Tree species recognition from close-range sensing: A review

• Published in: Remote sensing of environment 2024-11, Vol.313, p.114337, Article 114337
• Main Authors: Chen, Jianchang, Liang, Xinlian, Liu, Zhengjun, Gong, Weishu, Chen, Yiming, Hyyppä, Juha, Kukko, Antero, Wang, Yunsheng
• Format: Article
• Language: English
• Subjects: algorithms; automation; canopy; Classification; Close-range sensing; Data fusion; Deep learning; environment; Forest; forest stands; forests; international cooperation; landscapes; Machine learning; Phenology; Recognition; seasonal variation; Species; time series analysis; trees
• ISSN: 0034-4257
• DOI: 10.1016/j.rse.2024.114337

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 machine learning (ML) approaches are grouped into conventional ML and deep-learning (DL) categories. In each category, the reported studies/results are reviewed with respect to the spectral, spatial, and temporal domains of the used data sources, e.g., sensor and platform. Despite significant efforts in the field, the issues of automation, reliability, and robustness of the algorithms have only been partially resolved. The crucial elements in algorithm design what this work found and is worth careful consideration include forest types and stand conditions, seasonal variability and phenology, data characteristics and the corresponding feature selections, and the methodology. Future studies are recommended to focus on the fusion of multi-source data including passive and active multispectral data to integrate the spectral and structural information, the use of time-series data to enhance the role of phenological variances in species recognition, and the development of unsupervised DL tech