LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

作者：Mattia Balestra, Suzanne Marselis, Temuulen Tsagaan Sankey, Carlos Çabo, Xinlian Liang, Martin Mokroš, Xi Peng, Arunima Singh, Krzysztof Stereńczak, Cédric Vega, Grégoire Vincent, Markus Hollaus · 发表于：Current Forestry Reports · 年份：2024 · DOI：10.1007/s40725-024-00223-7 · 被引用次数：116 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Forest ecology and management

Abstract Purpose of the Review Many LiDAR remote sensing studies over the past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in the final data products. Here, we performed a structured literature review to analyze relevant studies on these topics published in the last decade and the main motivations and applications for fusion, and the methods used. We discuss the findings with a panel of experts and report important lessons, main challenges, and future directions. Recent Findings LiDAR fusion with other datasets, including multispectral, hyperspectral, and radar, is found to be useful for a variety of applications in the literature, both at individual tree level and at area level, for tree/crown segmentation, aboveground biomass assessments, canopy height, tree species identification, structural parameters, and fuel load assessments etc. In most cases, gains are achieved in improving the accuracy (e.g. better tree species classifications), and spatial-temporal resolution (e.g. for canopy height). However, questions remain regarding whether the marginal improvements reported in a range of studies are worth the extra investment, specifically from an operational point of view. We also provide a clear definition of “data fusion” to inform the scientific community on data fusion, combination, and integration. Summary This review provides a positive outlook for LiDAR fusion applications in the decade...