Geospatial (GIS) of Potential Soil Erosions Hazard Mapping ...

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97

International Journal of Electronics Communication and Computer Engineering

Volume 9, Issue 3, ISSN (Online): 2249–071X

Geospatial (GIS) of Potential Soil Erosions Hazard

Mapping in Hulu Langat, Malaysia

Omar Al-Kouri Department of Human Sciences, Geography , ART Faculty Taibah University, Medina, Kingdom of Saudi Arabia

Date of publication (dd/mm/yyyy): 16/05/2018

Abstract – The main goal of this study is to mapping

potential soil erosion hazard map. The mapped and prioritized

of Soil erosion hazard areas has been done by integrating

GIS-grid analysis to provide valuable information about the

most likely areas to be eroded using Arc GIS 9.1. The potential

soil erosion factors such as erosive rainfall, soil type and slope

steepness map was utilized in systematically method using

ArcGIS Model Builder; where, the Universal Soil Loss

Equation (USLE) for potential erosion map was adapted as

GIS function. The results showed that the high potential area

of soil erosion is found in North-East areas. Hula Langat

residential area which is about 70% of high potential risk;

whereas, Hulu Semenyih area has about 60% of high potential

risk. Each of Semenyih, Cheras, Baranang has low of high

potential risk about 30%, 25% in 10% respectively. Slope is

the greater influence factor

Keywords – Soil Erosion, Hazard Map, GIS Function,

Potential Risk, Residential Area, Slope Steepness.

I. INTRODUCTION

The development tool of Geography information

technology can support the new methods to a large extent

in monitoring and change detection on Soil erosion. The

role of GIS mapping and feature change detection generally

provides a source of input data or an aid for computing

coefficients and model parameters [12]. Experience has

shown that satellite data can be interpreted to derive

thematic information on (land change, soil, vegetation

change, water level change, etc), combined with

conventionally measured climatic parameters

(precipitation, humid, temperature etc) and topographic

parameters elevations (height, slope, contour), and provide

the necessary parameters inputs to the change in land and

soil [3, 5, 6].

A natural hazard is an aspect of the interaction between

human and nature arising from the common process. [10].

Natural hazards are governed by the coexistent state of

adjustment in the human use systems and the state of natural

in the natural events systems. These processes, whether

employing elaborate technical and social mechanisms or a

simple one, make possible the human occupancy of areas of

even frequent and recurrent natural hazards [10,11 -13].

Nowadays Geographic Information System (GIS) and

remote sensing technology are emerging as a powerful tool

for the assessment of risk and management of natural

hazards. Due to these techniques, natural hazard mapping

such as soil erosion hazard can be prepared now to delineate

soil erosion prone areas on the map. Such kind of maps will

help the civil authorities for quick assessment of potential

impact of a natural hazard and initiation of appropriate

measures for reducing the impact. Such data will help the

planners and decision-makers to take positive and in time

steps during pre-disaster situation. It will also help them

during post disaster activities for the assessment of damages

and losses occur due to erosion. Moreover, GIS provides a

broad range of tool for determining areas affected by

erosion or forecasting areas that is likely to be eroded due

to high land exploitation in the hilly, steep slope and heavy

rainfall area [1, 7, 9, and 13].

Agricultural land needs preserving for maintaining crops

quantity and quality. However, agriculture land is prone to

erosion that damage soil. Soil erosion is long term

phenomenon that is not easy to simplify or visualize for a

layman. The aim is to create potential soil erosion risk map

of Hulu Langat area base on GIS. The objectives of this

study are to create potential risk map of soil erosion using

ArcGIS model builder tool, to identify the priority areas for

soil conservation.

Review of previous and related literature reveals that GIS

map and temporal satellite-derived information has been

used in monitoring soil erosion effected parameters, soil

level change detection, and in other environmental

applications such as the use of Radar for soil level and

surface change [14, 15, 17,]. The study was conducted to

detect changes in soil and effected soil erosion parameters

change using GIS and temporal satellite data Ikons and

Land-sat for monitoring potential risk area of soil erosion

[2]. Many research have been studied and investigate the

potential soil erosion hazard map and there effected in

residential area using GIS and remote sensing data [10, 16].

However, their work was done for smaller areas. As such,

it was not wide and comprehensive enough to include larger

effected parameter of soil erosion [1, 7, 9, 13]. Also, they

didn't investigate the level of risk in potential erosion soil

hazard map of large periods. Mostly, they didn't use the

eroded due to high land exploitation in the hilly, steep slope

and heavy rainfall area analysis. This means low accuracy

of detection of potential erosion soil area. In this research,

we have included the whole effected parameters in a large

period.

