CoChat
Sign Up
Cited by 23

The DOI matches its Crossref record.

Modeling Gully Erosion Susceptibility to Evaluate Human Impact on a Local Landscape System in Tigray, Ethiopia

Busch, Robert, Hardt, Jacob, Nir, Nadav, Schütt, Brigitta·2021·Remote Sensing
DOI 10.3390/rs13102009
Open this paper in CoChat — free account

Abstract

In recent years, modeling gully erosion susceptibility has become an increasingly popular approach for assessing the impact of different land degradation factors. However, different forms of human influence have so far not been identified in order to form an independent model. We investigate the spatial relation between gully erosion and distance to settlements and footpaths, as typical areas of human interaction, with the natural environment in rural African areas. Gullies are common features in the Ethiopian Highlands, where they often hinder agricultural productivity. Within a catchment in the north Ethiopian Highlands, 16 environmental and human-related variables are mapped and categorized. The resulting susceptibility to gully erosion is predicted by applying the Random Forest (RF) machine learning algorithm. Human-related and environmental factors are used to generate independent susceptibility models and form an additional inclusive model. The resulting models are compared and evaluated by applying a change detection technique. All models predict the locations of most gullies, while 28% of gully locations are exclusively predicted using human-related factors.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/rs13102009

Cites

Statistics

Figures

Figure 1 Regional overview. Hill-shade map of the study catchment (red frame) and the surroundings based on ALOS Global DSM elevation data with 30 m × 30 m pixel size Figure 1. Regional overview. Hill-shade map of the study catchment (red frame) and the surroundings based on ALOS Global DSM elevation data with 30 m × 30 m pixel size (Version 2.2.), ©JAXA 2014.
Figure 1 Regional overview. Hill-shade map of the study catchment (red frame) and the surroundings based on ALOS Global DSM elevation data with 30 m × 30 m pixel size Figure 1. Regional overview. Hill-shade map of the study catchment (red frame) and the surroundings based on ALOS Global DSM elevation data with 30 m × 30 m pixel size (Version 2.2.), ©JAXA 2014.CC-BY · From the PDF
Figure 2 Overview map of the studied catchment area (red frame in Figure 1) and the mainstream (river) to which the watershed streams are tributary (ALOS Global DSM). ( a ) includes the drainage network, differentiated into gullies, main channels, and rivers. The white line marks the local topography-based division into lowlands (<1463 m above sea level (a.s.l.), transition (1463-1575 m a.s.l.), and hill country (>1575 m a.s.l.). ( b ) shows areas of human interaction (residential areas, pathways and hollow ways). Figure 2. Overview map of the studied catchment area (red frame in Figure 1) and the mainstream (river) to which the watershed streams are tributary (ALOS Global DSM). ( a ) includes the drainage network, differentiated into gullies, main channels, and rivers. The white line marks the local topography-based division into lowlands (<1463 m above sea level (a.s.l.), transition (1463-1575 m a.s.l.), and hill country (>1575 m a.s.l.). ( b ) shows areas of human interaction (residential areas, pathways and hollow ways).
