The Rapid Method of Soil Identification Based on Remote Sensing and Geographic Information Systems (Case Study of Moramo Watershed)

  • Jufri Karim Department of Geography, Faculty of Earth Sciences and Technology, Halu Oleo University, Indonesia
  • Totok Gunawan Department of Remote Sensing, Faculty of Geography, Gadjah Mada University, Indonesia
  • Tukidal Yunianto Department of Remote Sensing, Faculty of Geography, Gadjah Mada University, Indonesia
  • Hasbullah Syaf Department of Soil Science, Faculty of Agriculture, Halu Oleo University, Indonesia
  • Syamsu Alam Department of Soil Science, Faculty of Agriculture, Halu Oleo University, Indonesia
Keywords: image processing, land mapping, visual interpretation


The field-tested samples are done by stratified random sampling. Soil classification was obtained through observation of field profile morphology and soil analysis in the laboratory followed by supporting data such as temperature and rainfall. The Moramo River Basin (DAS) was used as the location of the case study in this experiment by observing 13 soil profiles. Soil properties and characteristics were observed for soil texture, clay mineral, soil pH (H2O and KCl), soil cation, saturation bases, and C-organic.  The soil naming was done to subgroup category based on Soil Taxonomy System in 2010 and paired with the land classification system of Soil Research Center in 1983, and WRB-FAO in 2006. The result showed that the accuracy of landform interpretation 89.6%, rocks 92.19%, accuracy of land use interpretation 90.63%, and accuracy of soil mapping 90.00%, so that the image ALOS AVNIR-2 can be utilized well to obtain parameter of the land unit for land mapping. The result of image data processing through RGB 341 composite image showed a high unidirectional frequency filter, histogram equalization, and analyzed with Geographic Information System, 15 units of landform, five-rock units. nine land-use units and 11 sub-soil sub-groups were obtained. The results of the soil classification in the Moramo Watershed (DAS) region in the subgroup category obtained 11 subgroups of land consisting of Lithic Udorthents, Typic Udifluvents, Aeric Endoaquents, Typic Fluvaquents, Typic Dystrudepts, Typic Eutrudepts, Ruptic-Alfic Eutrudepts, Lithic Dystrudepts, Oxyaquic Eutrudepts, Fluvaquentic Epiaquepts, Typic Endoaquepts.


