Skip to main content

Advertisement

SpringerLink
Log in

Simulating and assessing the evolution of collapsing gullies based on cellular automata-Markov and landscape pattern metrics: a case study in Southern China

  • Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Collapsing gullies, which involve considerable erosion and extreme landform changes, frequently occur in the granite region of Southern China. Capturing the evolution of collapsing gullies is useful and effective for predicting erosion amount and landform changes. However, the evolution of collapsing gullies is too complex to simulate using conventional models. The aim of this study is to modify the traditional cellular automata (CA)-Markov model for simulating the evolution of collapsing gullies and then quantify their morphology using landscape pattern metrics.

Materials and methods

A hillslope eroded by collapsing gullies located in Longmen Town of the granite region of Southern China is used as a case study. Three digital elevation models (DEMs) were derived on 11 March 2017, 21 July 2017, and 2 December 2017 from a remotely piloted vehicle and global positioning system (GPS) real-time kinematics. Rainfall data for the corresponding time was recorded by a tipping-bucket rain gauge. Using these data, the CA-Markov model for simulating the evolution of collapsing gullies was developed, and then the most accurate one was chosen to predict the evolution on 2 December 2018. Evolution of the case study hillslope was interpreted and assessed using landscape metrics to capture the erosional trends of collapsing gullies.

Results and discussion

The area differences of the modified CA-Markov model are lower than those of the traditional model while the kappa coefficients of the modified CA-Markov model are higher than those of the traditional model; that is, the modified CA-Markov model performs better for simulating and predicting the evolution of collapsing gullies. The kappa coefficients also demonstrate that both scouring and gravity impact collapsing gullies, and scouring force is more effective than gravity. Based on the evolution prediction, the erosion amount of collapsing gullies in the subsequent year is predicted to be 904.1 m3. Spatial pattern analysis showed that the mid-lower part of a hillslope eroded by collapsing gullies will continue to be intensively eroded and the ground surface will become more fragmented.

Conclusions

The use of a modified CA-Markov model and landscape pattern metrics provide an improved and effective method for understanding the spatial and temporal variations of collapsing gullies landform and ground surface, as well as better capturing the erosional trends of collapsing gullies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Aburas MM, Ho YM, Ramli MF, Ash’aari ZH (2017) Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an analytical hierarchy process and frequency ratio. Int J Appl Earth Obs 59:65–78

    Article  Google Scholar 

  • Ballesteros Canovas JA, Stoffel M, Martin-Duque JF, Corona C, Lucia A, Bodoque JM, Montgomery DR (2017) Gully evolution and geomorphic adjustments of badlands to reforestation. Sci Rep 7:45027–45035

    Article  CAS  Google Scholar 

  • Behera MD, Borate SN, Panda SN, Behera PR, Roy PS (2012) Modelling and analyzing the watershed dynamics using cellular automata (CA)-Markov model—a geo-information based approach. J Earth Syst Sci 121:1011–1024

    Article  Google Scholar 

  • Betts HD, DeRose RC (1999) Digital elevation models as a tool for monitoring and measuring gully erosion. Int J Appl Earth Obs 1:91–101

    Article  Google Scholar 

  • Boengiu S, Vladut A, Mariescu E (2012) Conditions of gully development within Piedmont areas with examples from the western part of the Getic Piedmont. J Environ Biol 33:407–415

    Google Scholar 

  • Cavalli M, Goldin B, Comiti F, Brardinoni F, Marchi L (2017) Assessment of erosion and deposition in steep mountain basins by differencing sequential digital terrain models. Geomorphology 291:4–16

    Article  Google Scholar 

  • Chaplot V, Brown J, Dlamini P, Eustice T, Janeau J-L, Jewitt G, Lorentz S, Martin L, Nontokozo-Mchunu C, Oakes E, Podwojewski P, Revil S, Rumpel C, Zondi N (2011) Rainfall simulation to identify the storm-scale mechanisms of gully bank retreat. Agr Water Manage 98:1704–1710

    Article  Google Scholar 

  • Chen X, Yu S, Zhang Y (2013) Evaluation of spatiotemporal dynamics of simulated land use/cover in China using a probabilistic cellular automata-Markov model. Pedosphere 23:243–255

