Soil loss rate estimation using a hybrid model of geographic information system coupled with fuzzy logic technique

2021 ◽  
Author(s):  
A. Halefom ◽  
I. Ahmad ◽  
M. A. Dar
Keyword(s):  
Information System ◽  
Fuzzy Logic ◽  
Geographic Information System ◽  
Hybrid Model ◽  
Soil Loss ◽  
Loss Rate ◽  
Geographic Information ◽  
Rate Estimation ◽  
Fuzzy Logic Technique ◽  
Soil Loss Rate

scholarly journals Soil loss estimation using geographic information system in enfraz watershed for soil conservation planning in highlands of Ethiopia

2016 ◽  
Vol 5 (2) ◽  
pp. 21-30 ◽  
Author(s):  
Gizachew Tiruneh ◽  
Mersha Ayalew
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Soil Loss ◽  
Satellite Images ◽  
Geographic Information ◽  
Rainfall Erosivity ◽  
Gis And Remote Sensing ◽  
Digital Elevation ◽  
Elevation Model ◽  
Soil Loss Rate

Accelerated soil erosion is a worldwide problem because of its economic and environmental impacts. Enfraz watershed is one of the most erosion-prone watersheds in the highlands of Ethiopia, which received little attention. This study was, therefore, carried out to spatially predict the soil loss rate of the watershed with a Geographic Information System (GIS) and Remote Sensing (RS). Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions was used to estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using soil map, vegetation cover (C) using satellite images, topography (LS) using Digital Elevation Model (DEM) and conservation practices (P ) using satellite images. Based on the analysis, about 92.31% (5914.34 ha) of the watershed was categorized none to slight class which under soil loss tolerance (SLT) values ranging from 5 to 11 tons ha-1 year-1. The remaining 7.68% (492.21 ha) of land was classified under moderate to high class about several times the maximum tolerable soil loss. The total and an average amount of soil loss estimated by RUSLE from the watershed was 30,836.41 ton year-1 and 4.81 tons ha-1year-1, respectively.Int. J. Agril. Res. Innov. & Tech. 5 (2): 21-30, December, 2015

scholarly journals Modelagem espacial de áreas suscetíveis a inundações baseada em um modelo multicritério hibrido e Sistema de Informação Geográfica: um estudo de caso aplicado a bacia do Rio Bengalas

2020 ◽  
Vol 13 (3) ◽  
pp. 1145
Author(s):  
Fabiano Peixoto Freiman ◽  
Camila De Oliveira Carvalho
Keyword(s):  
Information System ◽  
Fuzzy Logic ◽  
Geographic Information System ◽  
River Basin ◽  
Analytical Hierarchy Process ◽  
Rio De Janeiro ◽  
Geographic Information ◽  
Flood Susceptibility ◽  
Hierarchy Process

