A novel index for forest structure complexity mapping from single multispectral images

Due to the provisioning of essential ecosystem goods and services by forests, the monitoring of forests has attracted considerable attention within the academic community. However, the majority of remote sensing studies covering large areas primarily focus on tree cover due to resolution limitations. It is necessary to integrate innovative spatial methods and tools in the monitoring of forest ecosystems. Forest Structure Complexity, representing the spatial heterogeneity within forest structures, plays a pivotal role in influencing ecosystem processes and functions. In this study, we use multi-spectral remote sensing image data to extract the crown information of the single tree through deep learning technology; Subsequently, we analyze the relationship between each single tree and its neighboring trees, and explore the structural characteristics at tree level. Finally, we developed the canopy structural complexity index and applied it to Nordic forests, urban areas, savanna, rainforest, and the most complex tree plantations and natural forests in China Karst. This study aims to gain a deeper understanding of the forest structure complexity in diverse ecosystems and provide valuable information for sustainable forestry management and ecosystem conservation. The method developed in this study eliminates the need  for additional field measurement and radar data, offering robust tool support for extensive and efficient the monitoring of forest structure complexity, which has a wide application prospect.