Contribution of high-resolution remote sensing to spatial ecology of forest ecosystems at the single tree level: A systematic review

Abstract

Forest spatial ecology investigates the complex relationships between spatial patterns and ecological processes, offering critical insights into forest ecosystem dynamics. This review synthesizes findings from 66 studies, highlighting the growing significance of high-resolution remote sensing (HR-RS) technologies in the field. HR-RS is particularly valuable for capturing tree–tree interactions and tree–environment relationships that are difficult to detect using traditional field methods, especially in large or densely vegetated forests. HR-RS datasets, including imagery and point clouds, enable spatially explicit measurements of individual trees, capturing both quantitative attributes (e.g., height, crown size) and qualitative characteristics (e.g., species, health status). Among the reviewed studies, 35 % employed aerial imagery to detect features such as canopy gaps, snags, and pest outbreaks, while 40 % utilized point pattern analysis to assess tree–tree ecological interactions. LiDAR was widely used for its ability to represent forest 3D structure and biophysical attributes. Notably, 45.5 % of the studies focused on tree–environment relationships, using HR-RS to map environmental variables such as soil moisture and microclimate conditions. However, advanced technologies such as multispectral and hyperspectral LiDAR remain underutilized, revealing a gap in current research. To advance forest spatial ecology, future studies should prioritize multisensor data fusion, longitudinal UAV–LiDAR monitoring, and advanced 3D spatial analyses. The integration of machine learning and deep learning techniques will also be essential for improving tree classification and detecting spatial patterns, ultimately deepening our understanding of forest ecological processes.