Inter-Comparison of Four Models for Detecting Forest Fire Disturbance from MOD13A2 Time Series

نویسندگان

چکیده

Many models for change point detection from time series remote sensing images have been developed to date. For forest ecosystems, fire disturbance always an important topic. However, due a lack of benchmark datasets, it is difficult determine which model appropriate. Therefore, we collected and generated dataset specifically detection, named CUG-FFireMCD1. The CUG-FFireMCD1 contains total 132 pieces MODIS MOD13A2 series, each at least one disturbance. occurrence was determined using the National Cryosphere DesertDataCenter(NCDC) website, precise latitude longitude coordinates were FireCCI51 dataset. In addition, selected four commonly used validate advantages limitations through analysis. Finally, use results their applicable scenarios label additional points. are breaks additive season trend (BFAST), Prophet, continuous classification (CCDC), Landsat-based trends in recovery (LandTrendR). experiments show that BFAST outperformed other three with successful-detection-proportion rate 96.2% effect Prophet not as good BFAST, but also performs well, 87.9%. CCDC LandTrendR similar, success lower than can be data support labeling work. apply them perfectly best do some adaptation. summary, verified different types models, points marked credible. will surely provide reliable optimization accuracy verification detection.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14061446