Mapping vegetation types in semi-arid riparian regions using random forest and object-based image approach: A case study of the Colorado River Ecosystem,Grand Canyon,Arizona

Riparian regions are essential habitats for wildlife and play a vital role in agricultural production, but they are highly dynamic environments impacted by fluctuations of water levels. Monitoring vegetation types along narrow river corridors is complicated and requires high-resolution imagery and advanced remote sensing techniques due to the mixture of vegetation and other types of land covers. The primary aim of this paper is to develop a framework using airborne imagery, object-based image approach (OBIA), hyper-spectral analysis and Random Forest to classify vegetation along narrow, semi-arid riparian corridors via a case study of the Grand Canyon, the Colorado River. By analyzing hyper-spectral and field data with Random Forest, we found that the bandwidths from 642 to 682 nm and 750 to 870 nm were useful for vegetation classification in this case study. As a result, the red and near-infrared bands of aerial photos were used with ancillary data for species classification, and the overall accuracy (OA) of classification with these images reached up to 94.8% with a Kappa's coefficient of 0.93. The similarity of vegetation phenology caused most of the misclassified cases. Low cost unmanned aerial vehicles should be used to acquire more frequent data, which is essential to understand how vegetation patterns change over time.