for TSBs 
                         where vegetation exhibits strong seasonality and regularly spaced 
                         time series of satellite images are difficult to obtain due to 
                         persistent cloud cover.",
                  doi = "10.1080/15481603.2018.1550245",
                  url = "http://dx.doi.org/10.1080/15481603.2018.1550245",
                 issn = "1548-1603",
             language = "en",
           targetfile = "Reducing the effects of vegetation phenology on change detection 
                         in tropical seasonal biomes.pdf",
        urlaccessdate = "18 abr. 2024"
}