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 areas. 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 period of 2018, 2019, and 2020. 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. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. 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 multitemporal imagery for VTs classification.

Essential oil composition of aerial parts of the three ecotypes (Koohrang, Bazoft, and Samsami) of wild celery (Kelussia odoratissima) were determined, using GC/MS. The 22 constituents, corresponding to 95.55% of the essential oil of Bazoft ecotype, were identified, of which the major ones are such as z-ligustilide (47.31%) and 3-e-butyl phthalide (17.38%). In the Koohrang ecotype, 24 compounds were identified as 90.38% of its essential oil (the main compounds composed of z-ligustilide (33.73%) and 3-e-butyldiene phthalide (20.1%)). In the Samsami ecotype 24, compounds representing 93.64% of its essential oil were identified, and the main compounds were z-ligustilide (37.55%) and 3-e-butyl phthalide (19.92%). Overall, the analyses showed that z-ligustilide was the major compound that has been reported to have a positive impact on the nervous system, blood pressure, and cholesterol.