Efficient Localization and Spatial Distribution Modeling of Canopy Palms Using UAV Imagery

Understanding the spatial distribution of palms in tropical forests is essential for ecological monitoring, conservation strategies, and the sustainable integration of natural forest products into local and global supply chains. However, the analysis of remotely sensed data are challenged by overlapping palm and tree crowns, uneven shading across the canopy surface, and the heterogeneous nature of the forest landscapes, which often affect the performance of palm detection and segmentation algorithms. To overcome these issues, we introduce PalmDSNet, a deep learning framework for efficient detection, segmentation, and counting of canopy palms. To model spatial patterns, we introduce a bimodal reproduction algorithm that simulates palm propagation based on PalmDSNet outputs. We used UAV-captured imagery to create orthomosaics from 21 sites across western Ecuadorian tropical forests, covering a gradient from the everwet Chocó forests near Colombia to the drier forests of southwestern Ecuador. Expert annotations were used to create a comprehensive dataset, including 7,356 bounding boxes on image patches and 7,603 palm centers across five orthomosaics, encompassing a total area of 449 hectares. By integrating detection and spatial modeling, we effectively simulate the spatial distribution of palms in diverse and dense tropical environments, validating its utility for advanced applications in tropical forest monitoring and remote sensing analysis. The dataset can be accessed at 10.5281/zenodo.13822508, and the code to replicate the study is available at github.com/ckn3/palm-ds-sp.