Tree species recognition from close-range sensing: A review

作者：Jianchang Chen, Xinlian Liang, Zhengjun Liu, Weishu Gong, Yiming Chen, Juha M. Hyyppa, Antero Kukko, Yunsheng Wang · 发表于：Remote Sensing of Environment · 年份：2024 · DOI：10.1016/j.rse.2024.114337 · 被引用次数：50 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Species Distribution and Climate Change

Information on tree species across various spatial scales, from an individual tree to a forest stand and the broader landscape, contributes to an accurate and thorough understanding of forest conditions either as an individual characteristic or as an input of species-dependent models. However, tree species recognition is one of the most challenging tasks in forest remote sensing studies, due to the complexity of species compositions and canopy structures of forests, e.g., both cross-species similarities and intra-species variations commonly exist in spectral-, texture-, and structure- domains. Over the past two decades, the interest in using close-range sensing for tree species recognition has been rapidly growing. Recent research has highlighted the needs to further develop species recognition methods to elevate their performance in comparison with established remote sensing approaches, and to address new questions arising from spatial resolutions, data coverages, viewing geometries, and other data characteristics. This work provides an overview of the state-of-the-art of tree species recognition from close-range sensing data. The work summarizes the research works in the past decade, reviews the state of research, discusses prominent challenges, investigates impact factors, research gaps, and new potentials. Specifically, data from various sources, the features derived from each type of data, methodologies applied, and the targeted species are reviewed in detail. Relevant m...