A Comparison of Machine Learning Models for Mapping Tree Species Using WorldView-2 Imagery in the Agroforestry Landscape of West Africa

Abstract

Farmland trees are a vital part of the local economy as trees are used by farmers for fuelwood as well as food, fodder, medicines, fibre, and building materials. As a result, mapping tree species is important for ecological, socio-economic, and natural resource management. The study evaluates very high-resolution remotely sensed WorldView-2 (WV-2) imagery for tree species classification in the agroforestry landscape of the Kano Close-Settled Zone (KCSZ), Northern Nigeria. Individual tree crowns extracted by geographic object-based image analysis (GEOBIA) were used to remotely identify nine dominant tree species (Faidherbia albida, Anogeissus leiocarpus, Azadirachta indica, Diospyros mespiliformis, Mangifera indica, Parkia biglobosa, Piliostigma reticulatum, Tamarindus indica, and Vitellaria paradoxa) at the object level. For every tree object in the reference datasets, eight original spectral bands of the WV-2 image, their spectral statistics (minimum, maximum, mean, standard deviation, etc.), spatial, textural, and color-space (hue, saturation), and different spectral vegetation indices (VI) were used as predictor variables for the classification of tree species. Nine different machine learning methods were used for object-level tree species classification. These were Extra Gradient Boost (XGB), Gaussian Naïve Bayes (GNB), Gradient Boosting (GB), K-nearest neighbours (KNN), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR), Multi-layered Perceptron (MLP), Random Forest (RF), and Support Vector Machines (SVM). The two top-performing models in terms of highest accuracies for individual tree species classification were found to be SVM (overall accuracy = 82.1% and Cohen’s kappa = 0.79) and MLP (overall accuracy = 81.7% and Cohen’s kappa = 0.79) with the lowest numbers of misclassified trees compared to other machine learning methods.