ArticleRemote sensing2021
Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform.
Article in Remote sensing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Plant community responses to climate change in the Zayandeh-Rud Basin, Iran: Does woody plant encroachment follow global patterns?BMC ecology and evolution · 2026Article
- Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia.Heliyon · 2024Article
- NDVI as a potential tool for forecasting changes in geographical range of sycamore (Acer pseudoplatanus L.).Scientific reports · 2023Article
- Introducing ARTMO's Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape.Remote sensing · 2022Article
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Authors and funding
5 authors.
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Abstract
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.
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