Figure 2 Overview map of the studied catchment area (red frame in Figure 1) and the mainstream (river) to which the watershed streams are tributary (ALOS Global DSM). ( a ) includes the drainage network, differentiated into gullies, main channels, and rivers. The white line marks the local topography-based division into lowlands (<1463 m above sea level (a.s.l.), transition (1463-1575 m a.s.l.), and hill country (>1575 m a.s.l.). ( b ) shows areas of human interaction (residential areas, pathways and hollow ways). Figure 2. Overview map of the studied catchment area (red frame in Figure 1) and the mainstream (river) to which the watershed streams are tributary (ALOS Global DSM). ( a ) includes the drainage network, differentiated into gullies, main channels, and rivers. The white line marks the local topography-based division into lowlands (<1463 m above sea level (a.s.l.), transition (1463-1575 m a.s.l.), and hill country (>1575 m a.s.l.). ( b ) shows areas of human interaction (residential areas, pathways and hollow ways).CC-BY · From the PDF
Figure 3 Workflow for modeling gully erosion susceptibility. Figure 3. Workflow for modeling gully erosion susceptibility.
Figure 3 Workflow for modeling gully erosion susceptibility. Figure 3. Workflow for modeling gully erosion susceptibility.CC-BY · From the PDF
Figure 4 Comparison of the model performances by estimating the 'receiver operating characteristic' (ROC) curve for each model (A-overall; B-environmental; C-human-related). Corresponding 'area under the curve' (AUC) shares are shown in brackets. Figure 4. Comparison of the model performances by estimating the 'receiver operating characteristic' (ROC) curve for each model (A-overall; B-environmental; C-human-related). Corresponding 'area under the curve' (AUC) shares are shown in brackets.
Figure 4 Comparison of the model performances by estimating the 'receiver operating characteristic' (ROC) curve for each model (A-overall; B-environmental; C-human-related). Corresponding 'area under the curve' (AUC) shares are shown in brackets. Figure 4. Comparison of the model performances by estimating the 'receiver operating characteristic' (ROC) curve for each model (A-overall; B-environmental; C-human-related). Corresponding 'area under the curve' (AUC) shares are shown in brackets.CC-BY · From the PDF
Figure 5 Pixel-wise predicted Gully Erosion Susceptibility Maps (GESM) and change detection between Models B and C. ( a ) Model A-based on 16 environmental and human-related factors, ( b ) Model B-based on 13 environmental factors, ( c ) Model C-based on three human-related factors, ( d ) Change detection between Models B and C. Areas with low gully erosion susceptibility cover about 21% (Model A) with res Figure 5. Pixel-wise predicted Gully Erosion Susceptibility Maps (GESM) and change detection between Models B and C. ( a ) Model A-based on 16 environmental and human-related factors, ( b ) Model B-based on 13 environmental factors, ( c ) Model C-based on three human-related factors, ( d ) Change detection between Models B and C.
Figure 5 Pixel-wise predicted Gully Erosion Susceptibility Maps (GESM) and change detection between Models B and C. ( a ) Model A-based on 16 environmental and human-related factors, ( b ) Model B-based on 13 environmental factors, ( c ) Model C-based on three human-related factors, ( d ) Change detection between Models B and C. Areas with low gully erosion susceptibility cover about 21% (Model A) with res Figure 5. Pixel-wise predicted Gully Erosion Susceptibility Maps (GESM) and change detection between Models B and C. ( a ) Model A-based on 16 environmental and human-related factors, ( b ) Model B-based on 13 environmental factors, ( c ) Model C-based on three human-related factors, ( d ) Change detection between Models B and C.CC-BY · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 70

