Super-resolution supporting individual tree detection and canopy stratification using half-meter aerial data

Individual Tree Detection (ITD) can automatically recognize single trees and generate large-scale individual tree maps, providing essential insights for tree-by-tree forest management. However, ITD using remote sensing data becomes increasingly challenging as data quality and spatial resolution decrease. This paper proposes a Super-Resolution (SR)-based ITD method to predict individual tree locations, delineate crowns, and classify species from half-meter multi-modal aerial data. Based on the predicted ITD results, this study designs a tree-level forest canopy stratification method to further understand the forest structure. Specifically, we first fuse multi-modal data to represent tree features, including spectral RGB and NIR orthophoto images with a spatial resolution of 50 cm, and Canopy Height Model (CHM). The CHM is derived from the laser scanning data with a point density of approximately $5{\text{points/m}}^2$. Second, we integrate an SR module into the DCNN-based ITD model and employ multi-scale feature fusion (PANFPN) to address the challenges posed by limited data resolution. The SR module improves the spatial resolution of the data, while PANFPN integrates high- and low-level features to retain spatial details. Consequently, these components help mitigate the loss of tree features during the downsampling or pooling operation in DCNN models, preserving finer details. Third, we estimate tree height using Local Maxima (LM) filtering and derive crown size from ITD results to stratify the forest into three canopy classes: dominant (codominant), intermediate, and suppressed. The study sites are located in the Parkano region of Southwest Finland and encompass a diversity of tree species and forest stand types. Experiments demonstrate that the proposed SR module and PANFPN improve ITD performance, achieving an $mAP$ of 69.2% for the predicted bounding boxes and an $mAP$ of 64.3% for the segmented boundary masks. Our method is applicable to large-scale ITD and tree-level canopy stratification using half-meter multi-modal aerial data. The code is available at https://github.com/zmaomia/SR-Supporting-ITD.