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

• Published in: Geomatics, natural hazards and risk Vol. 11; no. 1; pp. 724 - 741
• Main Authors: Moayedi, Hossein, Jamali, Ali, Gibril, Mohamed Barakat A., Kok Foong, Loke, Bahiraei, Mehdi
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.01.2020; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Arid environments; Arid regions; Arid zones; Data analysis; Data mining; Datasets; Disaster management; Drought; Emergency preparedness; Environmental changes; Environmental management; Error reduction; Heat islands; Land cover; Land cover change; Land use; Landsat; Landsat satellites; machine learning; Mapping; Programming languages; Remote sensing; Resource management; Risk analysis; Risk management; Root-mean-square errors; Satellite imagery; Semi arid environments; semi-arid environment; Semiarid environments; Semiarid zones; Soil; Soils; Training; tree-base data mining; Urban heat islands; Urbanization; Vegetation
• ISSN: 1947-5705; 1947-5713
• DOI: 10.1080/19475705.2020.1745902

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.