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Estimation of the cover and management factor for modeling soil erosion using remote sensing

Song, Xian Feng ; Duan, Zheng LU ; Niu, Hai Shan and Kono, Yasuyuki (2009) In Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University 31(3). p.58-63
Abstract

The cover and management factor (C) of the Universal Soil Loss Equation (USLE) is difficult to be estimated over broad geographic areas where meteorology and soil erosion are poorly monitored. This paper presents a new approach to estimate C factor based on multi-temporal images of Landsat TM/ETM and a weather generator. The linear spectral unmixture algorithm was adopted to calculate the fractional abundance of ground cover, from which the potential soil loss ratio (PSLR) was estimated. The weather generator (CLIGEN) was used to simulate historical rainfall events and compute the distribution of rainfall erosivity indices. The annual C factor was finally estimated by means of weighting PSLR with the proportion of rainfall erosivity... (More)

The cover and management factor (C) of the Universal Soil Loss Equation (USLE) is difficult to be estimated over broad geographic areas where meteorology and soil erosion are poorly monitored. This paper presents a new approach to estimate C factor based on multi-temporal images of Landsat TM/ETM and a weather generator. The linear spectral unmixture algorithm was adopted to calculate the fractional abundance of ground cover, from which the potential soil loss ratio (PSLR) was estimated. The weather generator (CLIGEN) was used to simulate historical rainfall events and compute the distribution of rainfall erosivity indices. The annual C factor was finally estimated by means of weighting PSLR with the proportion of rainfall erosivity indices. The proposed method was applied to the upstream area of the Chaohe River for validation. The results showed that the estimated C values appropriately responded to the vegetation abundance and land use types, and the estimated soil loss fitted well to the observed records.

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author
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publishing date
type
Contribution to journal
publication status
published
subject
keywords
CLIGEN model, Cover and management factor, Linear spectral unmixture algorithm, Potential soil loss ratio(PSLR), Rainfall erosivity index
in
Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University
volume
31
issue
3
pages
6 pages
external identifiers
  • scopus:77954685101
ISSN
1000-1522
language
English
LU publication?
no
id
7c8d7510-b4b6-4717-b08b-6106cd5249a4
date added to LUP
2019-12-22 20:39:09
date last changed
2020-09-09 05:54:15
@article{7c8d7510-b4b6-4717-b08b-6106cd5249a4,
  abstract     = {<p>The cover and management factor (C) of the Universal Soil Loss Equation (USLE) is difficult to be estimated over broad geographic areas where meteorology and soil erosion are poorly monitored. This paper presents a new approach to estimate C factor based on multi-temporal images of Landsat TM/ETM and a weather generator. The linear spectral unmixture algorithm was adopted to calculate the fractional abundance of ground cover, from which the potential soil loss ratio (PSLR) was estimated. The weather generator (CLIGEN) was used to simulate historical rainfall events and compute the distribution of rainfall erosivity indices. The annual C factor was finally estimated by means of weighting PSLR with the proportion of rainfall erosivity indices. The proposed method was applied to the upstream area of the Chaohe River for validation. The results showed that the estimated C values appropriately responded to the vegetation abundance and land use types, and the estimated soil loss fitted well to the observed records.</p>},
  author       = {Song, Xian Feng and Duan, Zheng and Niu, Hai Shan and Kono, Yasuyuki},
  issn         = {1000-1522},
  language     = {eng},
  month        = {12},
  number       = {3},
  pages        = {58--63},
  series       = {Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University},
  title        = {Estimation of the cover and management factor for modeling soil erosion using remote sensing},
  volume       = {31},
  year         = {2009},
}