Advertisement

Chinese Geographical Science

, Volume 27, Issue 3, pp 415–430 | Cite as

An object-based approach for two-level gully feature mapping using high-resolution DEM and imagery: a case study on hilly loess plateau region, China

  • Kai Liu
  • Hu Ding
  • Guoan TangEmail author
  • A-Xing Zhu
  • Xin Yang
  • Sheng Jiang
  • Jianjun Cao
Article

Abstract

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model (DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.

Keywords

object-based image analysis gully feature hierarchical mapping gully erosion Digital Elevation Model (DEM) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgment

We thank Dr. WANG Lei from Northwest University for assisting in obtaining the high resolution DEM. Special thanks are also given to NA Jiaming and LIU Yiwen for their helpful comments on the manuscript.

References

  1. Anders N S, Seijmonsbergen A C, Bouten W, 2011. Segmentation optimization and stratified object-based analysis for semiautomated geomorphological mapping. Remote Sensing of Environment, 115(12): 2976–2985. doi: 10.1016/j.rse.2011.05. 007CrossRefGoogle Scholar
  2. Baatz M, Schäpe A, 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In Strobl J (eds.). Angewandte Geographische Informations-Verarbeitung XII. Karlsruhe, Germany: Wichmann Verlag, 12–23.Google Scholar
  3. Belgiu M, Drăguţ L, 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114(4): 24–31. doi: 10.1016/j.isprsjprs.2016.01.011CrossRefGoogle Scholar
  4. Blaschke T, Hay G J, Kelly M et al., 2014. Geographic objectbased image analysis: towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87(1): 180–191. doi: 10.1016/j.isprsjprs.2013.09.014CrossRefGoogle Scholar
  5. Blaschke T, Strobl J, 2001. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS, 6(1): 12–17Google Scholar
  6. Bocco G, Valenzuela C R, 1993. Integrating satellite-remote sensing and geographic information systems technologies in gully erosion research. Remote Sensing Reviews, 7(3-4): 233–240. doi: 10.1080/02757259309532179CrossRefGoogle Scholar
  7. Breiman L, 2011. Random forests. Machine Learning, 45(1): 5–32.CrossRefGoogle Scholar
  8. Casalí J, López J J, Giráldez J V, 1999. Ephemeral gully erosion in southern Navarra (Spain). Catena, 36(1): 65–84. doi: 10.1016/S0341-8162(99)00013-2CrossRefGoogle Scholar
  9. Castillo C, Pérez R, James M R et al., 2012. Comparing the accuracy of several field methods for measuring gully erosion. Soil Science Society of America Journal, 76(4): 1319–1332. doi: 10.2136/sssaj2011.0390CrossRefGoogle Scholar
  10. Clinton N, Holt A, Scarborough J et al., 2010. Accuracy assessment measures for object-based image segmentation goodness. Photogrammetric Engineering and Remote Sensing, 76(3): 289–299. doi: 10.14358/PERS.76.3.289CrossRefGoogle Scholar
  11. d’Oleire-Oltmanns S, Eisank C, Drăguţ L et al., 2013. An object-based workflow to extract landforms at multiple scales from two distinct data types. IEEE Transactions on Geoscience and Remote Sensing Letters, 10(4): 947–951. doi: 10.1109/LGRS.2013.2254465CrossRefGoogle Scholar
  12. d’Oleire-Oltmanns S, Marzolff I, Tiede D et al., 2014. Detection of gully-affected areas by applying object-based image analysis (OBIA) in the region of Taroudannt, Morocco. Remote Sensing, 6(9): 8287–8309. doi: 10.3390/rs6098287CrossRefGoogle Scholar
  13. Drăguţ L, Csillik O, Eisank C et al., 2014. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88(2): 119–127. doi: 10.1016/j.isprsjprs. 2013.11.018Google Scholar
  14. Drăguţ L, Eisank C, 2012. Automated object-based classification of topography from SRTM data. Geomorphology, 141(3): 21–33. doi: 10.1016/j.geomorph.2011.12.001Google Scholar
