Article Sidebar

Submitted
April 13, 2025
Accepted
June 30, 2025
Published
June 30, 2025
Download
PDF
Statistic
Read Counter : 0 Download : 0

Main Article Content

Abstract

This study aims to evaluate the surface carbon stock estimation model in Kendari City working area using Geographic Information Systems (GIS) and remote sensing approaches. Spatial analysis was carried out using Sentinel-2A satellite imagery with a resolution of 10 meters per pixel, employing the vegetation index method as the primary indicator for estimating carbon stocks. Field data were used as reference and validation for image interpretation results, which were processed using spatial statistical methods to produce an accurate and reliable surface carbon distribution model. The research findings indicate that the Kendari City area has a significantly varied distribution of carbon stocks, with values ranging from low to high at 203.669 tons of carbon per pixel. Areas with high vegetation cover, such as urban forests and green open spaces, exhibited higher carbon concentrations compared to settlement areas and densely urbanized regions. Model evaluation conducted through field validation methods revealed a high correlation between model predictions and actual field conditions. This research demonstrates that the integration of GIS and remote sensing is effective for rapidly and accurately mapping and evaluating potential surface carbon stocks. Spatial information about carbon reserves is crucial as a basis for formulating sustainable environmental management policies and climate change mitigation strategies in Kendari City. The results of this study recommend wider adoption of this method to support spatial data-based environmental management in other regions across Indonesia

