2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing
Mapping Cover and Management Factor based on Weather Generator and Remote Sensing Zheng Duan Xianfeng Song College of Resources and Environment Graduate University of Chinese Academy of Sciences Beijing 100049, China
[email protected] verification of the proposed approach was conducted with respect to soil loss mapping in the watershed of Chao River.
Abstract—The cover and management factor (C) in the Universal Soil Loss Equation is difficult to be parameterized in a large scale developing area due to the costs involved and the lack of soil erosion experiments. This paper presents a new approach based on weather generator and remote sensing to map C factor for use in soil loss prediction. The C factor value is originally defined as an average soil loss ratio weighted according to the distribution of rainfall erosivity index during the year, so we suggested simulating the annual distribution of rainfall erosivity index by CLIGEN model and estimating potential soil loss with the fractional abundances of ground covers resulted from Linear Spectral Mixture Analysis (LSMA) of Landsat TM data. The verification of the proposed approach was conducted with respect to a qualitative analysis of land use and soil loss mapping in the watershed of Chao River, Northeast China.
II.
A. Study area The study area is Chao River watershed that is mainly located within Hebei province in northeast China, between 40°35′-41°38′N latitude and 116°8′ ～ 117°29′E longitude (Fig. 1), totally about 4800 km2. Elevations range between 50 and 2200 meters. It is characterized by a temperate semiarid climate with four distinct seasons. Mean annual temperature is about 7 , with a mean minimum of -20 and a mean maximum of 32 . The annual precipitation is about 500 mm and the rainfall mainly occurs through strong storms between June and August. The main soil types are brown soil and cinnamon soil. The agriculture dominates the basin. The cropping system is one crop per annum and the cultivations are mainly maize and soy bean.
Keywords-cover and management factor; potential soil loss ratio; rainfall erosivity index; linear spectral mixture analysis; CLIGEN
I.
METHODS AND MATERIALS
INTRODUCTION
In the Universal Soil Loss Equation (USLE), the cover and management factor (C) reflects the effect of vegetation on soil erosion rates. The technology of remote sensing has been increasingly used to estimate C factor due to its rapid and repetitive acquisition of land cover changes over broad geographic areas in comparison with a traditional groundbased soil erosion experiment. The Normalized Difference Vegetation Index (NDVI) derived from remotely sensed imagery has often been used to estimate the C factor in most studies[1,2,3,4]. The recent work by de Asis and Omasa[5] further took into account the effect of the combination of various ground covers, in which the fractional abundances of ground covers were extracted from Landsat ETM data with linear spectral mixture analysis(LMSA). However, the C factor values estimated above are merely related to the particular moment at which remotely sensed imagery is acquired. Therefore they are not applicable to annual soil loss estimation by USLE. In this study, we present a new approach based on weather generator and remote sensing to estimate C factor for use in soil erosion prediction in a large scale developing region. It concerns both the seasonal distribution of erosive rainfalls and the annual variance of ground vegetation covers. The 978-0-7695-3563-0/08 $25.00 © 2008 IEEE DOI 10.1109/ETTandGRS.2008.100
Figure 1. Location of Chao basin and Miyun reservoir
B. Cover and management factor The C factor (C) is dependent on the distribution of rainfall runoff erosivity index as well as the annual variance in the vegetation cover. As defined in USLE, the C-factor is an average soil loss ratio weighted according to the distribution of rainfall erosivity index during the year[6]. (1) C = ∑ Ri ⋅ SLRi
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Where, C is the annual C-factor value, Ri is the fraction of rainfall erosivity index during ith stage of vegetation growth (0~1), and SLRi is the soil loss ratio during ith stage (0~1).
equations. The result (Fig. 2) indicates that the rainfall erosivity varies with time significantly. Based on the Thiessen polygons built with the coordinates of 14 rain gauge stations using GIS, the monthly distribution of rainfall erosivity index over the entire watershed area was interpolated in accordance with the rainfall erosivity index of each station.
