Land Use Change Modelling Using Logistic Regression, Random Forest and Additive Logistic Regression in Kubu Raya Regency, West Kalimantan

Alfa Nugraha Pradana(1*), Anik Djuraidah(2), Agus Mohamad Soleh(3)

(1) Department of Statistics, IPB University, Jl. Lingkar Akademik Kampus IPB Dramaga, Bogor 16680, Indonesia; Centre for International Forestry Research – World Agroforestry Centre, Jl. CIFOR, Situ Gede, Sindang Barang, Bo-gor 16115, Indonesia
(2) Department of Statistics, IPB University, Jl. Lingkar Akademik Kampus IPB Dramaga, Bogor 16680, Indonesia
(3) Department of Statistics, IPB University, Jl. Lingkar Akademik Kampus IPB Dramaga, Bogor 16680, Indonesia
(*) Corresponding Author

Abstract

Kubu Raya Regency is a regency in the province of West Kalimantan which has a wetland ecosystem including a high-density swamp or peatland ecosystem along with an extensive area of mangroves. The function of wetland ecosystems is essential for fauna, as a source of livelihood for the surrounding community and as storage reservoir for carbon stocks. Most of the land in Kubu Raya Regency is peatland. As a consequence, peat has long been used for agriculture and as a source of livelihood for the community. Along with the vast area of peat, the regency also has a potential high risk of peat fires. This study aims to predict land use changes in Kubu Raya Regency using three statistical machine learning models, specifically Logistic Regression (LR), Random Forest (RF) and Additive Logistic Regression (ALR). Land cover map data were acquired from the Ministry of Environment and Forestry and subsequently reclassified into six types of land cover at a resolution of 100 m. The land cover data were employed to classify land use or land cover class for the Kubu Raya regency, for the years 2009, 2015 and 2020. Based on model performance, RF provides greater accuracy and F1 score as opposed to LR and ALR. The outcome of this study is expected to provide knowledge and recommendations that may aid in developing future sustainable development planning and management for Kubu Raya Regency.

Keywords

land use change modelling;wetlands;logistic regression;random forest;additive logistic regression;Kubu Raya

Full Text:

PDF HTML

References

Abraham, Charlotte González, Cynthia Flores, Santana Sonia, Rodríguez Ramírez, and Marcela Olguín. (2023). Long ‑ Term Pathways Analysis to Assess the Feasibility of Sustainable Land ‑ Use and Food Systems in Mexico. Sus-tainability Science, 18(1), 469–84. doi: 10.1007/s11625-022-01243-7.

Abuhay, Wassie, Temesgen Gashaw, and Lewoye Tsegaye. (2023). Assessing Impacts of Land Use / Land Cover Changes on the Hydrology of Upper Gilgel Abbay Watershed Using the SWAT Model. Journal of Agriculture and Food Research, 12, 100535. doi: 10.1016/j.jafr.2023.100535.

Adeolu, Adesiji R., Thamer A. Mohammad, Nik N. Nik Daud, Alexander K. Sayok, Padfield Rory, and Evers Stepha-nie. (2018). Soil Carbon and Nitrogen Dynamics in a Tropical Peatland. Elsevier Inc.

Aditya, Jeremy, Prananto Budiman, Rudiyanto Rudiyanto, and Peter Grace. (2020). Drainage Increases CO 2 and N 2 O Emissions from Tropical Peat Soils. Global Change Biology, 26(8), 1–18. doi: 10.1111/gcb.15147.

Aguilera-Benavente, Francisco, and Nikolai Shurupov. (2023). Computers , Environment and Urban Systems Combining a Land Parcel Cellular Automata ( LP-CA ) Model with Participatory Approaches in the Simulation of Disrup-tive Future Scenarios of Urban Land Use Change Ram O. Computers, Environment and Urban Systems, 99, 101895. doi: 10.1016/j.compenvurbsys.2022.101895.

Akbar, Ali, Milad Zhoolideh, Hossein Azadi, Ju-hyoung Lee, and Jürgen Scheffran. (2023). Interactions of Land-Use Cover and Climate Change at Global Level : How to Mitigate the Environmental Risks and Warming Effects Intergovernmental Panel on Climate Change. Ecological Indicators, 146, 109829. doi: 10.1016/j.ecolind.2022.10 9829.

Baig Ca-ann, Mohammed Feras, Muhammad Raza, Ul Mustafa, Imran Baig, Husna Binti Takaijudin, and Muhammad Talha Zeshan. (2021). Assessment of Land Use Land Cover Changes and Future. Predictions Simulation, 14(3), 1–17. doi: 10.3390/w14030402.

