基于陆地卫星时间序列和优化离群值与阈值的耕地烧毁面积自动检测算法

Fire Pub Date : 2024-07-18 DOI:10.3390/fire7070257
Sumei Zhang, Huijuan Li, Hongmei Zhao
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摘要

鉴于全球火灾日益严重,准确探测小型和零散的耕地火灾一直是一项重大挑战。使用中等分辨率的卫星数据可以提高探测精度,但这种方法面临的主要挑战包括准确捕捉燃烧特征的年度和年际变化,以及识别这些变化时间序列中的异常值。在本研究中,我们将重点放在位于华北平原的河南省典型作物-秸秆燃烧区。我们基于 Landsat 5 图像中的近红外和短波红外数据,开发了一种自动燃烧区检测算法。我们的方法利用滤波和自动迭代算法整合了时间序列离群值分析,以确定检测烧毁区的最佳阈值。我们的研究结果表明,使用前时间序列和季节时间序列分析来区分与火灾有关的变化和植被的季节性及非季节性影响非常有效。最佳阈值验证结果表明,自动阈值方法高效可行,总体准确率超过 93%。所得到的烧毁面积地图的总精度达到 93.25%,远远超过 MCD64A1 火灾产品 76.5% 的检测精度,从而凸显了我们算法的功效。总之,我们的算法适用于检测大规模农田环境中的烧毁区域,并为未来检测算法的开发提供了宝贵的信息。
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An Automated Cropland Burned-Area Detection Algorithm Based on Landsat Time Series Coupled with Optimized Outliers and Thresholds
Given the increasingly severe global fires, the accurate detection of small and fragmented cropland fires has been a significant challenge. The use of medium-resolution satellite data can enhance detection accuracy; however, key challenges in this approach include accurately capturing the annual and interannual variations of burning characteristics and identifying outliers within the time series of these changes. In this study, we focus on a typical crop-straw burning area in Henan Province, located on the North China Plain. We develop an automated burned-area detection algorithm based on near-infrared and short-wave infrared data from Landsat 5 imagery. Our method integrates time-series outlier analysis using filtering and automatic iterative algorithms to determine the optimal threshold for detecting burned areas. Our results demonstrate the effectiveness of using preceding time-series and seasonal time-series analysis to differentiate fire-related changes from seasonal and non-seasonal influences on vegetation. Optimal threshold validation results reveal that the automatic threshold method is efficient and feasible with an overall accuracy exceeding 93%. The resulting burned-area map achieves a total accuracy of 93.25%, far surpassing the 76.5% detection accuracy of the MCD64A1 fire product, thereby highlighting the efficacy of our algorithm. In conclusion, our algorithm is suitable for detecting burned areas in large-scale farmland settings and provides valuable information for the development of future detection algorithms.
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