Classification and Spatio-Temporal Change Detection of Land Use/Land Cover Using Remote Sensing and Geographic Information System in the Manouba Region, NE Tunisia — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Classification and Spatio-Temporal Change Detection of Land Use/Land Cover Using Remote Sensing and Geographic Information System in the Manouba Region, NE Tunisia
Laboratoire Eau, Energie, Environnement, Ecole Nationale d’Ingénieurs de Sfax, Sfax, Tunisia
,
Faculte des Sciences de Gafsa, Campus Universitaire, Gafsa, Tunisia
,
Laboratoire Eau, Energie, Environnement, Ecole Nationale d’Ingénieurs de Sfax, Sfax, Tunisia
,
Geoengine, Geomatics and Geosciences Engineering, Rue du lac de Constance, Tunis, Tunisia
1 Laboratoire Eau, Energie, Environnement, Ecole Nationale d’Ingénieurs de Sfax, Sfax, Tunisia
2 Faculte des Sciences de Gafsa, Campus Universitaire, Gafsa, Tunisia
3 Laboratoire Eau, Energie, Environnement, Ecole Nationale d’Ingénieurs de Sfax, Sfax, Tunisia
4 Geoengine, Geomatics and Geosciences Engineering, Rue du lac de Constance, Tunis, Tunisia
Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and change assessment. In the first part of this study, the performance of machine learning classification algorithms was compared using Landsat 9 image (2023) of the Manouba government (Tunisia). Three different classification methods were applied: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Trees (RT). The classification aimed to identify five land use classes: urban area, vegetation, bare area, water and forest. A qualitative assessment was conducted using Overall Accuracy (OA) and the Kappa coefficient (K), derived from a confusion matrix. The results of the land cover classification demonstrated a high level of accuracy. The SVM method exhibited the best performance, with an overall accuracy of 93% and a kappa accuracy of 0.9. The ML method is the second-best classifier with an overall accuracy of 92% and a kappa accuracy of 0.88. The Random Trees method yielded the lowest accuracy among the three approaches, with an overall accuracy of 91% and a kappa accuracy of 0.87. The second part of the study focused on analyzing LULC changes in the study area. Based on the classification results, the SVM method was chosen to classify the Landsat 7 image acquired in 2000. LULC changes from 2000 to 2023 were investigated using change detection comparison. The findings indicate that over the last 23 years, vegetation land and urban areas in the study area have experienced significant increases of 31.94% and 5.47%, respectively. This study contributed to a better understanding of the classification process and dynamic LULC changes in the Manouba region. It provided valuable insights for decision-makers in planning land conservation and management.
Hassan, Z., Shabbir, R., Ahmad, S.S., Malik, A.H., Aziz, N., Butt, A. and Erum, S. (2016) Dynamics of Land Use and Land Cover Change (LULCC) Using Geospatial Techniques: A Case Study of Islamabad Pakistan. SpringerPlus, 5, Article No. 812. https://doi.org/10.1186/s40064-016-2414-z
Winkler, K., Fuchs, R., Rounsevell, M. and Herold, M. (2021) Global Land Use Changes Are Four Times Greater than Previously Estimated. Nature Communications, 12, Article No. 2501. https://doi.org/10.1038/s41467-021-22702-2
Chen, X. (2002) Using Remote Sensing and GIS to Analyze Land Cover Change and Its Impacts on Regional Sustainable Development. International Journal of Remote Sensing, 23, 107-124. https://doi.org/10.1080/01431160010007051
Kayitesi, N.M., Guzha, A.C. and Mariethoz, G. (2022) Impacts of Land Use Land Cover Change and Climate Change on River Hydro-Morphology—A Review of Research Studies in Tropical Regions. Journal of Hydrology, 615, Article ID: 128702. https://doi.org/10.1016/j.jhydrol.2022.128702
Tsegaye, L. and Bharti, R. (2022) The Impacts of LULC and Climate Change Scenarios on the Hydrology and Sediment Yield of Rib Watershed, Ethiopia. Environmental Monitoring and Assessment, 194, Article No. 717. https://doi.org/10.1007/s10661-022-10391-3
Gogoi, P.P., Vinoj, V., Swain, D., Roberts, G. Dash, J. and Tripathy, S. (2019) Land Use and Land Cover Change Effect on Surface Temperature over Eastern India. Scientific Reports, 9, Article No. 8859. https://doi.org/10.1038/s41598-019-45213-z
