Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed
map** of individual tree species. However, the in-depth mining and comprehending of the …

Accurate and automatic identification of tree species information at the individual tree scale
is of great significance for fine-scale investigation and management of forest resources and …

Abstract Collaboration between Light Detection and Ranging (LiDAR) point clouds and
deep learning has been proven to be an effective approach for vegetation map**. Current …

Plantation forests provide critical ecosystem services and have experienced worldwide
expansion during the past few decades. Accurate map** of tree species through remote …

The study of Land use/land cover change (LULCC) is crucial to understanding its impact on
hydrological processes and water resource availability. This research focuses on assessing …

As one of the most important components of urban space, the geometric and semantic
properties of road trees are crucial for the assessment and upgrade of urban environments …

Automatic and accurate individual tree species identification is essential for the realization of
smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote …

Traditional forest inventory supplies essential data for forest monitoring and management,
including tree species, but obtaining individual tree-level information is increasingly crucial …

Accurately classifying tree species in urban forests remains a major challenge due to the
limitations of existing methods. While hyperspectral imaging is capable of capturing detailed …

Recent advances have enabled machine learning methodologies developed for large
datasets to be applied to the small experimental datasets typically available for chemical …