LIU Kai, DING Hu, TANG Guoan, ZHU A-Xing, YANG Xin, JIANG Sheng, CAO Jianjun. 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[J]. Chinese Geographical Science, 2017, 27(3): 415-430. doi: 10.1007/s11769-017-0874-x
Citation: LIU Kai, DING Hu, TANG Guoan, ZHU A-Xing, YANG Xin, JIANG Sheng, CAO Jianjun. 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[J]. Chinese Geographical Science, 2017, 27(3): 415-430. doi: 10.1007/s11769-017-0874-x

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

doi: 10.1007/s11769-017-0874-x
Funds:  Under the auspices of Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China (No. 41271438, 41471316, 41401440, 41671389)
More Information
  • Corresponding author: TANG Guoan. E-mail: tangguoan@njnu.edu.cn
  • Received Date: 2016-08-12
  • Rev Recd Date: 2016-12-08
  • Publish Date: 2017-06-27
  • 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.
  • [1] Anders N S, Seijmonsbergen A C, Bouten W, 2011. Segmentation optimization and stratified object-based analysis for semi- automated geomorphological mapping. Remote Sensing of Environment, 115(12): 2976-2985. doi: 10.1016/j.rse.2011.05. 007
    [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.
    [3] Belgiu M, Dr?gu? 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.2016.01.011
    [4] Blaschke T, Hay G J, Kelly M et al., 2014. Geographic object- based image analysis: towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87(1): 180-191. doi:  10.1016/j.isprsjprs.2013.09.014
    [5] Blaschke T, Strobl J, 2001. What's wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS, 6(1): 12-17
    [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:  10.1080/02757259309532179
    [7] Breiman L, 2011. Random forests. Machine Learning, 45(1): 5-32.
    [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)00013-2
    [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/sssaj2011.0390
    [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.76.3.289
    [11] d'Oleire-Oltmanns S, Eisank C, Dr?gu? 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.2013.2254465
    [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/rs6098287
    [13] Dr?gu? 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. 2013.11.018
    [14] Dr?gu? L, Eisank C, 2012. Automated object-based classification of topography from SRTM data. Geomorphology, 141(3): 21-33. doi:  10.1016/j.geomorph.2011.12.001
    [15] Dr?gu? L, Eisank C, Strasser T. Local variance for multi-scale analysis in geomorphometry. Geomorphology, 2011, 130(3): 162-172. doi:  10.1016/j.geomorph.2011.03.011
    [16] Dr?gu? 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 903174803
    [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. 2011.11.020
    [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): 175-180
    [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-y
    [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.
    [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)
    [22] Ionita I, Fullen M A, Zg?obicki 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-z
    [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. 0123804
    [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.21.2.101
    [25] Knight J, Spencer J, Brooks A et al., 2007. Large-area, high- resolution remote sensing based mapping of alluvial gully erosion in Australia's tropical rivers. Fifth Australian Stream Management Conference, 199-204.
    [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.2013.11.003
    [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.2016.04.018
    [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/ijgi5120238
    [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.2012.01.007
    [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)
    [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 3313515293
    [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/s13567-015-0219-7
    [33] Martha T R, Kerle N, Van Westen C J et al., 2011. Segment optimization and data-driven thresholding for knowledge- based landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12): 4928-4943. doi:  10.1109/TGRS.2011.2151866
    [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: 3370-3376
    [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. 017
    [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) 00143-1
    [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.2013.07.002
    [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.2014.04.006
    [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. 2014.01.010
    [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. 2011.07.003
    [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.2011.05.013
    [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: 10.1029/96 WR03137
    [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. 2014.03.008
    [44] Valentin C, Poesen J, Li Y, 2005. Gully erosion: impacts, factors and control. Catena, 63(2):132-153. doi: 10.1016/j.Catena. 2005.06.001
    [45] Vrieling A 2006. Satellite remote sensing for water erosion assessment: a review. Catena, 65(1): 2-18. doi: 10.1016/j. Catena.2005.10.005
    [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 600857469
    [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/ 417325
    [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)90015-0
    [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.2005.06.002
    [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)
    [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)
    [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)
    [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:  10.1080/13658816.2014.922186
    [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)
    [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-z
    [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)
    [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, 77-92
    [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-z
    [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- 010-0064-6
    [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)
    [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.2012.02.019
    [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
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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

doi: 10.1007/s11769-017-0874-x
Funds:  Under the auspices of Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China (No. 41271438, 41471316, 41401440, 41671389)
    Corresponding author: TANG Guoan. E-mail: tangguoan@njnu.edu.cn

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.

LIU Kai, DING Hu, TANG Guoan, ZHU A-Xing, YANG Xin, JIANG Sheng, CAO Jianjun. 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[J]. Chinese Geographical Science, 2017, 27(3): 415-430. doi: 10.1007/s11769-017-0874-x
Citation: LIU Kai, DING Hu, TANG Guoan, ZHU A-Xing, YANG Xin, JIANG Sheng, CAO Jianjun. 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[J]. Chinese Geographical Science, 2017, 27(3): 415-430. doi: 10.1007/s11769-017-0874-x
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