Tree Species Classification in a Madagascar Tropical Dry Forest Using Multi-Temporal UAV Multispectral Imagery and Machine Learning

Accurate tree species mapping in tropical dry forests remains challenging because strong seasonal phenological variation and spectral overlap can limit discrimination from single-date imagery. This study evaluated the integration of wet- and dry-season unoccupied aerial vehicle (UAV) multispectral imagery, vegetation indices, Gray-Level Co-occurrence Matrix (GLCM) texture metrics, and digital surface model (DSM)-derived information for individual-tree classification in Kirindy Mitea National Park, Madagascar. UAV imagery at 11.3 cm ground sampling distance was linked to manually delineated crowns of 231 field-identified trees representing six canopy species. After missing-data filtering, 230 trees were retained and stratified into training (n = 186) and independent (n = 44) test datasets. Predictor selection was conducted exclusively on the training set: 68 candidate crown-level predictors were screened using Kruskal–Wallis tests with Benjamini–Hochberg false-discovery-rate correction, followed by Spearman correlation filtering (ρ ≥0.80), yielding 34 predictors. Random Forest (RF) and radial Support Vector Machine (SVM) classifiers were tuned using the same repeated five-fold cross-validation framework (five repetitions), without synthetic oversampling. On the independent test set, RF achieved an overall accuracy of 65.9% (95% CI: 50.1–79.5%), Cohen’s kappa of 0.45, balanced accuracy of 0.65, Macro-F1 of 0.40, and MCC of 0.46. SVM achieved an overall accuracy of 59.1% (95% CI: 43.3–73.7%), kappa of 0.33, balanced accuracy of 0.64, Macro-F1 of 0.38, and MCC of 0.36. The paired difference in overall correctness was not significant (McNemar p = 0.606). RF permutation importance identified wet-season CTVI median, dry-season NDWI median, wet-season NIR contrast median, and wet-season KNDVI intra-crown variability among the highest-ranking predictors. These results show that multi-season spectral, textural, and DSM-derived information provides complementary information for tree-species discrimination, while class-specific performance and uncertainty indicate that broader validation and larger samples are required before operational transfer to other sites.