A identificação de áreas suscetíveis a inundações é essencial para o gerenciamento de desastres e definição de políticas públicas. O objetivo deste trabalho é a apresentação de um método para identificação de áreas suscetíveis a inundações através da integração de informações geográficas provenientes de técnicas do Sensoriamento Remoto, as ferramentas do Sistema de Informação Geográfica (SIG), a lógica Fuzzy e a aplicação de Métodos de Análise Multicritério (MAM) Analytical Hierarchy Process (AHP). Para atingir o objetivo foi proposto um estudo de caso, localizado na Bacia do Rio Bengalas, nos municípios de Nova Friburgo e Bom Jardim (Região Serrana do Rio de Janeiro). A modelagem espacial multicritério foi realizada a partir da seleção de um conjunto de dados composto por informações geomorfológicas, hidrológicas e de uso e ocupação do solo. Como resultado, obteve-se um mapa de suscetibilidade a inundações para a região. A coerência do modelo gerado foi verificada a partir do histórico de inundações da bacia do Rio Bengalas. A metodologia, apresentou-se eficiente e adequada para a determinação de áreas suscetíveis a inundações, prevendo com sucesso a distribuição espacial de áreas com riscos a inundações.  Spatial modelling of flood-susceptible areas based on a hybrid multi-criteria model and Geographic Information System: a case study applied to the Bengalas River basin A B S T R A C TThe identification of areas susceptible to flooding is essential for disaster management and public policy making. The objective of this work is the presentation of a method for the identification of areas susceptible to floods through the integration of geographic information from Remote Sensing techniques, Geographic Information System (GIS) tools, Fuzzy logic and the application of Multicriteria Analysis Methods (MAM) Analytical Hierarchy Process (AHP). In order to achieve the objective, a case study was proposed, located in the Bengalas River Basin, in the municipalities of Nova Friburgo and Bom Jardim (Mountain Region of Rio de Janeiro). Multicriteria spatial modeling was performed by selecting a data set composed of geomorphological, hydrological and land use information. As a result, a flood susceptibility map was obtained for the region. The coherence of the generated model was verified from the flood history of the Bengalas River basin. The methodology was efficient and adequate for the determination of areas susceptible to floods, successfully predicting the spatial distribution of areas at risk of flooding.Keywords: flood susceptibility. Fuzzy logic. MAM. AHP. GIS. 

scholarly journals Remote Sensing and Geographic Information System in Water Erosion Assessment

2020 ◽  
Author(s):  
Nirmal Kumar ◽  
S. K. Singh ◽  
G. P. Obi Reddy ◽  
V. N. Mishra ◽  
R. K. Bajpai
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Soil Loss ◽  
Water Erosion ◽  
Geographic Information ◽  
Geographical Information ◽  
Comprehensive Overview

The aim of this review paper is to provide a comprehensive overview of geographical information system and remote sensing–based water erosion assessment. With multispectral and multi-temporal low cost data at various resolutions, remote sensing plays an important role for mapping the distribution and severity of water erosion and for modeling the risk and/or potential of soil loss. The ability of geographic information system to integrate spatial data of different types and sources makes its role unavoidable in water erosion assessment. The role of satellite data in identification of eroded lands and in providing inputs for erosion modeling has been discussed. The role of GIS in mapping eroded lands based on experts’ opinion, in generating spatial data inputs from sources other than remote sensing and in integrating the inputs to model the potential soil loss has been discussed.

scholarly journals Impact of land-use and land-cover change on soil erosion using the RUSLE model and the geographic information system: a case of Temeji watershed, Western Ethiopia

2021 ◽  
Author(s):  
Mitiku Badasa Moisa ◽  
Daniel Assefa Negash ◽  
Biratu Bobo Merga ◽  
Dessalegn Obsi Gemeda
Keyword(s):  
Land Use ◽  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Land Cover ◽  
Water Conservation ◽  
Soil Loss ◽  
Geographic Information ◽  
Lulc Change ◽  
The Impact

Abstract The impact of land-use land-cover (LULC) change on soil resources is getting global attention. Soil erosion is one of the critical environmental problems worldwide with high severity in developing countries. This study integrates the Revised Universal Soil Loss Equation model with a geographic information system to estimate the impacts of LULC conversion on the mean annual soil loss in the Temeji watershed. In this study, LULC change of Temeji watershed was assessed from 2000 to 2020 by using 2000 Landsat ETM+ and 2020 Landsat OLI/TIRS images and classified using supervised maximum likelihood classification algorithms. Results indicate that the majority of the LULC in the study area is vulnerable to soil erosion. High soil loss is observed when grassland and forest land were converted into cultivated land with a mean soil loss of 88.8 and 86.9 t/ha/year in 2020. Results revealed that about 6,608.5 ha (42.8%) and 8,391.8 ha (54.4%) were categorized under severe classes in 2000 and 2020, respectively. Accordingly, the soil loss severity class is directly correlated with the over-exploitation of forest resources and grasslands for agricultural purposes. These results can be useful for advocacy to enhance local people and stakeholder's participation toward soil and water conservation practices.

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