Improving UAV-Based LAI Estimation for Forests Over Complex Terrain by Reducing Topographic Effects on Multispectral Reflectance
Improving UAV-Based LAI Estimation for Forests Over Complex Terrain by Reducing Topographic Effects on Multispectral Reflectance
复制标题
通过减少地形对多光谱反射的影响来改进复杂地形下基于无人机的森林LAI估计
DOI：
10.1109/tgrs.2023.3337177
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发表时间：
2024
影响因子：
8.2
通讯作者：
Zhiqiang Cheng;Jing M. Chen;Zhenxiong Guo;Guofang Miao;Hongda Zeng;Rong Wang;Zhiqun Huang
中科院分区：
文献类型：
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作者：
Zhiqiang Cheng;Jing M. Chen;Zhenxiong Guo;Guofang Miao;Hongda Zeng;Rong Wang;Zhiqun Huang
叶面积指数(LAI)是表征陆地生态系统动态变化的关键参数，也是从遥感数据中提取的重要结构参数之一。然而，由于地形的变化给观测到的反射率带来了显著的不确定性，山区上空的LAI仍然很难可靠地反演。在这篇文章中，我们提出了一种新的方案来估计漫射辐射对基于比率的植被指数(VIS)的地形影响，这在现有的地形校正方案中还没有得到充分的考虑。在该方案中，首先利用无人机(UAV)光探测和测距(LiDAR)数据在斜率坐标系中对目标像素的天空视系数(SVF)和地形视因数(SVF)进行建模。基于这些视角因子，对入射太阳总辐射(ISRS)进行了校正，特别是对复杂地形上的漫射天空辐照度和相邻地形反射辐照度进行了校正。然后，我们重新计算了无人机图像的多光谱反射率，并评估了地形对归一化差异植被指数(NDVI)的影响，因为对红光和近红外(NIR)反射率的校正幅度有很大不同。最后，利用地形改正后的NDVI与野外实测LAI之间的关系建立经验模型，反演出大尺度的LAI分布。我们的结果表明，所提出的地形改正方案可以在一个生长季节显著改善LAI的反演。鉴于森林在全球复杂地形中的广泛分布，这项研究将对改进全球叶面积指数的绘制具有重要意义，而全球叶面积指数是陆地碳循环研究的关键。
Leaf area index (LAI) is a key parameter for characterizing the dynamics of terrestrial ecosystems and is also one of the important structural parameters that can be retrieved from remote sensing (RS) data. LAI over mountainous areas, however, is still difficult to retrieve reliably due to the topographical variation that introduces significant uncertainties into observed reflectance. In this article, we proposed a new scheme to estimate topographic influence on ratio-based vegetation indices (VIs) from diffuse radiation, which is not yet adequately considered in existing topographic correction schemes. In our scheme, unmanned aerial vehicle (UAV) light detecting and ranging (LiDAR) data were first used to model the sky view factor (SVF) and terrain view factor of target pixels in a slope coordinate system. Based on these view factors, the total incident solar radiation on slope (ISRS) was corrected, specifically for the diffuse sky irradiance and adjacent terrain-reflected irradiance over complex terrains. we then recalculated the multispectral reflectance of UAV images and evaluated the topographic effects on the normalized difference vegetation index (NDVI) because the magnitudes of correction on red and near-infrared (NIR) reflectances are quite different. Finally, large-scale LAI distribution was retrieved by empirical models developed from the relationships between terrain-corrected NDVI and field-measured LAI. Our results show that the proposed topographic correction scheme can significantly improve the LAI retrievals over a growing season. Given that forests are widely distributed in complex terrains around the globe, this study would have significance in improving the mapping of global LAI that is essential for terrestrial carbon cycle studies.