Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors
Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-...
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Format: | Article |
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Taylor & Francis Group
2021-08-01
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Series: | GIScience & Remote Sensing |
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Online Access: | http://dx.doi.org/10.1080/15481603.2021.1947623 |
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author | Hossein Shafizadeh-Moghadam Morteza Khazaei Seyed Kazem Alavipanah Qihao Weng |
author_facet | Hossein Shafizadeh-Moghadam Morteza Khazaei Seyed Kazem Alavipanah Qihao Weng |
author_sort | Hossein Shafizadeh-Moghadam |
collection | DOAJ |
description | Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment. |
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issn | 1548-1603 1943-7226 |
language | English |
last_indexed | 2024-03-11T23:08:55Z |
publishDate | 2021-08-01 |
publisher | Taylor & Francis Group |
record_format | Article |
series | GIScience & Remote Sensing |
spelling | doaj.art-ec59c9d2c27141c4a9667a0411d67e582023-09-21T12:34:17ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262021-08-0158691492810.1080/15481603.2021.19476231947623Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factorsHossein Shafizadeh-Moghadam0Morteza Khazaei1Seyed Kazem Alavipanah2Qihao Weng3Tarbiat Modares UniversityUniversity of TehranUniversity of TehranIndiana State UniversityMapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.http://dx.doi.org/10.1080/15481603.2021.1947623land use and land coversimple non-iterative clusteringmulti-temporal ndvitopographic dataarid and semi-arid region mappingclimate zones |
spellingShingle | Hossein Shafizadeh-Moghadam Morteza Khazaei Seyed Kazem Alavipanah Qihao Weng Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors GIScience & Remote Sensing land use and land cover simple non-iterative clustering multi-temporal ndvi topographic data arid and semi-arid region mapping climate zones |
title | Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors |
title_full | Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors |
title_fullStr | Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors |
title_full_unstemmed | Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors |
title_short | Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors |
title_sort | google earth engine for large scale land use and land cover mapping an object based classification approach using spectral textural and topographical factors |
topic | land use and land cover simple non-iterative clustering multi-temporal ndvi topographic data arid and semi-arid region mapping climate zones |
url | http://dx.doi.org/10.1080/15481603.2021.1947623 |
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