Assessment of critical land cover rehabilitation in South Sulawesi, Indonesia
DOI:
https://doi.org/10.15243/jdmlm.2025.122.6965Keywords:
Binturu, GEE Random Forest, Lamasi, land cover, Rante AlangAbstract
The wide areas of critical land in Indonesia are attracting a high level of attention due to the significant influence of global warming. Addressing this issue requires several priority efforts, such as critical land rehabilitation programs. The level of critical land rehabilitation can be evaluated with remote sensing technology. Therefore, this research aimed to assess critical land in South Sulawesi rehabilitated by PT Vale Indonesia Tbk (PT VI) as compensation for mining nickel from the forest areas. The assessment was conducted in three villages across Luwu, namely Binturu, Lamasi, and Rante Alang. Successful critical land rehabilitation was determined by monitoring land cover changes (LCC) based on satellite data generated using Harmonized Sentinel-2 from 2019 to 2023. Furthermore, analysis was performed using the Google Earth Engine (GEE) platform with the Random Forest machine learning algorithm and correlation matrix. The result showed five LC classes, including high-density, low-density, shrubs, bare land, and buildings. Before rehabilitation in 2019-2020, non-forested LC in the three villages was only at a proportion of 23.41%, while forested LC reached 58.92% and increased to 80-95% in 2021-2023. Critical land rehabilitation in Luwu was considered a success due to increased LC at high- and low-density classes, along with declines in bareland and buildings. Additionally, an inverse correlation was detected between high- and low-density LC classes and buildings, barelands, and shrubs. Elevation in high- and low-density LC could significantly contribute to mitigating climate change.
References
Alanis, R.E., Mora, A. and Marroquin, J.S. 2020. Muestreo Ecologico de la Vegetacion [Ecological Sampling of Vegetation], 1st ed., Universidad Autonoma de Nuevo Leon: Monterrey, Mexico, 2020, pp. 59-91.
Amani, M., Arsalan, G., Seyed, A.M., Mohammad, K., Armin, M.S., Mohammad, M., Moghaddam, S.H.A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., and Brisco, B. 2020. Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13. https://doi.org/10.1109/JSTARS.2020.3021052
Ambarwulan, W., Fajar, Y., Widiatmaka, W., Ati, R., Suria, D.T., Irman, F. and Muhrina, A.S.H. 2023. Modelling land use/land cover projection using different scenarios in the Cisadane Watershed, Indonesia: Implication on deforestation and food security. The Egyptian Journal of Remote Sensing and Space Sciences 26:273-283. https://doi.org/10.1016/j.ejrs.2023.04.002
Ariadi, A. Mukrimin, and Wahyuni. 2023. Analysis of land use/land cover change and erosion hazard levels in the social forestry area of KPH Ulubila, South Sulawesi Indonesia. IOP Conference Series: Earth and Environmental Science 1230(1):012047. https://doi.org/10.1088/1755-1315/1230/1/012047
Asti, N.A. and Noviani, P. 2023. Vegetation dynamics and Land Surface Temperature (LST) based on remote sensing in Jatigede reservoir, West Java Province: Preliminary study. Journal Geosains and Remote Sensing 4(2):67-76. https://doi.org/10.23960/jgrs.ft.unila.112
Basuki, I., Kauffman, J., Murdiyarso, D. and Anshari, G. 2016. Carbon stocks and emissions from degradation and conversion of tropical peat swamp forests in west Kalimantan, Indonesia. Proceedings of 15th International Peat Congress 2016, Kuching, Sarawak, Malaysia, 260-263. https://doi.org/10.13140/RG.2.2.22826.21445
Dinh, D.B., Ngo, D.T., Nguyen, H.D., Viet Nguyen, H.H. and Dang, N.T. 2023. Free satellite image data application for monitoring land use cover changes in the Kon Ha Nung plateau, Vietnam. Heliyon 9(1):e12864. https://doi.org/10.1016/j.heliyon.2023.e12864
Frimpong, B.F., Koranteng, A., Atta-Darkwa, T., Junior, O. F. and Zawi?a-Niedzwiecki, T. 2023. Land cover changes utilising Landsat satellite imageries for the Kumasi Metropolis and its adjoining municipalities in Ghana (1986-2022). Sensors 23(5):2644. https://doi.org/10.3390/s23052644
Gerwing, T.G., Hawkes, V.C., Gann, G.D. and Murphy, S.D. 2022. Restoration, reclamation, and rehabilitation: On the need for, and positing a definition of, ecological reclamation. Restoration Ecology 30(7):e13461. https://doi.org/10.1111/rec.13461
Ghaffarian, S., Farhadabad, A.R. and Kerle, N.2020. Post-disaster recovery monitoring with Google Earth Engine. Applied Sciences 10(13). https://doi.org/10.3390/app10134574
Gomes. V.C.F., Queiroz, G.R. and Ferreira, K.R. 2020. An overview of platforms for big earth observation data management and analysis. Journal Remote Sensing 12:1253. https://doi.org/10.3390/rs12081253
