Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia

Pradhan, Biswajeet; Chaudhari, Amruta; Adinarayana, J.; Buchroithner, Manfred

doi:10.1007/s10661-011-1996-8

Cited by 198 publications

(121 citation statements)

References 32 publications

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“…"P i " is monthly average of rainfall for month i (mm) and "P" represents the annual average of rainfall (mm) (Bagherzadeh 2012;Demirci & Karaburun 2011;Pradhan et al 2012).…”

Section: Preparation Of Usle Layersmentioning

confidence: 99%

“…(a) K factor identified based on soil map, (b) LS factor extracted from related DEM, (c) C factor layer generated from SPOT5 image, and (d) P factor computed from slope map of the study area. Kouli et al 2008;Ozcan et al 2007;Pradhan et al 2012;Prasannakumar et al 2012). The steeper and longer slope causes the higher risk of erosion (Demirci & Karaburun 2011 …”

Section: Soil Erodibility Factor (K)mentioning

confidence: 99%

“…Recently, due to population and industrial growth, massive development in hilly areas can be observed especially in Penang Island and Kuala Lumpur city . Therefore, many hilly areas are under heavy pressure from agricultural and urban development (Akgun et al 2011;2012;Pradhan 2010b;2012;Pradhan et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Khosrokhani

Pradhan

2013

Geomatics, Natural Hazards and Risk

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The main purpose of this paper is to assess and predict soil erosion and its dynamism observation at Kuala Lumpur metropolitan city using universal soil loss equation (USLE). To acquire USLE factors, multi-source datasets such as rainfall data of the past 20 years, DEM map, SPOT5 image and geological map of the study area were used to calculate spatial soil erosion of Kuala Lumpur metropolitan city. Results indicated that the highest spatial coverage (61.9%) occurred in very low and the lowest spatial coverage (3.8%) in severe soil loss class. Furthermore, a landslide inventory map of the same area was overlaid on the resultant soil erosion layer to demonstrate their correlation. A frequency ratio (FR) method was applied to determine the relationship between occurred landslides and each soil erosion class. The results indicated that the highest and the lowest FR equal to 0.8 and 2.1 and observed in very low and severe soil loss classes, respectively. Temporal assessment of soil erosion indicated that there is a decrement of soil loss content in severe class from 2000 to 2010. The result of the prognosis analysis showed that the spatial coverage of the study area in severe soil loss class equals to 48%. This result proved the significance of land use/land cover to curb soil erosion. The resultant maps may help the planners and managers to take proper decision for the conservation of soil as a noteworthy natural resource.

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“…"P i " is monthly average of rainfall for month i (mm) and "P" represents the annual average of rainfall (mm) (Bagherzadeh 2012;Demirci & Karaburun 2011;Pradhan et al 2012).…”

Section: Preparation Of Usle Layersmentioning

confidence: 99%

Section: Soil Erodibility Factor (K)mentioning

confidence: 99%

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

Khosrokhani

Pradhan

2013

Geomatics, Natural Hazards and Risk

Self Cite

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“…Due to rapid reforestation each year and the relative small ratio between average deforestation volume (177,252 m 3 ) and average growth volume (725,683 m 3 ), the NDVI in timber harvest ROIs recovered rapidly (Figure 12). In addition, it has been demonstrated that controlled deforestation and rapid plant replacement is environmentally acceptable in humid regions such as Malaysia [53]. Vegetation variation in cultivated areas is the direct result of agricultural tillage.…”

Section: Driving Forces Of Vegetation Cover Change In the Study Areamentioning

confidence: 99%

The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China

Yan

Kašanin‐Grubin

et al. 2017

Sustainability

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Natural climate change and human activities are the main driving forces associated with vegetation coverage change. Nanxiong Basin is a key ecosystem-service area at the national level with a dense population and highly representative of red-bed basins, which are considered as fragile ecological units in humid regions. In this study, the authors aimed to determine the trends in vegetation cover change over past two decades and the associated driving forces in this study area. The Normalized Difference Vegetation Index (NDVI) of 2000-2015, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing dataset along with the application of statistical methods and GIS (geographic information system) techniques were used to quantify vegetation cover change. The results show that human-induced factors can explain most variations at sites with significant cover change. That is to say that human activities are the main drivers of vegetation dynamics in this study area, which shows a significant reduction trend in vegetation cover during the industrialization and urbanization processes of the study period and noticeable recovery trend in 2000-2015 under the plantation and enclosed forest policy.

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“…These variables were selected because they have been successfully used in previous studies [31,[49][50][51][52][53][54][55]. Note that there are few people living in the study area.…”

Section: Influencing Factorsmentioning

confidence: 99%

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

Cao

Wang

Chen

et al. 2016

Water

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This study focused on landslide susceptibility analysis mapping of the Xulong hydropower station reservoir, which is located in the upstream of Jinsha River, a rapidly uplifting region of the Tibetan Plateau region. Nine factors were employed as landslide conditioning factors in landslide susceptibility mapping. These factors included the slope angle, slope aspect, curvature, geology, distance-to-fault, distance-to-river, vegetation, bedrock uplift and annual precipitation. The rapid bedrock uplift factor was represented by the slope angle. The eight factors were processed with the information content model. Since this area has a significant vertical distribution law of precipitation, the annual precipitation factor was analyzed separately. The analytic hierarchy process weighting method was used to calculate the weights of nine factors. Thus, this study proposed a component approach to combine the normalized eight-factor results with the normalized annual precipitation distribution results. Subsequently, the results were plotted in geographic information system (GIS) and a landslide susceptibility map was produced. The evaluation accuracy analysis method was used as a validation approach. The landslide susceptibility classes were divided into four classes, including low, moderate, high and very high. The results show that the four susceptibility class ratios are 12.9%, 35.06%, 34.11%and 17.92% of the study area, respectively. The red belt in the high elevation area represents the very high susceptibility zones, which followed the vertical distribution law of precipitation. The prediction accuracy was 85.74%, which meant that the susceptibility map was confirmed to be reliable and reasonable. This susceptibility map may contribute to averting the landslide risk in the future construction of the Xulong hydropower station.

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Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia

Cited by 198 publications

References 32 publications

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

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

The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

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