Beroho, Mohamed, Hamza Briak, El Khalil Cherif, Imane Boulahfa, Abdessalam Ouallali, Rachid Mrabet, Fassil Kebede, Alexandre Bernardino, and Khadija Aboumaria. (2023). Future Scenarios of Land Use / Land Cover ( LULC ) Based on a CA-Markov Simulation Model : Case of a Mediterranean Watershed in Morocco. Remote Sensing, MDPI, 15, 1162. doi: 10.3390/rs15041162.

Bohai, Western, Bay Using, Remote Sensing, Yongbin Zhang, Caiyao Kou, Mingyue Liu, Weidong Man, and Fuping Li. (2023). Estimation of Coastal Wetland Soil Organic Carbon Content in Topographic Data. Remote Sensing, MDPI, 15(4241), 1–20. doi: 10.3390/rs15174241.

Buya, Suhaimee. (2020). Modelling of Land-Use Change in Thailand Using Binary Logistic Regression and Multinomial Logistic Regression. Arabian Journal Of Geosciences, 13(12), 437. doi: 10.1007/s12517-020-05451-2.

Cao, Min, Ya Tian, Kai Wu, Min Chen, Yu Chen, Xue Hu, Zhongchang Sun, Lijun Zuo, Huadong Guo, Hui Lin, and Guonian Lü. (2023). Future Land-Use Change and Its Impact on Terrestrial Ecosystem Carbon Pool Evolution along the Silk Road under SDG Scenarios. Science Bulletin, 68(7), 740–49. doi: 10.1016/j.scib.2023.03.012.

Chen, Yuhan, Jia Wang, Nina Xiong, Lu Sun, and Jiangqi Xu. (2022). Impacts of Land Use Changes on Net Primary Productivity in Urban Agglomerations under Multi-Scenarios Simulation. Remote Sensing, MDPI, 14(1775), 1–21. doi: doi.org/10.3390/rs14071755.

Daba, Mekonnen H., and Songcai You. (2022). Quantitatively Assessing the Future Land-Use / Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA – Markov Model and Their Implications for Water Resources Management. Sustainability, MDPI, 14(1538), 1–29. doi: 10.3390/su14031538.

Danardono, Sunariya, M. I. T., Fikriyah, V. N., & Cholil, M. (2021). Spatiotemporal Variation of Terrestrial Carbon Se-questration in Tropical Urban Area (Case Study in Surakarta District, Indonesia). Quaestiones Geographicae, 40(3), 5–20. https://doi.org/10.2478/quageo-2021-0020

Emmanuel, Balogun, Abdulla Al, Ajeyomi Adedoyin, Zullyadini A. Rahaman, Ologun Emmanuel, Mahir Shahrier, Bushra Monowar, Muhammad Tauhidur, and Olarewaju Timilehin. (2023). Environmental and Sustainability Indicators Monitoring and Predicting the Influences of Land Use / Land Cover Change on Cropland Character-istics and Drought Severity Using Remote Sensing Techniques. Environmental and Sustainability Indicators, 18, 100248. doi: 10.1016/j.indic.2023.100248.

Fikriyah, V. N., Anggani, N. L., Kiat, U. E. I., Khikmah, F., Arroyan, W. A., & Rizki, M. F. (2023). Integrated Use Of Op-tical And Radar Data For Cropland Mapping Over The Mountain Slope Area In Boyolali, Indonesia. Ge-ographia Technica, 18(1/2023), 108–122. https://doi.org/10.21163/GT_2023.181.08

Gao, Chunliu, Deqiang Cheng, Javed Iqbal, and Shunyu Yao. (2023). Yellow River Region ( GYRR ) Land Cover and the Relationship Analysis with Mountain Hazards. Land, MDPI, 12(340), 1–24. doi: 10.3390/land12020340.

Gaur, Srishti. (2023). A Comprehensive Review on Land Use / Land Cover ( LULC ) Change Modelling for Urban De-velopment : Current Status and Future Prospects. Sustainability, MDPI, 15(903), 1–12. doi: 10.3390/ su15020903.

Géant, Chuma B., Mushagalusa N. Gustave, and Serge Schmitz. (2023). Mapping Small Inland Wetlands in the South ‑ Kivu Province by Integrating Optical and SAR Data with Statistical Models for Accurate Distribution Assess-ment Democratic Republic of Congo. Scientific Reports, 13(17626), 1–23. doi: 10.1038/s41598-023-43292-7.

Ghosh, Sasanka, and Arijit Das. (2020). Wetland Conversion Risk Assessment of East Kolkata Wetland : A Ramsar Site Using Random Forest and Support Vector Machine Model. Journal of Cleaner Production, 275, 123475. doi: 10.1016/j.jclepro.2020.123475.

