Analysis of Land-Cover Change Patterns and Their Temporal Association with Flood Occurrence in West Kendari District

Muh. Agung Apri Mars; Safril Kasim; La Gandri

doi:10.59976/jurit.v4i2.379

Authors

Muh. Agung Apri Mars Universitas Halu Oleo
Safril Kasim Universitas Halu Oleo
La Gandri Universitas Halu Oleo

DOI:

https://doi.org/10.59976/jurit.v4i2.379

Abstract

Urban expansion can alter land cover and reduce the land's capacity to absorb rainfall, thereby increasing surface runoff and flood potential. West Kendari District, part of Kendari City, Southeast Sulawesi Province, experienced substantial land cover change over the past decade and suffered severe flooding in 2024. This study aimed to analyze land cover change patterns in the district between 2014 and 2024 and to describe the temporal relationship between these changes and reported flood occurrences, without quantifying economic losses. Land cover was classified from Landsat 8 OLI/TIRS imagery for 2014 and 2019 and Landsat 9 OLI2/TIRS2 imagery for 2024, all at 30 meter spatial resolution and obtained from the USGS EarthExplorer archive, using the Maximum Likelihood Classification method into six classes. Classification accuracy was evaluated through a confusion matrix, with field validation using 26 reference points available only for the 2024 map, yielding an overall accuracy of 88.46 percent and a corrected Kappa coefficient of 0.851, indicating almost perfect agreement. The spatial analysis showed that forest cover declined by 412.47 hectares (36.8 percent), settlement area increased by 235.91 hectares (52.1 percent), and shrubland expanded by 362.51 hectares (502.6 percent), with the dominant transitions occurring from forest to shrubland and mixed garden, and from mixed garden to settlement. Flood events recorded by the Kendari City Regional Disaster Management Agency rose from none in 2014 to three in 2019 and five in 2024, coinciding in time with forest loss and settlement expansion, although a causal relationship could not be confirmed due to other contributing factors such as rainfall, drainage capacity, river conditions, sedimentation, and tidal influence. Further research incorporating rainfall data and economic loss assessment is recommended.

References

F. Nugraha, D. P. T. Baskoro, and S. D. Tarigan, “Evaluasi algoritma machine learning untuk klasifikasi dan prediksi penggunaan lahan,” Maj. Geogr. Indones., vol. 39, no. 1, pp. 19–28, 2025, doi: 10.22146/mgi.99150.

M. Arif, S. Sengupta, S. K. Mohinuddin, and K. Gupta, “Dynamics of land use and land cover change in peri-urban area of Burdwan City, India: a remote sensing and GIS-based approach,” GeoJournal, vol. 88, pp. 4189–4213, 2023.

Ridwan, “Studi kajian dampak perubahan tutupan lahan terhadap kejadian banjir di daerah aliran sungai,” ENVIRO J. Trop. Environ. Res., 2024.

M. Kamaraj and S. Rangarajan, “Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin,” Environ. Sci. Pollut. Res., vol. 29, pp. 86337–86348, 2022.

P. Lukas, A. M. Melesse, and T. T. Kenea, “Prediction of future land use/land cover changes using a coupled CA-ANN model in the upper Omo-Gibe river basin, Ethiopia,” Remote Sens., vol. 15, p. 1148, 2023.

V. Nasiri, A. Deljouei, F. Moradi, S. M. M. Sadeghi, and S. A. Borz, “Land use and land cover mapping using Sentinel-2, Landsat-8 satellite images, and Google Earth Engine: a comparison of two composition methods,” Remote Sens., vol. 14, no. 9, p. 1977, 2022.

K. A. Hossain, M. Masiero, and F. Pirotti, “Land cover change across 45 years in the world’s largest mangrove forest (Sundarbans): the contribution of remote sensing in forest monitoring,” Eur. J. Remote Sens., vol. 57, no. 1, 2024, doi: 10.1080/22797254.2022.2097450.

O. A. Fashae, “Comparative assessment of the changing pattern of land cover along the Southwestern coast of Nigeria using GIS and remote sensing techniques,” Sci. African, vol. 17, no. 6, p. e01286, 2022.

M. L. Edamo, “Capability of logistic regression in identifying flood-susceptible areas in a small watershed,” H2Open J., vol. 7, no. 5, pp. 351–374, 2024.

T. S. Miller, D. B. Wright, and S. Hassan, “Calibration of runoff curve numbers in highly modified tropical urban environments,” Hydrol. Process., vol. 37, no. 2, p. e14780, 2023.

M. N. Ahmad, Z. Shao, and A. Javed, “Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data,” Environ. Sci. Pollut. Res., vol. 30, no. 60, pp. 125741–125758, 2023, doi: 10.1007/s11356-023-30990-y.

L. M. Prayoga, “Land cover classification assessment using decision trees and maximum likelihood classification algorithms on Landsat 8 data,” DoubleClick J. Comput. Inf. Technol., vol. 6, no. 2, pp. 69–76, 2023, doi: 10.25273/DOUBLECLICK.V6I2.10606.

N. S. Ahmed and H. M. Abduljabbar, “Maximum likelihood classification for land cover change detection utilizing Landsat 8 imagery in Wasit Governorate,” Ibn Al Haitham J. Pure Appl. Sci., vol. 36, no. 1, 2023.

G. Mancino, A. Falciano, R. Console, and M. L. Trivigno, “Comparison between parametric and non-parametric supervised land cover classifications of Sentinel-2 MSI and Landsat-8 OLI data,” Geographies, vol. 3, no. 1, pp. 82–109, 2023, doi: 10.3390/geographies3010005.

