DOI: https://doi.org/10.14710/geoplanning.4.2.131-142

REMOTE SENSING AND GIS APPROACHES TO A QUALITATIVE ASSESSMENT OF SOIL EROSION RISK IN SERANG WATERSHED, KULONPROGO, INDONESIA

*Nursida Arif orcid  -  Muhammadiyah University of Gorontalo, Indonesia
Projo Danoedoro  -  Universitas Gadjah Mada, Indonesia
Hartono Hartono  -  Universitas Gadjah Mada, Indonesia

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Abstract
This research aims to determine the risk of soil erosion qualitatively by integrating remote sensing with the geographic information system. Factors that contributed to the occurrence of erosion in the area of study were analyzed using the method of the variation of combined input data of the factors controlling erosion (soil, climate, topography, vegetation, and humans). The input data were quantitative data changed into qualitative data that were obtained from field data and extracted from remote sensing imagery, i.e. SPOT 5. A number of parameters were calculated using the RUSLE model equation. The model was validated by observing the qualitative erosion indicators in the field (pedestal, tree root exposure, armor layers, rill erosion, and gully erosion) by observing slope steepness in each sample area. The area of study was Serang watershed located in Kulon Progo Regency, Yogyakarta. It is one of the critically potential watersheds viewed from the landform and land use. The results of various combinations generated the highest of accuracy by 90.57 % with extremely erosion dominating the area of study. The factors with the highest contribution to erosion in Serang Watershed were slope length and steepness (LS) and erodibility (K).
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Keywords: Remote sensing, Serang watershed, soil erosion

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Section: Original Research
Language : EN
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  1. Arsyad, S. (2010). Konservasi Tanah & Air. Institut Pertanian Bogor

  2. Asis, A. M. de, & Omasa, K. (2007). Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS Journal of Photogrammetry and Remote Sensing, 62(4), 309–324. [https://doi.org/10.1016/j.isprsjprs.2007.05.013">Crossref]

  3. Assouline, S., & Ben-Hur, M. (2006). Effects of rainfall intensity and slope gradient on the dynamics of interrill erosion during soil surface sealing. Catena, 66(3), 211–220. [https://doi.org/10.1016/j.catena.2006.02.005">Crossref]

  4. Bergsma, E. (2008). Erosion by Rain: its subprocesses and diagnostic micro-topographic features. Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands.

  5. Bot, A., & Benites, J. (2005). The importance of soil organic matter: key to drought-resistant soil and sustained food production. Food & Agriculture Org.

  6. Bouaziz, M., Leidig, M., & Gloaguen, R. (2011). Optimal parameter selection for qualitative regional erosion risk monitoring: A remote sensing study of SE Ethiopia. Geoscience Frontiers, 2(2), 237–245. [https://doi.org/10.1016/j.gsf.2011.03.004">Crossref]

  7. Bredeweg, B., Linnebank, F., Bouwer, A., & Liem, J. (2009). Garp3—Workbench for qualitative modelling and simulation. Ecological Informatics, 4(5), 263–281. [https://doi.org/10.1016/j.ecoinf.2009.09.009">Crossref]

  8. Desmet, P. J. J., & Govers, G. (1995). GIS-based simulation of erosion and deposition patterns in an agricultural landscape: a comparison of model results with soil map information. Catena, 25(1–4), 389–401. [https://doi.org/10.1016/0341-8162(95)00019-o">Crossref]

  9. Dibyosaputro, H. S., & others. (2012). Pola Persebaran Keruangan Erosi Permukaan sebagai Respon Lahan Terhadap Hujan di Daerah Aliran Sungai Secang, Kabupaten Kulonprogo, Daerah Istimewa Yogyakarta Indonesia. Universitas Gadjah Mada.

  10. Farhan, Y., Zregat, D., & Farhan, I. (2013). Spatial estimation of soil erosion risk using RUSLE approach, RS, and GIS techniques: a case study of Kufranja Watershed, Northern Jordan. Journal of Water Resource and Protection, 5(12), 1247. [https://doi.org/10.4236/jwarp.2013.512134">Crossref]

  11. Gutman, G., & Ignatov, A. (1998). The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 19(8), 1533–1543. [https://doi.org/10.1080/014311698215333">Crossref]

  12. Jabro, J. D., Stevens, W. B., Iversen, W. M., & Evans, R. G. (2010). Tillage depth effects on soil physical properties, sugarbeet yield, and sugarbeet quality. Communications in Soil Science and Plant Analysis, 41(7), 908–916. [https://doi.org/10.1080/00103621003594677">Crossref]

