Predicting defoliator abundance and defoliation measurements using Landsat-based condition scores,Remote Sensing in Ecology and Conservation

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Predicting defoliator abundance and defoliation measurements using Landsat-based condition scores
Remote Sensing in Ecology and Conservation ( IF 3.9 ) Pub Date : 2021-05-25 , DOI: 10.1002/rse2.211
Valerie J. Pasquarella ₁ , James G. Mickley _{2,

3} , Audrey Barker Plotkin ₄ , Richard G. MacLean ₅ , Riley M. Anderson ₆ , Leone M. Brown _{2,

7} , David L. Wagner ₂ , Michael S. Singer ₆ , Robert Bagchi ₂

Affiliation

Remote sensing imagery can provide critical information on the magnitude and extent of damage caused by forest pests and pathogens. However, monitoring short-term changes in deciduous forest condition caused by defoliating insects is challenging and requires approaches that directly account for seasonal vegetation dynamics. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016–2018 gypsy moth (Lymantria dispar) outbreak in southern New England. Our comparisons revealed that most models made reasonable predictions of changes in canopy condition and egg and larval abundances of L. dispar, indicating a strong correlation between our harmonic-based estimates of condition change and defoliator activity. The greatest differences in predictive ability were in the spectral domain, with assessments based on Tasseled Cap Greenness, Simple Ratio, and the Enhanced Vegetation Index ranking among the top models, and the commonly used Normalized Difference Vegetation Index consistently exhibiting poorer performance. We also observed notable differences in the magnitude of scores for different baseline periods. Additionally, we found that Landsat-based condition scores better explained larval abundance than egg mass counts, which have historically been used as a proxy for later-season larval abundance, indicating that our remote sensing approach may be more accurate and cost-effective for generating consistent retrospective assessments of L. dispar population abundance in addition to estimates of canopy damage. These findings provide important linkages between spectral changes detected using a harmonic modeling approach and biophysical aspects of defoliator activity, with potential to extend monitoring and prediction to regional or even continental scales.

中文翻译：

使用基于 Landsat 的条件评分预测落叶剂丰度和落叶测量

遥感图像可以提供有关森林病虫害造成的损害程度和程度的重要信息。然而，监测落叶昆虫引起的落叶林条件的短期变化具有挑战性，需要直接考虑季节性植被动态的方法。我们在 Google Earth Engine 中实施了一种先前发布的用于森林状况监测的谐波建模方法，并系统地评估了使用各种模型参数化生成的条件变化产品的相对能力，以预测 2016-2018 年吉普赛蛾（Lymantria dispar ）期间的害虫丰度和落叶。) 在新英格兰南部爆发。我们的比较表明，大多数模型对L. dispar冠层条件和卵和幼虫丰度的变化做出了合理的预测，表明我们基于谐波的条件变化估计与脱叶剂活动之间存在很强的相关性。预测能力的最大差异在光谱域，基于缨帽绿度、简单比率和增强植被指数排名的评估在顶级模型中，常用的归一化差异植被指数始终表现出较差的性能。我们还观察到不同基线期的分数大小存在显着差异。此外，我们发现基于 Landsat 的条件评分比卵质量计数更好地解释了幼虫丰度，卵质量计数历来被用作后期幼虫丰度的代表，这表明我们的遥感方法可能更准确且更具成本效益来生成一致的回顾性评估L. dispar种群丰度以及对树冠损害的估计。这些发现提供了使用谐波建模方法检测到的光谱变化与落叶剂活动的生物物理方面之间的重要联系，并有可能将监测和预测扩展到区域甚至大陆尺度。

更新日期：2021-05-25

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