(SSSA) Soil Science Society of America. (1994). Factors of Soil Formation: A Fiftieth Anniversary Retrospective. SSSA Special Publication nr 33. Madison, WI: SSSA
Alavipanaha, S. K., Matinfarb H. R., Rafiei Emamc, A., Khodaeid, K., Hasji Bagherie, R., & Yazdan Panahf, A. (2010). Criteria for selecting satellite data for studying land resources. DESERT, 15, 83-102.
Ashar, K., & La Ode. (2010). Landsat-7 ETM + Image Application and Geographic Information System in Solid Bitumen Surveys and Mapping (Cases in North Buton and Surrounding Districts of Southeast Sulawesi Province). Thesis. Remote Sensing Study Program, Faculty of Geography, Gadjah Mada University. Yogyakarta.
Bakosurtanal. (1988). Land System and Land Suitability Map Scale 1: 250,000, Sulawesi Sheet 2212, RePProT Series. National Survey and Mapping Coordinating Board.
Bauer, B. O. (2004). Encyclopedia of Geomorphology. Geomorphology, 1, 428–35. London: Routledge.
Christensen, S. D., Ransom, C. V., Edvarchuk, K. A., & Rasmussen, V. P. (2011). Efficiency and accuracy of wildland weed mapping methods. Invasive Plant Science and Management, 4(4), 458-465.
Danoedoro, P. (1996). Digital Image Processing. Theory and Application in the Field of Remote Sensing. Faculty of Geography, Gadjah Mada University. Yogyakarta.
Danoedoro, P. (2012). Introduction to Digital Remote Sensing. ANDI Yogyakarta publisher. Yogyakarta.
Darmawijaya, M. I. (1997). Land Classification; Theory Basis for Land Researchers and Agricultural Executors in Indonesia. Third print. Gadjah Mada University Press. Yogyakarta.
Director General of Land Rehabilitation and Social Forestry (RLPS). (2009). Guidelines for Monitoring and Evaluation of Watersheds (DAS).
FAO. (2006). World Reference Base for Soil Resources, by IUSS-ISRIC-FAO. World Soil Resources Reports No. 103. Rome
Javed, A., Khanday, M. Y., & Rais, S. (2011). Watershed Prioritization Using Land Use / Land Cover Parameters: A Remote Sensing and GIS Based Approach. Journal of the Geological Society of India, 78, 63-75.
Jensen, & John, R. (1986). Digital Image Processing Introductory: a Remote Sensing Perspective. Englewood Cliffs, NJ: Prentice Hall.
Jensen, L. L. F., & Gorte, B. G. H. (2002). Principle of remote sensing, Chapter 12 Digital image classification, ITC, Enchede, The Netherlands (2nd ed).
Mann, L. K., Anthony, W. K., Virginia, H. D., William, W. H., Robert, W., Larry, R. P., & Tom, L. A. (1999). The Role of Soil Classification in Geographic Information System Modeling of Habitat Pattern: Threatened Calcareous Ecosystems. Ecosystems, 2, 524-55.
Martinez-Casasnovas, J. A. (2003). A spatial information technology approach for the mapping and quantification of gully erosion. Catena, 50(2-4), 293-308.
Minasny, B., & McBratney, A.B. (2016). Digital Soil Mapping: a Brief history and some lessons. Geoderma, 264, 301-311.
News IDSN. (2009). Draft National Geospatial Information Law (TIGnas Bill) in Indonesian Development. Quarterly Bulletin No.10. Bakosurtanal. Bogor.
Pahlavan-Rad, M. R., Khormali, F., Toomanian, N., Brungard, C. W., Kiani, F., Komaki, C. B., & Bogaert, P. (2016). Legacy soil maps as a covariate in digital soil mapping: a case study from Northern Iran. Geoderma, 279, 141–148.
Prahasta, E. (2009). Geographic Information System: Basic Concepts (Geodetic and Geomatic Perspectives. First Printing Information Bandung. Bandung.
Raoofi, M., Refahi, H., Jalali, N., & Sarmadian, F. (2004). Find out more about satellite images to map and locate soil erosion. Iranian J. Agric. Sci., 35(4), 797-807 (In Persian).
Sartohasi, J. (2010). Soil Geomorphology and Its Application for Disaster Risk Reduction. Speech inauguration of Professor in the Faculty of Geography on November 24th. Yogyakarta.
Short, N. M. (1982). Landsat Tutorial Workbook - Basics of Satellite Remote Sensing. Washington DC: NASA.
Smith, M. J., & Pain, C. F. (2009). Applications of remote sensing in geomorphology. Progress in Physical Geography, 33(4), 568-582.
Soetoto. (1995). Image Interpretation for Geological Survey. Faculty of Geography UGM-Bakosurtanal. Yogyakarta.
Soil Survey Staff. (2010). Keys to Soil Taxonomy, USDA 11th ed. Natural Resources Conservation Service, Washington, DC.
Soil Survey Staff. (2017). Digital Soil Mapping. In Ditzler, C., Scheffe, K., & Monger, H. C. (Eds.), Soil Survey Manual (pp.295-354). Government Printing Office, Washington, DC.
Stumpf, F., Karsten S., Thorsten B., Schönbrodt-Stitt, Buzzo S., Dumperth G., Wadoux C., Alexandre, & Wei X. (2016). Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping. Thomas %J Journal of Plant Nutrition Scholten, and Soil Science. Zeitschrift fuer Pflanzenernaehrung und Bodenkunde, 179, 499–509.
Wahyunto, Murdiyati, S. R., & Ritung, S. (2004). Application of Remote Sensing Technology and Validation Tests for Detection of Spreading of Rice Fields and Land Use/Closure. Agricultural Informatics, 13, 746-769.
Yang, H., Zhang, X., Xu, M., Shao, S., Wang, X., Liu, W., Wu, D., Ma, Y., Bao, Y., Zhang, X., & Liu, H. (2020). Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China. Catena, 184, 104-259.
Yunianto, T. (1987). Remote Sensing Image Interpretation for Land Surveys. PUSPIC UGM-Bakosurtanal. Yogyakarta.
Zhang, G., Liu, F., & Song, X. (2017). Recent progressand future prospect of digital soil mapping: A review. J. Integr. Agric., 16, 2871-2885.