Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario Remote Environment
Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario Remote Environment
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发表时间：
2008
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通讯作者：
P. Wolter;P. Townsend;B. Sturtevant;Clayton C. Kingdon
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作者：
P. Wolter;P. Townsend;B. Sturtevant;Clayton C. Kingdon
昆虫和疾病影响着美国和加拿大的大片森林。要了解这种干扰对生态系统的影响，就需要了解宿主物种在景观中的分布模式。在这项研究中，我们绘制了分布和丰富的寄主物种的云杉芽虫（Choristoneura fumiferana），以方便景观规模的规划和建模的爆发动态。我们使用多时相，多季节Landsat数据和128地面实况图（和120个额外的验证图）来绘制断面积（BA），为北方明尼苏达州和邻近的安大略640万公顷的森林。偏最小二乘回归用于确定地面数据和大地卫星传感器数据之间的关系。随后，BA被映射到所有森林，以及两个特定的宿主树属（云杉和冷杉）。这些偏最小二乘回归分析得出的总体森林BA估计值的R2为0.62，RMSE为4.67 m2 ha-1（20%的BA），白色云杉相对BA，R2为0.88（RMSE=12.57 m 2 ha − 1 [测量的20%]），香脂相对BA的R2为0.64（RMSE=6.08 m 2 ha − 1 [测量的33%]）。我们还使用这种方法来估计落叶和针叶树种的相对BA，R2值分别为0.86，RMSE值分别为9.89 m 2 ha − 1（测量值的23%）和9.78 m 2 ha − 1（测量值的16%）。值得注意的是，冬季图像（有积雪）和短波红外指数-特别是短波红外/可见光比率-加强了我们开发的模型。由于地面测量主要是在含有云杉和冷杉的森林中进行的，因此模拟结果不适用于以松树和雪松等非目标针叶树为主的林分。PLS回归已被证明是一个有效的建模工具，在空间异质性森林的森林结构的区域特征，使用多时相Landsat传感器数据。
Insects and disease affect large areas of forest in the U.S. and Canada. Understanding ecosystem impacts of such disturbances requires knowledge of host species distribution patterns on the landscape. In this study, we mapped the distribution and abundance of host species for the spruce budworm ( Choristoneura fumiferana ) to facilitate landscape scale planning and modeling of outbreak dynamics. We used multitemporal, multi-seasonal Landsat data and 128 ground truth plots (and 120 additional validation plots) to map basal area (BA), for 6.4 million hectares of forest in northern Minnesota and neighboring Ontario. Partial least-squares (PLS) regression was used to determine relationships between ground data and Landsat sensor data. Subsequently, BA was mapped for all forests, as well as for two speci fi c host tree genera ( Picea and Abies ). These PLS regression analyses yielded estimates for overall forest BA with an R 2 of 0.62 and RMSE of 4.67 m 2 ha − 1 (20% of measured BA), white spruce relative BAwith an R 2 of 0.88 (RMSE=12.57 m 2 ha − 1 [20% of measured]), and balsam fi r relative BA with an R 2 of 0.64 (RMSE=6.08 m 2 ha − 1 [33% of measured]). We also used this method to estimate the relative BA of deciduous and coniferous species, each with R 2 values of 0.86 and RMSE values of 9.89 m 2 ha − 1 (23% of measured) and 9.78 m 2 ha − 1 (16% of measured), respectively. Of note, winter imagery (with snow cover) and shortwave infrared-based indices – especially the shortwave infrared/visible ratio – strengthened the models we developed. Because ground measurements were made largely in forest stands containing spruce and fi r, modeled results are not applicable to stands dominated by non-target conifers such as pines and cedar. PLS regression has proven to be an effective modeling tool for regional characterization of forest structure within spatially heterogeneous forests using multi-temporal Landsat sensor data.