II. STUDY AREA

The study area located on the west coast of Peninsular

Malaysia it is some portion of Selangor state located in

District of Hulu Langat sub District of Sermban, see Figure

1. The study area is about (69503.408 ha) (695.03 Km2)

and bounded by101° 31'E to 102° 11' E and 2° 52'N to 3°

17’ N It has an altitude between 900m to 1960m above

mean sea level.


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Fig. 1. Show the Hulu Langat, Malaysia.

III. MATERIAL AND METHOD

A- Data Table 1: shows the dataset (digital topographical map,

soil map and rainfall map) that are essential to perform

geospatial processing to produce soil erosion map.

Table 1: Required data set

Data type Data year Source of data

digital topographical

map

2015 UPM Library

Soil map 2010 MACRES

Rain Fall map 2015 MACRES

A brief description of each map and its classifications are

listed as follows: Rain fall factor map (erosivity surface)

was obtained from MACRES, dated for the year 2015. The

map classification was based on rain fall level measured by

cm. The map was classified to 7 classes of between 450 and

750 cm. These classes was used as ready input of R factor

in ULSE, show in figure 2. Soil map that was obtained from

MACRES was classified to 7 classes (LAA, MUN, RGM,

SDG, STP, Tavy and Urban); the majority soil type of the

area is STP followed by RGM, show in figure 3.

Fig. 2. Hulu Langat Rainfall factor map

Fig. 3. Hulu Langat Soil map.

Table 2: present 11 soil types and its equivalent K factor

for Selangor state, where Hulu Langat district have 7 types.

The table was obtained from MACRES (2010) [19].

Therefore, Hulu Langat soil Erodibility map was produced

by matching the soil type and the equivalent k factor

Table 2: Soil type and Soil equivalent K factor

(MACRES, 2010).

Soil Type K-

Value

Soil Type K-

Value

MUN 0.325 STP 0.181

LAA 0.286 KRJ 0.179

SDG 0.243 SGR 0.155

TVY 0.209 KDH 0.143

RGM 0.181 PRH 0.090

PRG 0.041 Water and Urban

areas

0

Digital topographical maps of the Hulu Langat were

obtained from UPM Library, dated for the year 2015. The

map is based on contour lines with 20m interval, which used

to calculate the LS factor by using ArcGIS built-in geo-

processing functions same as defined in the study of

MACRES (2010) figure 4 and 5.

Fig. 4. Digital Topological (contour) map


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Fig. 5. Hulu Langat Slope map

IV. FACTORS INFLUENCING EROSION

Soil erosions as it is natural phenomena, there are factors

that influence its occurrence and directions; the common

known factors are climate, vegetation and land slope. These

factors also define its potential risk based on the community

The climate factor affecting erosion hazard is mainly

characterized by erosivity as the potential ability of rain to

cause erosion; it is a function of the physical characteristics

of rainfall [1, 7].

The climatic sub-factors that influence erosion are

rainfall amount, intensity, and frequency. During periods of

frequent rainfall, a greater percentage of the rainfall will

become runoff due to high soil moisture or saturated

conditions [5]. Vegetation is probably the most important

physical factor influencing soil erosion. A good cover of

vegetation shields the soil from the impact of raindrops. It

also binds the soil together, making it more resistant to

runoff. A vegetative cover provides organic matter, slows

runoff, and filters sediment. On a graded slope, the

condition of vegetative cover will determine whether

erosion will be stopped or only slightly halted. A dense,

robust cover of vegetation is one of the best protections

against soil erosion.

The vegetation factors, affecting erosion hazard vary

with the season, crops, degree of maturity, as well as with

the kind of vegetative material, namely, roots, plant tops,

and plant residue. Soil with sparse vegetation is more

susceptible to erosion. This is because the roots of plants,

especially trees, bind soils and rocks together. Vegetation

cover leaves and branches plus the top layer of organic

matter covering the ground, known as litter, also acts to

protect the soil from the force of intense rains [8].

According to [3, 15 steepness and roughness affect

Erodibility. Generally, the longer the slope the greater the

potential of erosion. The greatest erosion potential is at the

base of the slope, where runoff velocity greatest and runoff

concentrates. Slope steepness, along with surface

roughness, and the amount and intensity of rainfall control

the speed at which runoff flows down a slope. The steeper

the slope, the faster the water will flow. The faster it flows,

the more likely it will cause erosion and increase

sedimentation

Slope steepness is an important factor governing the

efficacy of splash erosion as slope angle steepness more soil

is splash down slope. The relationship between slope

steepness and sheet erosion and rill erosion are equivocal.

However, although it is assumed in equations for predicting

soil loss that slope steepness is an important factor

controlling severity of erosion [12-14].

