Predicting Tree Mortality Using Spectral Indices Derived from Multispectral UAV Imagery

编号 030034203

推送时间 20220509

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Predicting Tree Mortality Using Spectral Indices Derived from Multispectral UAV Imagery

来源期刊 REMOTE SENSING

期 第342期

发表时间 20220504

关键词 tree mortality;  UAV;  spectral indices;  random forest;  forest management;  trembling aspen;  lodgepole pine;  white spruce; 

摘要 Past research has shown that remotely sensed spectral information can be used to predict tree health and vitality. Recent developments in unmanned aerial vehicles (UAVs) have now made it possible to derive such information at the tree and stand scale from high-resolution imagery. We used visible and multispectral bands from UAV imagery to calculate a set of spectral indices for 52,845 individual tree crowns within 38 forest stands in western Canada. We then used those indices to predict the mortality of these canopy trees over the following year. We evaluated whether including multispectral indices leads to more accurate predictions than indices derived from visible wavelengths alone and how the performance varies among three different tree species (Picea glauca,?Pinus contorta,?Populus tremuloides). Our results show that spectral information can be effectively used to predict tree mortality, with a random forest model producing a mean area under the receiver operating characteristic curve (AUC) of 89.8% and a balanced accuracy of 83.3%. The exclusion of multispectral indices worsened the model performance, but only slightly (AUC = 87.9%, balanced accuracy = 81.8%). We found variation in model performance among species, with higher accuracy for the broadleaf species (balanced accuracy = 85.2%) than the two conifer species (balanced accuracy = 73.3% and 77.8%). However, all models overpredicted tree mortality by a major degree, which limits the use for tree mortality predictions on an individual level. Further improvements such as long-term monitoring, the use of hyperspectral data and cost-sensitive learning algorithms, and training the model with a larger and more balanced data set are necessary. Nevertheless, our results demonstrate that imagery from UAVs has strong potential for predicting annual mortality for individual canopy trees.

服务人员 付贺龙

服务院士 唐守正

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