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18-27. https://doi.org/10.1016/j.rse.2017.06.031
Hird, J.N., Kariyeva, J. and McDermid, G.J. 2021. Satellite time series and Google Earth Engine democratize the process of forest-recovery monitoring over large areas. Remote Sensing 13(23):4745. https://doi.org/10.3390/rs13234745
Ibitoye, B., Akomian, F.A., Sabin, G. and Brice, S. 2024. Land use/land cover change and carbon footprint in tropical ecosystems in Benin, West Africa. Trees, Forest and People 15:100488. https://doi.org/10.1016/j.tfp.2023.100488
James, L.A., Beach, T.P. and Richter, D.D. 2020. Floodplain and terrace legacy sediment as a widespread record of anthropogenic geomorphic change. Annals of the American Association of Geographers 111(3): 742-755. https://doi.org/10.1080/24694452.2020.1835460
Jiayu, Li., Wang, J. and Zhou, W. 2024. Different impacts of urbanization on ecosystem services supply and demand across old, new and non-urban areas in the ChangZhuTan urban agglomeration, China. Landscape Ecology 39(6):107. https://doi.org/10.1007/s10980-024-01900-5
Kamrul, I., Jashimuddin, M., Nath, B. and Nath, T.K. 2018. Land use classification and change detection by using multi-temporal remote sense imagery: The case of Chunati wildlife sanctuary. The Egyption Journal of Remote Sensing and Space Science 21:37-47. https://doi.org/10.1016/j.ejrs.2016.12.005
Kolli, M.K., Opp, C., Karthe, D. and Groll, M. 2020. Mapping of major land-use changes in the Kolleru Lake freshwater ecosystem by using landsat satellite images in Google Earth Engine. Water 12(9), Article 9. https://doi.org/10.3390/w12092493
Manjunatha, M.C. and Basavarajappa, H.T. 2017. Antropogenic pressure on forest cover and its change detection analysis using geoinformatics in Holalkere Taluk of Chitradurga District, Karnataka, India. International Journal of Scientific Research in Science and Tecnology 3(1). https://doi.org/10.32628/IJSRST173124
Memedi, M., Sadikov, A., Groznik, V., Zabkar, J., Mozina, M., Bergquist, F., Johansson, A., Haubenberger, D. and Nyholm, D. 2015. Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease. Sensors 15(9). https://doi.org/10.3390/s150923727
Moncrieff, G.R. 2022. Continuous land cover change detection in a critically endangered shrubland ecosystem using neural networks. Remote Sensing 14(12):2766. https://doi.org/10.3390/rs14122766
Morales, N.S., Fernandez, I.C., Duran, L.P. and Perez-Martínez, W.A. 2023. RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the identification of priority areas for ecological restoration. Land 12:303. https://doi.org/10.3390/land12020303
Nunez-Florez, R., Perez-Gomez, U. and Fernandez-Mendez, F. 2019. Functional diversity criteria for selecting urban trees. Urban Forestry & Urban Greening 38:251-266. https://doi.org/10.1016/j.ufug.2019.01.005
Rakuasa, H., Melianus, S. and Philia, C.L. 2022. Analysis and prediction of land cover changes using the Celular Automata-Markov Chain model in the Wae Ruhu Das, Ambon City. Jurnal Tanah dan Sumberdaya Lahan 9(2):285-295, (in Indonesian). https://doi.org/10.21776/ub.jtsl.2022.009.2.9
Rumenah, R.E. and Priati, A. 2010. Potential Land and Critical Land. Faculty of Geography, Gadjah Mada University, Yogyakarta (in Indonesian).
Sadono, R., Pujiono, E. and Lestari, L. 2020. Land cover changes and carbon storage before and after community forestry program in Bleberan Village, Gunung Kidul, Indonesia, 1999-2018. Forest Science and Technology 16(3):134-144. https://doi.org/10.1080/21580103.2020.1801523
Sidhu, N., Pebesma, E. and Camara, G. 2018. Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing 51(1):486-500. https://doi.org/10.1080/22797254.2018.1451782
Sukanya, G,. Deepak, K. and Rina, K. 2022. Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with Google Earth Engine (GEE). Environmental Challenges 9:100605. https://doi.org/10.1016/j.envc.2022.100605
Tian, F., Brandt, M., Liu, Y.Y., Verger, A., Tagesson, T., Diouf, A.A., Rasmussen, K., Mbow, C., Wang, Y. and Fensholt, R. 2016. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sensing of Environment 177:265-276. https://doi.org/10.1016/j.rse.2016.02.056
Western, D. 2004. The challenge of integrated rangeland monitoring: synthesis address. African Journal of Range and Forage Science 21(2):129-136. https://doi.org/10.2989/10220110409485844
Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X. and Gao, Y. 2019. Automatic land-cover mapping using Landsat Time-Series data based on Google Earth Engine. Remote Sensing 11(24), Article 24. https://doi.org/10.3390/rs11243023
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