Quantitative Attribution Analysis of the Spatial Differentiation of Gully Erosion in the Black Soil Region of Northeast China

Gully erosion is the major soil erosion type in the black soil region of Northeast China. However, studies on multifactor synthesis at a large scale and on the driving mechanism of spatial differentiation are still relatively lacking for gully erosion in this region. In this study, the simulation of gully erosion and its quantitative attribution analysis have been conducted in the Sancha River catchment in Northeast China, based on high-resolution satellite imagery mapping and the geodetector method. A total of 18 indicators in 6 categories, including topography, climate and weather, soil properties, lithology, land use, have been taken into consideration. The influence of each influencing factor and its interactive influence on gully erosion were quantitatively evaluated. The results showed that at the large catchment scale, the submeter images had a strong capacity for the recognition of a permanent gully and obtained satisfactory results. According to the results of the geodetector, lithology and soil type are the main factors that affect the spatial differentiation of gully erosion in the Sancha River basin, because their interpretation power for gully density and gully intensity was close to 10%. The lithology belonged to gray–white matter rhyolite, spherulite rhyolite, and crystal clastic tuff, with the highest gully density and intensity. The interpretation power of the secondary factors, including rainfall erosivity, watershed area, elevation, soil erodibility, land use pattern, slope, and distance from the river, amounted to more than 1%. The interactions among most driving factors showed nonlinear enhancement. The influence of the interaction between lithology and soil type appeared to be the largest. In particular, the lithology of different soil types accounted for 28.7% and 32.5% of the gully density and gully intensity. The interaction of factors had a stronger influence on the spatial differentiation of gully erosion than any single factor.

[1]  Hao Li,et al.  A case‐study on history and rates of gully erosion in Northeast China , 2021, Land Degradation & Development.

[2]  Yonghua Zhao,et al.  Quantitative Analysis of Factors Influencing Spatial Distribution of Soil Erosion Based on Geo-Detector Model under Diverse Geomorphological Types , 2021, Land.

[3]  M. Meadows,et al.  Effect of Land Use Change on Gully Erosion Density in the Black Soil Region of Northeast China From 1965 to 2015: A Case Study of the Kedong County , 2021, Frontiers in Environmental Science.

[4]  Wei Chen,et al.  GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran , 2020, Remote. Sens..

[5]  Wei Chen,et al.  GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models , 2020, Applied Sciences.

[6]  Xiao Wang,et al.  Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China , 2020, Remote. Sens..

[7]  X. Duan,et al.  The gully erosion rates in the black soil region of northeastern China: Induced by different processes and indicated by different indexes , 2019, CATENA.

[8]  Qiang Xu,et al.  Evaluation of gully head retreat and fill rates based on high-resolution satellite images in the loess region of China , 2019, Environmental Earth Sciences.

[9]  H. Pourghasemi,et al.  Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. , 2019, The Science of the total environment.

[10]  Zhengfu Bian,et al.  Analysis of the Development of an Erosion Gully in an Open-Pit Coal Mine Dump During a Winter Freeze-Thaw Cycle by Using Low-Cost UAVs , 2019, Remote. Sens..

[11]  Biswajeet Pradhan,et al.  A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran) , 2019, Sensors.

[12]  Yongsheng Wang,et al.  Geodetection analysis of the driving forces and mechanisms of erosion in the hilly-gully region of northern Shaanxi Province , 2019, Journal of Geographical Sciences.

[13]  Yanfang Hao,et al.  Gully Erosion Induced by Snowmelt in Northeast China: A Case Study , 2019, Sustainability.

[14]  Saro Lee,et al.  Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. , 2019, The Science of the total environment.

[15]  Jiang-bo Gao,et al.  Quantitative attribution analysis of soil erosion in different geomorphological types in karst areas: Based on the geodetector method , 2019, Journal of Geographical Sciences.

[16]  H. Pourghasemi,et al.  Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. , 2017, The Science of the total environment.

[17]  Shuwen Zhang,et al.  Gully erosion regionalization of black soil area in northeastern China , 2017, Chinese Geographical Science.

[18]  Qing Wang,et al.  Gully Erosion Mapping and Monitoring at Multiple Scales Based on Multi-Source Remote Sensing Data of the Sancha River Catchment, Northeast China , 2016, ISPRS Int. J. Geo Inf..