Keywords

Carbon Stocks Geographic Information Systems Kendari City Remote Sensing

Article Details

References

  1. Ahmed, H. G. I., Mohamed, M. H., & Saleh, S. S. (2021). A GIS model for exploring the water pumped storage locations using remote sensing data. Egyptian Journal of Remote Sensing and Space Science, 24(3), 515–523. https://doi.org/10.1016/j.ejrs.2021.09.006
  2. Al-Bahrani, H. S., Al-Rammahi, A. H., Al-Mamoori, S. K., Al-Maliki, L. A., & AL-Ansari, N. (2022). Groundwater detection and classification using remote sensing and GIS in Najaf, Iraq. Groundwater for Sustainable Development, 19, 100838. https://doi.org/10.1016/j.gsd.2022.100838
  3. AlHamaydeh, M., Al-Shamsi, G., Aly, N., & Ali, T. (2021). Seismic risk quantification and GIS-based seismic risk maps for Dubai-UAE_Dataset. Data in Brief, 39, 107566. https://doi.org/10.1016/j.dib.2021.107566
  4. Aljenaid, S., Abido, M., Redha, G. K., AlKhuzaei, M., Marsan, Y., Khamis, A. Q., Naser, H., AlRumaidh, M., & Alsabbagh, M. (2022). Assessing the spatiotemporal changes, associated carbon stock, and potential emissions of mangroves in Bahrain using GIS and remote sensing data. Regional Studies in Marine Science, 52, 102282. https://doi.org/10.1016/j.rsma.2022.102282
  5. Alqahtany, A. (2023). GIS-based assessment of land use for predicting increase in settlements in Al Ahsa Metropolitan Area, Saudi Arabia for the year 2032. Alexandria Engineering Journal, 62, 269–277. https://doi.org/10.1016/j.aej.2022.07.020
  6. Bedair, S., Sayed, S. A., & AlMetwaly, W. M. (2022). Enhancing Hybrid Learning using Open Source GIS-Based Maps Archiving System. Egyptian Journal of Remote Sensing and Space Science, 25(3), 779–793. https://doi.org/10.1016/j.ejrs.2022.07.003
  7. Çetinkaya, C., Erbaş, M., Kabak, M., & Özceylan, E. (2023). A mass vaccination site selection problem: An application of GIS and entropy-based MAUT approach. Socio-Economic Planning Sciences, 85(May). https://doi.org/10.1016/j.seps.2022.101376
  8. Chowdhury, M. S., & Hafsa, B. (2022). Multi-decadal land cover change analysis over Sundarbans Mangrove Forest of Bangladesh: A GIS and remote sensing based approach. Global Ecology and Conservation, 37(May), e02151. https://doi.org/10.1016/j.gecco.2022.e02151
  9. Chukwuma, E. C., Okonkwo, C. C., Ojediran, J. O., Anizoba, D. C., Ubah, J. I., & Nwachukwu, C. P. (2021). A Gis Based Flood Vulnerability Modelling Of Anambra State Using An Integrated Ivfrn-Dematel-Anp Model. Heliyon, 7(9), e08048. https://doi.org/10.1016/j.heliyon.2021.e08048
  10. Desalegn, H., & Mulu, A. (2021). Mapping flood inundation areas using GIS and HEC-RAS model at Fetam River, Upper Abbay Basin, Ethiopia. Scientific African, 12, e00834. https://doi.org/10.1016/j.sciaf.2021.e00834
  11. Dhinsa, D., Tamiru, F., & Tadesa, B. (2022). Groundwater potential zonation using VES and GIS techniques: A case study of Weserbi Guto catchment in Sululta, Oromia, Ethiopia. Heliyon, 8(8), e10245. https://doi.org/10.1016/j.heliyon.2022.e10245
  12. El-Hadidy, S. M., & Morsy, S. M. (2022). Expected spatio-temporal variation of groundwater deficit by integrating groundwater modeling, remote sensing, and GIS techniques. Egyptian Journal of Remote Sensing and Space Science, 25(1), 97–111. https://doi.org/10.1016/j.ejrs.2022.01.001
  13. Elbeih, S. F. (2021). Evaluation of agricultural expansion areas in the Egyptian deserts: A review using remote sensing and GIS. Egyptian Journal of Remote Sensing and Space Science, 24(3P2), 889–906. https://doi.org/10.1016/j.ejrs.2021.10.004
  14. Elboshy, B., Alwetaishi, M., M. H. Aly, R., & Zalhaf, A. S. (2022). A suitability mapping for the PV solar farms in Egypt based on GIS-AHP to optimize multi-criteria feasibility. Ain Shams Engineering Journal, 13(3), 101618. https://doi.org/10.1016/j.asej.2021.10.013
  15. Endayani, S., Sadono, R., Kusumandari, A., & Hartono. (2019). Social and economic vulnerability in the sub-watershed of Karang mumus, East Kalimantan Province. Jurnal Manajemen Hutan Tropika, 25(2), 93–103. https://doi.org/10.7226/jtfm.25.2.93