  1. Castillo, 2016, A century of gully erosion research: Urgency, complexity and study approaches, Earth-Sci. Rev., 160, 300, 10.1016/j.earscirev.2016.07.009 · DOI
  2. Richard, 2004, Estimation of runoff critical shear stress for soil erosion from soil shear strength, Catena, 57, 233, 10.1016/j.catena.2003.11.007 · DOI
  3. Haregeweyn, 2015, Soil erosion and conservation in Ethiopia, Prog. Phys. Geogr. Earth Environ., 39, 750, 10.1177/0309133315598725 · DOI
  4. Taddese, 2001, Land degradation: A challenge to Ethiopia, Environ. Manag., 27, 815, 10.1007/s002670010190 · DOI
  5. Valentin, 2005, Gully erosion: Impacts, factors and control, Catena, 63, 132, 10.1016/j.catena.2005.06.001 · DOI
  6. Poesen, 2003, Gully erosion and environmental change: Importance and research needs, Catena, 50, 91, 10.1016/S0341-8162(02)00143-1 · DOI
  7. Arabameri, A., Pradhan, B., Pourghasemi, H.R., Rezaei, K., and Kerle, N. (2018). Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms. Appl. Sci., 8. · DOI
  8. Arabameri, A., Blaschke, T., Pradhan, B., Pourghasemi, H.R., Tiefenbacher, J.P., and Bui, D.T. (2020). Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study. Sensors, 20. · DOI
  9. Hosseinalizadeh, 2019, Gully headcut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models, Geoderma, 342, 1, 10.1016/j.geoderma.2019.01.050 · DOI
  10. Pourghasemi, 2017, Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling, Sci. Total Environ., 609, 764, 10.1016/j.scitotenv.2017.07.198 · DOI
  11. Bocco, 1991, Gully erosion: Processes and models, Prog. Phys. Geogr. Earth Environ., 15, 392, 10.1177/030913339101500403 · DOI
  12. Dotterweich, 2008, The history of soil erosion and fluvial deposits in small catchments of central Europe: Deciphering the long-term interaction between humans and the environment—A review, Geomorphology, 101, 192, 10.1016/j.geomorph.2008.05.023 · DOI
  13. Sidle, 2019, Hydrogeomorphic processes affecting dryland gully erosion: Implications for modelling, Prog. Phys. Geogr. Earth Environ., 43, 46, 10.1177/0309133318819403 · DOI
  14. Nyssen, 2004, Human impact on the environment in the Ethiopian and Eritrean highlands—a state of the art, Earth-Sci. Rev., 64, 273, 10.1016/S0012-8252(03)00078-3 · DOI
  15. Sidle, R.C., and Ochiai, H. (2006). Landslides: Processes, Prediction, and Land Use, American Geophysical Union. · DOI
  16. Bull, 1997, Gully processes and modelling, Prog. Phys. Geogr. Earth Environ., 21, 354, 10.1177/030913339702100302 · DOI
  17. Kirkby, 2009, Gully processes and gully dynamics, Earth Surf. Process. Landf., 34, 1841, 10.1002/esp.1866 · DOI
  18. Nir, N., Knitter, D., Hardt, J., and Schütt, B. (2021). Human movement and gully erosion: Investigating feedback mechanisms using Frequency Ratio and Least Cost Path analysis in Tigray, Ethiopia. PLoS ONE, 16. · DOI · Paper page
  19. Frankl, 2012, Gully development and its spatio-temporal variability since the late 19th century in the northern Ethiopian Highlands, Afr. Focus, 25, 25, 10.21825/af.v25i2.4955 · DOI
  20. Nyssen, 2005, Rainfall erosivity and variability in the Northern Ethiopian Highlands, J. Hydrol., 311, 172, 10.1016/j.jhydrol.2004.12.016 · DOI
  21. Abebe, B.A. (2014). Modeling the Effect of Climate and Land Use Change on the Water Resources in Northern Ethiopia: The Case of Suluh River Basin. [Ph.D. Thesis, Freie Universität Berlin].
  22. Conoscenti, 2008, Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: A test in Southern Sicily, Nat. Hazards, 46, 287, 10.1007/s11069-007-9188-0 · DOI
  23. Shit, P.K., Pourghasemi, H.R., and Bhunia, G.S. (2020). Geomorphic Threshold and SCS-CN-Based Runoff and Sediment Yield Modelling in the Gullies of Dwarka–Brahmani Interfluve, West Bengal, India. Gully Erosion Studies from India and Surrounding Regions, Springer International Publishing. · DOI
  24. Schumm, 1979, Geomorphic Thresholds: The Concept and Its Applications, Trans. Inst. Br. Geogr., 4, 485, 10.2307/622211 · DOI
