干旱区生态环境知识资源中心(Arid): Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors

Arid

DOI	10.1080/15481603.2021.1947623
	Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors
	Shafizadeh-Moghadam, Hossein; Khazaei, Morteza; Alavipanah, Seyed Kazem; Weng, Qihao
通讯作者	Shafizadeh-Moghadam, H (corresponding author), Tarbiat Modares Univ, Dept Water Engn & Management, Tehran, Iran.
来源期刊	GISCIENCE & REMOTE SENSING
ISSN	1548-1603
EISSN	1943-7226
出版年	2021
卷号	58 期号:6 页码:914-928
英文摘要	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.
英文关键词	Land use and land cover simple non-iterative clustering multi-temporal NDVI topographic data arid and semi-arid region mapping climate zones
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000677763600001
WOS关键词	SUPPORT VECTOR MACHINE ; RANDOM FOREST ; CROPLAND EXTENT ; NEURAL-NETWORK ; CLIMATE ; ACCURACY ; DYNAMICS ; IMAGERY ; CHINA ; INDEX
WOS类目	Geography, Physical ; Remote Sensing
WOS研究方向	Physical Geography ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363445
作者单位	[Shafizadeh-Moghadam, Hossein] Tarbiat Modares Univ, Dept Water Engn & Management, Tehran, Iran; [Khazaei, Morteza; Alavipanah, Seyed Kazem] Univ Tehran, Fac Geog, Dept Remote Sensing & GIS, Tehran, Iran; [Weng, Qihao] Indiana State Univ, Ctr Urban & Environm Change, Dept Earth & Environm Syst, Terre Haute, IN 47809 USA
推荐引用方式 GB/T 7714	Shafizadeh-Moghadam, Hossein,Khazaei, Morteza,Alavipanah, Seyed Kazem,et al. Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors[J],2021,58(6):914-928.
APA	Shafizadeh-Moghadam, Hossein,Khazaei, Morteza,Alavipanah, Seyed Kazem,&Weng, Qihao.(2021).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,58(6),914-928.
MLA	Shafizadeh-Moghadam, Hossein,et al."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 58.6(2021):914-928.