Resumen
Time series analysis combined with remote sensing data allows for the study of abrupt changes in the environment due to significant and severe disturbances such as deforestation, agricultural activities, fires, and urban expansion, as well as gradual changes such as climate variability and forest degradation in the ecosystem. The precision of any change detection analysis is highly dependent upon its ability to separate actual changes and fluctuations on a seasonal scale. One of the efficient methods in this context is using the Breaks for Additive Seasonal and Trend (BFAST) set of algorithms. This study aims to perform a comprehensive and comparative evaluation of different Vis? performance in forest degradation with the Landsat 8 images and BFASTMonitor approach. Through evaluation, the study also considers the potential effects of different forest types and deforestation scales in the Marmara region of Turkey. For this purpose, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Normalized Burn Ratio (NBR) vegetation indices (VI) were selected for a comparative evaluation. The overall accuracy of VIs in deciduous forests was around 85% for NDVI, NDMI, and NBR, and 78.80% for EVI, while in coniferous forests, the overall accuracy demonstrated higher values of about 88% for NDVI, NDMI, and EVI, and 87.28% for NBR. Consequently, water-sensitive VIs that utilize shortwave infrared bands proved to be slightly more sensitive in detecting forest disturbances while chlorophyll-sensitive VIs represented lower accuracy for both forest types. Overall, all VIs faced an underestimation error in deforested area detection that was observable through negative BIAS. The results illuminate that BFASTMonitor can be considered as a tool in monitoring forest environments due to its acceptable deforestation determination capability in deciduous and coniferous forests, with slightly higher performance for small-scale deforestation patterned regions.