Abstract
Soil erosion rate in Uthai Thani province of Thailand was assessed employing Universal Soil Loss Equation (USLE). Remote sensing and Geographic Information System (GIS) were used for the data interpretation and analysis, and analysis. The study identifies six erosion categories from hazard severity point of view in the study area. The estimated erosion rate rnages from 0 to 279.32 tons/ha/year and the upland area under sugarcane cultivation being most severe prone areas from the aspect of potential soil productivity. The study relates to the on-site physical effects of soil erosion in terms of soil loss, not take other factors, like soil erosion and crop productivity relationship; and off-site damage, in to account. The study recommends the need of long-range conservation measures to address the problem of soil degradation.
1. Introduciton
Of the world's land degradation problems, soil erosion is the first order category (Hitzhusen, 1993). Water and wind erosion account for the most land degradation on every continent except Australia. Available geologic records on erosion of different continents show that Asia has the highest erosion relate (74 ton/acre/yr) and the Australia has the lowest rate of 14 ton/acre/yr (El-Swaify, 1994). The fact that "soil erosion is the foreseeable result of poor or incorrect land use and that it can not be overcome unless and use and land management are improved" has generally not been appreciated (Sanders, 1992).
Many high priorities natural resource conservation projects throughout the world depend upon accurate erosion assessments and predictions (Onstand and young, 1988). Such assessments and predictions are rather important from agricultural production point of view as the soil is the basis of production. The need is not merely the quantifying the erosion rate but such outcome of erosion assessment by utilized in for policy formulation of maintaining the land productivity and the environment as a whole.
Universal Soil Loss Equation (USLE) is the most popular empirically based model used globally for erosion prediction and control. The major limitations of adopting quantitative 'western" models and convservation planning technologies to torpical regions need carefully valid site-specific data. This study attempts to quantify the soil loss, its spatial distribution an associated land use types in Uthai Thani province of Thailand, More importantly, the study aims to extend the realization of increased concern on the valuable soil resources that study aims to extend the realization of increased concern on the valuable soil resources that has to be protected from a sustainable agriculture point of view.
2. Methology
2.1 The study Area
The study area, Uthai Tahni province, is situated between 14o55' to 15o50'N and 99o05' to 100o 10'E in the western continental highlands of Thailand. The extreme high and low temperatures range from 16.6o C to 40.1oC with an annual rainfall of 1,139.2 mm. The cumulative rainfall of the months August to October constitutes more than half of the annual rainfall (Charuppat, 1992). The topography ranges from flat alluvial to undulating hills and mountains with an elevation of 14 to 1,554 m. a.s.l. Majority of the area falls under the soil order Ultisols. Besides forest, the major land use types include paddy, corn, cassavea. Sorghum and sugarcane. Except for sugarcane and mungbean, the areas under other corps have decreased on 1992/93 compared to 1981/82. Crop yield has declined for all the field crops.
2.2. Materials
Various existing secondary sources of information were used in this study which included : Landsat TM
data acquired on 21 February 1995; Topographic map (1:50,000); Soil map (1:100,000); Land use map (1:250,000); is ohyetal map; published and unpublished exports, documents and literature.
2.3 Method
From USLE, soil loss is the function of six different factors as shown in the following equitation. The preparations of data for input to the equitation are discussed here under.
A=R*K*L*S*C*P
Where,
A= computed soil loss per unit area ( tons/ ha / yr)
R= Rainfall erosivity
K= Soil erodivility
L= Slope length
S= Slop steepness
C= Cover types
P= Management and Conservation practice
2.3.1 Determining the factor values
R- factor
R factor (rainfail erosivity) is the principal function of USLE Maximum rainfall intensity for 30 minutes (30) expressed as a kinetic energy of rainfall , is used to compute R factor as it is reported to have best correlation with the soil loss than the lower or the higher intensity. R is expressed in terms of annual erosivity in ton /ha/yr. In this study, the equation
R= 38.5+0.35r
where,
r = total rainfall amount in m.m.
as used by El-swaify et al (1985); Funnpheng et al (1993),was also used. Isohytal map ( based on 30 year's record) was used to delineate the different rainfall regimes in the study area. The computed values for different factors are presented in Table 1.