Nayak, S. and Mandal, M. (2012) Impact of Land Use and Land Cover Changes on Temperature Trends over Western India. Current Science, 102, 1166-1173. http://www.jstor.org/stable/24107759
Abebe, G., Getachew, D. and Ewunetu, A. (2022) Analysing Land Use/Land Cover Changes and Its Dynamics Using Remote Sensing and GIS in Gubalafito District, Northeastern Ethiopia. SN Applied Sciences, 4, 3Article No. 30. https://doi.org/10.1007/s42452-021-04915-8
Chamling, M. and Bera, B. (2020) Spatio Temporal Patterns of Land Use/Land Cover Change in the Bhutan—Bengal Foothill Region between 1987 and 2019: Study towards Geospatial Applications and Policy Making. Earth Systems and Environment, 4, 117-130. https://doi.org/10.1007/s41748-020-00150-0
Kouassi, J.L., Amos, G., Lucien, D., Yeboi, B. and Christophe, K. (2021) Assessing Land Use and Land Cover Change and Farmers Perceptions of Deforestation and Land Degradation in South-West Cote d’Ivoire, West Africa. Land, 10, Article 429. https://doi.org/10.3390/land10040429
Robert, P. and Janine, B. (2017) Land Changes in Slovakia: Past Processes and Future Directions. Applied Geography, 85, 163-175. https://doi.org/10.1016/j.apgeog.2017.05.009
Shahfahada, Talukdara, S., Naikooa, M.W., Rahmana, A., Gagnonb, A.S., Towfiqul Islamc, A.R.M. and Mosavi, A. (2023) Comparative Evaluation of Operational Land Imager Sensor on Board Landsat 8 and Landsat 9 for Land Use Land Cover Mapping over a Heterogeneous Landscape. Geocarto International, 38, Article ID: 2152496. https://doi.org/10.1080/10106049.2022.2152496
Paul, S.S. (2014) Analysis of Land Use and Land Cover Change in Kiskatinaw River Watershed: A Remote Sensing, Gis & Modeling Approach. Master’s Thesis, University of Northern British Columbia, Prince George.
Solaimani, K., Arekhi, R, Tamartash, R. and Miryaghobzadeh, M. (2010) Land Use/Cover Change Detection Based on Remote Sensing Data (A Case Study: Neka Basin). Agriculture and Biology Journal of North America, 1, 1148-1157. https://doi.org/10.5251/abjna.2010.1.6.1148.1157
Gaur, S. and Singh, R. (2023) A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future. Sustainability, 15, Article 903. https://doi.org/10.3390/su15020903
Zhou, Q. and Jing, X. (2022) Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping. Remote Sensing, 14, Article 5764. https://doi.org/10.3390/rs14225764
Mathan, M. and Krishnaveni, M. (2020) Monitoring Spatio-Temporal Dynamics of Urban and Peri-Urban Land Transitions Using Ensemble of Remote Sensing Spectral Indices—A Case Study of Chennai Metropolitan Area, India. Environmental Monitoring and Assessment, 192, Article No. 15. https://doi.org/10.1007/s10661-019-7986-y
Tewabe, D. and Fentahun, T. (2020) Assessing Land Use and Land Cover Change Detection Using Remote Sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science, 6, Article ID: 1778998. https://doi.org/10.1080/23311843.2020.1778998
Yasir, M., Hui, S., Binghu, H. and Rahman, S.U. (2020) Coastline Extraction and Land Use Change Analysis Using Remote Sensing (RS) and Geographic Information System (GIS) Technology—A Review of the Literature. Reviews on Environmental Health, 35, 453-460. https://doi.org/10.1515/reveh-2019-0103
Wu, J., Li, Y., Li, N. and Shi, P. (2017) Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data. Risk Analysis, 38, 17-30. https://doi.org/10.1111/risa.12806
Saha, A.K. and Agrawal, S. (2020) Mapping and Assessment of Flood Risk in Prayagraj District, India: A GIS and Remote Sensing Study. Nanotechnology for Environmental Engineering, 5, Article No. 11. https://doi.org/10.1007/s41204-020-00073-1
Vivekananda, G.N., Swathi, R. and Sujith, A. (2021) Multi-Temporal Image Analysis for LULC Classification and Change Detection. European Journal of Remote Sensing, 54, 189-199. https://doi.org/10.1080/22797254.2020.1771215
Reis, S. (2008) Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey. Sensors, 8, 6188-6202. https://doi.org/10.3390/s8106188
Attri, P., Chaudhry, S. and Sharma, S. (2015) Remote Sensing & GIS Based Approaches for LULC Change Detection—A Review. International Journal of Current Engineering and Technology, 5, 3126-3137.
