The impact of sheet and gully erosion on soil aggregate losses in the black soil region of Northeast China

Kedong County is typical of the black soil region of northeast China in being highly susceptible to accelerated soil erosion by gullying. Using data sourced from Corona satellite imagery for 1965, SPOT5 for 2005 and GF-1 for 2015, the spatial distribution of gullies in the research area was mapped. Land use data for 1965, 2005, and 2015 were obtained from the topographic map of 1954, and from Landsat images for 2005 and 2015. Over the last 50 years, the extent of gully erosion in the study area has increased markedly, most notably on cultivated land, while gully density rose from 2,756.16 m2/km2 to 14,294.19 m2/km2. Cultivating land on slopes, especially on slopes greater than ∼4°, may rapidly aggravate gully erosion. The greatest increases in gully density occurred in situations when cultivated land and other/degraded land were transformed, which gully erosion density increased by 49,526.69 m2/km2. Other/degraded land is the most vulnerable land in the study area, with the highest gully erosion density. In these cases, gully density initially increases and, although the “Grain for Green” project has been implemented, gully erosion density has not always declined in the recent past.

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Effects of gully erosion and gully filling on soil degradation in the black soil region of Northeast China

Journal of Mountain Science ◽

10.1007/s11629-013-2560-5 ◽

2013 ◽

Vol 10 (5) ◽

pp. 913-922 ◽

Cited By ~ 9

Author(s):

Wen-jian Tang ◽

Hong-hu Liu ◽

Bao-yuan Liu

Keyword(s):

Northeast China ◽

Soil Degradation ◽

Black Soil ◽

Gully Erosion ◽

Black Soil Region

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Effects of gully erosion and gully filling on soil depth and crop production in the black soil region, northeast China

Environmental Earth Sciences ◽

10.1007/s12665-012-1863-0 ◽

2012 ◽

Vol 68 (6) ◽

pp. 1723-1732 ◽

Cited By ~ 30

Author(s):

Honghu Liu ◽

Tianyu Zhang ◽

Baoyuan Liu ◽

Gang Liu ◽

G. V. Wilson

Keyword(s):

Northeast China ◽

Crop Production ◽

Soil Depth ◽

Black Soil ◽

Gully Erosion ◽

Black Soil Region

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Influence of different nitrogen forms application on rice photosynthesis: fluorescence with water-saving irrigation in black soil region of Songnen Plain, Northeast China

Paddy and Water Environment ◽

10.1007/s10333-018-0670-y ◽

2018 ◽

Vol 16 (4) ◽

pp. 795-804 ◽

Cited By ~ 1

Author(s):

Zheng Ennan ◽

Yang Hua ◽

Zhang Zhongxue

Keyword(s):

Northeast China ◽

Black Soil ◽

Water Saving ◽

Songnen Plain ◽

Nitrogen Forms ◽

Black Soil Region

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Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

Remote Sensing ◽

10.3390/rs12030487 ◽

2020 ◽

Vol 12 (3) ◽

pp. 487 ◽

Cited By ~ 3

Author(s):

Biwei Wang ◽

Zengxiang Zhang ◽

Xiao Wang ◽

Xiaoli Zhao ◽

Ling Yi ◽

...

Keyword(s):

Regional Scale ◽

Black Soil ◽

Google Earth ◽

Gully Erosion ◽

Image Feature ◽

Gradient Boosting ◽

Light Gradient ◽

Optical Images ◽

Black Soil Region ◽

Object Based

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

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