Y. Gurjar, R. Wen, E. Farahbakhsh, and R. Chandra, “Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji,” 2025. doi: 10.48550/arXiv.2509.13388.

Endarsih, “Model spasial perubahan penggunaan lahan dan tutupan lahan terhadap bencana banjir di Daerah Aliran Sungai Sikilang, Pasaman Barat,” EL-JUGHRAFIYAH, 2024.

C. J. White, H. Carlsen, and A. W. Robertson, “Projecting localized extreme rainfall under accelerated climate change scenarios,” Clim. Change, vol. 178, no. 1, p. 12, 2025.

R. Mulyasari, N. Haerudin, D. Rizkiani, and I. Akbari, “Pengaruh perubahan tutupan lahan terhadap pola banjir di Ibu Kota Provinsi Lampung: studi menggunakan SIG dan pendekatan temporal,” OPHIOLITE J. Geol. Terap., vol. 6, no. 1, pp. 29–35, 2024, doi: 10.56099/ophi.v6i1.p29-35.

C. M. Kaya and L. Derin, “Parameters and methods used in flood susceptibility mapping: a review,” J. Water Clim. Chang., vol. 14, pp. 1935–1960, 2023.

I. R. Khan, S. I. Elmahdy, R. Rustum, Q. Khan, and M. M. Mohamed, “Floods modelling and analysis for Dubai using HEC-HMS model and remote sensing using GIS,” Sci. Rep., vol. 14, p. 25039, 2024.

K. Ma, X. Li, Q. Liu, W. Zhou, and W. Li, “Application of SCS-CN method in flood hazard mapping,” Front. Environ. Sci., vol. 13, p. 1713793, 2025.

H. H. Nguyen, M. Venohr, A. Gericke, A. Sundermann, E. A. R. Welti, and P. Haase, “Dynamics in impervious urban and non-urban areas and their effects on run-off, nutrient emissions, and macroinvertebrate communities,” Landsc. Urban Plan., vol. 231, p. 104639, 2023.

A. M. Vincent, K. Parthasarathy, and P. Jidesh, “Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization,” Appl. Soft Comput., vol. 148, p. 110846, 2023.

Z. Luo, J. Tian, J. Zeng, and F. Pilla, “Resilient landscape pattern for reducing coastal flood susceptibility,” Sci. Total Environ., vol. 856, p. 159087, 2023.

M. D. K. L. Gunathilaka and S. L. J. Fernando, “Accuracy assessment of unsupervised land use and land cover classification using remote sensing and geographical information systems,” Int. J. Environ. Eng. Educ., vol. 4, no. 3, pp. 76–82, 2022.

E. Allegri, M. Zanetti, S. Torresan, and A. Critto, “Pluvial flood risk assessment for 2021-2050 under climate change scenarios in the Metropolitan City of Venice,” Sci. Total Environ., vol. 914, 2024.

T. S. Onajomo, “Geospatial assessment of land use/cover, rainfall, and flood incidents in Eti-Osa, Lagos, Nigeria,” South African J. Geomatics, vol. 11, no. 2, pp. 202–218, 2022.

H. Zhang, H. Jia, C. Li, and Q. Zhang, “Exploring the driving factors of urban flood at the catchment scale: a case study of multitype megacities in China,” Ecol. Indic., vol. 166, p. 112513, 2024.

A. Salvati, “Flood susceptibility mapping using support vector regression and hyper-parameter optimization,” J. Flood Risk Manag., vol. 16, no. 4, p. e12920, 2023.

J. Wang, M. Bretz, M. A. A. Dewan, and M. A. Delavar, “Machine learning in modelling land-use and land cover-change (LULCC): current status, challenges and prospects,” Sci. Total Environ., vol. 822, p. 153559, 2022.

M. Abdelkareem and A. M. Mansour, “Risk assessment and management of vulnerable areas to flash flood hazards in arid regions using remote sensing and GIS-based knowledge-driven techniques,” Nat. Hazards, vol. 117, no. 3, pp. 2269–2295, 2023.

L. Ouyang et al., “Mapping impervious surface using phenology-integrated and Fisher transformed linear spectral mixture analysis,” Remote Sens., vol. 14, no. 7, p. 1673, 2022.

K. L. Wong, Y. X. Chen, and H. S. Lim, “River buffer zone encroachment and channel capacity degradation in emerging Asian megacities,” Land use policy, vol. 137, p. 106981, 2024.

T. E. Idowu, R. M. Waswa, K. Lasisi, M. Nyadawa, and V. Okumu, “Object-based land use/land cover change detection of a coastal city using multi-source imagery: a case study of Lagos, Nigeria,” South African J. Geomatics, vol. 9, pp. 136–148, 2022.

M. S. Chowdhury, “Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting,” Environ. Challenges, 2024.

M. Banjara, A. Bhusal, A. B. Ghimire, and A. Kalra, “Impact of land use and land cover change on hydrological processes in urban watersheds: analysis and forecasting for flood risk management,” Geosciences, vol. 14, no. 2, p. 40, 2024, doi: 10.3390/geosciences14020040.

R. Madhuri, K. Srinivasa Raju, and A. Vasan, “Flood-susceptibility-based building risk under climate change, Hyderabad, India,” J. Water Clim. Chang., vol. 14, no. 7, pp. 2150–2163, 2023.

O. O. Agumagu, R. Marchant, and L. C. Stringer, “Land use and land cover change dynamics in the Niger Delta region of Nigeria from 1986 to 2024,” Land, vol. 14, no. 4, p. 765, 2025, doi: 10.3390/land14040765.

B. Pradhan, “Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model,” Geosci. Front., vol. 14, no. 6, p. 101625, 2023.