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Estimating potential soil sheet Erosion in a Brazilian semiarid county using USLE, GIS, and remote sensing data

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Abstract

The present study aimed to estimate soil erosion in Machados County, Brazil. Rainfall erosivity was calculated using monthly and annual precipitation averages over a 30-year interval, soil erodibility was obtained with a granularity-based equation, and topography and land cover were obtained from DEM data and Sentinel – 2B imagery, respectively. A GIS interface was used to spatialize parameter results and for topography and land cover analysis. The achieved results allowed surmising that the soil loss for the study region risk is low, but significant, with a mean value of 8.11 t/ha year. About a quarter of the total area presented high soil loss, above 20 t/ha year. The biggest influential factors were soil erodibility, with a mean value of 0.028, and land cover, averaging 0.1409. The topographic factor averaged 3.414 and rain erosivity, found to be 2747.22 mm/year, is considered low for the region. Given a lack of conservative practices observed during field work, the soil stewarship P factor was considered 1 for the assessment. The use of orbital images to obtain C factor and the expression applied to calculate soil erodibility provided adequate results. In addition, there is a need for research to monitor and quantify erosion processes in Brazilian semiarid, as well as their erosion tolerance.

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Authors and Affiliations

  1. Forest Sciences Department, Universidade Federal Rural de Pernambuco, Paulista, Pernambuco, Brazil

    Cassiano José Lages Marinho Falcão

  2. Forest Sciences Department, Universidade Federal Rural de Pernambuco, Recife, Pernambuco, Brazil

    Simone Mirtes de Araújo Duarte & Aline da Silva Veloso

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  1. Cassiano José Lages Marinho Falcão
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  2. Simone Mirtes de Araújo Duarte
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  3. Aline da Silva Veloso
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Correspondence to Cassiano José Lages Marinho Falcão.

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Falcão, C.J., Duarte, S.M. & da Silva Veloso, A. Estimating potential soil sheet Erosion in a Brazilian semiarid county using USLE, GIS, and remote sensing data. Environ Monit Assess 192, 47 (2020). https://doi.org/10.1007/s10661-019-7955-5

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