Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin'anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning
Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin'anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning
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DOI：
10.3390/rs13040792
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
J. Qiu;Heng Wang;Wenjuan Shen;Yali Zhang;H. Su;Mingshi Li
中科院分区：
文献类型：
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作者：
J. Qiu;Heng Wang;Wenjuan Shen;Yali Zhang;H. Su;Mingshi Li
火灾后的许多现场因素，包括火灾严重程度、管理策略、地形和当地气候，是森林管理者和恢复生态学家制定应对气候变化的森林植被恢复计划所关注的问题。利用植被变化跟踪器算法，对中国东北部大兴安岭地区1987年至2016年的森林干扰进行了制图。利用支持向量机(SVM)分类器和历史火灾记录，从VCT得到的扰动斑块中分离出烧伤斑块。然后，应用逐步多元线性回归(SMLR)、支持向量机和随机林(RF)方法对植被恢复特征与各种影响因素之间的统计关系进行了评估。结果表明，VCT获取的森林扰动事件具有较高的空间精度，大部分年份的精度在70%~86%之间。提出的VCT-支持向量机算法提取的年火斑的总体准确率超过92%。火灾后植被恢复的建模精度较高，验证结果表明，RF算法比支持向量机和最小二乘支持向量机具有更好的预测精度。总之，地形变量(例如海拔)和气象变量(例如火灾后第二年的年降水量、火灾后第五年的平均相对湿度和火灾后第三年的极端最高温度)共同影响着这一寒温带大陆性季风气候区域的植被恢复。
Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing’anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region.