  25. Arabameri, 2019, Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques, Catena, 180, 282, 10.1016/j.catena.2019.04.032 · DOI
  26. Rahmati, 2016, Gully erosion susceptibility mapping: The role of GIS-based bivariate statistical models and their comparison, Nat. Hazards, 82, 1231, 10.1007/s11069-016-2239-7 · DOI
  27. Choi, 2020, Gully erosion susceptibility mapping using artificial intelligence and statistical models, Geomat. Nat. Hazards Risk, 11, 821, 10.1080/19475705.2020.1753824 · DOI
  28. Zabihi, 2018, Spatial modelling of gully erosion in Mazandaran Province, northern Iran, Catena, 161, 1, 10.1016/j.catena.2017.10.010 · DOI
  29. Boardman, 2006, Soil erosion science: Reflections on the limitations of current approaches, Catena, 68, 73, 10.1016/j.catena.2006.03.007 · DOI
  30. Jungerius, 2002, Road construction and gully erosion in West Pokot, Kenya, Earth Surf. Process. Landf., 27, 1237, 10.1002/esp.423 · DOI
  31. Nyssen, 2002, Impact of road building on gully erosion risk: A case study from the Northern Ethiopian Highlands, Earth Surf. Process. Landf., 27, 1267, 10.1002/esp.404 · DOI
  32. Alewell, 2019, Using the USLE: Chances, challenges and limitations of soil erosion modelling, Int. Soil Water Conserv. Res., 7, 203, 10.1016/j.iswcr.2019.05.004 · DOI
  33. Evans, 2016, The new assessment of soil loss by water erosion in Europe. Panagos P. et al., 2015 Environmental Science & Policy 54, 438–447—A response, Environ. Sci. Policy, 58, 11, 10.1016/j.envsci.2015.12.013 · DOI
  34. Morgan, 1998, The European Soil Erosion Model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf, Process. Landf., 23, 527, 10.1002/(SICI)1096-9837(199806)23:6<527::AID-ESP868>3.0.CO;2-5 · DOI
  35. Woodward, 1999, Method to predict cropland ephemeral gully erosion, Catena, 37, 393, 10.1016/S0341-8162(99)00028-4 · DOI
  36. Flanagan, D.C., Ascough, J.C., Nearing, M.A., and Laflen, J.M. (2001). The Water Erosion Prediction Project (WEPP) Model. Landscape Erosion and Evolution Modeling, Springer Science and Business Media LLC. · DOI
  37. Gayen, 2019, Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms, Sci. Total Environ., 668, 124, 10.1016/j.scitotenv.2019.02.436 · DOI
  38. Rahmati, 2017, Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework, Sci. Total Environ., 579, 913, 10.1016/j.scitotenv.2016.10.176 · DOI
  39. Harden, 1992, Incorporating roads and footpath in watershed-scale hydrologic and soil erosion models, Phys. Geogr., 13, 368, 10.1080/02723646.1992.10642463 · DOI
  40. Tarboton, 1992, A physical basis for drainage density, Geomorphology, 5, 59, 10.1016/0169-555X(92)90058-V · DOI
  41. Selige, 2002, Spatial prediction of soil attributes using terrain analysis and climate regionalization, Göttinger Geograph. Abh., 115, 13
  42. Panagos, 2015, The new assessment of soil loss by water erosion in Europe, Environ. Sci. Policy, 54, 438, 10.1016/j.envsci.2015.08.012 · DOI
  43. Jasiewicz, 2013, Geomorphons—A pattern recognition approach to classification and mapping of landforms, Geomorphology, 182, 147, 10.1016/j.geomorph.2012.11.005 · DOI
  44. Kiss, 2004, Determination of drainage network in digital elevation models, utilities and limitations, J. Hung. Geomath., 2, 16
  45. Bisrat, 2018, Identification of surface water storing sites using Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI), J. Nat. Resour. Dev., 8, 91, 10.5027/jnrd.v8i0.09 · DOI
  46. Dube, 2014, Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District–Zimbabwe, Phys. Chem. Earth Parts A/B/C, 67–69, 145, 10.1016/j.pce.2014.02.002 · DOI
  47. Moore, 1991, Digital terrain modelling: A review of hydrological, geomorphological, and biological applications, Hydrol. Process., 5, 3, 10.1002/hyp.3360050103 · DOI
  48. Beven, 1979, A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant, Hydrol. Sci. Bull., 24, 43, 10.1080/02626667909491834 · DOI · Paper page
  49. Breiman, 2001, Random Forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
