Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario

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• 作者：Peter T. Wolter ; Philip A. Townsend ; Brian R. Sturtevant ; Clayton C. Kingdon
• 关键词：Landsat ; Multi-temporal ; Partial least squares regression ; Forest structure ; Spruce budworm ; Minnesota ; Ontario
• 刊名：Remote Sensing of Environment
• 出版年：2008
• 出版时间：15 October 2008
• 年：2008
• 卷：112
• 期：10
• 页码：3971-3982
• 全文大小：1278 K

文摘

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 multi-temporal, 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 specific host tree genera (Picea and Abies). These PLS regression analyses yielded estimates for overall forest BA with an R² of 0.62 and RMSE of 4.67 m² ha^− 1 (20 % of measured BA), white spruce relative BA with an R² of 0.88 (RMSE = 12.57 m² ha^− 1 [20 % of measured]), and balsam fir relative BA with an R² of 0.64 (RMSE = 6.08 m² ha^− 1 [33 % of measured]). We also used this method to estimate the relative BA of deciduous and coniferous species, each with R² values of 0.86 and RMSE values of 9.89 m² ha^− 1 (23 % of measured) and 9.78 m² 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 fir, 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.