  13. Kamaludin, H., Lihan, T., Ali Rahman, Z., Mustapha, M. A., Idris, W. M. R., & Rahim, S. A. (2013). Integration of remote sensing, RUSLE and GIS to model potential soil loss and sediment yield (SY). Hydrology and Earth System Sciences Discussions, 10(4), 4567–4596. [https://doi.org/10.5194/hessd-10-4567-2013">Crossref]

  14. la Rosa, D., & Van Diepen, C. (2002). Qualitative and quantitative land evaluation. In 1.5. Land use and land cover, Encyclopedia of Life Support System (EOLSS. UNESCO)(Verheye W, ed.), Eolss Publ, Oxford, UK.

  15. Liao, Z., Wang, B., Xia, X., & Hannam, P. M. (2012). Environmental emergency decision support system based on Artificial Neural Network. Safety Science, 50(1), 150–163. [https://doi.org/10.1016/j.ssci.2011.07.014">Crossref]

  16. McCool, D. K., Foster, G. R., Mutchler, C. K., & Meyer, L. D. (1989). Revised slope length factor for the Universal Soil Loss Equation. Transactions of the ASAE, 32(5), 1571–1576. [https://doi.org/10.13031/2013.31192">Crossref]

  17. Morgan, R. P. C. (2009). Soil erosion and conservation. John Wiley & Sons.

  18. Parveen, R., & Kumar, U. (2012). Integrated approach of universal soil loss equation (USLE) and geographical information system (GIS) for soil loss risk assessment in Upper South Koel Basin, Jharkhand. Journal of Geographic Information System, 4(6), 588. [https://doi.org/10.4236/jgis.2012.46061">Crossref]

  19. Pradhan, B., Lee, S., & Buchroithner, M. F. (2010). A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems, 34(3), 216–235. [https://doi.org/10.1016/j.compenvurbsys.2009.12.004">Crossref]

  20. Pradhan, B., & Saro, L. (2007). Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model. Earth Science Frontiers, 14(6), 143–151. [https://doi.org/10.1016/s1872-5791(08)60008-1">Crossref]

  21. Renard, K. G., Foster, G. R., Weesies, G. A., & Porter, J. P. (1991). RUSLE: Revised universal soil loss equation. Journal of Soil and Water Conservation, 46(1), 30–33.

  22. Santoso, H. B., & Senawi, M. P. (2012). Arahan Penggunaan Lahan Optimal Berdasarkan Aspek Biofisik dan Kebutuhan Minimal Lahan Pertanian untuk Pengendalian Erosi di Das Serang. Universitas Gadjah Mada.

  23. Shin, G. J. (1999). The analysis of soil erosion analysis in watershed using GIS. Ph. D. Dissertation, Department of Civil Engineering, Gang-won National University.

  24. Stocking, M., & Murnaghan, N. (2000). Land degradation--guidelines for field assessment. Overseas Development Group, University of East Anglia, Norwich, UK, 120.

  25. Utomo, W. H. (1994). Erosi dan Konservasi Tanah. Penerbit IKIP Malang

  26. Vrieling, A., Sterk, G., & Vigiak, O. (2006). Spatial evaluation of soil erosion risk in the West Usambara Mountains, Tanzania. Land Degradation & Development, 17(3), 301–319. https://doi.org/10.1002/ldr.711">[Crossref]

  27. Widarsih, S., & Senawi, M. P. (2012). Pendugaan Erosi, Kemampuan dan Kekritisan Lahan untuk Rehabilitasi Sub DAS Tinalah, DAS Progo. Universitas Gadjah Mada.

  28. Wischmeier, W. H., Smith, D. D., & others. (1978). Predicting rainfall erosion losses-a guide to conservation planning. Predicting Rainfall Erosion Losses-a Guide to Conservation Planning.

  29. Xu, L., Xu, X., & Meng, X. (2012). Risk assessment of soil erosion in different rainfall scenarios by RUSLE model coupled with Information Diffusion Model: A case study of Bohai Rim, China. Catena, 100, 74–82. [https://doi.org/10.1016/j.catena.2012.08.012">Crossref]

  30. Ypsilantis, B. (2011). Upland Soil Erosion Monitoring and Assessment. Bureau of Land Management, National Operations Center.

  31.  


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