  50. Liaw, 2001, Classification and Regression by RandomForest, R News, 2, 18
  51. Breiman, 1996, Bagging Predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 · DOI · Paper page
  52. Micheletti, 2014, Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping, Math. Geosci., 46, 33, 10.1007/s11004-013-9511-0 · DOI
  53. Nicodemus, 2011, Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures, Brief. Bioinform., 12, 369, 10.1093/bib/bbr016 · DOI
  54. Shit, P.K., Pourghasemi, H.R., and Bhunia, G.S. (2020). Modeling of Gully Erosion Based on Random Forest Using GIS and R. Gully Erosion Studies from India and Surrounding Regions, Springer International Publishing. · DOI
  55. Braun, A.C. (2021). More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses. Prog. Phys. Geogr. Earth Environ. · DOI
  56. Calle, 2010, Letter to the Editor: Stability of Random Forest importance measures, Brief. Bioinform., 12, 86, 10.1093/bib/bbq011 · DOI
  57. Ahnert, 1994, Equilibrium, scale and inheritance in geomorphology, Geomorphology, 11, 125, 10.1016/0169-555X(94)90077-9 · DOI
  58. Dotterweich, 2013, The history of human-induced soil erosion: Geomorphic legacies, early descriptions and research, and the development of soil conservation—A global synopsis, Geomorphology, 201, 1, 10.1016/j.geomorph.2013.07.021 · DOI
  59. Berking, 2010, Runoff in two semi-arid watersheds in a geoarcheological context: A case study of Naga, Sudan, and Resafa, Syria, Geoarchaeology, 25, 815, 10.1002/gea.20333 · DOI
  60. Berking, 2016, Problems Concerning Ancient Water Management in the Mediterranean, J. Anc. Stud., 6, 74
  61. Torri, D., Rossi, M., Brogi, F., Marignani, M., Bacaro, G., Santi, E., Tordoni, E., Amici, V., and Maccherini, S. (2018). Badlands and the Dynamics of Human History, Land Use, and Vegetation Through Centuries. Badlands Dynamics in a Context of Global Change, Elsevier Science. · DOI
  62. Young, 1973, The role of rainfall impact in soil detachment and transport, Water Resour. Res., 9, 1629, 10.1029/WR009i006p01629 · DOI
  63. Zhang, 2018, Identifying sediment transport capacity of raindrop-impacted overland flow within transport-limited system of interrill erosion processes on steep loess hillslopes of China, Soil Tillage Res., 184, 109, 10.1016/j.still.2018.07.007 · DOI
  64. Zheng, 2006, Effect of Vegetation Changes on Soil Erosion on the Loess Plateau, Pedosphere, 16, 420, 10.1016/S1002-0160(06)60071-4 · DOI
  65. Zhou, 2008, Effect of vegetation cover on soil erosion in a mountainous watershed, Catena, 75, 319, 10.1016/j.catena.2008.07.010 · DOI
  66. Bryan, 1997, The significance of soil piping processes: Inventory and prospect, Geomorphology, 20, 209, 10.1016/S0169-555X(97)00024-X · DOI
  67. Derbyshire, 2001, Geological hazards in loess terrain, with particular reference to the loess regions of China, Earth-Sci. Rev., 54, 231, 10.1016/S0012-8252(01)00050-2 · DOI
  68. Frechen, 2011, Late Quaternary landscape evolution in a small catchment on the Chinese Loess Plateau, Quat. Int., 234, 159, 10.1016/j.quaint.2009.12.018 · DOI
  69. Wilson, 1994, Erosional Impact of Hikers, Horses, Motorcycles, and Off-Road Bicycles on Mountain Trails in Montana, Mt. Res. Dev., 14, 77, 10.2307/3673739 · DOI
  70. Weaver, 1978, Trampling effects of hikers, motorcycles and horses in meadows and forests, J. Appl. Ecol., 15, 451, 10.2307/2402604 · DOI

Cited by

Sorted by citations

Metadata

Authors
Busch, R., Hardt, J., Nir, N., & Schütt, B.
Affiliations
Physical Geography, Institute of Geographical Sciences, Freie Universität Berlin, Malteserstraße 74-100, 12449 Berlin, Germany
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2021-05-20
Volume
13
Issue
10
Pages
2009
ISSN
2072-4292
DOI
10.3390/rs13102009
Cited by
23
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Deutsche Forschungsgemeinschaft (404354728)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review