Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in GIScience and Remote Sensing on 28/11/2019, available online: https://www.tandfonline.com/doi/full/10.1080/15481603.2018.1550245
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes
AU - Silveira, Eduarda Martiniano de Oliveira
AU - Espírito-Santo, Fernando Del Bon
AU - Acerbi-Júnior, Fausto Weimar
AU - Galvão, Lênio Soares
AU - Withey, Kieran Daniel
AU - Blackburn, George Alan
AU - de Mello, José Márcio
AU - Shimabukuro, Yosio Edemir
AU - Domingues, Tomas
AU - Scolforo, José Roberto Soares
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in GIScience and Remote Sensing on 28/11/2019, available online: https://www.tandfonline.com/doi/full/10.1080/15481603.2018.1550245
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - remote sensing
KW - geostatistics
KW - seasonality
KW - LULCC
U2 - 10.1080/15481603.2018.1550245
DO - 10.1080/15481603.2018.1550245
M3 - Journal article
VL - 56
SP - 699
EP - 717
JO - GIScience & Remote Sensing
JF - GIScience & Remote Sensing
SN - 1943-7226
IS - 5
ER -