C. Simulation of rainfall erosivity index by CLIGEN There are 14 rain gauge stations in the study area, which meet the requirement of the station quantity in USLE. Since there is not complete logging of historical rainfall events, the CLIGEN, a stochastic weather generator, was adopted to simulate rainfall events. The use of CLIGEN has been verified[7,8], and it generates four precipitation related variables for each wet day, including precipitation amount P(mm), storm duration D (h), time to peak as a fraction of storm duration, tp, and the ratio of peak intensity to the average intensity, ip. Based on the four simulated variables, a double exponential function is adopted to describe the rainfall pattern by fitting the distribution of rainfall intensity as follows[9]. i p exp[b(t − t p )],0 ≤ t ≤ t p ⎧ (2) i(t) = ⎨ i exp bt t t t ,t t 1.0 − − − < ≤ [ ( ) /( 1 )] p p p p p ⎩ Where, i(t) is the ratio of rainfall intensity at time t to the average intensity during a precipitation event, b is a parameter implicitly depending on tp and ip: (3) i p (1 − exp( −bt p ) − bt p = 0
D. Estimation of potential soil loss ratio using remote sensing 1) Selection of remote sensing images by seasonality: The occurrence period of soil erosion as well as the growth period of crops and natural vegetation in the study area was taken into account in the selection of remote sensing images. The Fig. 2 implies that the soil erosion in Chao River watershed mainly occurs during April to October, which covers the entire vegetation growth period according to our field investigation. The remote sensing image is a snapshot to ground covers at a particular time. Therefore, the more seasonal images are selected, the more accurately it reflects the seasonal variance in canopy of ground covers as well as its impact on soil erosion rates. As the growth period of vegetation is also in rainy season, the image quality is often poor due to the impact of cloud on optical remote sensor. The six high quality scenes of Landsat TM acquired in May, July and September in 1990 and 1991 were finally selected. These images represent the different growth stages of crops and natural vegetation and reflect the basic trend of the variance in canopy of vegetation. 2) Extraction of Fractional Abundance of Ground Covers with LSMA: The LSMA is based on the assumption that the spectral signature of a given pixel is the linear, proportionweighted combination of the endmember spectra[10]. An endmember is a pure surface material or land-cover type that is assumed to have a unique spectral signature. The LSMA has been proved to have a reasonable interpretation, a better fitting capacity, and an acceptable accuracy although the spectral mixture of endmembers is complicated and non-linear related [11,12]. The LSMA formula is presented as follow:
Based on rainfall intensity for a precipitation event, the total storm kinetic energy and the maximum 30-min rainfall intensity can be calculated using the method proposed by Yu[7]. − bt ⎡ αi I ⎤ − ( I / I )e p −I / I (4) − e p 0 ⎞⎟ ⎥ E = Pe0 ⎢1 − p 0 ⎛⎜ e p 0 ⎝ ⎠ ⎣⎢ bt p I p ⎦⎥ (5)
I 30 = 2( Pi p /bt p )(1 − exp( − bt p / 2 D)
(6)
R = EI 30 /173.5 -1
Where, E is the total kinetic energy, e0=0.29 MJ·ha ·mm, α=0.72, I0=20 mm·h-1, Ip is peak intensity (mm·h-1), I30 is the maximum 30-min rainfall intensity if D is less than or equal to 30 min, I30=2P (mm/h), and R is the rainfall erosivity index. To overcome the uncertainty in the distribution of rainfall event in a single year, the daily precipitation data ranging from 1975 to 1991 was adopted to calculate the average annual distribution of rainfall erosivity index using the above 1
n
Gi = ∑ (rij Fj ) + εi j =1
30
25
25
20
20
15
15
10
10
5
5
0
0 Ja n
M ar
M ay
Ju ly
S ep
% of annual erosivity index (R
precipitation (P) (mm)
and 30
n
∑F
j
= 1 , 0 ≤ Fj ≤ 1
(7)
j =1
where, Gi is the spectral reflectance of the mixed pixel in band i, rij is the reflectance of the endmember j in band i, Fj is the fractional abundance of the endmember j, and εi is the residual error in band i, and n is the number of endmembers. The selection of endmembers is the most critical step in the performance of LSMA. In this work, total four endmembers were identified including vegetation, bare soil, water/shadow and non-photosynthetic materials. The number of endmembers is less than that of bands of Landsat
P R
Nov
Figure 2. Monthly precipitation (P) and percent of annual erosivity index (R) at Nanguan rain gauge station
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ratio by the distribution of rainfall erosivity index during the year (Fig. 3 C).
