Geomorphic factors influencing the spatial distribution of eroded Chernozems in automated digital soil erosion mapping

被引:3
|
作者
Buryak, Zhanna A. [1 ]
Ukrainsky, Pavel A. [1 ]
Gusarov, Artyom, V [2 ]
Lukin, Sergey, V [3 ]
Beylich, Achim A. [4 ]
机构
[1] Belgorod State Natl Res Univ, Fed & Reg Ctr Aerosp & Ground Monitoring Objects &, Belgorod 308015, Russia
[2] Kazan Fed Univ, Inst Geol & Petr Technol, Kazan 420008, Russia
[3] Belgorod State Natl Res Univ, Inst Earth Sci, Belgorod 308015, Russia
[4] Geomorphol Field Lab GFL, N-7584 Selbustrand, Norway
基金
俄罗斯科学基金会;
关键词
Soil erosion; Voronic and Vermic Chernozems; Vorony -Calcic Chernozems; Slope; Relief; Morphometry; DEM; Ordinal regression; Central Russian Upland; Belgorod Oblast; LAND-USE; DEGRADATION; REGRESSION; CLIMATE; RATES; MODELS; TRENDS;
D O I
10.1016/j.geomorph.2023.108863
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Among the factors influencing the intensity of soil erosion by water, the relief sets the basic conditions for the occurrence of erosion-sedimentation processes: the geometry of the contact surface with water runoff and its primary spatial characteristics. We developed a model that describes the categories of the intensity of soil erosion by water only by geomorphic parameters using a DEM. The study was conducted in arable soils in the southeastern part of the Central Russian Upland, namely, typical Chernozems (TCh) in the forest-steppe zone and ordinary Chernozems (OCh) of the northern steppe of the temperate climate zone. Based on 1146 ground soilerosion survey points, the relationship between the category of soil erosion intensity and terrain parameters (slope steepness, slope length and exposure, topographic position index, and slope profile curvature, etc.) was analyzed. The results of the revealed dependencies for the studied soils made it possible to develop prognostic models using ordinal logistic regression with an assessment of their accuracy. It was found that the model for OCh soils shows in all parameters a stronger relationship between geomorphic factors and the category of soil erosion intensity than the model for TCh soils. The regression model for OCh exceeds the model for TCh by 12 % in overall accuracy. For both soils, eroded areas are determined with much less accuracy (50-60 %) than noneroded areas (80-95 %). Based on the modeling results, maps of soil erosion by water were constructed, where belonging to the category of soil erosion intensity was determined by the maximum probability. It is also shown that soil erosion intensity modeling based only on a set of geomorphic predictors is not inferior in accuracy to the conventional visual-expert cartographic method and can be a more objective and efficient alternative in automated digital soil erosion mapping.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Spatial distribution of soil organic carbon and its influencing factors at different soil depths in a semiarid region of Chin
    Wu, Lizhi
    Li, Long
    Yao, Yunfeng
    Qin, Fucang
    Guo, Yuefeng
    Gao, Yuhan
    Zhang, Meili
    ENVIRONMENTAL EARTH SCIENCES, 2017, 76 (19)
  • [32] Exploring the spatial distribution and influencing factors of soil PH value of cultivated land in Sichuan Province
    Li Wang
    Wenying Xiong
    Hang Chen
    Fei Meng
    Yongzhong Tan
    Environmental Earth Sciences, 2024, 83
  • [33] Spatial distribution and influencing factors of urban soil organic carbon stocks in Xi'an City, China
    Zhenwen Fang
    Sha Zhou
    Shaohong Zhang
    Wenchao Xing
    Xiaoling Feng
    Qiaoling Yang
    Fazhu Zhao
    Kang Liu
    Jun Wang
    Urban Ecosystems, 2023, 26 : 677 - 688
  • [34] Influencing Factors on Bioavailability and Spatial Distribution of Soil Selenium in Dry Semi-Arid Area
    Farooq, Muhammad Raza
    Zhang, Zezhou
    Yuan, Linxi
    Liu, Xiaodong
    Rehman, Abdul
    Banuelos, Gary S.
    Yin, Xuebin
    AGRICULTURE-BASEL, 2023, 13 (03):
  • [35] Spatial Distribution and Influencing Factors of Soil Fungi in a Degraded Alpine Meadow Invaded by Stellera chamaejasme
    Liu, Yongmei
    Zhao, Fan
    Wang, Lei
    He, Wei
    Liu, Jianhong
    Long, Yongqing
    AGRICULTURE-BASEL, 2021, 11 (12):
  • [36] Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector
    Qiao, Pengwei
    Yang, Sucai
    Lei, Mei
    Chen, Tongbin
    Dong, Nan
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 664 : 392 - 413
  • [37] Spatial distribution and influencing factors of urban soil organic carbon stocks in Xi'an City, China
    Fang, Zhenwen
    Zhou, Sha
    Zhang, Shaohong
    Xing, Wenchao
    Feng, Xiaoling
    Yang, Qiaoling
    Zhao, Fazhu
    Liu, Kang
    Wang, Jun
    URBAN ECOSYSTEMS, 2023, 26 (03) : 677 - 688
  • [38] Exploring the spatial distribution and influencing factors of soil PH value of cultivated land in Sichuan Province
    Wang, Li
    Xiong, Wenying
    Chen, Hang
    Meng, Fei
    Tan, Yongzhong
    ENVIRONMENTAL EARTH SCIENCES, 2024, 83 (01)
  • [39] Soil Factors Influencing Nematode Spatial Variability in Soybean
    Brito Freitas, Jose Roberto
    Moitinho, Mara Regina
    Teixeira, Daniel De Bortoli
    Bicalho, Elton da Silva
    da Silva Junior, Joao Fernandes
    Siqueira, Diego Silva
    Figueiredo Barbosa, Bruno Flavio
    Martins Soares, Pedro Luiz
    Pereira, Gener Tadeu
    AGRONOMY JOURNAL, 2017, 109 (02) : 610 - 619
  • [40] Some soil factors influencing accelerated water erosion of arable land
    Evans, R
    PROGRESS IN PHYSICAL GEOGRAPHY, 1996, 20 (02) : 205 - 215