  16. Endayani, S., Sadono, R., Kusumandari, A., Hartono, & Baiquni, M. (2022). Horizontal and Vertical Geometric Accuracy of Agisoft Photoscan and Pix4D Mapper Softwares at Kebun Raya Universitas Mulawarman in Samarinda, East Kalimantan, Indonesia. International Journal on Advanced Science, Engineering and Information Technology, 12(5), 2132–2139. https://doi.org/10.18517/ijaseit.12.5.16362
  17. Endayani, S., Stefano, A., Fathiah, Hamka, & Lisnawati, A. (2023). Forest Land Change Assessment of Karang Mumus Sub-Watershed Area. Jurnal Manajemen Hutan Tropika, 29(1), 35–44. https://doi.org/10.7226/jtfm.29.1.35
  18. Endayani, S., Stefano, A., Fathiah, Purbawati, & Rosanti, I. (2022). Monitoring Forest Area Change Using Quickbird. Biotropia, 29(2), 161–170. https://doi.org/10.11598/btb.2022.29.2.1690
  19. Erdem, F., Atun, R., Yigit Avdan, Z., Atila, I., & Avdan, U. (2021). Drought analysis of Van Lake Basin with remote sensing and GIS technologies. Egyptian Journal of Remote Sensing and Space Science, 24(3), 1093–1102. https://doi.org/10.1016/j.ejrs.2021.10.006
  20. Ghoneim, S. M., Yehia, M. A., Salem, S. M., & Ali, H. F. (2022). Integrating remote sensing data, GIS analysis and field studies for mapping alteration zones at Wadi Saqia area, central Eastern Desert, Egypt. Egyptian Journal of Remote Sensing and Space Science, 25(1), 323–336. https://doi.org/10.1016/j.ejrs.2022.02.001
  21. Gupta, L., Agrawal, N., Dixit, J., & Dutta, S. (2022). A GIS-based assessment of active tectonics from morphometric parameters and geomorphic indices of Assam Region, India. Journal of Asian Earth Sciences: X, 8(July 2021), 100115. https://doi.org/10.1016/j.jaesx.2022.100115
  22. Gürtekin, E., & Gökçe, O. (2021). Estimation of erosion risk of Harebakayiş sub-watershed, Elazig, Turkey, using GIS based RUSLE model. Environmental Challenges, 5(June). https://doi.org/10.1016/j.envc.2021.100315
  23. Isihak, S., Akpan, U., & Bhattacharyya, S. (2022). Evolution of GIS-based rural electrification planning models and an application of OnSSET in Nigeria. Renewable and Sustainable Energy Transition, 2(May 2021), 100019. https://doi.org/10.1016/j.rset.2022.100019
  24. Khalil, N., Mhanna, M., & Assaf, E. H. (2021). Horizontal corridor optimization of highway using GIS & CFSC method in mountainous areas. Egyptian Journal of Remote Sensing and Space Science, 24(3), 509–514. https://doi.org/10.1016/j.ejrs.2021.08.008
  25. Kumar, M., Singh, P., & Singh, P. (2022). Integrating GIS and remote sensing for delineation of groundwater potential zones in Bundelkhand Region, India. Egyptian Journal of Remote Sensing and Space Science, 25(2), 387–404. https://doi.org/10.1016/j.ejrs.2022.03.003
  26. Larkin, A., Gu, X., Chen, L., & Hystad, P. (2021). Predicting perceptions of the built environment using GIS, satellite and street view image approaches. Landscape and Urban Planning, 216, 104257. https://doi.org/10.1016/j.landurbplan.2021.104257
  27. Li, S., Zhang, M., Yuan, F., Li, X., Wang, C., Long, J., & Jiao, J. (2022). Isotope spatiotemporal analysis and prospecting indication based on GIS in Tibet. Ore Geology Reviews, 147(May), 104997. https://doi.org/10.1016/j.oregeorev.2022.104997
  28. Li, W., Winter, P. L., Milburn, L. A., & Padgett, P. E. (2021). A dual-method approach toward measuring the built environment - sampling optimization, validity, and efficiency of using GIS and virtual auditing. Health and Place, 67, 102482. https://doi.org/10.1016/j.healthplace.2020.102482
  29. Liu, H., Zhang, Z., Chen, T., Yang, J., Li, Q., & He, D. (2021). Simplified Modelling of Transient Ground Potential Rise of GIS in Ultra-high-voltage Substations with Hybrid Reactive Power Compensation. Electric Power Systems Research, 199(51507095), 107441. https://doi.org/10.1016/j.epsr.2021.107441
  30. Liu, Q., Zhai, G., & Lu, X. (2021). Integrated land-sea surveying and mapping of intertidal zone based on high-definition remote sensing images and GIS technology. Microprocessors and Microsystems, 82(January), 103937. https://doi.org/10.1016/j.micpro.2021.103937