TM. The principle component analysis was used to reduce redundant information of spectral characteristics of endmembers and improve pixel purity index. The conversion from the digital number to reflectance value was conducted according to the method described in the Landsat 7 Users Handbook. The LSMA formula was solved with a constrained quadratic programming method, and the low root mean square error of spectral unmixing results indicates it has a better accuracy. Because the historical images were adopted in this work, it is difficult to verify the LSMA-derived factional abundances of endmembers by ground truth data. Instead, the correlation analysis was conducted between the fraction of vegetative cover and the value of NDVI, and their high correlation coefficient of 0.9 implies that the result is good for further process. 3) Calculation of potential soil loss ratio. In soil erosion experiment of USLE, soil loss ratio (SLR) is defined as the ratio of soil loss under actual conditions to losses experienced under standard conditions of clean-tilled continuous-fallow[13]. The impact of rainfall, soil and topography on soil loss is removed by mathematical division, so the SLR must represent the comprehensive effect of ground cover on soil erosion rates. Referring to the work conducted by de Asis and Omasa, the water/shadow was not considered in the process of estimating potential soil loss ratio, hence, the fractions of vegetation, bare soil, and non-photosynthetic material were rescaled to ensure their summary equal to one. We made use of the fractional abundance of these ground covers derived from remote sensing images to estimate potential soil loss ratio on a pixel-by-pixel basis as follows: α × Fbs (8) SLR = 1 + Fveg + FNPM
Figure 3. The SLRs in May, July and September and the annual average C-factor value
A. Cover and management factor There is a lack of the standard database of C factor and the national unified standard of estimating C factor in China, so this paper made a quantitative analysis on the C factors estimated in this work. By overlaying the estimated C factor map with land use map, the C values for different land use types were calculated out by zonal statistics (Table 1). The fraction of vegetative cover is one of main indicators classifying land use type, so the annual C factor values that are significantly affected by vegetation should vary regularly among land use types. TABLE I.
Land use types Wood land Shrubbery land Sparse woodland Orchards High coverage grassland Medium coverage grassland Low coverage grassland Shoaly land
where, SLR is the potential soil loss ratio, Fbs is the fraction of bare soil, Fveg is the fraction of vegetation, FNPM is the fraction of non-photosynthetic materials, and α is the correction coefficient with a default value one. The potential soil loss ratio at the time when the images were acquired (May, July and September) was estimated using the above equation, and the resulted maps of soil loss ratio are shown in Fig. 3 (SLR5, SLR7 and SLR9). It should be noted that the potential soil loss ratio was estimated with a default α=1, and may be further adjusted to improve accuracy by comparing with the experimental data of soil loss. III.
ANNUAL C FOR LAND USE IN CHAO RIVER BASIN C factor 0.03 0.07 0.09 0.15
Land use types Urban land Rural residential area Other construction Sandy land Exposed rock and shingle land
C factor 0.15 0.41 0.18 0.67
mountainous dry land
0.24
0.37
hilly dry land
0.27
0.53
plain dry land
0.18
0.12 0.22
0.17
According to Table 1, the annual C factor values among forest lands (woodland, shrubbery land and sparse woodland) increase while their fractions of canopy cover decrease according to the definition of National Standard for Land Use Classification. Same characteristics exist within grasslands (high coverage grassland, medium coverage grassland and low coverage grassland). The above interrelation proves that the C factor values estimated in this work are relatively consistent with each other.
RESULTS AND DISCUSSION
With the help of GIS, the monthly potential soil loss ratio during April to October may be simply calculated by the linear interpolation of the estimated SLRs in May, July and September. Thus, the annual C factor can be further computed by multiplying distribution of potential soil loss
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[3]
B. Soil erosion assessment After obtaining the C factor, the soil loss in the study area was predicted using USLE. The result shows that the average annual soil erosion in the basin is 1.137kg/m2, which is at the low risk of soil erosion. The soil erosion risk map was generated following the National Standard of Mapping Soil Erosion Risk from the Ministry of Water Resources of China. The map shows that severe erosion areas are mainly located in sandy lands or the cropping lands scattered along river system. The above result is close to the outcomes from the studies conducted in this basin[14,15], which makes an indirect validation of the C factor values estimated to some degree. IV.
[4]
[5]
[6] [7] [8]
CONCLUSION
This study proposed a new approach to estimate the cover and management factor in the USLE, in which two alternative methods were concerned for generating the parameters of C factor. That is, the potential soil loss ratio was derived from remote sensing images using LSMA while the rainfall erosivity index was calculated by CLIGEN model. The proposed approach offers a practicable way to the estimation of C factor for use in predicting soil erosion over broad geographic areas where there is a lack of detailed historical measurement data. Because of the complexity of estimating C factor, the long-term ground experiments of soil erosion should be further carried out to improve the method.
[9]
[10]
[11]
[12]
ACKNOWLEDGMENT [13]
This study is supported by the 2007 Grant-in-Aid Program of Kurita Water and Environment Foundation of Japan.
[14]
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