  15. Drăguţ L, Eisank C, Strasser T. Local variance for multi-scale analysis in geomorphometry. Geomorphology, 2011, 130(3): 162–172. doi: 10.1016/j.geomorph.2011.03.011Google Scholar
  16. Drăguţ L, Tiede D, Levick S R, 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859–871. doi: 10.1080/13658810 903174803CrossRefGoogle Scholar
  17. Duro D C, Franklin S E, Dubé M G, 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118(3): 259–272. doi: 10.1016/j.rse. 2011.11.020CrossRefGoogle Scholar
  18. Fadul H M, Salih A A, Imad-eldin A A et al., 1999. Use of remote sensing to map gully erosion along the Atbara River, Sudan. International Journal of Applied Earth Observation and Geoinformation, 1(3): 175–180CrossRefGoogle Scholar
  19. Gao H, Li Z, Jia L et al., 2016. Capacity of soil loss control in the Loess Plateau based on soil erosion control degree. Journal of Geographical Sciences, 26(4): 457–472. doi: 10.1007/s11442-016-1279-yCrossRefGoogle Scholar
  20. Gómez-Gutiérrez Á, Conoscenti C, Angileri S E et al., 2015. Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations. Natural Hazards, 79(1): 291–314.CrossRefGoogle Scholar
  21. He Fuhong, Gao Bingjian, Wang Huanzhi et al., 2013. Study on the relationship between gully erosion and topographic factors based on GIS in small watershed of Jiaodong Peninsula. Geographical Research, 32(10): 1856–1864. (in Chinese)Google Scholar
  22. Ionita I, Fullen M A, Zgłobicki W et al., 2015. Gully erosion as a natural and human-induced hazard. Natural Hazards, 79(1): 1–5. doi: 10.1007/s11069-015-1935-zCrossRefGoogle Scholar
  23. Jiang S, Tang G, Liu K, 2015. A new extraction method of loess shoulder-line based on Marr-Hildreth operator and terrain mask. PloS One, 10(4): e0123804. doi: 10.1371/journal.pone. 0123804CrossRefGoogle Scholar
  24. Karami A, Khoorani A, Nuhegar A et al., 2015. Gully erosion mapping using object-based and pixel-based image classification methods. Environmental & Engineering Geoscience, 21(2): 101–110. doi: 10.2113/gseegeosci.21.2.101CrossRefGoogle Scholar
  25. Knight J, Spencer J, Brooks A et al., 2007. Large-area, highresolution remote sensing based mapping of alluvial gully erosion in Australia’s tropical rivers. Fifth Australian Stream Management Conference, 199–204.Google Scholar
  26. Kurtz C, Stumpf A, Malet J P et al., 2014. Hierarchical extraction of landslides from multiresolution remotely sensed optical images. ISPRS Journal of Photogrammetry and Remote Sensing, 87(1): 122–136. doi: 10.1016/j.isprsjprs.2013.11.003CrossRefGoogle Scholar
  27. Li Z, Zhang Y, Zhu Q et al., 2017. A gully erosion assessment model for the Chinese Loess Plateau based on changes in gully length and area. Catena, 148(1): 195–203. doi: 10.1016/j.Catena.2016.04.018CrossRefGoogle Scholar
  28. Liu K, Ding H, Tang G, et al., 2016. Detection of catchment-scale gully-affected areas using Aerial Vehicle (UAV) on the Chinese Loess Plateau. ISPRS International Journal of Geo-Information, 5(12): 238. doi: 10.3390/ijgi5120238CrossRefGoogle Scholar
  29. Liu Y, Bian L, Meng Y et al., 2012. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 68(3): 144–156. doi: 10.1016/j.isprsjprs.2012.01.007CrossRefGoogle Scholar
  30. Lu Guonian, Qian Yadong, Chen Zhongming, 1998. Study of automated extraction of shoulder line of valley from grid digital elevation model. Scientia Geographica Sinica, 18(6): 567–573. (in Chinese)Google Scholar
  31. Lucieer A, de Jong S, Turner D, 2014. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in Physical Geography, 38(1): 97–116. doi: 10.1177/030913 3313515293CrossRefGoogle Scholar
