Assessing soil erosion hazard -a raster based GIS approach with spatial principal component analysis (SPCA) View Full Text


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Article Info

DATE

2015-12

AUTHORS

Md. Rejaur Rahman, Z. H. Shi, Cai Chongfa, Zhu Dun

ABSTRACT

Soil erosion is one of the most serious environmental problems affecting the quality of soil, land, and water resources upon which humans depend for their sustenance. A soil erosion hazard map is highly useful for environmental planning, soil conservation and management in soil erosion prone areas. In order to assess the soil erosion hazard, remote sensing (RS) and geographical information system (GIS) technologies were adopted, and a numerical model was developed using spatial principal component analysis (SPCA). Here, an integrated soil erosion hazard index (SEHI) was computed and classified into four levels of soil erosion hazard viz. low, average, high, and very high. In the process, nine factors were selected together with the degree of importance of the factors in hazard of the soil erosion. Integrated RS and GIS techniques and models were applied to generate the necessary factors for the SPCA approach. In addition, erosion hazard and calculated rate of soil erosion were used to find out the risk of soil erosion. Soil erosion risk was identified as actual occurrence and potential risk. Results show that, in general, an average hazardous condition of soil erosion was found in the area. The potential risk was more extensive in terms of involved area compared to the actual occurrence, and both actual occurrence and potential risk were higher at the mid-level elevation of the area. This study highlighted that priority should be given where the actual occurrence is high to very high and the probability of potential risk is average to high for protecting the land at present and in the future as well. Therefore, the application of SPCA combined with RS and GIS provided an effective methodology to solve the complex decisional problem for soil erosion hazard and risk assessment. More... »

PAGES

853-865

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12145-015-0219-1

DOI

http://dx.doi.org/10.1007/s12145-015-0219-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034107907


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