Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality
Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality
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DOI：
10.1016/j.rse.2019.111238
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发表时间：
2019-09-15
影响因子：
13.5
通讯作者：
Meentemeyer, Ross K.
中科院分区：
文献类型：
--
作者：
He, Yinan;Chen, Gang;Meentemeyer, Ross K.
森林生态系统越来越受到各种干扰的影响，包括新出现的传染病（ElD），导致美国西部树木大量死亡。特别是在过去的十年里，EID疫情在森林地区的爆发更加频繁和严重，导致大量树木死亡。虽然可以通过遥感观察树木死亡，但其症状可能与疾病和非疾病干扰（例如野火和干旱）有关。物种分布模型广泛用于了解物种对某些栖息地条件的空间偏好，由于空间和光谱分辨率有限，这可能会限制不确定的遥感方法。在这项研究中，我们整合了多传感器遥感和物种分布模型，绘制了 2005 年至 2016 年 80,000 公顷森林面积中因疾病引起的树木死亡情况。我们选择了橡树突然死亡（由病原体 P. ramorum 引起）作为一种快速传播的新发传染病的案例研究，该疾病在过去的 20 年里已经杀死了加利福尼亚州数以百万计的橡树 (Quercus spp.) 和 tanoak (Lithocarpus densifiorus)。 过去几十年。为了平衡对疾病分布模式的精细监测和大尺度令人满意的覆盖范围的需求，我们的方法应用光谱分解来使用每年的陆地卫星时间序列提取亚像素疾病的存在。通过采用物种分布模型生成的疾病感染概率，结果得到了改善。我们使用来自高空间分辨率 NAIP（国家农业影像计划）和高光谱 AVIRIS（机载可见光/红外成像光谱仪）传感器的图像样本、Google Earth (R) 图像和现场观测来校准和验证该方法。研究结果显示，从 2005 年到 2016 年，每年橡树猝死感染率为 7%，总体绘图准确度在 76% 到 83% 之间。与单独使用遥感相比，多传感器遥感和物种分布模型的结合大大减少了对疾病影响的高估，导致检测受疾病影响的树木平均减少了 26%。这种整合策略证明了在经历多重干扰的森林景观中绘制长期、因病引起的树木死亡图谱的有效性。
Forest ecosystems have been increasingly affected by a variety of disturbances, including emerging infectious diseases (ElDs), causing extensive tree mortality in the Western United States. Especially over the past decade, EID outbreaks occurred more frequently and severely in forest landscapes, which have killed large numbers of trees. While tree mortality is observable from remote sensing, its symptom may be associated with both disease and non-disease disturbances (e.g., wildfire and drought). Species distribution modeling is widely used to understand species spatial preferences for certain habitat conditions, which may constrain uncertain remote sensing approaches due to limited spatial and spectral resolution. In this study, we integrated multi-sensor remote sensing and species distribution modeling to map disease-caused tree mortality in a forested area of 80,000 ha from 2005 to 2016. We selected sudden oak death (caused by pathogen P. ramorum) as a case study of a rapidly spreading emerging infectious disease, which has killed millions of oak (Quercus spp.) and tanoak (Lithocarpus densifiorus) in California over the past decades. To balance the needs for fine-scale monitoring of disease distribution patterns and satisfactory coverage at broad scales, our method applied spectral unmixing to extract sub-pixel disease presence using yearly Landsat time series. The results were improved by employing the probability of disease infection generated from a species distribution model. We calibrated and validated the method with image samples from high-spatial resolution NAIP (National Agriculture Imagery Program), and hyperspectral AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensors, Google Earth (R) imagery, and field observations. The findings reveal an annual sudden oak death infection rate of 7% from 2005 to 2016, with overall mapping accuracies ranging from 76% to 83%. The integration of multi-sensor remote sensing and species distribution modeling considerably reduced the overestimation of disease effects as compared to the use of remote sensing alone, leading to an average of 26% decrease in detecting disease-affected trees. Such integration strategy proved the effectiveness of mapping long-term, disease-caused tree mortality in forest landscapes that have experienced multiple disturbances.