New Emerging and Comprehensive Land Mapping Unit at Detailed Scale: Integrating Random Forest Analysis and Remote Sensing Techniques for Sustainable Land Management

Aditya Nugraha Putra, Reni Ustiatik, Novandi Rizky Prasetya, Erza Aulia Adara, Istika Nita, Syamsu Ridzal Indra Hadi, Soemarno Soemarno, Sudarto Sudarto, Sri Rahayu Utami, Mochammad Munir, Mochtar Lutfi Rayes

Abstract

Precise and detailed land mapping is essential for sustainable land management, environmental conservation, and regional planning, especially in complex and diverse landscapes. This study aims to present an innovative framework for the development of Land Mapping Units (LMUs) at a detailed scale (1:20,000), through the integration of Random Forest (RF) analysis and high-resolution remote sensing data. This study was conducted in the South Malang Plateau, Indonesia (the area characterized by karst, tectonic, volcanic, and alluvial landforms) from June to December 2024. As part of the methodology, the study utilized a combination of geospatial data, including geological maps, DEM-derived topographical indices, and remote sensing indices (Normalized Difference Soil Index/NDSI, Soil Adjusted Vegetation Index/SAVI, Normalized Difference Water Index/NDWI, Modified Soil Adjusted Vegetation Index/MSAVI). A total of 10,903 field observation points were analyzed, with 70% used for model training and 30% for validation. The results show that RF-based LMUs achieved R2 of 0.93 and Root Mean Square Error (RMSE) of 0.645, which is reliable to use. The LMUs provide a comprehensive understanding of landform-specific characteristics, including soil fertility linked to parent material, erosion sensitivity, and slope variability. These insights support applications in precision agriculture, disaster mitigation, and environmental planning. Moreover, the result can guide informed decision-making to prioritize sustainable land management that effectively prevents land degradation in the South Malang Plateau region, as stated in the Sustainable Development Goals (SDGs). The study demonstrates the potential of combining machine learning and remote sensing to refine spatial analysis and address the limitations of manual mapping methods. The proposed framework is scalable and adaptable to other diverse landscapes, making it a valuable tool for advancing sustainable land management in a rapidly changing world.

Keywords

geographic information systems; Land Mapping Units (LMU); machine learning; remote sensing; topography

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References

Adugna, T., Xu, W., & Fan, J. (2022). Comparison of Random Forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sensing, 14(3), 574. https://doi.org/10.3390/rs14030574

Aryal, J., Sitaula, C., & Frery, A. C. (2023). Land use and land cover (LULC) performance modeling using machine learning algorithms: A case study of the city of Melbourne, Australia. Scientific Reports, 13(1), 13510. https://doi.org/10.1038/s41598-023-40564-0

Bachri, S., Fathoni, M. N., Sumarmi, Masruroh, H., Wibowo, N. A., Khusna, N., ..., & Yudha, L. (2023). Geomorphological mapping and landform characterization of Semeru volcano after the eruption in 2021. IOP Conference Series: Earth and Environmental Science, 1180(1), 012004. https://doi.org/10.1088/1755-1315/1180/1/012004

Bao, Y., Yao, F., Meng, X., Wang, J., Liu, H., Wang, Y., ..., & Mouazen, A. M. (2024). A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China. Soil and Tillage Research, 238, 106010. https://doi.org/10.1016/j.still.2024.106010

Barré, P., Durand, H., Chenu, C., Meunier, P., Montagne, D., Castel, G., ..., & Cécillon, L. (2017). Geological control of soil organic carbon and nitrogen stocks at the landscape scale. Geoderma, 285, 50–56. https://doi.org/10.1016/j.geoderma.2016.09.029

Binte Mostafiz, R., Noguchi, R., & Ahamed, T. (2021). Agricultural land suitability assessment using satellite remote sensing-derived soil-vegetation indices. Land, 10(2), 223. https://doi.org/10.3390/land10020223

Bouguerra, H., Tachi, S. E., Bouchehed, H., Gilja, G., Aloui, N., Hasnaoui, Y., ..., & Navarro-Pedreño, J. (2023). Integration of high-accuracy geospatial data and machine learning approaches for soil erosion susceptibility mapping in the Mediterranean Region: A case study of the Macta Basin, Algeria. Sustainability, 15(13), 10388. https://doi.org/10.3390/su151310388

Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., & Edwards, T. C. (2015). Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239–240, 68–83. https://doi.org/10.1016/j.geoderma.2014.09.019

Chaoyong, W., Aslam, R. W., Quddoos, A., Naz, I., Tariq, A., Ullah, S., ..., & Almutairi, K. F. (2024). SAR image integration for multi-temporal analysis of Lake Manchar Wetland dynamics using machine learning. Scientific Reports, 14(1), 26669. https://doi.org/10.1038/s41598-024-76730-1