    Article  Google Scholar 

  • D’Ambrosio D, Gregorio SD, Gabriele S, Gaudio R (2001) A cellular automata model for soil erosion by water. Pergamon 26:33–39

    Google Scholar 

  • Gage EA, Cooper DJ (2017) Relationships between landscape pattern metrics, vertical structure and surface urban Heat Island formation in a Colorado suburb. Urban Ecosyst 20:1229–1238

    Article  Google Scholar 

  • Ghosh P, Mukhopadhyay A, Chanda A, Mondal P, Akhand A, Mukherjee S, Nayak SK, Ghosh S, Mitra D, Ghosh T, Hazra S (2017) Application of cellular automata and Markov-chain model in geospatial environmental modeling—a review. Remote Sens Appl Soc Environ 5:64–77

    Google Scholar 

  • Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr 63:101–112

    Article  Google Scholar 

  • Hancock GR, Willgoose GR (2011) Gully development, evolution and erosion using a landscape model. 19th International Congress on Modelling and Simulation, Perth, pp 2627–2633

    Google Scholar 

  • Jiang F, Huang Y, Wang M, Lin J, Zhao G, Ge H (2014a) Effects of rainfall intensity and slope gradient on steep colluvial deposit erosion in Southeast China. Soil Sci Soc Am J 78:1741–1752

    Article  CAS  Google Scholar 

  • Jiang F, Huang Y, Wang M, Lin J, Zhao J, Ge H (2014b) Effects of repetitive rainfalls on rill erosion of colluvial deposit in granite slope collapse. Chinese Sci Soil Water Conserv 12:1–7

    Google Scholar 

  • Kakembo V, Xanga WW, Rowntree K (2009) Topographic thresholds in gully development on the hillslopes of communal areas in Ngqushwa Local Municipality, Eastern Cape, South Africa. Geomorphology 110:188–194

    Article  Google Scholar 

  • Kamusoko C, Aniya M, Adi B, Manjoro M (2009) Rural sustainability under threat in Zimbabwe—simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Appl Geogr 29:435–447

    Article  Google Scholar 

  • Khalili AE, Raclot D, Habaeib H, Lamachère JM (2013) Factors and processes of permanent gully evolution in a Mediterranean marly environment (Cape Bon, Tunisia). Hydrolog Sci J 58:1519–1531

    Article  Google Scholar 

  • Kumar S, Radhakrishnan N, Mathew S (2013) Land use change modelling using a Markov model and remote sensing. Geomat Nat Haz Risk 5:145–156

    Article  CAS  Google Scholar 

  • Leyland J, Darby SE (2008) An empirical–conceptual gully evolution model for channelled sea cliffs. Geomorphology 102:419–434

    Article  Google Scholar 

  • Li W, Li B, Shi Y (1999) Markov-chain simulation of soil textural profiles. Geoderma 92:37–52

    Article  Google Scholar 

  • Lin J, Huang Y (2010) Review of study on formation mechanism of slope disintegration erosion and its problems. Chinese Res Soil Water Conserv 17:41–44

  • Liu R, Men C, Wang X, Xu F, Yu W (2016) Application of spatial Markov chains to the analysis of the temporal-spatial evolution of soil erosion. Water Sci Technol 74:1051–1059

    Article  Google Scholar 

  • Liu X, Yuan L (2012) A cellular automata model for the process of rill erosion. International Symposium on Geomatics for Integrated Water Resource Management, Lanzhou, pp 1–3. https://doi.org/10.1109/GIWRM.2012.6349546

    Book  Google Scholar 

  • Liu X, Zhang D (2014) Application of 3D laser scanning to monitoring the landform changes of collapsing hill and gully: a case study of Liantanggang collapsing hill and gully in the Wuhua County of Guangdong. Chinese Trop Geogr 34:133–140

    Google Scholar 

  • Liu X, Zhang D (2015a) Study on erosion process of collapsing hill and gully by three-dimensional monitoring: an example of Liantanggang in Wuhua County of Guangdong Province. Chinese J Soil Water Conserv 29:26–31

    Google Scholar 

  • Liu X, Zhang D (2015b) Temporal-spatial analyses of collapsed gully erosion based on three-dimensional laser scanning. Trans Chin Soc Agric Eng 31:204–211