  32. Machado G, Mendoza M R, Corbellini L G, 2015. What variables are important in predicting bovine viral diarrhea virus? A random forest approach. Veterinary Research, 46(7): 1–15. doi: 10.1186/s13567-015-0219-7Google Scholar
  33. Martha T R, Kerle N, Van Westen C J et al., 2011. Segment optimization and data-driven thresholding for knowledgebased landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12): 4928–4943. doi: 10.1109/TGRS.2011.2151866CrossRefGoogle Scholar
  34. McInnes J, Vigiak O, Roberts A M, 2011. Using Google Earth to map gully extent in the West Gippsland region (Victoria, Australia). International Congress on Modelling and Simulation, 49: 3370–3376Google Scholar
  35. Myint S W, Gober P, Brazel A et al., 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5): 1145–1161. doi: 10.1016/j.rse.2010.12. 017CrossRefGoogle Scholar
  36. Poesen J, Nachtergaele J, Verstraeten G et al., 2003. Gully erosion and environmental change: importance and research needs. Catena, 50(2): 91–133. doi: 10.1016/S0341-8162(02) 00143-1CrossRefGoogle Scholar
  37. Puissant A, Rougier S, Stumpf A 2014. Object-oriented mapping of urban trees using Random Forest classifiers. International Journal of Applied Earth Observation and Geoinformation, 26(2): 235–245. doi: 10.1016/j.jag.2013.07.002CrossRefGoogle Scholar
  38. Shruthi R B V, Kerle N, Jetten V et al., 2014. Object-based gully system prediction from medium resolution imagery using Random Forests. Geomorphology, 216(7): 283–294. doi: 10.1016/j.geomorph.2014.04.006CrossRefGoogle Scholar
  39. Shruthi R B V, Kerle N, Jetten V et al., 2015. Quantifying temporal changes in gully erosion areas with object oriented analysis. Catena, 128(5): 262–277. doi: 10.1016/j.Catena. 2014.01.010CrossRefGoogle Scholar
  40. Shruthi R B V, Kerle N, Jetten V, 2011. Object-based gully feature extraction using high spatial resolution imagery. Geomorphology, 134(3): 260–268. doi: 10.1016/j.geomorph. 2011.07.003CrossRefGoogle Scholar
  41. Stumpf A, Kerle N 2011. Object-oriented mapping of landslides using Random Forests. Remote Sensing of Environment, 115(10): 2564–2577. doi: 10.1016/j.rse.2011.05.013CrossRefGoogle Scholar
  42. Tarboton D G, 1997. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research, 33(2): 309–319. doi: 10.1029/96WR03137CrossRefGoogle Scholar
  43. Tarolli P, 2014. High-resolution topography for understanding Earth surface processes: opportunities and challenges. Geomorphology, 216(7): 295–312. doi: 10.1016/j.geomorph. 2014.03.008CrossRefGoogle Scholar
  44. Valentin C, Poesen J, Li Y, 2005. Gully erosion: impacts, factors and control. Catena, 63(2): 132–153. doi: 10.1016/j.Catena. 2005.06.001CrossRefGoogle Scholar
  45. Vrieling A 2006. Satellite remote sensing for water erosion assessment: a review. Catena, 65(1): 2–18. doi: 10.1016/j.Catena.2005.10.005CrossRefGoogle Scholar
  46. Vrieling A, Rodrigues S C, Bartholomeus H et al., 2007. Automatic identification of erosion gullies with ASTER imagery in the Brazilian Cerrados. International Journal of Remote Sensing, 28(12): 2723–2738. doi: 10.1080/01431160 600857469CrossRefGoogle Scholar
  47. Wang T, He F, Zhang A et al., 2014. A quantitative study of gully erosion based on object-oriented analysis techniques: a case study in Beiyanzikou catchment of Qixia, Shandong, China. The Scientific World Journal, (4): 417325. doi: 10.1155/2014/417325Google Scholar
  48. Woodcock C E, Strahler A H, 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21(3): 311–332. doi: 10.1016/0034-4257(87)90015-0CrossRefGoogle Scholar
  49. Wu Y, Cheng H, 2005. Monitoring of gully erosion on the Loess Plateau of China using a global positioning system. Catena, 63(2): 154–166. doi: 10.1016/j.catena.2005.06.002CrossRefGoogle Scholar