Cracknell, M. J., & Reading, A. M. (2014). Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers & Geosciences, 63, 22–33. https://doi.org/10.1016/j.cageo.2013.10.008

Dan, N. N., Ping, L. G., & Lang, L. P. C. (2018). Land unit mapping and evaluation of land suitability for agro – forestrye in Thua Thien Hue Province – VietNam as an Example. IOP Conference Series: Earth and Environmental Science, 159, 012012. https://doi.org/10.1088/1755-1315/159/1/012012

Djodjic, F., Bieroza, M., & Bergström, L. (2021). Land use, geology and soil properties control nutrient concentrations in headwater streams. Science of The Total Environment, 772, 145108. https://doi.org/10.1016/j.scitotenv.2021.145108

Du, L., You, X., Li, K., Meng, L., Cheng, G., Xiong, L., & Wang, G. (2019). Multi-modal deep learning for landform recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 63–75. https://doi.org/10.1016/j.isprsjprs.2019.09.018

Fang, Q., Hong, H., Algeo, T. J., Huang, X., Sun, A., Churchman, G. J., ..., & Liu, Y. (2019). Microtopography-mediated hydrologic environment controls elemental migration and mineral weathering in subalpine surface soils of subtropical monsoonal China. Geoderma, 344, 82–98. https://doi.org/10.1016/j.geoderma.2019.03.008

Fidelus-Orzechowska, J., Gorczyca, E., Bukowski, M., & Krzemień, K. (2021). Degradation of a protected mountain area by tourist traffic: Case study of the Tatra National Park, Poland. Journal of Mountain Science, 18(10), 2503–2519. https://doi.org/10.1007/s11629-020-6611-4

Haregeweyn, N., Tsunekawa, A., Tsubo, M., Fenta, A. A., Ebabu, K., Vanmaercke, M., ..., & Poesen, J. (2023). Progress and challenges in sustainable land management initiatives: A global review. Science of The Total Environment, 858, 160027. https://doi.org/10.1016/j.scitotenv.2022.160027

Heung, B., Bulmer, C. E., & Schmidt, M. G. (2014). Predictive soil parent material mapping at a regional-scale: A Random Forest approach. Geoderma, 214–215, 141–154. https://doi.org/10.1016/j.geoderma.2013.09.016

Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202

Kasahun, M., & Legesse, A. (2024). Machine learning for urban land use/cover mapping: Comparison of artificial neural network, random forest and support vector machine, a case study of Dilla Town. Heliyon, 10(20), e39146. https://doi.org/10.1016/j.heliyon.2024.e39146

Kirkpatrick, J. B., Green, K., Bridle, K. L., & Venn, S. E. (2014). Patterns of variation in Australian alpine soils and their relationships to parent material, vegetation formation, climate and topography. CATENA, 121, 186–194. https://doi.org/10.1016/j.catena.2014.05.005

Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer New York. https://doi.org/10.1007/978-1-4614-6849-3

Lihawa, F. (2017). Daerah Aliran Sungai Elo erosi, sedimentasi dan longsoran. Yogyakarta: Deepublisher. Retrieved from https://repository.ung.ac.id/get/karyailmiah/8472/Fitryane-Lihawa-Daerah-Aliran-Sungai-Alo-Erosi-Sedimentasi-dan-Longsoran.pdf

Löbmann, M. T., Maring, L., Prokop, G., Brils, J., Bender, J., Bispo, A., & Helming, K. (2022). Systems knowledge for sustainable soil and land management. Science of The Total Environment, 822, 153389. https://doi.org/10.1016/j.scitotenv.2022.153389

Mamo, T. A., Tolossa, D., Senbeta, F., & Zeleke, T. (2022). Factors influencing smallholder farmers’ decision to abandon introduced sustainable land management technologies in Central Ethiopia. Caraka Tani: Journal of Sustainable Agriculture, 37(2), 385–405. https://doi.org/10.20961/carakatani.v37i2.60720

Masruroh, H., Sahrina, A., Sumarmi, Rohman, F., & Trihatmoko, E. (2024). Geomorphology mapping and landslide susceptibility for disaster risk reduction and sustainability environment in the Sub DAS Maspo, Mt. Welirang, East Java, Indonesia. IOP Conference Series: Earth and Environmental Science, 1314(1), 012117. https://doi.org/10.1088/1755-1315/1314/1/012117

Matyukira, C., & Mhangara, P. (2024). Advances in vegetation mapping through remote sensing and machine learning techniques: A scientometric review. European Journal of Remote Sensing, 57(1), 2422330. https://doi.org/10.1080/22797254.2024.2422330