Girma, Rediet, Christine Fürst, and Awdenegest Moges. (2022). Land Use Land Cover Change Modelling by Integrat-ing Artificial Neural Network with Cellular Automata-Markov Chain Model in Gidabo River Basin , Main Ethi-opian Rift. Environmental Challenges, 6, 100419. doi: 10.1016/j.envc.2021.100419.

Huo, Jingeng, Zhenqin Shi, Wenbo Zhu, Hua Xue, and Xin Chen. (2022). A Multi-Scenario Simulation and Optimiza-tion of Land Use with a Markov – FLUS Coupling Model : A Case Study in Xiong ’ an New Area , China. Sus-tainability, MDPI, 14(2425), 1–20. doi: 10.3390/su14042425.

Huu, Cuong Nguyen, Cuong Nguyen Van, Tien Nguyen, and Ngoc My. (2022). AGRICULTURE AND Modelling Land-Use Changes Using Logistic Regression in Western Highlands of Vietnam : A Case Study of Lam Dong Province. Agriculture And Natural Resources, 56(5), 935–44. doi: 10.34044/j.anres.2022.56.5.08.

Jafarpour, Kamran, Ali Shamsoddini, Mir Najaf, Faizah Binti, Che Ros, and Ali Khedmatzadeh. (2022). Predicting Spa-tial and Decadal of Land Use and Land Cover Change Using Integrated Cellular Automata Markov Chain Model Based Scenarios ( 2019 – 2049 ) Zarriné-R ū d River Basin in Iran ✩. Environmental Challenges, 6:100399. doi: 10.1016/j.envc.2021.100399.

Kumar, Nitesh, and Maurya Sana. (2023). Land Use / Land Cover Dynamics Study and Prediction in Jaipur City Using CA Markov Model Integrated with Road Network. GeoJournal, 88(1), 137–60. doi: 10.1007/s10708-022-10593-9.

Li, Xiang, Zhaoshun Liu, Shujie Li, and Yingxue Li. (2022). Multi-Scenario Simulation Analysis of Land Use Impacts on Habitat Quality in Tianjin Based on the PLUS Model Coupled with the InVEST Model. Sustainability, MDPI, 14(6923), 1–18. doi: 10.3390/su14116923.

Liu, Lintao, Shouchao Yu, Hengjia Zhang, Yong Wang, and Chao Liang. (2023). Analysis of Land Use Change Drivers and Simulation of Different Future Scenarios : Taking Shanxi Province of China as an Example. International Journal of Environmental Research and Public Health, MDPI, 20(1626), 1–19. doi: 10.3390/ijerph20021626.

Liu, Zhen, Robert Gilmore, and Pontius Jr. (2021). The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping. 13(19), 3922. doi: 10.3390/rs13193922.

M L Assidik, I Soekarno, Widyaningtias, I. A. Humam. (2021). Water Balance Analysis and Hydraulic Structure Design to Prevent Peatland Fires. Science, Environmental, 758(1), 1–7. doi: 10.1088/1755-1315/758/1/012006.

N. Karasiak, J.‑F. Dejoux, C. Monteil, D. Sheeren. (2022). Spatial Dependence between Training and Test Sets : Another Pitfall of Classification Accuracy Assessment in Remote Sensing. Machine Learning, 111(7), 2715–40. doi: 10.1007/s10994-021-05972-1.

Paulina Guarderas, Franz Smith, and March Dufrene. (2022). Land Use and Land Cover Change in a Tropical Mountain Landscape of Northern Ecuador : Altitudinal Patterns and Driving Forces. Plose One, 17, 1–26. doi: 10.1371/ journal.pone.0260191.

Penny, Jessica, Carlos M. Ordens, Steve Barnett, Slobodan Djordjevi, and Albert S. Chen. (2023). Small-Scale Land Use Change Modelling Using Transient Groundwater Levels and Salinities as Driving Factors – An Example from a Sub-Catchment of Australia ’ s Murray-Darling Basin. Agricultural Water Management, 278, 108174. doi: 10.1016/j.agwat.2023.108174.

Rahsia, Shandra Andina, Evi Gusmayanti, and Rossie W. Nusantara. (2021). Emisi Karbondioksida ( CO 2 ) Lahan Gambut Pasca Kebakaran Tahun 2018 Di Kota Pontianak. Jurnal Ilmu Lingkungan, 18(2), 384–391. doi: 10.14710/jil.18.2.384-391.