Darem, A.A., Alhashmi, A.A., Almadani, A.M., Alanazi, A.K. and Sutantra, G.A. (2023) Development of a Map for Land Use and Land Cover Classification of the Northern Border Region Using Remote Sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science, 26, 341-350. https://doi.org/10.1016/j.ejrs.2023.04.005
Singh, R.K., Sinha, V.S.P. and Joshi, P.K. (2021) A Multinomial Logistic Model-Based Land Use and Land Cover Classification for the South Asian Association for Regional Cooperation Nations Using Moderate Resolution Imaging Spectroradiometer Product. Environment, Development and Sustainability, 23, 6106-6127. https://doi.org/10.1007/s10668-020-00864-1
Shi, D. and Yang, X. (2015) Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery. In: Li, J. and Yang, X., Eds., Monitoring and Modeling of Global Changes: A Geomatics Perspective, Springer, Dordrecht, 265-279. https://doi.org/10.1007/978-94-017-9813-6_13
Adugna, T., Xu, W. and Fan, J. (2022) Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sensing, 14, Article 574. https://doi.org/10.3390/rs14030574
Lu, D. and Weng, Q. (2007) A Survey of Image Classification Methods and Techniques for Improving Classification Performance. International Journal of Remote Sensing, 28, 823-870. https://doi.org/10.1080/01431160600746456
Dixon, G., Leni, C., Antonio, C., Sofronio, C. and Sangjun, I. (2015) Land Use Characterization and Change Detection of a Small Mangrove Area in Banacon Island, Bohol, Philippines Using a Maximum Likelihood Classification Method. Forest Science and Technology, 11, 197-205. https://doi.org/10.1080/21580103.2014.996611
Institut nationale de satatistique (2020) Rapport INS.
Direction generale de l’amenagement du territoire (2011) Atlas du gouvernerat de Manouba.
Bunyangha, J., Majaliwa, M.J.G., Muthumbi, A.W., Gichuki, N.N. and Egeru, A. (2021) Past and Future Land Use/Land Cover Changes from Multitemporal Landsat Imagery in Mpologoma Catchment, Eastern Uganda. The Egyptian Journal of Remote Sensing and Space Science, 24, 675-685. https://doi.org/10.1016/j.ejrs.2021.02.003
Talukdar, S., Uddin, K., Akhter, S., Ziaul, S., Reza, A., Islam, T. and Mallick, J. (2021) Modeling Fragmentation Probability of Land-Use and Land-Cover Using the Bagging, Random Forest, and Random Subspace in the Teesta River Basin, Bangladesh. Ecological Indicators, 126, Article ID: 107612. https://doi.org/10.1016/j.ecolind.2021.107612
Sundarakumar, K., Harika, M., Begum, S.A., Yamini, S. and Balakrishna, K. (2012) Land Use and Land Cover Change Detection and Urban Sprawl Analysis of Vijayawada City Using Multitemporal Landsat Data. International Journal of Engineering Science and Technology, 4, 170-178.
Alam, A., Bhat, M.S. and Maheen, M. (2020) Using Landsat Satellite Data for Assessing the Land Use and Land Cover Change in Kashmir Valley. GeoJournal, 85, 1529-1543. https://doi.org/10.1007/s10708-019-10037-x
Motlagh, Z.K., Lotfi, A. and Pourmanafi, S. (2020) Spatial Modeling of Land-Use Change in a Rapidly Urbanizing Landscape in Central Iran: Integration of Remote Sensing, CA-Markov, and Landscape Metrics. Environmental Monitoring and Assessment, 192, Article No. 695. https://doi.org/10.1007/s10661-020-08647-x
Janecek, A., Gansterer, W., Damel, M. and Ecker, G. (2008) On the Relationship between Feature Selection and Classification Accuracy. JMLR: Workshop and Conference Proceedings, 4, 90-105.
