Large-Scale Populus euphratica Distribution Mapping Using Time-Series Sentinel-1/2 Data in Google Earth Engine

Accurate and efficient large-scale mapping of P. euphratica distribution is of great importance for managing and protecting P. euphratica forests, policy making, and realizing sustainable development goals in the ecological environments of desert areas. In large regions, numerous types of vegetation exhibit spectral characteristics that closely resemble those of P. euphratica, such as Tamarix, artificial forests, and allee trees, posing challenges for the accurate identification of P. euphratica. To solve this issue, this paper presents a method for large-scale P. euphratica distribution mapping. The geographical distribution characteristics of P. euphratica were first utilized to rapidly locate the appropriate region of interest and to further reduce background complexity and interference from other similar objects. Spectral features, indices, phenological features, and backscattering features extracted from all the available Sentinel-2 MSI and Sentinel-1 SAR data from 2021 were regarded as the input for a random forest model used to classify P. euphratica in the GEE platform. The results were then compared with the results from the method using only spectral features and index features, the results from the method that only added phenological features, and the results from the method that added phenological features and backscattering features by visually and quantitatively referencing field-surveyed samples, UAV data, and high-spatial-resolution data from Google Earth Data and Map World. The comparison indicated that the proposed method, which adds both phenological and time-series backscattering features, could correctly distinguish P. euphratica from other types of vegetation that have spectral information similar to P. euphratica. The rates of omission errors (OEs), commission errors (CEs), and overall accuracy (OA) for the proposed method were 12.53%, 11.01%, and 89.32%, respectively, representing increases of approximately 9%, 17%, and 13% in comparison with the method using only spectral and index features. The proposed method significantly improved the accuracy of P. euphratica classification in terms of both omission and, especially, commission.