Vegetation Types Mapping Using Multi-Temporal Landsat Images and Google Earth Engine Platform in Heterogeneous Semi-Steppe Rangelands

Vegetation Types (VTS) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid rangelands. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the ۱۲-months period of ۲۰۲۰. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. Also, the single image of May ۲۰۲۰ chosen as the reference for classification comparison. We then compared single-date and multi-temporal datasets of Landsat ۸ images within the Google Earth Engine (GEE) platform as the input to the Random Forest (RF) classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of ۵۱% and ۶۴%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of ۷۴% and an overall accuracy of ۸۱%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multi-temporal imagery for VTs classification.

کلیدواژه ها:

vegetation types classification ، multi-temporal images ، Random Forest ، Google Earth Engine