Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models

  • Alireza Arabameri
  • Biswajeet PradhanEmail author
  • Khalil Rezaei


Remote sensing is recognized as a powerful and efficient tool that provides a comprehensive view of large areas that are difficult to access, and also reduces costs and shortens the timing of projects. The purpose of this study is to introduce effective parameters using remote sensing data and subsequently predict gully erosion using statistical models of Density Area (DA) and Information Value (IV), and data mining based Random Forest (RF) model and their ensemble. The aforementioned models were employed at the Tororud-Najarabad watershed in the northeastern part of Semnan province, Iran. For this purpose, at first using various resources, the map of the distribution of the gullies was prepared with the help of field visits and Google Earth images. In order to analyse the earth's surface and extraction of topographic parameters, a digital elevation model derived from PALSAR (Phased Array type L-band Synthetic Aperture Radar) radar data with a resolution of 12.5 meters was used. Using literature review, expert opinion and multi-collinearity test, 15 environmental parameters were selected with a resolution of 12.5 meters for the modelling. Results of RF model indicate that parameters of NDVI (normalized difference vegetation index), elevation and land use respectively had the highest effect on the gully erosion. Several techniques such as area under curve (AUC), seed cell area index (SCAI), and Kappa coefficient were used for validation. Results of validation indicated that the combination of bivariate (IV and DA models) with the RF data-mining model has increased their performance. The prediction accuracy of AUC and Kappa values in DA, IV and RF are (0.745, 0.782, and 0.792) and (0.804, 0.852, and 0.860) and these values in ensemble models of DA-RF and IV-RF are (0.845, and 0.911) and (0.872, and 0.951) respectively. Results of SCAI show that ensemble models had a good performance, so that, with increasing of sensitivity, the values of SCAI have decreased. Based on results, determination of gullies and assessing the process of gullying through remote sensing technology in combination with field observations and accurate statistical and computer methods can be a suitable methodology for predicting areas with gully erosion potential.

Key words

remote sensing ALOS PALSAR gully erosion random forest GIS 


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  1. Abdulkareem, J.H., Sulaiman, W.N.A., Pradhan, B., and Jamil, N.R., 2018a, Long-term hydrologic impact assessment of non-point source pollution measured through Land Use/Land Cover (LULC) changes in a tropical complex catchment. Earth Systems and Environment, 2, 67–84. Scholar
  2. Abdulkareem, J.H., Pradhan, B., Sulaiman, W.N.A., and Jamil, N.R., 2018b, Quantification of runoff as influenced by morphometric characteristics in a rural complex catchment. Earth Systems and Environment, 2, 145–162. Scholar
  3. Arabameri, A., Pradhan, B., Rezaei, K., Yamani, M., Pourghasemi, H.R., and Lombardo, L., 2018a, Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm. Land Degradation & Development. Scholar
  4. Arabameri, A., Pradhan, B., Pourghasemi, H.R., Rezaei, K., and Kerle, N., 2018b, Spatial Modelling of gully erosion using GIS and R programing: a comparison among three data mining algorithms. Applied Sciences, 8, 1369. Scholar
  5. Al-Abadi, A.M. and Al-Ali, A.K., 2018, Susceptibility mapping of gully erosion using GIS-based statistical bivariate models: a case study from Ali Al-Gharbi District, Maysan Governorate, southern Iraq. Environmental Earth Sciences, 77, 249.CrossRefGoogle Scholar
  6. Althuwaynee, O.F., Pradhan, B., Park, H.J., and Lee, J.H., 2014, A novel ensemble bivariate statistical evidential belief function with knowledge- based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21–36.CrossRefGoogle Scholar
