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Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data

机译:利用MODIS-NDVI数据监测桦木中几何蛾的暴发时空动态

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Defoliation caused by repeated outbreaks of cyclic geometrid moths is the most prominent natural disturbance factor in the northern-boreal birch forest. Evidence suggests that recent changes in outbreak distribution and duration can be attributed to climate warming. There is hence an immediate need for methods that can be applied to characterize the geographical distribution of outbreaks. Here we assess the reliability of MODIS (Moderate Resolution Imaging Spectroradiometer) 16-day NDVI data for generating time series of the distribution of defoliation caused by moths attacking birch forest in Fennoscandia. We do so by first establishing the relationship between ground measures of moth larval density and a defoliation score based on MODIS-NDVI. We then calibrate and validate a model with the MODIS-NDVI defoliation score as a classifier to discriminate between areas with and without visible defoliation as identified from orthophotos and provide two examples of application of the model. We found the MODIS defoliation score to be a valid proxy for larval density (R-2=0.88-0.93) above a certain, low threshold (a defoliation score of -5%). Areas with and without visible defoliation could be discriminated based on defoliation score with a substantial strength of agreement (max kappa = 0.736), and the resulting model was able to predict the proportion of area with visible defoliation in independent test areas with good reliability across the range of proportions. We conclude that satellite-derived defoliation patterns can be an invaluable tool for generating indirect population dynamical data that permits the development of targeted monitoring on relevant regional scales.
机译:在北方-北方桦树森林中,周期性爆发的周期性飞蛾引起的落叶是最突出的自然干扰因素。有证据表明,近期暴发分布和持续时间的变化可归因于气候变暖。因此,迫切需要可用于表征暴发的地理分布特征的方法。在这里,我们评估了MODIS(中等分辨率成像光谱仪)16天NDVI数据的可靠性,该数据可用于生成由飞蛾袭击Fennoscandia的桦木森林引起的落叶分布的时间序列。为此,我们首先建立基于MODIS-NDVI的蛾类幼虫密度的地面量度与落叶指数之间的关系。然后,我们使用MODIS-NDVI落叶分数作为分类器来校准和验证模型,以区分从正射照片中识别出的有和没有可见落叶的区域,并提供了该模型的两个应用示例。我们发现,MODIS落叶指数可以有效地替代高于某个特定低阈值(落叶分数为-5%)的幼虫密度(R-2 = 0.88-0.93)。可以根据具有相当强的一致性(最大kappa = 0.736)的落叶分数来区分具有和没有可见落叶的区域,并且所得模型能够预测独立测试区域中具有可见落叶的区域比例,并且在整个测试过程中具有良好的可靠性。比例范围。我们得出的结论是,卫星衍生的落叶模式可能是生成间接人口动态数据的无价工具,从而可以在相关区域规模上开展有针对性的监测。

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