Raihan, Asif, Tarig Ali, Maruf Mortula, and Rahul Gawai. (2023). Spatiotemporal Analysis of the Impacts of Climate Change on UAE Mangroves Spatiotemporal Analysis of the Impacts of Climate Change on Mangroves Located in the United Arab Emirates. Journal of Sustainable Development of Energy Water and Environment Systems, 11(3), 1–19. doi: 10.13044/j.sdewes.d11.0460.

Ren, Dong-Feng, Aihua Cao, and Fei-yue Wang. (2023). Response and Multi-Scenario Prediction of Carbon Storage and Habitat Quality to Land Use in Liaoning Province , China. Sustainability, MDPI, 15(4500), 1–23. doi: 10.3390 /su15054500.

Robinson, Bo Sun and Derek T. (2018). Comparison of Statistical Approaches for Modelling Land-Use Change. Land MDPI, 7(144), 1–33. doi: 10.3390/land7040144.

Salako, Gabriel, David J. Russell, Andres Stucke, and Einar Eberhardt. (2023). Assessment of Multiple Model Algo-rithms to Predict Earthworm Geographic Distribution Range and Biodiversity in Germany : Implications for Soil ‑ Monitoring and Species ‑ Conservation Needs. Biodiversity and Conservation, 32(7), 2365–94. doi: 10.1007/ s10531-023-02608-9.

Salmona, Yuri Botelho, Eraldo Aparecido, Trondoli Matricardi, David Lewis Skole, Andrade Silva, Osmar De Ara, Coelho Filho, Marcos Antonio Pedlowski, James Matos Sampaio, Leidi Cahola, and Reuber Albuquerque. (2023). A Worrying Future for River Flows in the Brazilian Cerrado Provoked by Land Use and Climate Changes. Sustainability, MDPI, 15(4251), 1–24. doi: 10.3390/su15054251.

Seena, Sahadevan, Christiane Baschien, Juliana Barros, Kandikere R. Sridhar, Manuel A. S. Graça, Heikki Mykrä, and Mirco Bundschuh. (2023). Ecosystem Services Provided by Fungi in Freshwaters: A Wake-up Call. Hydrobio-logia, 850(12–13), 2779–94. doi: 10.1007/s10750-022-05030-4.

Siddik, Sifat, Shibli Sadik, Atikur Rahman, and Nazrul Islam. (2022). The Impact of Land Use and Land Cover Change on Groundwater Recharge in Northwestern Bangladesh. Journal of Environmental Management, 315(4), 115130. doi: 10.1016/j.jenvman.2022.115130.

Tsiripidis, Ioannis. (2023). Simulating Future Land Use and Cover of a Mediterranean Mountainous Area : The Effect of Socioeconomic Demands and Climatic Changes. Land, MDPI, 12(253), 1–23. doi: 10.3390/land12010253.

Verburg, Peter H., Peter Alexander, Tom Evans, Nicholas R. Magliocca, Ziga Malek, Mark D. A. Rounsevell, and Jasper Van Vliet. (2019). ScienceDirect Beyond Land Cover Change : Towards a New Generation of Land Use Mod-els. Current Opinion in Environmental Sustainability, 38, 77–85. doi: 10.1016/j.cosust.2019.05.002.

Wang, Baixue, and Weiming Cheng. (2022). Effects of Land Use / Cover on Regional Habitat Quality under Different Geomorphic Types Based on InVEST Model. Remote Sensing, MDPI, 14(1279), 1–34. doi: doi.org/10. 3390/rs14051279.

Xia, Chuyu, Jian Zhang, Jing Zhao, Fei Xue, Qiang Li, Kai Fang, and Zhuang Shao. (2023). Exploring Potential of Ur-ban Land-Use Management on Carbon Emissions - A Case of Hangzhou , China. Ecological Indicators, 146, 109902. doi: 10.1016/j.ecolind.2023.109902.

Yang, Haijiang, Xiaohua Gou, Bing Xue, Weijing Ma, Wennong Kuang, Zhenyu Tu, Linlin Gao, Dingcai Yin, and Jun-zhou Zhang. (2023). Research on the Change of Alpine Ecosystem Service Value and Its Sustainable Develop-ment Path. Ecological Indicators, 146(3), 109893. doi: 10.1016/j.ecolind.2023.109893.

Zarandian, Ardavan, and Fatemeh Mohammadyari. (2023). Scenario Modelling to Predict Changes in Land Use / Cover Using Land Change Modeler and InVEST Model : A Case Study of Karaj Metropolis , Iran. Environmental Monitoring and Assessment, 195(273), 1–22. doi: 10.1007/s10661-022-10740-2.

Article Metrics

Abstract view(s): 982 time(s)
PDF: 390 time(s) HTML: 458 time(s)

Refbacks

  • There are currently no refbacks.