  7. Althuwaynee, O.F., Pradhan, B., and Lee, S., 2012. Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 44, 120–135.CrossRefGoogle Scholar
  8. Angileri, S.E., Conoscenti, C., Hochschild, V., Märker, M., Rotigliano, E., and Agnesi, V., 2016, Water erosion susceptibility mapping by applying stochastic gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy). Geomorphology, 262, 61–76. Scholar
  9. Allouche, O., Tsoar, A., and Kadmon, R., 2006, Assessing the accuracy of species distribution models: prevalence, kappa, and the true skill statistic (TSS). Journal of Applied Ecology, 43, 1223–1232. Scholar
  10. Aertsena, W., Kinta, V., Orshovena, J., Özkanb, K., and Muysa, B., 2010, Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221, 1119–1130. Scholar
  11. Ayele, G.K., Gessess, A.A., Addisie, M.B., Tilahun, S.A., Tebebu, T.Y., Tenessa, D.B., Langendoen, E.J., Nicholson, C.F., and Steenhuis, T.S., 2016, A biophysical and economic assessment of a community- based rehabilitated gully in the Ethiopian highlands. Land Degradation & Development, 27, 270–280. Scholar
  12. Ballesteros Cánovas, J.A., Stoffe, M., Martín-Duque, J.F., Corona, C., Lucía, A., Bodoque, J.M., and Montgomery, D.R., 2017, Gully evolution and geomorphic adjustments of badlands to reforestation. Scientific Reports, 7, 45027. Scholar
  13. Bingner, R.L., Wells, R.R., Momm, H.G., Rigby, J.R., and Theurer, F.D., 2016, Ephemeral gully channel width and erosion simulation technology. Natural Hazards, 80, 1949–1966. Scholar
  14. Breiman, L., 2001, Random forests. Journal of Machine learning, 45, 5–32. Scholar
  15. Bui, D.T., Bui, Q.-T., Nguyen, Q.-P., Pradhan, B., Nampak, H., and Trinh, P.T., 2017a, A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32–44.CrossRefGoogle Scholar
  16. Bui, D.T., Tuan, T.A., Hoang, N.D., Thanh, N.Q., Nguyen, D.B., Van Liem, N., and Pradhan, B., 2017b, Spatial prediction of rainfallinduced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14, 447–458.CrossRefGoogle Scholar
  17. Chaplot, V., Coadou le Brozec, E., Silvera, N., and Valentin, C., 2005, Spatial and temporal assessment of linear erosion in catchments under sloping lands of northern Laos. Catena, 63, 167–184. Scholar
  18. Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D.T., Duan, Z., and Ma, J., 2017, A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147–160.CrossRefGoogle Scholar
  19. Conforti, M., Aucelli, P.P.C., Robustelli, G., and Scarciglia, F., 2010, Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Natural Hazards, 56, 881–898. Scholar
  20. Conoscenti, C., Agnesi, V., Cama, M., Alamaru Caraballo-Arias, N., and Rotigliano, E., 2018, Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity. Land Degradation and Development, 29, 724–736. Scholar
  21. Chu, T. and Lindenschmidt, K.E., 2017, Comparison and validation of digital elevation models derived from InSAR for a flat inland delta in the high latitudes of northern Canada. Canadian Journal of Remote Sensing, 43, 109–123. Scholar
  22. Conoscenti, C., Angileri, S., Cappadonia, C., Rotigliano, E., Agnesi, V., and Märker, M., 2014, Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology, 204, 399–411. Scholar
  23. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., and Hess, K.T., 2007, Random forests for classification in ecology. Journal of Ecology, 88, 2783–2792. Scholar
  24. Dubuis, A., 2013, Predicting spatial patterns of plant biodiversity: from species to communities. Ph.D. Thesis University of Lausanne, Lausanne, 295 p. Scholar
  25. Elith, J., Leathwick, J.R., and Hastie, T., 2008, A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813. Scholar
  26. Fielding, A.H. and Bell, J.F., 1997, A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38–49. Scholar
