Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data
Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data
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DOI：
10.3390/rs9090929
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发表时间：
2017-09-01
期刊：
影响因子：
5
通讯作者：
Jin, Yufang
中科院分区：
文献类型：
--
作者：
Byer, Sarah;Jin, Yufang
2012年至2016年，加州持续五年的干旱导致内华达州山脉森林的树木死亡率大幅上升。在景观层面监测森林健康和树木枯死对植被和灾害管理战略至关重要。我们研究了中分辨率成像光谱仪（MODIS）的多光谱图像的能力，在检测和解释最近严重干旱的影响，在内华达州森林。利用中分辨率成像光谱仪植被指数（维斯）和水指数的时间序列，开发了遥感度量标准，以表示基线森林健康状况和干旱压力。我们使用随机森林算法，训练森林航空探测调查数据，检测树木死亡率的遥感指标和地形变量的基础上。树木死亡率的地图估计表明，我们的两个阶段的随机森林模型能够检测的空间格局和严重程度的树木死亡率，与整体生产者的准确性为96.3%的分类随机森林（CRF）和回归随机森林（RRF）的RMSE为7.19死树每英亩。CRF的总体遗漏错误范围从重度死亡率分类的19%到低死亡率分类的27%。对这些模型的解释表明，干旱发生前生产力较高的森林更容易受到干旱的影响，因此更有可能出现树木死亡。这一方法突出了将基线森林健康数据和干旱压力测量纳入了解森林对严重干旱的反应的重要性。
A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability of multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) in detecting and explaining the impacts of the recent severe drought in Sierra Nevada forests. Remote sensing metrics were developed to represent baseline forest health conditions and drought stress using time series of MODIS vegetation indices (VIs) and a water index. We used Random Forest algorithms, trained with forest aerial detection surveys data, to detect tree mortality based on the remote sensing metrics and topographical variables. Map estimates of tree mortality demonstrated that our two-stage Random Forest models were capable of detecting the spatial patterns and severity of tree mortality, with an overall producer's accuracy of 96.3% for the classification Random Forest (CRF) and a RMSE of 7.19 dead trees per acre for the regression Random Forest (RRF). The overall omission errors of the CRF ranged from 19% for the severe mortality class to 27% for the low mortality class. Interpretations of the models revealed that forests with higher productivity preceding the onset of drought were more vulnerable to drought stress and, consequently, more likely to experience tree mortality. This method highlights the importance of incorporating baseline forest health data and measurements of drought stress in understanding forest response to severe drought.