Abstract Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spectral and structural signatures of defoliation by insects at the individual tree level have not been well studied. Additionally, the predictive power of spectral and structural metrics for mapping canopy defoliation has seldom been compared. These critical knowledge gaps prevent us from consistently detecting and mapping canopy defoliation by herbivorous insects across multiple scales. During the peak of a gypsy moth outbreak in Long Island, New York in summer 2016, we leveraged bi-temporal airborne imaging spectroscopy (IS, i.e., hyperspectral imaging) and LiDAR measurements at 1 m spatial resolution to explore the spectral and structural signatures of canopy defoliation in a mixed oak-pine forest. We determined that red edge and near-infrared spectral regions within the IS data were most sensitive to crown-scale defoliation severity. LiDAR measurements including B70 (i.e., 70th bincentile height), intensity skewness, and kurtosis were effectively able to detect structural changes caused by herbivorous insects. In addition to canopy leaf loss, increased exposure of understory and non-photosynthetic materials contributed to the detected spectral and structural signatures. Comparing the ability of individual sensors to map canopy defoliation, the LiDAR-only Ordinary Least-Square (OLS) model performed better than the IS-only model (Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R-squared = 0.63, RMSE = 19.11%). The IS + LiDAR model improved on performance of the individual sensors (Adj. R-squared = 0.81, RMSE = 14.46%). Our study improves our understanding of spectral and structural signatures of defoliation by herbivorous insects and presents a novel approach for mapping insect defoliation at the individual tree level. Additionally, with the current and next generation of spaceborne sensors (e.g., WorldView-3, Landsat, Sentinel-2, HyspIRI, and GEDI), higher accuracy and frequent monitoring of insect defoliation may become more feasible across a range of spatial scales, which are critical for ecological research and management of forest resources including the economic consequences of forest insect infestations (e.g., reduced growth and increased mortality), as well as for informing and testing of carbon cycle models.

中文翻译：

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使用双时空机载成像光谱和 LiDAR 测量在单个树级别绘制草食性昆虫的冠层落叶图

摘要 食草昆虫造成的落叶是一种普遍的森林干扰驱动因素，影响全球森林健康和生态系统动态。与费时费力的实地调查相比，遥感提供了在大空间和时间尺度上绘制草食性昆虫对冠层落叶的唯一现实方法。然而，昆虫在个体树木水平上落叶的光谱和结构特征尚未得到很好的研究。此外，很少比较光谱和结构度量对映射冠层落叶的预测能力。这些关键的知识差距使我们无法在多个尺度上始终如一地检测和绘制草食性昆虫的冠层落叶情况。2016 年夏季，在纽约长岛爆发吉普赛蛾的高峰期，我们利用双时空机载成像光谱（IS，即高光谱成像）和 1 m 空间分辨率的 LiDAR 测量来探索混合橡木松林中冠层落叶的光谱和结构特征。我们确定 IS 数据中的红边和近红外光谱区域对冠级落叶严重程度最敏感。包括 B70（即第 70 个二进制百分位数高度）、强度偏度和峰度在内的 LiDAR 测量值能够有效地检测由食草昆虫引起的结构变化。除了冠层叶片损失外，林下和非光合材料暴露的增加也有助于检测到光谱和结构特征。比较单个传感器绘制树冠落叶的能力，LiDAR-only Ordinary Least-Square (OLS) 模型比 IS-only 模型表现更好（Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R-squared = 0.63, RMSE = 19.11%）。IS + LiDAR 模型改进了单个传感器的性能（调整 R 平方 = 0.81，RMSE = 14.46%）。我们的研究提高了我们对草食性昆虫落叶的光谱和结构特征的理解，并提出了一种在个体树木水平上绘制昆虫落叶图的新方法。此外，随着当前和下一代星载传感器（例如 WorldView-3、Landsat、Sentinel-2、HyspIRI 和 GEDI）的出现，对昆虫落叶的更高准确度和频繁监测可能在一系列空间尺度上变得更加可行，