  27. Forkuor, G. and Maathuis, B.H.P., 2012, Comparison of SRTM and ASTER derived digital elevation models over two regions in Ghana: implications for hydrological and environmental modeling. In: Piacentini, T. (ed.). Studies on Environmental and Applied Geomorphology. IntechOpen, p. 219–240. Scholar
  28. Gómez-Gutiérrez, Á., Conoscenti, C., Angileri, S.E., Rotigliano, E., and Schnabel, S., 2015, Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations. Natural Hazards, 79, 291–314. Scholar
  29. Guo-liang, D., Yong-shuang, Z., Javed, I., and Xin, Y., 2017, Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science, 14, 249–268. Scholar
  30. Govers, G., Merckx, R., Wesemael, B.V., and Van Oost, K., 2017, Soil conservation in the 21st century: why we need smart agricultural intensification. SOIL, 3, 45–59. Scholar
  31. Ghosh, S. and Kumar Guchhait, S., 2016, Geomorphic threshold estimation for gully erosion in the lateritic soil of Birbhum, West Bengal, India. SOIL Discussion Papers. Scholar
  32. Gurbanov, E.A. and Ganieva, S.A., 2017, Intensity of gully erosion in arid zone of Azerbaijan Republic (by the example of the region of the Mingechaur water reservoir). Aridnye Ekosistemy, 23, 46–51. Scholar
  33. Ghumman, A.R., Al-Salamah, I.S., Al-Saleem, S.S., and Haider, H., 2017, Evaluating the impact of lower resolutions of digital elevation model on rainfall-runoff modeling for ungauged catchments. Environmental Monitoring and Assessment, 189, 54. Scholar
  34. Hao, L., Cruse, R.H., Xiaobing, L., and Xingyi, Z., 2016, Effects of topography and land use change on gully development in typical mollisol region of northeast China. Chinese Geographical Science, 26, 779–788. Scholar
  35. Heidary, F. and sabohy, R., 2015, Investigating the factors affecting the growth of gulls and determining their propagation model in Kerman Province (case study of Baft, Rabor and Rhine Area). Engineering Sciences of Iran, 9, 1–10. (In Persian)Google Scholar
  36. Hastie, T., 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2rd edition). In Springer series in statistics New York, 533 p.CrossRefGoogle Scholar
  37. Ionita, I., 2011, The human impact on soil erosion and gullying in the Moldavian Plateau, Romania. Landform Analyses, 17, 71–73. Scholar
  38. Ionita, I., Fullen, M.A., Zgłobicki, W., and Poesen, J., 2015, Gully erosion as a natural and human-induced hazard. Natural Hazards, 79, S1–S5. Scholar
  39. Jebur, M.N., Pradhan, B., and Tehrany, M.S., 2013, Detection of vertical slope movement in highly vegetated tropical area of Gunung pass landslide, Malaysia, using L-band InSAR technique. Geosciences Journal, 18, 61–68. Scholar
  40. Johansen, K., Taihei, S., Tindall, D., and Phinn, S., 2012, Object-based monitoring of gully extent and volume in North Australia using LIDAR data. Proceedings of GEOBIA 2012, The 4th International Conference on Geographic Object-Based Image Analysis, Rio de Janeiro, May 7–9, p. 168–173.Google Scholar
  41. Keesstra, S.D., Bouma, J., Wallinga, J., Tittonell, P., Smith, P., Cerdà, A., Montanarella, L., Quinton, J.N., Pachepsky, Y., van der Putten, W.H., Bardgett, R.D., Moolenaar, S., Mol, G., Jansen, B., and Fresco, L.O., 2016, The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. SOIL, 2, 111–128. Scholar
  42. Kou, M., Jiao, J., Yin, Q., Wang, N., Wang, Z., Li, Y., Yu, W., Wei, Y., Yan, F., and Cao, B., 2016, Successional trajectory over 10years of vegetation restoration of abandoned slope croplands in the hill-gully region of the loess plateau. Land Degradation & Development, 27, 919–932. Scholar
  43. Lee, S. and Min, K., 2001, Statistical analysis of landslide susceptibility at Yonging, Korea. Environmental Geology, 40, 1095–1113. Scholar
  44. Marker, M., Pelacani, S., and Schroder, B., 2012, A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy. Geomorphology, 125, 530–540. Scholar
