scholarly journals Bivariate Based Susceptibility Mapping for Gully Erosion in Wanjoga River Catchment Upper Tana Basin, Kenya

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Cecilia Wawira Ireri ◽  
George Krhoda ◽  
Mukhovi Stellah
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Arid Regions ◽  
Rainfall Variability ◽  
Gully Erosion ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Error Matrix ◽  
Conditioning Factors ◽  
Semi Arid

Gullies occur in semi-arid regions characterized by rainfall variability and seasonality, increased overland flow, affecting ecological fragility of an area. In most gully prone areas, extent of land affected by gullies is increasing. Thus, predicting susceptibility to gully erosion in semi-arid environment is an important step towards effectively rehabilitating and prevention against gully erosion. Proneness to gully occurrence was assessed against; Land cover/land use, slope, soil characteristics, rainfall variability and elevation, and modelled using geographical information system (GIS)-based bivariate statistical approach. Objectives of the study were; a) to assess influence of geomorphological factors on gully erosion, b) analyze and develop gully erosion susceptibility map, c) verify gully susceptibility images using error matrix of class labels in classified map against ground truth reference data. Total of 66 gullied areas (width and depth ≥ ranging 0.5), were mapped using 15m resolution Landsat images for 2018 and field surveys to estimate susceptibility to gully erosion by Global Mapper software in GIS. The images were verified using 120 pixels of known 15 gully presence or absence to produce an error matrix based on comparison of actual outcomes to predicted outcomes. Influence of conditioning factors to gully erosion showed a significant positive relationship between gully susceptibility and gully conditioning factors with consistency value; CR =0.097; value< 0.1, indicating, individual conditioning factors had an importance in influencing gully erosion. Slope (43%) and soil lithotype (25%), most influenced gully susceptibility, while land cover/land use (12%) and rainfall (12%) had least impact. Verification results showed satisfactory agreement between susceptibility map and field data on gullied areas at approximately 76.2%, an error of positive value of 4% and a negative value of 7%. Thus, production of susceptibility map by bivariate statistical method represents a useful tool for ending long and short-term gully emergencies by planning conservation of semi-arid regions.

scholarly journals Impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand

2012 ◽  
Vol 7 (No. 1) ◽  
pp. 10-17 ◽  
Author(s):  
S. Wijitkosum
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
National Park ◽  
Land Use Changes ◽  
Influential Factors ◽  
Geographical Information ◽  
Adjacent Area ◽  
Erosion Risk ◽  
Study Results

Soil erosion has been considered as the primary cause of soil degradation since soil erosion leads to the loss of topsoil and soil organic matters which are essential for the growing of plants. Land use, which relates to land cover, is one of the influential factors that affect soil erosion. In this study, impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand, were investigated by applying remote sensing technique, geographical information system (GIS) and the Universal Soil Loss Equation (USLE). The study results revealed that land use changes in terms of area size and pattern influenced the soil erosion risk in Pa Deng in the 1990&ndash;2010 period. The area with smaller land cover obviously showed the high risk of soil erosion than the larger land cover did.

scholarly journals Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Mei Yang ◽  
Jingjue Chen ◽  
Tianqiang Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Hybrid Model ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Support Vector ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

scholarly journals Extreme rainfall deficits were not the cause of recurring colonial era famines of southern Indian semi-arid regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ranjini Ray ◽  
Atreyee Bhattacharya ◽  
Gaurav Arora ◽  
Kushank Bajaj ◽  
Keyle Horton ◽  
...  
Keyword(s):  
Arid Regions ◽  
Human Impacts ◽  
Rainfall Variability ◽  
Extreme Rainfall ◽  
Southern India ◽  
Colonial Era ◽  
Regional Rainfall ◽  
Semi Arid ◽  
Rainfall Deficits

AbstractUsing information contained in the eighteenth to twentieth century British administrative documents, preserved in the National Archives of India (NAI), we present a 218-year (1729–1947 AD) record of socioeconomic disruptions and human impacts (famines) associated with ‘rain failures’ that affected the semi-arid regions (SARs) of southern India. By mapping the southern Indian famine record onto long-term spatiotemporal measures of regional rainfall variability, we demonstrate that the SARs of southern India repeatedly experienced famines when annual rainfall reduced by ~ one standard deviation (1 SD), or more, from long-term averages. In other words, ‘rain failures’ listed in the colonial documents as causes of extreme socioeconomic disruptions, food shortages and human distress (famines) in the southern Indian SARs were fluctuations in precipitation well within the normal range of regional rainfall variability and not extreme rainfall deficits (≥ 3 SD). Our study demonstrates that extreme climate events were not necessary conditions for extreme socioeconomic disruptions and human impacts rendered by the colonial era famines in peninsular India. Based on our findings, we suggest that climate change risk assessement should consider the potential impacts of more frequent low-level anomalies (e.g. 1 SD) in drought prone semi-arid regions.

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