Fig. 6. The Influence of Slope Factor

V. DETERMINATION AND CLASSIFICATION OF

EROSION FEATURE SET

Morgan indicated a method to combine the erosion

hazard factors in the compilation of an erosion hazard map

by using a scoring system for rating erosion hazard. In this

method each unit is rated on a scale from 1 to 5 on the basis

of erosion hazard factors, i.e. erosivity, slope steepness,

Erodibility, ground cover and human occupation. The

scoring is arranged so that 1 is associated with a low erosion

hazard and 5 with a high erosion hazard. The five factors

scores are summed to give a total score which is compared

with an arbitrarily chosen classification system to

categorize areas which vary from very low up to very high

erosion hazard classes11-14].

The soil loss is predicted on basis of soil loss equation

according to Wischmeier and Smith [17] as follows formula

1:

A = R. K. L. S. C. P (1)

Where:

A = Is the computed soil loss on tons per acre per year.

R = The rainfall and runoff factors.

K = The Erodibility factor.

L = The slope length factors.

S = The slope steepness factor.

C = The crop and management factor.

P = The support practice factor.

In order carry out an erosion hazard survey efficiently a

systematic approach can be adopted based on the land

resource survey, resulting in a breakdown of the area into

land units. The degree of erosion hazard within each land

unit can be obtained by the evaluating each soil loss factor.

Saro (2011) studied the evaluation of the hazard of soil

erosion and its verification at Boun, Korea, using a GIS and

remote sensing. The precipitation, topographic, soil, and

land use data were collected, processed, and constructed

into a spatial database using GIS. Areas that had suffered

soil erosion were analysed and mapped using the Universal

Soil Loss Equation (USLE)[20].

The factors that influence soil erosion are rainfall

erosivitiy (R) from the precipitation database, soil


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Erodibility (K) from the soil database, slope length and

steepness (LS) from the topographic database, and crop and

management (C) and conservation supporting practices (P)

from the land use database. Land use was classified from

Landsat Thematic Mapper satellite images.

In Malaysia, Soil erosion risk map was developed for

Langkawi Island using the USLE, remote sensing and GIS;

Spatial modelling requires generating a representative

raster layers based on original data for the following

parameters: rainfall erosivity, slope length/gradient, soil

Erodibility and conservation practices[8]. The analysis was

performed using IDRISI, a raster based GIS software.

Recently, the use of USLE has been extended as a useful

tool to predict soil losses and planning control practices in

agricultural watersheds by estimating the factor values in a

grid cell basis. The study was performed in the Kazan

Watershed located in Central Anatolia, Turkey to predict

soil erosion risk by the integrating USLE and GIS

methodology for planning conservation measures in the site

[6].

VI. CALCULATING THE LS FACTOR

There is an about two well-known methods for

calculating Length Slope (LS) factor: i) Weischmeier and

Smith (1968) and ii) Moore and Burch (1986)[13].

Weischmeier and Smith (1968) method consist of two

inputs regarding the slope (gradient and length). However,

the method proposed by Moore and Burch (1986) [12-13]

found to be computerize due most of its inputs depend on

digital raster metadata, such as cell size (also known as grid

size).

The method of determining potential risk areas of soil

erosion is based on MACRES (2003) using the four factors

(rain Erosivity, soil Erodibility, slope gradient and slope

length). However, method of calculating Length Slope (LS)

factor will be based on Moore and Burch (1986)[22], where

the calculation depend on three type of entries: the slope

gradient (in degrees), flow accumulation (calculated from

slope) and cell size (obtained from ArcGIS default

parameters of data conversion to raster). In contrast, the

method of Weischmeier and Smith (1968) requires

calculating each of slope gradients (provide as ArcGIS

function) and slope length (which is not provided as ArcGIS

function). Therefore, the method of Moore and Burch

(1986) was used in this project of it simple implementation

of USLE in ArcGIS. In This research had undertaken the

following steps as fellow:

1. A new toolbox was created for soil erosion and within

it a new model was created.

2. Data format for each of the TIN, R and K factor maps

were unified for using geoprocessing function

(specifically Map Algebra), where inputmaps are

converted to raster maps. This was done in ArcGIS

Model Builder by inserting “TIN to Raster” function

into model builder and establishing the connection

between it and the input data (TIN data). The same was

done for each of K and R factor by converting them

using “Polygon to Raster” function.

3. The LS factor was calculated from the derived formula

that was proposed by Moore and Burch (1986). The

formula and the raster topographical map were entered

using Map Algebra (Figure 6). The entered expression

of the formula 2 is :

Pow (Flow Accumulation (Flow Direction (Tin2 Raster))

*20/22.1, 0.4)* Pow (Sin (slope (Tin2 Raster)* 0.01745) /

0.09, 1.4) (2)

Where, Tin2Raster is the raster topography map.