  45. Martínez, L.J. and Correa, N.A., 2016, Digital elevation models to improve soil mapping in mountainous areas: case study in Colombia. In: Zinck, J.A., Metternicht, G., Bocco, G., and Del Valle, H.F. (eds.), Geopedology an Integration of Geomorphology and Pedology for Soil and Landscape Studies. Springer International Publishing, Cham, p. 377–388.Google Scholar
  46. Mashi, S.A., Yaro, A., and Jenkwe, E.D., 2015, Causes and consequences of gully erosion: perspectives of the local people in Dangara area, Nigeria. Environment, Development and Sustainability, 17, 1431–1450. Scholar
  47. McCloskey, G.L., Wasson, R.J., Boggs, G.S., and Douglas, M., 2016, Timing and causes of gully erosion in the riparian zone of the semi-arid tropical Victoria River, Australia. Geomorphology, 266, 96–104. Scholar
  48. Mekuriaw, A., 2017, Assessing the effectiveness of land resource management practices on erosion and vegetative cover using GIS and remote sensing techniques in Melaka watershed, Ethiopia. Environmental Systems Research, 6, 16. Scholar
  49. Moore, I.D., Gessler, P.E., Nielsen, G.A., and Peterson, G.A., 1993, Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57, 443–452. Scholar
  50. Montanarella, L., Pennock, D.J., McKenzie, N., Badraoui, M., Chude, V., Baptista, I., Mamo, T., Yemefack, M., Aulakh, M.S., Yagik Hong, S.Y., Vijarnsom, P., Zhang, G., Arrouays, D., Black, H., Krasilnikov, P., Sobocka, J., Alegre, J., Henriquez, C.R., Mendonca-Santos, M.L., Taboada, M., Espinosa-Victoria, D., Alshankiti, A., Alavi Panah S.K., Elsheikh, E.A.E.M., Hempel, J., Arbestian, M.C., Nachtergaele, F., and Vargas, R., 2016, World’s soils are under threat. SOIL, 2, 79–82. Scholar
  51. Nampak, H., Pradhan, B., and Manap, M.A., 2014, Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283–300.CrossRefGoogle Scholar
  52. Nicodemus, K.K., 2011, Letter to the editor: on the stability and ranking of predictors from random forest variable importance measures. Briefings in Bioinformatics, 12, 369–373. Scholar
  53. Novara, A., Keesstra, S., Cerdà, A., Pereira, P., and Gristina, L., 2016, Understanding the role of soil erosion on 15 CO2-C loss using 13C isotopic signatures in abandoned Mediterranean agricultural land. Science of the Total Environment, 550, 330–336. Scholar
  54. Oh, H.J. and Pradhan, B., 2011, Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37, 1264–1276.CrossRefGoogle Scholar
  55. Pakoksung, K. and Takagi, M., 2016, Digital elevation models on accuracy validation and bias correction in vertical. Modeling Earth Systems and Environment, 2, 11. Scholar
  56. Poesen, J., 2011, Challenges in gully erosion research. Landform Analysis, 17, 5–9. Scholar
  57. Poesen, J., Vanwalleghem, T., de Vente, J., Knapen, A., Verstraeten, G., and Martínez-Casasnovas, J.A., 2006, Gully erosion in Europe. In: Boardman, J. and Poesen, J. (eds.), Soil Erosion in Europe. Wiley, Chichester, p. 515–536.CrossRefGoogle Scholar
  58. Poesen, J., Nachtergaele, J., Verstracten, G., and Volentin, C., 2003, Gully erosion and environmental change: importance and research needs. Catena, 50, 133–160. Scholar
  59. Pourghasemi, H.R., Yousefi, S., Kornejady, A., and Cerdà, A., 2017, Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, 609, 764–775. Scholar
  60. Pradhan, B., 2013, A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.CrossRefGoogle Scholar
  61. Prosdocimi, M., Burguet, M., Di Prima, S., Sofia, G., Terol, E., Rodrigo Comino, J., Cerdà, A., and Tarolli, P., 2017, Rainfall simulation and Structure-from-Motion photogrammetry for the analysis of soil water erosion in Mediterranean vineyards. Science of the Total Environment, 574, 204–215. Scholar
  62. Prosdocimi, M., Cerdà, A., and Tarolli, P., 2016, Soil water erosion on Mediterranean vineyards: a review. Catena, 141, 1–21. Scholar
