6533b825fe1ef96bd1283249
RESEARCH PRODUCT
Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
Jochem VerrelstAli NaghipourAtaollah EbrahimiMasoumeh AghababaeiEsmaeil Asadisubject
Earth observation010504 meteorology & atmospheric sciencesComputer scienceNDVIScienceQvegetation types classification04 agricultural and veterinary sciences15. Life on landTime optimal01 natural sciencesNormalized Difference Vegetation IndexRandom forestIdentification (information)Vegetation typesmachine learning040103 agronomy & agriculturevegetation types classification; multi-temporal images; machine learning; Google Earth Engine; NDVI0401 agriculture forestry and fisheriesGeneral Earth and Planetary SciencesGoogle Earth EngineCartographymulti-temporal images0105 earth and related environmental sciencesdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-11-19 | Remote Sensing |