Calculation of USLE for potential soil erosion in ArcGIS

Model Builder was done using Map Algebra (Figure 7). The

entered expression of the USLE is :

R_factor_Raster*K_factor_Raster*LS_factor

The model was enhanced to enable users to add their data

through defining each of TIN, R factor and K factor as input

parameter for the model (Figure 8).

c

Fig. 7. Customized function for LS factor calculation and

USLE potential map calculation using Map Algebra

The model was saved with the following diagram layout,

show in figure 8.

Fig. 8. USLE model diagram layout based on ArcGIS

Model Builder


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The model was tested using the available dataset of Hulu

Langat district, where it produce the soil erosion potential

risk map. The model was tested using the available dataset

of Hulu Langat district, where it produce the soil erosion

potential risk map.

The produced soil erosion potential risk map view was

classified to match the ones was produced by MACRES

[19] for Selangor state. The soil erosion classification

scheme was adopted from Department of Environment,

Malaysia. This was done to validate the produced map by

sub setting Hulu Langat district part from Selangor state.

The potential risk map of soil erosion was overlaid with

residential boundaries in Hulu Langat district to view the

severity in each residential boundary (Mukim).from the

overlay, it was determined the priority for each mukim area

for soil conservation.

V. RESULT AND DISCUSSION

The implementation soil erosion model in ArcGIS using

Model Builder, have provided a systematic and simple

method to view and study the adopted soil erosion model.

In addition, the Model Builder have provided a method for

developing a soil erosion function, which simplify the usage

of the soil erosion model; where users have only to define

their data set, the model process the spatial data and display

the results on screen. Based on the developed potential soil

erosion model that requires 3 input (K factor, R factor and

TIN), the final map was utilized in figure 9. The

classification method used for displaying potential risk was

based on the soil erosion scheme that was developed by

Department of Environment, Malaysia and used by

MACRES (2010)[9], see figure 10.

Fig. 9. A: The potential soil erosion risk map of Hulu

Langat district

Fig. 10. B Soil Erosion for Selangor state (MACRES,

2010)

The visual comparison of the two maps shows there is

high potential soil erosion in Hulu Langat district near is

upper part, where about third quart of its map shows less

potential risk. However, the comparison shows that

MACRES (2010) [19] map have general pattern of soil

erosion for Hulu Langat District; because the map was

produce for whole Selangor state. Whereas, the produce

map in this project show more details of soil erosion, though

both maps were geoprocessed based on raster function and

calculations.

VI. SOIL EROSION FOR RESIDENTIAL AREAS IN

HULU LANGAT DISTRICT

Figure 11 show 6 residential areas (Hulu Langat, Cheras,

Semenyih, Hulu Semenyih and Baranang) in Hulu Langat

district, where high potential of soil erosion is found in

North-East areas. Hulu Langat residential area has about

70% of high potential risk; whereas, Hulu Semenyih area

has about 60% of high potential risk. Each of Semenyih,

Cheras, Baranang has low of high potential risk about 30%,

25% in 10% respectively. Slope is the greater influence

factor in the study area where the high erosion risk located

in the steep area. The potential erosion risk decrease in the

urban area and water body which is represented in green

colour see figure 11; the overlay of the residential area with

the final result of potential soil erosion of hulu langat done

to show simplify visualization of soil erosion in the study

area.


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Fig. 11. Potential soil erosion risk map for residential areas

in Hulu Langat district

VII. CONCLUSION

The potential soil erosion risk map for Hulu Langat area

was produced using USLE model in ArcGIS model builder.

Hazard assessment and risk mapping can prove very useful

tool for advance conservation planning,. Based on this

study, the input factor maps are the important aspect for

producing accurate erosion risk map. This research was

found that the implementation of USLE model in ArcGIS

Model Builder was useful in term of organization. In

addition, the model was simplified by making it’s usage like

a function of ArcGIS. The produced potential soil erosion

risk map have shown some level of details compared with

the map that was produces by MACRES (2010), where their

work have been generalized for whole state of Selangor.

Whereas, the produced map is for the Hulu Langat district.

ACKNOWLEDGMENT

We thank the NASA Earth System for the LANDSAT

products and Taibah university the Deanship Science

Foundation for Resources Satellite Data and Application.

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Mapping Soil Erosion,” Progress in Soil Science Digital Soil

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AUTHOR’S PROFILE

Omar M. Al-kouri received a B.S. in Geography from

Damascus University in 1998 and M.S. in Geography information system & Remote Sensing from University

Putra Malaysia in 2004 and Ph.D. in Geography

information system & Geomatic engineering from University Putra Malaysia in 2009. He has been an

Assistant Professor at the Geography information system & mapping Dept., Taibah University (Kingdom of Saudi Arabia) since Jan. 2011. His research interest is in the areas of Geo hazard mapping

using GIS Technology. E-mail: [email protected]

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