Geometry-Constrained Reference Sample Construction from Forest Inventory Compartments for Dominant Tree Species Mapping

Abstract

Forest inventory compartments provide extensive and management-relevant reference information for satellite-based tree species mapping, but their dominant-species attributes are defined at the stand level rather than for individual image pixels. Existing applications commonly derive training samples from compartment centres or assign polygon labels to enclosed pixels, which may introduce boundary effects, uneven class representation, and disproportionate contributions from individual compartments. However, the intermediate step of converting inventory polygons into spatially controlled pixel-level reference samples has received comparatively limited attention. Here, we developed a geometry-constrained reference sample construction framework that integrates interior-position screening, class balancing, source compartment contribution control, and spatial-spacing constraints. The framework was evaluated for mapping Korean pine, larch, white birch, and spruce in a temperate mixed forest in northeastern China using Sentinel-1/2 time series and ancillary predictors. Predictor–classifier combinations were selected using compartment-grouped out-of-fold evaluation, sampling workflows were compared on 60 independently withheld compartments, and the final map was further assessed using 306 independent reference points. Relative to centroid sampling, the geometry-constrained workflow increased compartment-level macro-F1 from 0.612 to 0.709. XGBoost with optical time series and ancillary predictors achieved the best development-set performance, while inclusion of the complete Sentinel-1 time series provided no further gain. The final model achieved an overall accuracy of 0.827 and a macro-F1 of 0.820 on the independent reference points. Aggregation of 10 m predictions further enabled compartment-level characterization of mapped dominant species, dominance strength, and mixing intensity. These results demonstrate that reference sample construction is a consequential step in tree species mapping from polygon-based forest inventories and provide a practical approach for linking pixel-level remote sensing classification with forest management units.