  31. Lumbreras, M., Diarce, G., Martin-Escudero, K., Campos-Celador, A., & Larrinaga, P. (2022). Design of district heating networks in built environments using GIS: A case study in Vitoria-Gasteiz, Spain. Journal of Cleaner Production, 349(August 2021), 131491. https://doi.org/10.1016/j.jclepro.2022.131491
  32. Massano, M., Macii, E., Lanzini, A., Patti, E., & Bottaccioli, L. (2023). A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas. Engineering, 26, 198–213. https://doi.org/10.1016/j.eng.2022.06.020
  33. Murugesan, M., Venkatesan, P., Kumar, S., Thangavelu, P., Rose, W., John, J., Castro, M., Manivannan, T., Mohan, V. R., & Rupali, P. (2022). Epidemiological investigation of the COVID-19 outbreak in Vellore district in South India using Geographic Information Surveillance (GIS). International Journal of Infectious Diseases, 122, 669–675. https://doi.org/10.1016/j.ijid.2022.07.010
  34. Pepe, M., Costantino, D., Alfio, V. S., Restuccia, A. G., & Papalino, N. M. (2021). Scan to BIM for the digital management and representation in 3D GIS environment of cultural heritage site. Journal of Cultural Heritage, 50, 115–125. https://doi.org/10.1016/j.culher.2021.05.006
  35. Quan, S. J., & Bansal, P. (2021). A systematic review of GIS-based local climate zone mapping studies. Building and Environment, 196(November 2020), 107791. https://doi.org/10.1016/j.buildenv.2021.107791
  36. Stefano, A., Endayani, S., & Sadono, R. (2021). Combining the Traditional and Modern Architecture in Taman Samarendah Plan, Samarinda City, East Kalimantan Province, Indonesia. International Journal on Advanced Science, Engineering and Information Technology, 11(2), 705–711. https://doi.org/10.18517/ijaseit.11.2.8341
  37. Taloor, A. K., Adimalla, N., & Goswami, A. (2021). Remote Sensing and GIS applications in Geoscience. Applied Computing and Geosciences, 11, 100065. https://doi.org/10.1016/j.acags.2021.100065
  38. Tamiru, H., & Wagari, M. (2021). Evaluation of data-driven model and GIS technique performance for identification of Groundwater Potential Zones: A case of Fincha Catchment, Abay Basin, Ethiopia. Journal of Hydrology: Regional Studies, 37(July), 100902. https://doi.org/10.1016/j.ejrh.2021.100902
  39. Turek, T., & Stepniak, C. (2021). Areas of integration of GIS technology and smart city tools. Research findings. Procedia Computer Science, 192, 4681–4690. https://doi.org/10.1016/j.procs.2021.09.246
  40. Villacreses, G., Martínez-Gómez, J., Jijón, D., & Cordovez, M. (2022). Geolocation of photovoltaic farms using Geographic Information Systems (GIS) with Multiple-criteria decision-making (MCDM) methods: Case of the Ecuadorian energy regulation. Energy Reports, 8, 3526–3548. https://doi.org/10.1016/j.egyr.2022.02.152
  41. Wu, X., Xu, C., Xu, X., Chen, G., Zhu, A., Zhang, L., Yu, G., & Du, K. (2022). A Web-GIS hazards information system of the 2008 Wenchuan Earthquake in China. Natural Hazards Research, 2(3), 210–217. https://doi.org/10.1016/j.nhres.2022.03.003
  42. Xiao, Y., Yan, Y., Yu, Y. S., Wang, B., & Liang, Y. H. (2022). Research on pose adaptive correction method of indoor rail mounted inspection robot in GIS Substation. Energy Reports, 8, 696–705. https://doi.org/10.1016/j.egyr.2022.03.155
  43. Yousef, G. A., Elazony, M. A., Abdelsattar, A., Sewailam, M. M., & Elsaid, O. H. (2022). Applying an integrated Remote Sensing-GIS approach in the documentation of handicraft centers at New Valley Governorate, Egypt. Egyptian Journal of Remote Sensing and Space Science, 25(3), 731–739. https://doi.org/10.1016/j.ejrs.2022.04.004
  44. Yudhana, A., Sulistyo, D., & Mufandi, I. (2021). GIS-based and Naïve Bayes for nitrogen soil mapping in Lendah, Indonesia. Sensing and Bio-Sensing Research, 33, 100435. https://doi.org/10.1016/j.sbsr.2021.100435
  45. Zhang, S., Liu, X., Li, R., Wang, X., Cheng, J., Yang, Q., & Kong, H. (2021). AHP-GIS and MaxEnt for delineation of potential distribution of Arabica coffee plantation under future climate in Yunnan, China. Ecological Indicators, 132, 108339. https://doi.org/10.1016/j.ecolind.2021.108339