Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/33427
metadata.artigo.dc.title: Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes
metadata.artigo.dc.creator: Silveira, Eduarda Martiniano de Oliveira
Espírito-Santo, Fernando Del Bon
Acerbi-Júnior, Fausto Weimar
Galvão, Lênio Soares
Withey, Kieran Daniel
Blackburn, George Alan
Mello, José Márcio de
Shimabukuro, Yosio Edemir
Domingues, Tomas
Scolforo, José Roberto Soares
metadata.artigo.dc.subject: Remote sensing
Geostatistics
Seasonality
Tropical seasonal biomes
Vegetation phenology
Sensoriamento remoto
Geoestatística
Sazonalidade
Biomas sazonais tropicais
Fenologia da vegetação
metadata.artigo.dc.publisher: Taylor & Francis
metadata.artigo.dc.date.issued: 2018
metadata.artigo.dc.identifier.citation: SILVEIRA, E. M. de O. et al. Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes. GIScience & Remote Sensing, [S. l.], 2018. DOI: https://doi.org/10.1080/15481603.2018.1550245.
metadata.artigo.dc.description.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.
metadata.artigo.dc.identifier.uri: https://www.tandfonline.com/doi/full/10.1080/15481603.2018.1550245
http://repositorio.ufla.br/jspui/handle/1/33427
metadata.artigo.dc.language: en_US
Appears in Collections:DCF - Artigos publicados em periódicos

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