Soil Erosion Assessment in the Red Soil Region of Southeast China Using an Integrated Index

Soil Science ◽  
2009 ◽  
Vol 174 (10) ◽  
pp. 574-581 ◽  
Author(s):  
Yin Liang ◽  
Decheng Li ◽  
Chunli Su ◽  
Xianzhang Pan
2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.


2015 ◽  
Vol 511 ◽  
pp. 176-185 ◽  
Author(s):  
Yihong Xu ◽  
Jixin Qiao ◽  
Shaoming Pan ◽  
Xiaolin Hou ◽  
Per Roos ◽  
...  

2019 ◽  
Vol 11 (11) ◽  
pp. 3103
Author(s):  
Dong Huang ◽  
Xiaohuan Yang ◽  
Hongyan Cai ◽  
Zuolin Xiao ◽  
Dongrui Han

Soil erosion (SE) processes are closely related to natural conditions and human activities, posing a threat to environment and society. Identifying the human impact on regional SE changes is increasingly essential for pertinent SE management. Jiangxi province is studied here as a representative area of hilly-red-soil regions within southern China. The main objectives of this study were to investigate the changing trend of SE within Jiangxi and identify human impacts on regional SE change from the perspective of spatial differences, through a new approach based on a gravity-center model. Our results showed that SE status presented an overall amelioration from 1990 to 2015, while the average soil erosion modulus (SEM) declined from 864 to 281 Mg/(km2·a). Compared to the situation under human and natural impacts, human-induced spatial differences of SE change demonstrated that the western and northwest regions showed stronger negative effects; the southern region shifted towards negative effects; the northeast region presented a much weaker negative effect. Our results indicated that 4 cities with strong negative effects need more attention in further SE management suited to their local conditions and development, and also suggested that the approach based on a gravity-center has potential for identifying the human impact on regional SE change from the perspective of spatial patterns.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11630
Author(s):  
Man Liu ◽  
Guilin Han

Background Soil erosion can affect the distribution of soil nutrients, which restricts soil productivity. However, it is still a challenge to understand the response of soil nutrients to erosion under different soil types. Methods The distribution of soil nutrients, including soil organic carbon (SOC), soil organic nitrogen (SON), and soil major elements (expressed as Al2O3, CaO, Fe2O3, K2O, Na2O, MgO, TiO2, and SiO2), were analyzed in the profiles from yellow soils, red soils, and lateritic red soils in an erosion region of Southeast China. Soil erodibility K factor calculated on the Erosion Productivity Impact Calculator (EPIC) model was used to indicate erosion risk of surface soils (0∼30 cm depth). The relationships between these soil properties were explored by Spearman’s rank correlation analysis, further to determine the factors that affected the distribution of SOC, SON, and soil major elements under different soil types. Results The K factors in the red soils were significantly lower than those in the yellow soils and significantly higher than those in the lateritic red soils. The SON concentrations in the deep layer of the yellow soils were twice larger than those in the red soils and lateritic red soils, while the SOC concentrations between them were not significantly different. The concentrations of most major elements, except Al2O3 and SiO2, in the yellow soils, were significantly larger than those in the red soils and lateritic red soils. Moreover, the concentrations of major metal elements positively correlated with silt proportions and SiO2 concentrations positively correlated with sand proportions at the 0∼80 cm depth in the yellow soils. Soil major elements depended on both soil evolution and soil erosion in the surface layer of yellow soils. In the yellow soils below the 80 cm depth, soil pH positively correlated with K2O, Na2O, and CaO concentrations, while negatively correlated with Fe2O3 concentrations, which was controlled by the processes of soil evolution. The concentrations of soil major elements did not significantly correlate with soil pH or particle distribution in the red soils and lateritic red soils, likely associated with intricate factors. Conclusions These results suggest that soil nutrients and soil erodibility K factor in the yellow soils were higher than those in the lateritic red soils and red soils. The distribution of soil nutrients is controlled by soil erosion and soil evolution in the erosion region of Southeast China.


2015 ◽  
Vol 7 (10) ◽  
pp. 14309-14325 ◽  
Author(s):  
Zhanyu Zhang ◽  
Liting Sheng ◽  
Jie Yang ◽  
Xiao-An Chen ◽  
Lili Kong ◽  
...  

Author(s):  
Cesar Puente ◽  
Gustavo Olague ◽  
Stephen V. Smith ◽  
Stephen H. Bullock ◽  
Miguel A. González-Botello ◽  
...  

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