  63. Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H.R., and Feizizadeh, B., 2017, Evaluating the influence of geo-environmental factors on gully erosion in a semiarid region of Iran: an integrated framework. Science of the Total Environment, 579, 913–927. Scholar
  64. Rahmati, O., Haghizadeh, A., Pourghasemi, H.R., and Noormohamadi, F., 2016, Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Natural Hazards, 82, 1231–1258. Scholar
  65. Rodriguez-Galiano, V., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P., and Jeganathan, C., 2012, Random Forest classification of Mediterranean land covers using multi-seasonal imagery and multiseasonal texture. Journal of Remote Sensing of Environment, 121, 93–107. Scholar
  66. Slimane, A.B., Raclot, D., Evrard, O., Sanaa, M., Lefevre, I., and Bissonnais, Y.L., 2016, Relative contribution of rill/interrill and gully/channel erosion to small reservoir siltation in Mediterranean environments. Land Degradation & Development, 27, 785–797. Scholar
  67. Tamene, L., Adimassu, Z., Aynekulu, E., and Yaekob, T., 2017, Estimating landscape susceptibility to soil erosion using a GIS-based approach in Northern Ethiopia. International Soil and Water Conservation Research, 5, 221–230. Scholar
  68. Tarolli, P. and Sofia, G., 2016, Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology, 255, 140–161. Scholar
  69. Tehrany, M.S., Pradhan, B., and Jebur, M.N., 2013, Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79.CrossRefGoogle Scholar
  70. Tehrany, M.S., Pradhan, B., and Jebur, M.N., 2014, Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343.CrossRefGoogle Scholar
  71. Tehrany, M.S., Pradhan, B., Mansor, S., and Ahmad, N., 2015, Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91–101.CrossRefGoogle Scholar
  72. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M.N., and Tehrany, M.S., 2014, Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124–135.CrossRefGoogle Scholar
  73. Valentin, C., Poesen, J., and Li, Y., 2005, Gully erosion: impacts, factors and control. CATENA, 63, 132–153. Scholar
  74. Van Westen, C.J., Rangers, N., Terlien, M.T.J., and Soeters, R., 1996, Prediction of the occurrence of slope instability phenomena through GIS based hazard zonation. Geologische Rundschau, 86, 404–414. Scholar
  75. Wang, L., Wei, S., Horton, R., and Shao, M., 2011, Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China. Catena, 87, 90–100. Scholar
  76. Zabihi, M., Mirchooli, F., Motevalli, A., Darvishan, A.K., Pourghasemi, H.R., Zakeri, M.A., and Sadighi, F., 2018, Spatial modelling of gully erosion in Mazandaran Province, northern Iran. Catena, 161, 1–13. Scholar
  77. Zakerinejad, R. and Maerker, M., 2015, An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran. Natural Hazards, 79, 25–50. Scholar
  78. Zakerinejad, R. and Maerker, M., 2014, Prediction of gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: a case study in the Mazayejan Plain, Southwest Iran. Supplementi di Geografia Fisica e Dinamica Quaternaria, 37, 67–76. Scholar
  79. Zheng, F., 2006, Effect of vegetation changes on soil erosion on the Loess Plateau. Pedosphere, 16, 420–427. Scholar
  80. Zhang, W., Wang, W., and Chen, L., 2012, Constructing DEM based on InSAR and the relationship between InSAR DEM’s precision and terrain factors. Energy Procedia, 16, 184–189. Scholar
  81. Zhou, C., Ge, L.E.D., and Chang, H.C., 2005, A case study of using external DEM in InSAR DEM generation. Geo-Spatial Information Science, 8, 14–18. Scholar

Copyright information

© The Association of Korean Geoscience Societies and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Alireza Arabameri
    • 1
  • Biswajeet Pradhan
    • 2
    Email author
  • Khalil Rezaei
    • 3
  1. 1.Department of GeomorphologyTarbiat Modares UniversityTehranIran
  2. 2.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia
  3. 3.Faculty of Earth SciencesKharazmi UniversityTehranIran

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