MAPPING FOREST DISTURBANCE USING PURE FOREST INDEX TIME SERIES AND CCDC ALGORITHM

نویسندگان

چکیده

Abstract. Forest dynamics are closely related to climate change, natural disasters, and ecological diversity. The accumulated Landsat archive provides an unprecedented opportunity for long-term forest monitoring globally. However, using time series detect small-scale low-intensity disturbance events is still challenging since the moderate spatial resolution of images mixed pixel problem. Towards improving ability vegetation index (VI) in characterizing sub-pixel dynamics, this paper introduced spectral mixture analysis (SMA) develop a novel Pure Index (PFI). Continuous Change Detection Classification (CCDC) algorithm was used based on PFI series. Cross-comparison shows that far superior other conventional VI indicating conditions it can enhance signal suppress noises from background. Time further demonstrates superiority accurately dynamics. high overall accuracy 0.96 map generated by proposed approach achieved. This study highlights tracking subtle changes heterogeneous landscape.

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ژورنال

عنوان ژورنال: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

سال: 2022

ISSN: ['1682-1777', '1682-1750', '2194-9034']

DOI: https://doi.org/10.5194/isprs-archives-xlviii-3-w1-2022-1-2022