    Google Scholar 

  • Marko K, Zulkarnain F, Kusratmoko E (2016) Coupling of Markov chains and cellular automata spatial models to predict land cover changes (case study: upper Ci Leungsi catchment area). IOP Conference Series: Earth and Environmental Science 47:012032. https://doi.org/10.1088/1755-1315/47/1/012032

    Article  Google Scholar 

  • Mitsova D, Shuster W, Wang X (2011) A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape Urban Plan 99:141–153

    Article  Google Scholar 

  • Mondal MS, Sharma N, Garg PK, Kappas M (2016) Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egypt J Remote Sens Space Sci 19:259–272

    Google Scholar 

  • Olsen LM, Dale VH, Foster T (2007) Landscape patterns as indicators of ecological change at Fort Benning, Georgia, USA. Landscape Urban Plan 79:137–149

    Article  Google Scholar 

  • Palmate SS, Pandey A, Mishra SK (2017) Modelling spatiotemporal land dynamics for a trans-boundary river basin using integrated cellular automata and Markov chain approach. Appl Geogr 82:11–23

    Article  Google Scholar 

  • Pastor J, Bonde J, Johnston C, Naiman RJ (1993) Markovian analysis of the spatially dependent dynamics of beaver ponds. Lectures on Mathematics in the Life Science 23:5–27

    Google Scholar 

  • Sang L, Zhang C, Yang J, Zhu D, Yun W (2011) Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model 54:938–943

    Article  Google Scholar 

  • Shruthi RBV, Kerle N, Jetten V (2011) Object-based gully feature extraction using high spatial resolution imagery. Geomorphology 134:260–268

    Article  Google Scholar 

  • Sidorchuk A (2006) Stages in gully evolution and self-organized criticality. Earth Surf Proc Land 31:1329–1344

    Article  Google Scholar 

  • Skaggs R, Ghosh S (1999) Assessing changes in soil erosion rates: a Markov chain analysis. J Agr Appl Econ 31:611–622

    Article  Google Scholar 

  • Stavi I, Perevolotsky A, Avni Y (2010) Effects of gully formation and headcut retreat on primary production in an arid rangeland: natural desertification in action. J Arid Environ 74:221–228

    Article  Google Scholar 

  • Xiong L, Tang G, Zhu AX, Yuan B, Lu B, Dang T (2017) Paleotopographic controls on modern gully evolution in the loess landforms of China. Sci China Earth Sci 60:438–451

    Article  Google Scholar 

  • Yeh CT, Huang SL (2009) Investigating spatiotemporal patterns of landscape diversity in response to urbanization. Landscape Urban Plan 93:151–162

    Article  Google Scholar 

  • Zhang D, Liu X (2011) Evolution and phases divison of collapsed gully erosion landform. Chinese J Subtrop Resour Environ 6:23–28

    Google Scholar 

  • Zhou D, Lin Z, Liu L (2012) Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Sci Total Environ 439:260–274

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Thanks are also extended to Zach Zopp, Yi Wang, and Yu Li for their valuable suggestions.

Funding

This work was supported by the National Natural Science Foundation of China (41601557 and 41571272). Dr. Xiang Ji is a visiting scholar at the University of Wisconsin-Madison with financial support provided by the Chinese Government Scholarship Program.

Author information

Authors and Affiliations

  1. College of Resources and Environment, Fujian Agriculture and Forestry University, 15 Shangxiadian Road, Fuzhou, 350002, China

    Xiang Ji, Jinshi Lin, Fangshi Jiang, Shaoxin Li, Mingming Yu & Yanhe Huang

  2. Department of Biological Systems Engineering, University of Wisconsin–Madison, 460 Henry Mall, Madison, WI, 53706, USA

    Anita Thompson

Authors
  1. Xiang Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anita Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinshi Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fangshi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shaoxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mingming Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yanhe Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanhe Huang.

Additional information

Responsible editor: Jun Zhou

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ji, X., Thompson, A., Lin, J. et al. Simulating and assessing the evolution of collapsing gullies based on cellular automata-Markov and landscape pattern metrics: a case study in Southern China. J Soils Sediments 19, 3044–3055 (2019). https://doi.org/10.1007/s11368-019-02281-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11368-019-02281-y

Keywords

Search

Navigation