PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals.

• Abdullah, A. Y., Masrur, A., Adnan, M. S., Baky, M. A., Hassan, Q. K., & Dewan, A. (2019). Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7), Article 790. https://doi.org/10.3390/rs11070790

• Aja, D., Miyittah, M. K., & Angnuureng, D. B. (2022). Quantifying mangrove extent using a combination of optical and radar images in a wetland complex, Western Region, Ghana. Sustainability, 14(24), Article 16687. https://doi.org/10.3390/su142416687

• Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban land use and land cover change analysis using random forest classification of landsat time series. Remote Sensing, 14(11), Article 2654. https://doi.org/10.3390/rs14112654

• Balha, A., Mallick, J., Pandey, S., Gupta, S., & Singh, C. K. (2021). A comparative analysis of different pixel and object-based classification algorithms using multi-source high spatial resolution satellite data for LULC Mapping. Earth Science Informatics, 14(4), 2231–2247. https://doi.org/10.1007/s12145-021-00685-4

• Fisher, J. R., Acosta, E. A., Dennedy‐Frank, P. J., Kroeger, T., & Boucher, T. M. (2017). Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality. Remote Sensing in Ecology and Conservation, 4(2), 137–149. https://doi.org/10.1002/rse2.61

• Gaur, S., & Singh, R. (2023). A comprehensive review on land use/land cover (LULC) change modeling for urban development: Current status and future prospects. Sustainability, 15(2), Article 903. https://doi.org/10.3390/su15020903

• Geidam, K. K., Adnan, N. A., & Alhaji Umar, B. (2020). Analysis of land use land cover changes using remote sensing data and Geographical Information Systems (GIS) at an urban set up of Damaturu, Nigeria. Journal of Science and Technology, 12(2), 24-37. https://doi.org/10.30880/jst.2020.12.02.003

• Gondwe, J. F., Lin, S., & Munthali, R. M. (2021). Analysis of land use and land cover changes in urban areas using remote sensing: Case of Blantyre City. Discrete Dynamics in Nature and Society, 2021, 1–17. https://doi.org/10.1155/2021/8011565

• Husain, M. A., Kumar, P., & Gonencgil, B. (2023). Assessment of spatio-temporal land use/cover change and its effect on land surface temperature in lahaul and Spiti, India. Land, 12(7), Article 1294. https://doi.org/10.3390/land12071294

• Isola, F., Lai, S., Leone, F., & Zoppi, C. (2023). Land take and landslide hazard: Spatial assessment and policy implications from a study concerning Sardinia. Land, 12(2), Article 359. https://doi.org/10.3390/land12020359

• Johnson, T., Butcher, J., Santell, S., Schwartz, S., Julius, S., & LeDuc, S. (2022). A review of climate change effects on practices for mitigating water quality impacts. Journal of Water and Climate Change, 13(4), 1684–1705. https://doi.org/10.2166/wcc.2022.363

• Konrad, C. P. (2014). Effects of Urban Development on Floods [Fact Sheet]. U. S. Geological Survey. https://doi.org/10.3133/fs07603

• Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1), 3–10. https://doi.org/10.1016/j.gsf.2015.07.003

• Lokman, M. (2004). Exploring the interface: The enigmatic mangroves. Kolej Universiti Sains dan Teknologi Malaysia.

• Maryantika, N., & Lin, C. (2017). Exploring changes of land use and mangrove distribution in the economic area of Sidoarjo District, East Java using multi-temporal landsat images. Information Processing in Agriculture, 4(4), 321–332. https://doi.org/10.1016/j.inpa.2017.06.003

• Nath, B., Niu, Z., & Singh, R. (2018). Land use and land cover changes, and environment and risk evaluation of Dujiangyan City (SW China) using remote sensing and GIS techniques. Sustainability, 10(12), Article 4631. https://doi.org/10.3390/su10124631

• Neidhardt, H., & Shao, W. (2023). Impact of climate change-induced warming on groundwater temperatures and quality. Applied Water Science, 13(12), Article 235. https://doi.org/10.1007/s13201-023-02039-5

• Ngondo, J., Mango, J., Liu, R., Nobert, J., Dubi, A., & Cheng, H. (2021). Land-use and land-cover (LULC) change detection and the implications for coastal water resource management in the Wami–Ruvu Basin, Tanzania. Sustainability, 13(8), Article 4092. https://doi.org/10.3390/su13084092

• Rahman, H. A. (2014). An overview of environmental disaster in Malaysia and preparedness strategies. Iranian Journal of Public Health, 43(3), 17–24.

• Romali, N. S., Yusop, Z., Zaki, N. I. M., Sulaiman, M. K., Ghani, N. A. A. A., & Sulaiman, S. (2019). Flood damage function model for residential area in Kuantan: A preliminary study. International Journal of Integrated Engineering, 11(2). https://doi.org/10.30880/ijie.2019.11.02.022

• Saddique, N., Mahmood, T., & Bernhofer, C. (2020). Quantifying the impacts of land use/land cover change on the water balance in the afforested river basin, Pakistan. Environmental Earth Sciences, 79(19), Article 448. https://doi.org/10.1007/s12665-020-09206-w

• Saini, R., & Rawat, S. (2023). Land use land cover classification in remote sensing using Machine Learning Techniques. In 2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP) (pp. 99-104). IEEE Publishing. https://doi.org/10.1109/ihcsp56702.2023.10127126

• Samra, R. M. A. (2021). Dynamics of human-induced lakes and their impact on land surface temperature in Toshka Depression, Western Desert, Egypt. Environmental Science and Pollution Research, 29(14), 20892–20905. https://doi.org/10.1007/s11356-021-17347-z

• Seyam, M. M., Haque, M. R., & Rahman, M. M. (2023). Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: A case study at Bhaluka in Mymensingh, Bangladesh. Case Studies in Chemical and Environmental Engineering, 7, Article 100293. https://doi.org/10.1016/j.cscee.2022.100293

• Shahbudin, S., Zuhairi, A., Kamaruzzaman, Y., & Jalal, K. C. A. (2009, October 20-22). Impact of coastal development on mangrove distribution in Kuantan, Pahang. In International Workshop on Integrated Coastal Zone Management (pp. 1-11). Izmir, Turkey.

• Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine Learning classifiers for satellite observations - A review. Remote Sensing, 12(7), Article 1135. https://doi.org/10.3390/rs12071135

• Tewabe, D., & 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(1), Article 1778998. https://doi.org/10.1080/23311843.2020.1778998

• Viana, C. M., Girão, I., & Rocha, J. (2019). Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sensing, 11(9), Article 1104. https://doi.org/10.3390/rs11091104

• Vicenteserrano, S., Perezcabello, F., & Lasanta, T. (2008). Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of landsat images. Remote Sensing of Environment, 112(10), 3916–3934. https://doi.org/10.1016/j.rse.2008.06.011

• Yulianto, F., Suwarsono, Nugroho, U. C., Nugroho, N. P., Sunarmodo, W., & Khomarudin, M. R. (2020). Spatial-temporal dynamics land use/land cover change and flood hazard mapping in the upstream citarum watershed, West Java, Indonesia. Quaestiones Geographicae, 39(1), 125–146. https://doi.org/10.2478/quageo-2020-0010

• Zaidi, S., M., Akbari, A., & Ishak, W., M., (2014) A critical review of flood history in Kuantan River basin: Challenges and potential solutions. International Journal of Civil Engineering and Geo-Environment, 5, 1-5.