干旱区生态环境知识资源中心(Arid): Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran

Arid

DOI	10.1080/19475705.2020.1745902
	Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran
	Moayedi, Hossein 1,2; Jamali, Ali 3; Gibril, Mohamed Barakat A.4; Kok Foong, Loke 5,6; Bahiraei, Mehdi 7
通讯作者	Jamali, Ali ; Kok Foong, Loke
来源期刊	GEOMATICS NATURAL HAZARDS & RISK
ISSN	1947-5705
EISSN	1947-5713
出版年	2020
卷号	11 期号:1 页码:724-741
英文摘要	Land Use Land Cover (LULC) mapping has been used in different environmental applications including disaster management, risk analysis, heat island mapping, and the effects of urbanization on environmental changes such as floods and droughts in the recent decade. The earth's natural surface coverage including urban infrastructure, surface vegetation, bare soil can be identified with LULC maps. Besides, LULC is one of the most important tasks in natural hazards, planning activities, resource management, and global monitoring studies. The present study aimed to improve the level of land used and land cover change mapping in a semi-arid environment using Landsat 8 Operational Land Imager (OLI) image. In this research, several tree-based algorithms for LULC mapping are used and compared. These algorithms are used in a combination model implementation of WEKA 3.8 and R programming language to provide a method of a fit-for-purpose algorithms for LULC mapping. It is found that Reduced Error Pruning Tree (REP Tree) is the best approach according to the training and test datasets which are four classes including the build-up, soil, roads, and vegetation region pixels in a semi-arid environment. For the training dataset, the best values of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) is determined, 99.7313, 0.002 and 0.0354 as well as for the test dataset are 99.7313, 0.002 and 0.0354, respectively. Finally, trained models of the implemented data mining algorithms are used for the classification of a different dataset without using any training data where the Logical Analysis of Data Tree (LAD Tree) algorithm shows the best performance in terms of OA, MAE, and RMSE with values of 99.2815, 0.0057, and 0.0557 respectively.
英文关键词	Land cover change machine learning tree-base data mining semi-arid environment
类型	Article
语种	英语
国家	Vietnam ; Iran ; U Arab Emirates
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000527816400001
WOS关键词	RANDOM FOREST ; CLASSIFICATION ; REGRESSION ; NETWORKS ; MODEL ; ROCK
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314605
作者单位	1.Duy Tan Univ, Inst Res & Dev, Da Nang, Vietnam; 2.Duy Tan Univ, Fac Civil Engn, Da Nang, Vietnam; 3.Apadana Inst Higher Educ, Fac Surveying Engn, Shiraz, Iran; 4.Univ Sharjah, Res Inst Sci & Engn, GIS & Remote Sensing Ctr, Sharjah, U Arab Emirates; 5.Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam; 6.Ton Duc Thang Univ, Fac Civil Engn, Ho Chi Minh City, Vietnam; 7.Razi Univ, Fac Engn, Kermanshah, Iran
推荐引用方式 GB/T 7714	Moayedi, Hossein,Jamali, Ali,Gibril, Mohamed Barakat A.,et al. Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran[J],2020,11(1):724-741.
APA	Moayedi, Hossein,Jamali, Ali,Gibril, Mohamed Barakat A.,Kok Foong, Loke,&Bahiraei, Mehdi.(2020).Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran.GEOMATICS NATURAL HAZARDS & RISK,11(1),724-741.
MLA	Moayedi, Hossein,et al."Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran".GEOMATICS NATURAL HAZARDS & RISK 11.1(2020):724-741.