Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq
The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change de...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402308461X |
| _version_ | 1797429928339177472 |
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| author | Abdulqadeer Rash Yaseen Mustafa Rahel Hamad |
| author_facet | Abdulqadeer Rash Yaseen Mustafa Rahel Hamad |
| author_sort | Abdulqadeer Rash |
| collection | DOAJ |
| description | The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change detection analysis has been limited. This study monitors and analyzes LULC changes in the study area from 1991 to 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). The results showed that the RF algorithm produced the most accurate maps of the three-decade study period, accompanied by a high kappa coefficient (0.93–0.97) compared with the SVM (0.91–0.95), ANN (0.91–0.96), KNN (0.92–0.96), and XGBoost (0.92–0.95) algorithms. Consequently, the RF classifier was implemented to categorize all obtainable satellite images. Socioeconomic changes throughout these transition periods were revealed by the change detection results. Rangeland and barren land areas decreased by 11.33 % (−402.03 km2) and 6.68 % (−236.8 km2), respectively. The transmission increases of 13.54 % (480.18 km2), 3.43 % (151.74 km2), and 0.71 % (25.22 km2) occurred in agricultural land, forest, and built-up areas, respectively. The outcomes of this study contribute significantly to LULC monitoring in developing regions, guiding stakeholders to identify vulnerable areas for better land use planning and sustainable environmental protection. |
| first_indexed | 2024-03-09T09:20:00Z |
| format | Article |
| id | doaj.art-ad0a12bbab3040bb9cd2fe057f880d6c |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-09T09:20:00Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ad0a12bbab3040bb9cd2fe057f880d6c2023-12-02T07:01:38ZengElsevierHeliyon2405-84402023-11-01911e21253Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, IraqAbdulqadeer Rash0Yaseen Mustafa1Rahel Hamad2Dept. of Petroleum Geosciences, Faculty of Science, Soran University, 44008, Soran, Erbil, Iraq; Soran Research Centre, Soran University, Soran, Erbil, Iraq; Corresponding author. Dept. of Petroleum Geosciences, Faculty of Science, Soran University, 44008, Soran, Erbil, Iraq.Dept. of Environmental Sciences, Faculty of Science, University of Zakho, Duhok, IraqDept. of Petroleum Geosciences, Faculty of Science, Soran University, 44008, Soran, Erbil, Iraq; Soran Research Centre, Soran University, Soran, Erbil, IraqThe identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change detection analysis has been limited. This study monitors and analyzes LULC changes in the study area from 1991 to 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). The results showed that the RF algorithm produced the most accurate maps of the three-decade study period, accompanied by a high kappa coefficient (0.93–0.97) compared with the SVM (0.91–0.95), ANN (0.91–0.96), KNN (0.92–0.96), and XGBoost (0.92–0.95) algorithms. Consequently, the RF classifier was implemented to categorize all obtainable satellite images. Socioeconomic changes throughout these transition periods were revealed by the change detection results. Rangeland and barren land areas decreased by 11.33 % (−402.03 km2) and 6.68 % (−236.8 km2), respectively. The transmission increases of 13.54 % (480.18 km2), 3.43 % (151.74 km2), and 0.71 % (25.22 km2) occurred in agricultural land, forest, and built-up areas, respectively. The outcomes of this study contribute significantly to LULC monitoring in developing regions, guiding stakeholders to identify vulnerable areas for better land use planning and sustainable environmental protection.http://www.sciencedirect.com/science/article/pii/S240584402308461XSupervised classificationChange detectionSocioeconomicsRemote sensingLandsat imagery |
| spellingShingle | Abdulqadeer Rash Yaseen Mustafa Rahel Hamad Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq Heliyon Supervised classification Change detection Socioeconomics Remote sensing Landsat imagery |
| title | Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq |
| title_full | Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq |
| title_fullStr | Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq |
| title_full_unstemmed | Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq |
| title_short | Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq |
| title_sort | quantitative assessment of land use land cover changes in a developing region using machine learning algorithms a case study in the kurdistan region iraq |
| topic | Supervised classification Change detection Socioeconomics Remote sensing Landsat imagery |
| url | http://www.sciencedirect.com/science/article/pii/S240584402308461X |
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