Using Geospatial-Driven Territorial Planning and Land Suitability Analysis for Sustainable Coconut Agriculture in Johor, Malaysia

Abstract

This research assesses land suitability and territorial planning for sustainable coconut agriculture in Johor, Malaysia, using Geographic Information Systems (GIS) and remote sensing technologies. The study aims to generate a comprehensive coconut land suitability map, evaluate land viability using Weighted Overlay Analysis (WOA) and Principal Component Analysis (PCA), and develop territorial plans that consider soil suitability, climate conditions, and environmental impacts. Key variables analyzed include digital elevation model (DEM), soil bulk density, land use/land cover, soil moisture, NDVI, soil pH, annual rainfall, temperature, soil type, and soil water content. Each of these factors was reclassified and assigned weights through Weighted Overlay Analysis (WOA) and Principal Component Analysis (PCA) to produce detailed suitability maps. The final maps classified areas into suitability categories: very suitable, suitable, moderately suitable, unsuitable, and very unsuitable, based on a predefined scale. These results clearly show that there is a significant loss of suitability regarding coconut cultivation from 2018 to 2024 due to driving factors like urbanization, degradation of land, and fluctuating environmental conditions. Therefore, key areas that were found highly suitable for coconut farming include Pontian, Muar, Batu Pahat, and Kota Tinggi, while areas of lower suitability include Pulau Tioman. The study thus concludes that adaptive land management strategies and continuous monitoring are needed to promote sustainable coconut agriculture, hence optimizing land use in Johor.

Keywords

• GIS
• LAND SUITABILITY
• SUSTAINABLE AGRICULTURE
• TERRITORIAL PLANNING

References

1. Farooq, M. S., Uzair, M., Raza, A., Habib, M., Xu, Y., Yousuf, M., ... & Ramzan Khan, M. (2022). Uncovering the research gaps to alleviate the negative impacts of climate change on food security: a review. Frontiers in plant science, 13, 927535.
2. Prakash, S., & Verma, A. K. (2022). Anthropogenic activities and Biodiversity threats. International Journal of Biological Innovations, IJBI, 4(1), 94-103.
3. Ikendi, S. (2023). Ecological conservation, biodiversity, and agricultural education as integrated approaches for envisioning the future of sustainable agriculture in North America. International Journal of Sustainable Development & World Ecology, 30(2), 152-163.
4. Montagnini, F., Grover, E. C., Hering, P., & Bachmann, G. (2024). Conclusions: Agroforestry for Biodiversity Conservation and Food Sovereignty—Lessons Learned and Pending Challenges. In Integrating Landscapes: Agroforestry for Biodiversity Conservation and Food Sovereignty (pp. 707-732). Cham: Springer International Publishing.
5. Habibullah, M. S., Din, B. H., Tan, S. H., & Zahid, H. (2022). Impact of climate change on biodiversity loss: global evidence. Environmental Science and Pollution Research, 29(1), 1073-1086.
6. Khan, N., Ray, R. L., Sargani, G. R., Ihtisham, M., Khayyam, M., & Ismail, S. (2021). Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability, 13(9), 4883.
7. Sapry, H., & Zulkifli, M. (2024). The impact of economic instability on household food security and framework to develop a sustainable food supply chain. Journal of Future Sustainability, 4(4), 231-242.
8. Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 3(4), 254-256.

9. Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745.
10. Sagaydak, A., & Sagaydak, A. (2021). Agricultural land consolidation vs. land fragmentation in Russia. International Journal of Engineering and Geosciences, 7(2), 128-141.
11. Qu, Y., WANG, S., Tian, Y., Jiang, G., Zhou, T., & Meng, L. (2023). Territorial spatial planning for regional high-quality development – An analytical framework for the identification, mediation and transmission of potential land utilization conflicts in the Yellow River Delta. Land Use Policy, 125, 106462–106462.
12. Huang, R., Nie, Y., Duo, L., Zhang, X., Wu, Z., & Xiong, J. (2021). Construction land suitability assessment in rapid urbanizing cities for promoting the implementation of United Nations sustainable development goals: a case study of Nanchang, China. Environmental Science and Pollution Research, 28, 25650-25663.
13. Isa, N. A., Salleh, S. A., Wan Mohd, W. M. N., & Chan, A. (2018). Kuala Lumpur city of tomorrow: Integration of geospatial urban climatic information in city planning. Theoretical and Empirical Researches in Urban Management, 13(4), 5-27.
14. Isa, N. A., Wan Mohd, W. M. N., Salleh, S. A., Gee Ooi, M. C., & Chan, A. (2020). Land cover impacts towards thermal variation in the Kuala Lumpur city. Journal of Urban and Regional Analysis, 12(1), 91-111.
15. Jonuzi, E., Alkan, T., Durduran, S. S., Selvi, H. Z. (2024). Using GIS-supported MCDA method for appropriate site selection of parking lots: The case study of the city of Tetovo, North Macedonia. International Journal of Engineering and Geosciences, 9(1), 86-98.
16. Umesha, S., Manukumar, H. M., & Chandrasekhar, B. (2018). Sustainable agriculture and food security. In Biotechnology for sustainable agriculture (pp. 67-92). Woodhead Publishing.
17. Yakar, M., & Dogan, Y. (2019). 3D Reconstruction of Residential Areas with SfM Photogrammetry. In Advances in Remote Sensing and Geo Informatics Applications: Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018 (pp. 73-75). Springer International Publishing.
18. Peter, B. G., Messina, J. P., Lin, Z., & Snapp, S. S. (2020). Crop climate suitability mapping on the cloud: A geovisualization application for sustainable agriculture. Scientific Reports, 10(1), 15487.
19. Mathenge, M., Sonneveld, B. G., & Broerse, J. E. (2022). Application of GIS in agriculture in promoting evidence-informed decision making for improving agriculture sustainability: a systematic review. Sustainability, 14(16), 9974.
20. Guliyev , İsmail ., & Hüseynov, R. (2024). Comparative character and monitoring of some parameters of the soil and vegetation by remote sensing in the zone of Zangilan . Advanced Remote Sensing, 4(1), 28–35.
21. Alfaloji, M. (2022). Water budget estimation using remote sensing observations and GLDAS-CLSM for Limpopo River Basin. Advanced Remote Sensing, 2(2), 85–93.
22. Ahamed, T. (Ed.). (2022). Remote Sensing Application: Regional Perspectives in Agriculture and Forestry (Vol. 59). Springer Nature.
23. Salleh, S. A., Latif, Z. A., Pradhan, B., Wan Mohd, W. M. N., & Chan, A. (2014). Functional relation of land surface albedo with climatological variables: a review on remote sensing techniques and recent research developments. Geocarto International, 29(2), 147-163.
24. Economic Planning Unit. (n.d.). SDG roadmap for Malaysia (Phase 2: 2021–2025).
25. Malaysian Green Technology & Climate Change Corporation. (2023). Green practices guideline for agriculture sector. Malaysian Green Technology & Climate Change Corporation.
26. Malaysıa Natıonal Pathway For Food Systems Transformatıon by Mınıstry of Agrıculture and Food Industrıes. (2021).
27. Kaya, Y., & Polat, N. (2023). A linear approach for wheat yield prediction by using different spectral vegetation indices. International Journal of Engineering and Geosciences, 8(1), 52-62.
28. Sarı, F., & Koyuncu, F. (2021). Multi criteria decision analysis to determine the suitability of agricultural crops for land consolidation areas. International Journal of Engineering and Geosciences, 6(2), 64-73.
29. Malay Mail. (2022, December 8). Johor plans to develop 1,325 hectares of coconut plantation, says state rep. Malay Mail.
30. Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69.
31. Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123..
32. Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. A., & McDowell, R. W. (2019). Development of a model using matter element, AHP and GIS techniques to assess the suitability of land for agriculture. Geoderma, 352, 80-95.
33. Francisco, J. T., Casisirano, J. D., Blanco, A. C., Rivera, R. L., Canja, L. H., Francisco, M. D., & Barrientos, R. M. (2024). Enhancement of the Fıeld Assessment Protocols and Suıtabılıty Maps for Coconut. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 251-258.
34. Arumugam, T., & Hatta, M. A. M. (2022). Improving Coconut Using Modern Breeding Technologies: Challenges and Opportunities. Plants, 11(24), 3414.
35. Pawlak, K., & Kołodziejczak, M. (2020). The role of agriculture in ensuring food security in developing countries: Considerations in the context of the problem of sustainable food production. Sustainability, 12(13), 5488.
36. Sundram, P. (2023). Food security in ASEAN: progress, challenges and future. Frontiers in Sustainable Food Systems, 7, 1260619.
37. Pabuayon, I. M., Medina, S. M., Medina, C. M., Manohar, E. C., & Villegas, J. I. P. (2008). Economic and environmental concerns in Philippine upland coconut farms: an analysis of policy, farming systems and socio-economic issues. Economy & Environment Program for Southeast Asia, IDRC—CRDI, Singapore.
38. Agcaoili, S. (2023). Analysis on the Land Suitability for Coconut Cultivation using Geographic Information System. Indian Journal of Science and Technology, 16(20), 1477-1486.
39. Yeniay, E., & Şık, A. (2023). Spatial ecological risk analysis in peach farming in Manisa. Advanced GIS, 3(2), 59–67