Adam, E., Mutanga, O., Odindi, J. and Abdel-Rahman, E.M. (2014) Land-Use/Cover Classification in a Heterogeneous Coastal Landscape Using RapidEye Imagery: Evaluating the Performance of Random Forest and Support Vector Machines Classifiers. International Journal of Remote Sensing, 35, 3440-3458. https://doi.org/10.1080/01431161.2014.903435
Cortes, C. and Vladimir, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018
Kuhn, M. and Johnson, K. (2016) Applied Predictive Modeling. Springer Nature, New York.
Friedl, M.A. and Brodley, C.E. (1997) Decision Tree Classification of Land Cover from Remotely Sensed Data. Remote Sensing of Environment, 61, 399-409. https://doi.org/10.1016/S0034-4257(97)00049-7
Valero, M.J.A. and Alzate, B.E. (2019) Comparison of Maximum Likelihood, Support Vector Machines, and Random Forest Techniques in Satellite Image Classification. Tecnura, 23, 13-26. https://doi.org/10.14483/22487638.14826
Dong, S., Chen, Z., Gao, B., Guo, H., Sun, D. and Pan, Y. (2020) Stratified Even Sampling Method for Accuracy Assessment of Land Use/Land Cover Classification: A Case Study of Beijing, China. International Journal of Remote Sensing, 41, 6427-6443. https://doi.org/10.1080/01431161.2020.1739349
Congalton, R.G. (1991) A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37, 35-46. https://doi.org/10.1016/0034-4257(91)90048-B
Singh, A. (1989) Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing, 10, 989-1003. https://doi.org/10.1080/01431168908903939
Kamaraj, M. and Rangarajan, S. (2021) Predicting the Future Land Use and Land Cover Changes for Bhavani Basin, Tamil Nadu, India Using QGIS MOLUSCE Plugin. https://doi.org/10.21203/rs.3.rs-616393/v1
Alshari, E.A. and Gawali, B.W. (2022) Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE. Journal of Sensors, 2022, Article ID: 7419031. https://doi.org/10.1155/2022/7419031
Fetene, D.T., Lohani, T.K. and Mohammed, A.K. (2023) LULC Change Detection Using Support Vector Machines and Cellular Automata-Based ANN Models in Guna Tana Watershed of Abay Basin, Ethiopia. Environmental Monitoring and Assessment, 195, Article No. 1329. https://doi.org/10.1007/s10661-023-11968-2
Mehari, M., Li, J.H. and Melesse, M. (2022) Land Use Land Cover Change Analysis and Detection of Its Drivers Using Geospatial Techniques: A Case of South-Central Ethiopia. All Earth, 34, 309-332. https://doi.org/10.1080/27669645.2022.2139023
Prasad, S.V.S., Satya Savithri, T. and Murali Krishna, I.V. (2017) Comparison of Accuracy Measures for RS Image Classification Using SVM and ANN Classifiers. International Journal of Electrical and Computer Engineering, 7, 1180-1187. https://doi.org/10.11591/ijece.v7i3.pp1180-1187
Talukdar, S., Singha, P., Mahato, S., Shahfahad, P.S., Liou, Y.A. and Rahman, A. (2020) Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations A Review. Remote Sensing, 12, Article 1135. https://doi.org/10.3390/rs12071135
Boumiza, K. (2023) 2022 annus horribilis ou l’année Kais Saied. https://africanmanager.com/,2022
Abu Kubi, M. (2003) Detection and Mapping of the Land Use/Land Cover (LULC) Changes in the Jordan Valley Using LANDSAT Imageries. In: Gitas, I.Z., Miguel, S. and Ayanz, J., Eds., Environmental Monitoring in the South-Eastern Mediterranean Region Using RS/GIS Techniques, CIHEAM, Chania, 69-84.
Rane, N.L., Achari, A., Choudhary, S.P. and Giduturi, M. (2023) Effectiveness and Capability of Remote Sensing (RS) and Geographic Information Systems (GIS): A Powerful Tool for Land use and Land Cover (LULC) Change and Accuracy Assessment. International Journal of Innovative Science and Research Technology, 8, 286-295.