Table 1 Computed values for different factors
| R-Factor |
| Rainfall amount (mm) |
Computed R value |
1000-1200
1200-1400
1400-1600
1600-2000 |
423.5
493.5
563.5
688.5 |
| L - Factor |
| Average slope length (m) |
Computed L value |
50
75
100
125 |
0.72
1.62
1.83
2.00 |
|
C&P Factor Combined |
| Landuse type |
Computed CP value |
Rice paddy
Sugarcane
Orchards
Forest
Water body |
0.010
0.300
0.010
0.004
0.000 |
| K and S Factor |
| Soil groups |
Computed K value |
Computed S value |
Clayey oxic paleustults
Clayey plinthic paleaquults
Siliceous typic utipasamments
Clayey plinthic tropqualfs
Loamy oxic paleustults
Sandy spodic quartzipsamments
Clayey typic tropaqualfs
Clayey skeletal paleustults
Silty typic haplustalfs
Loamy paleustults
Clayey paleustults
Loamy aeric tropaqualfs
Clayey typic pellusterts
Clayey tpic haplustults
Loamy tpic paleudults
Loamy oxic paleustults
Sandy ustoxic quartzisammens
Mixed aeric tropaqualfs
Clayey oxic plinthaquults
Loarmy utlic haplustalfs
Clayey typic printhustulf
Skeletal oxic haplustults
Clayey aeric paleaquults
Loamy aeric paleaquults
Loamy aeric paleaquults
Loamy aquic ustifiluvents
Mixed aeric tropaquepts
Skeletal petroferric haplustults
Loamy oxic paleustults
Loamy udorthentic haplustolls
Loamy typic ustifluvents
Aquic quartzisamments
Clyey oxic haplustults
Siliceous oxic paleustults
Loamy oxic paleustults |
0.15
0.35
0.50
0.21
0.17
0.50
0.21
0.33
0.33
0.17
0.15
0.24
0.15
0.33
0.17
0.27
0.50
0.35
0.27
0.21
0.21
0.03
0.33
0.27
0.27
0.35
0.15
0.33
0.03
0.03
0.35
0.50
0.27
0.50
0.17 |
0.66
0.09
0.32
0.66
0.91
0.32
0.09
0.32
0.09
0.32
0.66
0.09
0.32
0.66
0.91
0.09
0.09
0.09
0.32
0.32
0.09
0.09
0.32
0.09
0.09
2.91
0.09
2.91
0.09
0.66
0.32
0.09
0.32
0.09
0.66 |
|
K-factor
K factor termed as soil erodibility is the integrated effect of processes that regulate rainfall acceptance and the resistance of the soil to particle detachment and subsequent transport. These processes are influenced by soil properties , of which soil texture is an important factor that influences erodibility. In Thailand, combine LS nomograph is however, available, it overestimates erosion rates on steep slopes. In this study soil erodibility was estinmated based on the characteristics of the soils using the following formula developed by Wischmeier and Smith, 1978 ( cited by Rainis, 1995).
100k = ( 2.1M1.4) 10 -4 ( 12-a ) + 3.25 (b-2) + 2.5 ( c-3)
where,
a = % of organic matter
b = soil structure class
c = soil permeability class
M= (% silt + % very fie sand) or ( 100- % clay)
L- factor
L factor which is the function of slope length along with the S factor ( slop steepness) represents the topographical factor commonly expressed as LS factor. Many researchers have used these two L and S factor as combined LS factor. Some of the researchers have also been found to consider the L and S factor as constant. In this study, L and S were computed separately. To establish the slope length, a Digital Elevation Model (DEM) was created in ERDAS and grouped in to four major classes of slope length with the help of overlaid drainage system. L factors for each slope length class were computed using with the help of overlaid drainage system. L factors for each slope length class were computed using the equation L =(meter/ 22.13)0.4 as used by Funnpheng et al, 1993.
S -factor
S factor termed as slope steepness factor is important in determining the velocity of the sediment runoff through water erosion. Since S factor is basically the function of the slope gradient, a conversion index developed in Thailand, was used to compute S factor for associated soil groups.
C and P - factor
C factor is the constant values for various land cover types and P factor is the constant values for management and conservation prractices. In the absence of separate information for these two factors, they wee treated as a single combind CP factor
2.3.2 Analytical methodology
Figure 1 persents the analytical methodology of the study. A digital image processing was performed to classify the Landsat TM data employing maximum likelihood classifier in supervised classification. Entire analysis was done done using GIS (PC ARC/INFO). The resultand soil loss rate clacualted from the analysis were further grouped in to six major groups to show the hazard severity in relation to the harzard severity in relation o the spatial distribution and their areal extent. Soil erosion rate with respect to land use types was calculated to difference the vulnerabl earea fromconservtionpoint of view.
Figure 1. Analytical methodology for soil erosion assessment
