Abstract
Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.
Highlights
Academic Editors: Bingfang Wu, Abstract: Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem
There are few studies of single shrub species classification based on unmanned aerial vehicle (UAV) RGB images in the Gobi region where the surface background is relatively complex
We proposed a semi-automated workflow to classify individual shrub species based on readily available high-resolution UAV RGB images using a combination of object-based image analysis (OBIA) and random forest (RF)
Summary
Academic Editors: Bingfang Wu, Abstract: Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Different spectral and texture index images were derived from UAV RGB images as variables for species classification. We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. We analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. The shrub species and their spatial distributions reflect the species composition of a shrub community and the spatial configurations of different species, both of which are fundamental indices in ecological research and key metrics in vegetation surveys of the Gobi region [3,4]
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