[19]  Shuwen Zhang,et al.  Integrated Use of GCM, RS, and GIS for the Assessment of Hillslope and Gully Erosion in the Mushi River Sub-Catchment, Northeast China , 2016 .

[20]  J. Poesen,et al.  How fast do gully headcuts retreat , 2016 .

[21]  J. Svenning,et al.  Reply to Feeley and Rehm: Land-use intensification increases risk of species losses from climate change , 2015, Proceedings of the National Academy of Sciences.

[22]  Pierre Karrasch,et al.  Measuring gullies by synergetic application of UAV and close range photogrammetry - A case study from Andalusia, Spain , 2015 .

[23]  Kun Bu,et al.  Remote sensing monitoring of gullies on a regional scale: A case study of Kebai region in Heilongjiang Province, China , 2015, Chinese Geographical Science.

[24]  Norman Kerle,et al.  Quantifying temporal changes in gully erosion areas with object oriented analysis , 2015 .

[25]  Yan-sui Liu,et al.  Sand stabilization effect of feldspathic sandstone during the fallow period in Mu Us Sandy Land , 2015, Journal of Geographical Sciences.

[26]  P. Fiener,et al.  Use and misuse of the K factor equation in soil erosion modeling: An alternative equation for determining USLE nomograph soil erodibility values , 2014 .

[27]  Panos Panagos,et al.  Soil erodibility in Europe: a high-resolution dataset based on LUCAS. , 2014, The Science of the total environment.

[28]  J. Poesen,et al.  A review of topographic threshold conditions for gully head development in different environments , 2014 .

[29]  J. A. Gomez,et al.  Comparing the accuracy of several field methods for measuring gully erosion , 2012 .

[30]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..

[31]  Jeffrey J. McDonnell,et al.  Connectivity at the hillslope scale: identifying interactions between storm size, bedrock permeability, slope angle and soil depth. , 2009 .

[32]  Kevin Bishop,et al.  Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model , 2009 .

[33]  Baoyuan Liu,et al.  Development of gullies and sediment production in the black soil region of northeastern China , 2008 .

[34]  Baoyuan Liu,et al.  Short-term gully retreat rates over rolling hill areas in black soil of Northeast China , 2007 .

[35]  L. H. Cammeraat,et al.  Identification of vulnerable areas for gully erosion under different scenarios of land abandonment in Southeast Spain , 2007 .

[36]  I. Ioniţă,et al.  Gully development in the Moldavian Plateau of Romania , 2006 .

[37]  Jesús Álvarez-Mozos,et al.  Accuracy of methods for field assessment of rill and ephemeral gully erosion , 2006 .

[38]  J. Poesen,et al.  Gully erosion: Impacts, factors and control , 2005 .

[39]  Yongqiu Wu,et al.  Monitoring of gully erosion on the Loess Plateau of China using a global positioning system , 2005 .

[40]  Huan Liu,et al.  Discretization: An Enabling Technique , 2002, Data Mining and Knowledge Discovery.

[41]  Jean Poesen,et al.  Short-term bank gully retreat rates in Mediterranean environments , 2001 .

[42]  C. F. Lee,et al.  Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .

[43]  Thomas Blaschke,et al.  Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment , 2020, Remote. Sens..

[44]  Li Zhen,et al.  Estimating gully development rates in Hilly Loess Region of Western Shanxi province based on QuickBird images , 2012 .

[45]  E. Rotigliano,et al.  Multi parametric GIS analysis to assess gully erosion susceptibility : a test in southern Sicily, Italy , 2011 .

[46]  Yilan Liao,et al.  Risk assessment of human neural tube defects using a Bayesian belief network , 2010 .

[47]  Zhu Meng-jun.  Preliminary Results of Gully Erosion by Remote Sensing Monitoring in Weiliantan,Gonghe Basin,Qinghai Province , 2009 .

[48]  Zhang Shuwen,et al.  Application of Corona and Spot Imagery on Erosion Gully Research in Typical Black Soil Regions of Northeast China , 2006 .

[49]  J. Poesen,et al.  Gully erosion and environmental change: importance and research needs , 2003 .