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Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery

机译:云杉芽虫树宿主物种分布和丰富映射,使用多时间哨声-1和哨照-2卫星图像

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Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a valuable data source for seamless regional-scale mapping of forest composition. This study developed and evaluated new models to map the distribution and abundance of SBW host species at 20 m spatial resolution using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery in combination with several site variables for a total of 191 variables in northern New Brunswick, Canada using the Random Forest (RF) algorithm. We found Sentinel-2 multi-temporal single spectral bands and numerous spectral vegetation indices (SVIs) yielded the classification of SBW host species with an overall accuracy (OA) of 72.6% and kappa coefficient (K) of 0.65. Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.0% and K: 0.66). The use of Sentinel-1 SAR data with elevation resulted in a reasonable OA of 57.5% and K of 0.47. These spatially explicit up-to-date SBW host species maps are essential for identifying susceptible forests, monitoring SBW defoliation, and minimizing forest losses from insect impacts at landscape scale in the current SBW outbreak in the region.
机译:云芽(Choristoneura Fumiferana; SBW)是加拿大东北部最具破坏性的森林虫害。 SBW发生以及其损伤的程度和严重程度高度依赖于森林的特征和宿主物种的可用性即,云杉(Picea SP。)和Balsam FIR(Abies Balsamea(L.)磨坊。)。遥感卫星图像代表了森林成分无缝区域级映射的有价值的数据源。本研究开发并评估了使用Sentinel-1合成孔径雷达(SAR)和Sentinel-2多光谱图像以20米的空间分辨率映射SBW主机种类的分布和丰度,以及总共191个变量的几个站点变量在新的布伦瑞克,加拿大使用随机森林(RF)算法。我们发现Sentinel-2多颞单光谱带和许多光谱植被索引(SVIS)产生了SBW宿主物种的分类,总精度为72.6%,Kappa系数(K)为0.65。将Sentinel-1 SAR数据与哨子-2变量加上高度,仅略微改善了模型的性能(OA:73.0%和K:0.66)。使用抬高的Sentinel-1 SAR数据的使用产生了57.5%和k的合理OA为0.47。这些空间明确的最新的SBW主机物种地图对于识别敏感的森林,监测SBW脱落和最小化景观规模的昆虫影响最小化森林损失的必要性是必不可少的。

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