Mersha, E., Degefa, S., Argaw, M., & Mengist, W. (2024). Impact of land use land cover changes on ecosystem service values: Implication for landscape management. Journal of Landscape Ecology, 18(1), 61–85. https://doi.org/10.2478/jlecol-2025-0004

Mujiyo, M., Widijanto, H., Herawati, A., Rochman, F., & Rafirman, R. (2018). Potensi lahan untuk budidaya pisang di Kecamatan Jenawi Karanganyar. Caraka Tani: Journal of Sustainable Agriculture, 32(2), 142–148. https://doi.org/10.20961/carakatani.v32i2.17020

Nikparvar, B., & Thill, J.-C. (2021). Machine learning of spatial data. ISPRS International Journal of Geo-Information, 10(9), 600. https://doi.org/10.3390/ijgi10090600

Otekunrin, O. A., Otekunrin, O. A., Fasina, F. O., Omotayo, A. O., & Akram, M. (2020). Assessing the zero hunger target readiness in Africa in the face of COVID-19 Pandemic. Caraka Tani: Journal of Sustainable Agriculture, 35(2), 213–227. https://doi.org/10.20961/carakatani.v35i2.41503

Oyawale, A. A., Adeoti, F. O., Ajayi, T. R., & Omitogun, A. A. (2020). Applications of remote sensing and geographic information system (GIS) in regional lineament mapping and structural analysis in Ikare Area, Southwestern Nigeria. Journal of Geology and Mining Research, 12(1), 13–24. https://doi.org/10.5897/JGMR2019.0310

Patria, A., & Putra, P. S. (2020). Development of the Palu–Koro Fault in NW Palu Valley, Indonesia. Geoscience Letters, 7(1), 1. https://doi.org/10.1186/s40562-020-0150-2

Phan, D. C., Trung, T. H., Truong, V. T., Sasagawa, T., Vu, T. P. T., Bui, D. T., ..., & Nasahara, K. N. (2021). First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam. Scientific Reports, 11(1), 9979. https://doi.org/10.1038/s41598-021-89034-5

Reddy, G. P. O., Ramamurthy, V., & Singh, S. K. (2018). Integrated remote sensing, GIS, and GPS applications in agricultural land use planning. Geospatial Technologies in Land Resources Mapping, Monitoring and Management, 21, 489–515. Springer International Publishing. https://doi.org/10.1007/978-3-319-78711-4_24

Reddy, G. P. O., & Singh, S. K. (2018). Geospatial technologies in land resources mapping, monitoring and management. Springer International Publishing. https://doi.org/10.1007/978-3-319-78711-4

Reinhart, H., Putra, R. D., & Rafida, M. R. (2023). Karst ecosystem services and their roles in the management of Gunung Sewu UNESCO global geopark. Sustinere: Journal of Environment and Sustainability, 7(3), 220–233. https://doi.org/10.22515/sustinere.jes.v7i3.349

Rozaki, Z., Kamarudin, M. F., Aziz, A. A., & Senge, M. (2023). Exploring agricultural resilience in volcano-prone regions: A case study from Mount Merapi, Indonesia. Caraka Tani: Journal of Sustainable Agriculture, 38(2), 284–296. https://doi.org/10.20961/carakatani.v38i2.72390

Sahrina, A., Aksa, F. I., Labib, M. A., Purwanto, & Irawan, L. Y. (2022). Landscape as a nature laboratory in geography learning in South Malang, Indonesia. Proceedings of the 2nd International Conference on Social Knowledge Sciences and Education (ICSKSE 2022), 696, 111–127. Atlantis Press SARL. https://doi.org/10.2991/978-2-494069-63-3_12

Schmidt, F. H., & Ferguson, J. H. A. (1951). Rainfall types based on wet and dry period ratios for Indonesia with Western New Guinea. (Vol. 27). Verhandelingen, Djawatan Meteorologi dan Geofisik, Djakarta. Retrieved from https://www.cabidigitallibrary.org/doi/full/10.5555/19510600016

Simon, A., Wilhelmy, M., Klosterhuber, R., Cocuzza, E., Geitner, C., & Katzensteiner, K. (2021). A system for classifying subsolum geological substrates as a basis for describing soil formation. CATENA, 198, 105026. https://doi.org/10.1016/j.catena.2020.105026

Soedwiwahjono, & Pamardhi-Utomo, R. (2020). A strategy for the sustainable development of the karst area in Wonogiri. IOP Conference Series: Earth and Environmental Science, 447(1), 012057. https://doi.org/10.1088/1755-1315/447/1/012057

Srivastava, N., & Saxena, N. (2023). Applications of artificial intelligence and machine learning in geospatial data. Advances in Geospatial Technologies, pp. 196–219. IGI Global. https://doi.org/10.4018/978-1-6684-7319-1.ch010