  50. Yan Yechao, Zhang Shuwen, Li Xiaoyan et al., 2005. Temporal and spatial variation of erosion gullies in Kebai black soil region of Heilongjiang during the past 50 years. Acta Geographica Sinica, 60(6): 1016–1020. (in Chinese)Google Scholar
  51. Yan Yechao, Zhang Shuwen, Yue Shuping, 2006. Application of Corona and Spot imagery on erosion gully research in typical black soil regions of Northeast China. Resources Science, 28(6): 154–160. (in Chinese)Google Scholar
  52. Yang Feng, Zhou Yi, Cheng Min, 2016. Loess shoulder-line constrained method for waterworn gullies extraction on loess plateau. Mountain Research, 34(4): 504–510. (in Chinese)Google Scholar
  53. Yu B L, Shu S, Liu H X et al., 2014. Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China. International Journal of Geographical Information Science, 28(11): 2328–2355. doi: 10.1080/13658816.2014.922186CrossRefGoogle Scholar
  54. Zhang Jiao, Zheng Fenli, Wen Leilei et al., 2011. Methodology of dynamic monitoring gully erosion process using 3D laser scan technology. Bulletin of Soil and Water Conservation, 31(6): 89–94. (in Chinese)Google Scholar
  55. Zhang Shuwen, Li Fei, Li Tianqi et al., 2015. Remote sensing monitoring of gullies on a regional scale: a case study of Kebai region in Heilongjiang Province, China. Chinese Geographical Science, 25(5): 602–611. doi: 10.1007/s11769-015-0780-zCrossRefGoogle Scholar
  56. Zhang Wenjie, Cheng Weiming, Li Baolin et al., 2014. The Relationship between gully erosion and geomorphological factors in the hill and ravine region of the Loess Plateau. Journal of Geo-information Sciences, 1(1): 87–94. (in Chinese)Google Scholar
  57. Zheng F, Wang B 2014. Soil erosion in the Loess Plateau region of China. In: Tsunekawa et al. (eds.). Restoration and Development of the Degraded Loess Plateau, China. Springer Japan, 77–92CrossRefGoogle Scholar
  58. Zheng Zhenmin, Fu Bojie, Feng Xiaoming, 2016. GIS-based analysis for hotspot identification of tradeoff between ecosystem services: a case study in Yanhe Basin, China. Chinese Geographical Science, 26(4): 1–12. doi: 10.1007/s 11769-016-0816-zCrossRefGoogle Scholar
  59. Zhou Y, Tang G, Yang X et al., 2010. Positive and negative terrains on northern Shaanxi Loess Plateau. Journal of Geographical Sciences, 20(1): 64–76. doi: 10.1007/s11442-010-0064-6CrossRefGoogle Scholar
  60. Zhou Yi, Tang Guoan, Xi Yu, et al., 2013. A shoulder-lines connection algorithm using improved snake model. Geomatics and Information Science of Wuhan University, 38(1): 82–85. (in Chinese)Google Scholar
  61. Zhu T X, 2012. Gully and tunnel erosion in the hilly Loess Plateau region, China. Geomorphology, 153: 144–155. doi: 10.1016/j.geomorph.2012.02.019CrossRefGoogle Scholar
  62. Zhu Y, Cai Q, 2014. Rill erosion processes and its factors in different soils. In: Li Y et al. (eds). Gully Erosion Under Global Change. Chengdu, China: Sichuan Science and Technology Press, 96–108.Google Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Kai Liu
    • 1
    • 2
  • Hu Ding
    • 1
    • 2
  • Guoan Tang
    • 1
    • 2
    Email author
  • A-Xing Zhu
    • 3
    • 4
  • Xin Yang
    • 1
    • 2
  • Sheng Jiang
    • 1
    • 2
  • Jianjun Cao
    • 1
    • 5
  1. 1.Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of EducationNanjingChina
  2. 2.State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)NanjingChina
  3. 3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
  4. 4.Department of GeographyUniversity of Wisconsin-MadisonMadisonUSA
  5. 5.School of Environmental ScienceNanjing Xiaozhuang UniversityNanjingChina

Personalised recommendations