Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

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, esp...

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Autores principales:	Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi, Jochem Verrelst
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	vegetation types classification multi-temporal images machine learning Google Earth Engine NDVI Science Q
Acceso en línea:	https://doaj.org/article/0f0ca420f4d8450db0dd225d30a96692
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id	oai:doaj.org-article:0f0ca420f4d8450db0dd225d30a96692
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spelling	oai:doaj.org-article:0f0ca420f4d8450db0dd225d30a966922021-11-25T18:55:22ZVegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform10.3390/rs132246832072-4292https://doaj.org/article/0f0ca420f4d8450db0dd225d30a966922021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4683https://doaj.org/toc/2072-4292Vegetation 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.Masoumeh AghababaeiAtaollah EbrahimiAli Asghar NaghipourEsmaeil AsadiJochem VerrelstMDPI AGarticlevegetation types classificationmulti-temporal imagesmachine learningGoogle Earth EngineNDVIScienceQENRemote Sensing, Vol 13, Iss 4683, p 4683 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vegetation types classification multi-temporal images machine learning Google Earth Engine NDVI Science Q
spellingShingle	vegetation types classification multi-temporal images machine learning Google Earth Engine NDVI Science Q Masoumeh Aghababaei Ataollah Ebrahimi Ali Asghar Naghipour Esmaeil Asadi Jochem Verrelst Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
description	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.
format	article
author	Masoumeh Aghababaei Ataollah Ebrahimi Ali Asghar Naghipour Esmaeil Asadi Jochem Verrelst
author_facet	Masoumeh Aghababaei Ataollah Ebrahimi Ali Asghar Naghipour Esmaeil Asadi Jochem Verrelst
author_sort	Masoumeh Aghababaei
title	Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
title_short	Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
title_full	Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
title_fullStr	Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
title_full_unstemmed	Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
title_sort	vegetation types mapping using multi-temporal landsat images in the google earth engine platform
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0f0ca420f4d8450db0dd225d30a96692
work_keys_str_mv	AT masoumehaghababaei vegetationtypesmappingusingmultitemporallandsatimagesinthegoogleearthengineplatform AT ataollahebrahimi vegetationtypesmappingusingmultitemporallandsatimagesinthegoogleearthengineplatform AT aliasgharnaghipour vegetationtypesmappingusingmultitemporallandsatimagesinthegoogleearthengineplatform AT esmaeilasadi vegetationtypesmappingusingmultitemporallandsatimagesinthegoogleearthengineplatform AT jochemverrelst vegetationtypesmappingusingmultitemporallandsatimagesinthegoogleearthengineplatform
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Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

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