Sukarman, S., & Ritung, S. (2013). Development and acceleration strategy of soil resources mapping in Indonesia. Jurnal Sumberdaya Lahan, 7(1), 1–14. https://doi.org/10.2018/jsdl.v7i1.6421

Sumarmi, S., Kresnajaya, A., A, A. H., & Fadlurrahman, I. (2022). Assessing the suitability of land in South Malang for conversion to oil palm plantations and the possible environmental impacts. KnE Social Sciences, 7(16), 124–132. https://doi.org/10.18502/kss.v7i16.12160

Trivedi, M. B., Marshall, M., Estes, L., De Bie, C. A. J. M., Chang, L., & Nelson, A. (2023). Cropland mapping in tropical smallholder systems with seasonally stratified sentinel-1 and sentinel-2 spectral and textural features. Remote Sensing, 15(12), 3014. https://doi.org/10.3390/rs15123014

Ullah, S., Abbas, M., & Qiao, X. (2024). Impact assessment of land-use alteration on land surface temperature in Kabul using machine learning algorithm. Journal of Spatial Science, 1–23. https://doi.org/10.1080/14498596.2024.2364283

Ullah, S., Qiao, X., & Tariq, A. (2025a). Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: A path toward sustainability. Scientific Reports, 15(1), 3092. https://doi.org/10.1038/s41598-025-87234-x

Ullah, W., Ahmad, K., Tahir, A. A., Ullah, S., Rafiq, M. T., & Abdullah-Al-Wadud, M. (2025b). Impacts of tourism on LULC and LST dynamics in district Buner and Shangla, Pakistan. Scientific Reports, 15(1), 9304. https://doi.org/10.1038/s41598-025-94230-8

Wang, C., Zhang, W., Ji, Y., Marino, A., Li, C., Wang, L., ..., & Wang, M. (2024). Estimation of aboveground biomass for different forest types using data from sentinel-1, sentinel-2, ALOS PALSAR-2, and GEDI. Forests, 15(1), 215. https://doi.org/10.3390/f15010215

Wang, F., Chen, H., Lian, J., Fu, Z., & Nie, Y. (2018). Preferential flow in different soil architectures of a small karst catchment. Vadose Zone Journal, 17(1), 1–10. https://doi.org/10.2136/vzj2018.05.0107

Wang, Q., Shi, W., Li, Z., & Atkinson, P. M. (2016). Fusion of sentinel-2 images. Remote Sensing of Environment, 187, 241–252. https://doi.org/10.1016/j.rse.2016.10.030

Weiss, A. (2001). Topographic position and landforms analysis. Poster presentation, ESRI user conference, San Diego, CA, 200. Retrieved from https://env761.github.io/assets/files/tpi-poster-tnc_18x22.pdf

Wicaksono, K. P., Permanasari, P. N., & Saitama, A. (2023). Analysis the potential of Malang Regency as a center for oil palm plantations in East Java. International Journal of Environment, Agriculture and Biotechnology, 8(4), 162–168. https://doi.org/10.22161/ijeab.84.17

Woś, B., & Pietrzykowski, M. (2021). Characteristics of technogenic soils developed from Neogene and Quaternary sediments substrate on reclaimed sulphur and sand extraction mine sites. Soil Science Annual, 71(4), 344–351. https://doi.org/10.37501/soilsa/126996

Zaka, M. M., & Samat, A. (2024). Advances in remote sensing and machine learning methods for invasive plants study: A comprehensive review. Remote Sensing, 16(20), 3781. https://doi.org/10.3390/rs16203781

Zeraatpisheh, M., Bottega, E. L., Bakhshandeh, E., Owliaie, H. R., Taghizadeh-Mehrjardi, R., Kerry, R., ..., & Xu, M. (2022). Spatial variability of soil quality within management zones: Homogeneity and purity of delineated zones. CATENA, 209, 105835. https://doi.org/10.1016/j.catena.2021.105835

Zhan, J. (2023). The sustainable management of land systems. Frontiers in Sustainable Resource Management, 2, 1240771. https://doi.org/10.3389/fsrma.2023.1240771

Zhang, A., Tariq, A., Quddoos, A., Naz, I., Aslam, R. W., Barboza, E., ..., & Abdullah-Al-Wadud, M. (2025). Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models. Scientific Reports, 15(1), 6993. https://doi.org/10.1038/s41598-025-92034-4

Zhao, W., Xiong, L., Ding, H., & Tang, G. (2017). Automatic recognition of loess landforms using Random Forest method. Journal of Mountain Science, 14(5), 885–897. https://doi.org/10.1007/s11629-016-4320-9

DOI: https://doi.org/10.20961/carakatani.v40i3.97530

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