Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Khosrokhani, Maryam; Pradhan, Biswajeet

doi:10.1080/19475705.2013.794164

Cited by 50 publications

(25 citation statements)

References 46 publications

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“…Due to low temporal resolution of rainfall data used in this, the rain events that actually responsible for large volume of sediment yield or soil loss cannot be identified [15]. Therefore temporal distribution R-factors throughout the year and timing of the most severe events is an important characteristic of erosivity factor [16].…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of Rainfall-Runoff Erosivity Factor for Cameron Highland, Pahang, Malaysia

Sholagberu¹,

Mustafa²,

Yusof³

et al. 2016

J. Ecol. Eng.

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Rainfall-runoff is the active agent of soil erosion which often resulted in land degradation and water quality deterioration. Its aggressiveness to induce erosion is usually termed as rainfall erosivity index or factor (R). R-factor is one of the factors to be parameterized in the evaluation of soil loss using the Universal Soil Loss Equation and its reversed versions (USLE/RUSLE). The computation of accurate R-factor for a particular watershed requires high temporal resolution rainfall (pluviograph) data with less than 30-minutes intensities for at least 20 yrs, which is available only in a few regions of the world. As a result, various simplified models have been proposed by researchers to evaluate R-factor using readily available daily, monthly or annual precipitation data. This study is thus aimed at estimating R-factor and to establish an approximate relationship between R-factor and rainfall for subsequent usage in the estimation of soil loss in Cameron highlands watershed. The results of the analysis showed that the least and peak (critical) R-factors occurred in the months of January and April with 660.82 and 2399.18 MJ mm ha -1 h -1 year -1 respectively. Also, it was observed that erosivity power starts to increase from the month of January through April before started falling in the month of July. The monthly and annual peaks (critical periods) may be attributed to increased rainfall amount due to climate change which in turn resulted to increased aggressiveness of rains to cause erosion in the study area. The correlation coefficient of 0.985 showed that there was a strong relationship rainfall and R-factor.

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Section: Resultsmentioning

confidence: 99%

“…The , Pi -monthly average of rainfall for month i (mm) and P -annual average of rainfall (mm) [Khosrokhani and Pradhan, 2014]. In a study conducted by Bol [1978] in Indonesia generated an empirical model as show Equation (11) relating R-factor to rainfall P (mm).…”

Section: Some Available Methodsmentioning

confidence: 99%

Evaluation of Rainfall-Runoff Erosivity Factor for Cameron Highland, Pahang, Malaysia

Sholagberu¹,

Mustafa²,

Yusof³

et al. 2016

J. Ecol. Eng.

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“…To estimate soil erosion and to develop optimal soil erosion management plans, many erosion models based on the empirical models, such as Universal Soil Loss Equation (USLE -USDA Agriculture Handbook 282, first published 1965 and updated version was 1978 in USDA Agriculture Handbook 537) & (Wischmeier and Smithl, 1978) (Lal, 1994;Ni and Li, 2003;Lee, 2004;Dabral et al 2008;Rahman et al 2009;Zhang et al, 2009;Kim et al, 2012;Vijith et al, 2012;Alexakis et al, 2013;Arar and Chenchouni, 2013;Khosrokhani and Pradhan, 2013;Naqvi et al, 2013;and Rozos et al, 2013). Each model has its own characteristics and application scopes (Boggs et al, 2001;Lu et al, 2004;Dabral et al, 2008;Tian et al, 2009).…”

Section: Multiple Modelling Approachmentioning

confidence: 99%

Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

Rahaman

Aruchamy

Jegankumar

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation-RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h -1 / y -1 . Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

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“…A spatiao-temporal assessment of soil erosion derived from the USLE model was developed using a geographic information system (GIS) (Khosrokhania & Pradhan 2013). A spatial resolution of 1.0 km was adopted for all coverages for consistency with the MODIS data used in the wind erosion model, and to match the drought data.…”

Section: Modelling the Risk Of Soil Erosion By Watermentioning

confidence: 99%

Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques

Al-Bakri

Brown

Gedalof

et al. 2014

Geomatics, Natural Hazards and Risk

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(2016) Modelling desertification risk in the northwest of Jordan using geospatial and remote sensing techniques, Geomatics, Natural Hazards and Risk, 7:2, 531-549, DOI: 10.1080DOI: 10. /19475705.2014 Remote sensing, climate, and ground data were used within a geographic information system (GIS) to map desertification risk in the north-west of Jordan. The approach was based on modelling wind and water erosion and incorporating the results with a map representing the severity of drought. Water erosion was modelled by the universal soil loss equation, while wind erosion was modelled by a dust emission model. The extent of drought was mapped using the evapotranspiration water stress index (EWSI) which incorporated actual and potential evapotranspiration. Output maps were assessed within GIS in terms of spatial patterns and the degree of correlation with soil surficial properties. Results showed that both topography and soil explained 75% of the variation in water erosion, while soil explained 25% of the variation in wind erosion, which was mainly controlled by natural factors of topography and wind. Analysis of the EWSI map showed that drought risk was dominating most of the rainfed areas. The combined effects of soil erosion and drought were reflected on the desertification risk map. The adoption of these geospatial and remote sensing techniques is, therefore, recommended to map desertification risk in Jordan and in similar arid environments.

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Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Cited by 50 publications

References 46 publications

Evaluation of Rainfall-Runoff Erosivity Factor for Cameron Highland, Pahang, Malaysia

Evaluation of Rainfall-Runoff Erosivity Factor for Cameron Highland, Pahang, Malaysia

Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques

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