#UAV-LiDAR

对2015-2025年66篇UAV-LiDAR森林结构属性估算文献进行文献计量分析。

对比无人机RGB与LiDAR传感器在甘蔗田地形测绘中的精度与适用性。

Abstract Context Spatially explicit and accurate maize crop height maps are essential for supporting site-specific management in precision agriculture. Although low-cost unmanned aerial system (UAS)-based multispectral imagery offers a promising approach for maize crop height estimation, it’s accuracy under varying canopy conditions remains uncertain. Aims In this study, we used 3D point clouds to estimate maize crop height during sparse and dense canopy cover based on UAS-captured multispectral images and compare with light detection and ranging (LiDAR)-based estimates. Methods The 3D point clouds from multispectral images and LiDAR data were processed using a spatially localized grid (SLG) filtering approachto derive digital terrain model (DTM) and canopy surface model (CSM), which were then used to estimate maize crop height.Furthermore, two strategies were explored — (1) grid expansion, and (2) using ground reference derived during sparse canopy aspotential tools to mitigate issues during dense canopy cover for multispectral image-based estimations. Results Results showed that the crop height estimated from multispectral images during sparse canopy cover show comparable accuracy (r = 0.9, nRMSE = 32%) to LiDAR-based estimation (r = 0.81, nRMSE = 17%). The accuracy of multispectral-based estimation, however, significantly declined under dense canopy cover, where no meaningful correlation was observed (r = − 0.1, nRMSE = 61%). In contrast, LiDAR-based estimates show consistent level of accuracy under the same conditions (r = 0.78, nRMSE = 15%). When grid expansion strategy was implemented for multispectral-based estimation during the dense canopy cover, the nRMSE was observed to decrease up to 28%. Meanwhile, with the use of sparse canopy cover ground reference, the nRMSE decreased up to 17%, comparable to the LiDAR-based estimate. Conclusion Overall, these results demonstrate the potential of deriving crop heights cost-effectively using multispectral imaging from UAS. This approach offers a scalable and economically viable alternative to expensive LiDAR systems, enabling wider integration of crop height information into precision agriculture applications.

Corn canopy structure and biomass assessment using UAV-based monocular depth and structure-from-motion。Smart Agricultural Technology

利用无人机搭载向上光谱仪与多元线性回归，从多光谱影像直接反演地表反射率。

用无人机多光谱成像与机器学习量化田间作物除草剂药害和干燥剂响应。

提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。

用无人机RGB影像提取植被指数监测小麦和油菜长势并生成精准干预区。