Assessment of Soil Erosion Susceptibility Using Empirical Modeling

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1 98 ACTA METEOROLOGICA SINICA VOL.27 Assessment of Soil Erosion Susceptibility Using Empirical Modeling GUO Jianping 1 (Hï ), NIU Tao 1 (Ú 7), Pooyan RAHIMY 2, WANG Fu 3 (fi L), ZHAO Haiying 4 (ë =), and ZHANG Jiahua 5 (ÜZu) 1 Institute of Atmospheric Composition Observation and Services, Chinese Academy of Meteorological Sciences, Beijing , China 2 Institute of Geo-Information Science and Earth Observation of the University of Twente, 7500 AA Enschede, the Netherlands 3 School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu , China 4 Shanxi Meteorological Observatory, Taiyuan , China 5 Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing , China (Received March 27, 2012; in final form September 22, 2012) ABSTRACT Soil erosion is one of the most serious land degradation problems all over the world, causing irreversible land quality reduction. In this paper, we modify the Revised Universal Soil Loss Equation (RUSLE) model by replacing the factors of slope length and gradient with Sediment Transport Index (STI). The Digital Elevation Model, terrain parameters, Normalized Difference Vegetation Index (NDVI), and rainfall data are used as inputs to the model. Along with the application of remote sensing techniques and ground survey measurements, erosion susceptibility maps are produced. The revised models are then used to obtain the optimal estimate of soil erosion susceptibility at Alianello of southern Italy, which is prone to soil erosion. The soil loss estimated from the modified RUSLE model shows a large spatial variance, ranging from 10 to as much as 7000 ton ha 1 yr 1. The high erosion susceptible area constitutes about 46.8% of the total erosion area, and when classified by land cover type, 33% is mixed bare with shrubs and grass, followed by 5.29% of mixture of shrubs and trees, with shrubs having the lowest percentage of 0.06%. In terms of slope types, very steep slope accounts for a total of 40.90% and belongs to high susceptibility, whereas flat slope accounts for only 0.12%, indicating that flat topography has little effect on the erosion hazard. As far as the geomorphologic types are concerned, the type of moderate steep steep slopes with moderate to severe erosion is most favorable to high soil erosion, which comprises about 9.34%. Finally, we validate the soil erosion map from the adapted RUSLE model against the visual interpretation map, and find a similarity degree of 71.9%, reflecting the efficiency of the adapted RUSLE model in mapping the soil erosion in this study area. Key words: soil, erosion, susceptibility, RUSLE, geomorphology Citation: Guo Jianping, Niu Tao, Pooyan Rahimy, et al., 2013: Assessment of soil erosion susceptibility using empirical modeling. Acta Meteor. Sinica, 27(1), , doi: /s Introduction Soil erosion is a hazard traditionally associated with agriculture in the tropical and semi-arid areas and has great long-term effects on soil productivity and sustainable agriculture (Morgan, 1995). Soil erosion is correlated with about 85% of land degradation in the world, leading to up to 17% reduction in crop productivity (Oldeman et al., 1990). To exactly assess the soil erosion is still challenging, partly due to the diverse factors affecting the estimation of soil erosion, especially due to the anthropologic activities. Gener- Supported by the Special Project on Public Welfare of Forestry ( ), China Meteorological Administration Special Public Welfare Research Fund (GYHY ), National Science and Technology Support Program of China (2008BAC40B02), National Natural Science Foundation of China ( and ), Chinese Academy of Meteorological Sciences Basic Research Project (2011Y002 and 2010Z002), and One Hundred Talents Program of the Chinese Academy of Sciences. Corresponding author: jpguo@cams.cma.gov.cn. The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2013

2 NO.1 GUO Jianping, NIU Tao, Pooyan RAHIMY, et al. 99 ally, there are two approaches to assess the soil erosion susceptibility, i.e., the qualitative method (visual image interpretation) and the quantitative method (erosion models). Besides the visual interpretation of soil erosion from satellite images or aerial photographs, a large number of erosion models exist, which can be divided into empirical models and physical-based models (Morgan, 1995). Empirical models have a statistical basis, whereas physical-based models intend to describe the controlling processes on a storm event basis. Nevertheless, many models contain both empirical and physically based components. Recent reviews of several state-of-the-art erosion models are given by Merritt et al. (2003) and Vrieling (2006). Compared with the traditional field measurements of soil erosion, remotely sensed data can greatly improve the erosion mapping through direct erosion detection or through the use of erosion controlling factors. The intermediate parameter, like Normalized Difference Vegetation Index (NDVI), is a good example, which is one of the key input parameters for the empirical models. Also, satellite imagery has the potential to provide regional spatial data for several input parameters to erosion models (e.g., King and Delpont, 1993; Pelletier, 1985). Furthermore, the automatic extraction of eroded lands from satellite imagery is a sound alternative for visual interpretation techniques. For instance, Servenay and Prat (2003) applied an unsupervised classification algorithm to multispectral SPOT (Systéme Pour l Observation de la Terre) HRV (High Resolution Visible) data to distinguish four stages of erosion. The most widely used model is the Universal Soil Loss Equation (USLE), which is an empirical model assessing long-term averages of sheet and rill erosion, based on plot data collected in eastern USA (Wischmeier and Smith, 1978). The USLE and its adapted versions, i.e., RUSLE (Renard et al., 1997) and MUSLE (Smith et al., 1984), have been widely applied to various spatial scales in different environments worldwide. However, the model is restricted to evaluating sheet and rill erosion on short slopes. Moreover, Most of previous studies have not sufficiently realized that under different conditions, the empirical relationships of the USLE may not be valid. The aim of this study is to characterize the soil erosion susceptibility in the region of Alianello of southern Italy by means of an adapted empirical model called RUSLE. The model will be cross-validated with the visual interpretation of soil erosion based on high resolution aerial photos. Also, the study intends to identify which factor (e.g., land cover, slope gradient, and geomorphologic types) more favorably affects the soil loss estimation. Major climate characteristics in the study area are described in Section 2. The data and methods employed are given briefly in Section 3. Section 4 focuses on erosion inventory from direct visual interpretation and the soil erosion susceptibility assessment based on the RUSLE model. The main conclusions are summarized in Section Study area The soil erosion susceptibility assessment is carried out over Alianello, a typical Mediterranean area located in the Agri River valley of southern Italy, between and N, and E (Fig. 1). The overall study area covers 7.34 km 2, mainly composed of rugged badland, with altitude ranging from 213- to 419-m above sea level (ASL). The economy of the study area is almost exclusively based on agriculture. The climate in the area is of the meso- Mediterranean type with mildly wet winters and hot, dry summers (UNESCO, 1963) and with irregular distribution of rainfall, which has a strong gradient from the coastline to the interior mountains. In the coastal areas, the summer drought (June September) is strong, with a rainfall of less than 100 mm and a mean monthly temperature in the warmest months being greater than 23. At higher altitudes ( m) of inland, the climate becomes cooler (20 23 ) with rainfall in summer being about the same. Above 1600 m, the rainfall in the critical summer months is more than 150 mm. The climate and land-use have further influenced vegetation cover, where herbs and shrubs take over the place of deforested areas in the badlands (Basso et al.,

3 100 ACTA METEOROLOGICA SINICA VOL.27 Fig. 1. The study area of Alianello in southern Italy. The upper right inset is RGB composite of the Alianello area digitized from IKONOS (Greek word for image ) satellite image, whereas the lower left one is a geographic map of southern Italy. Both of them are mapped in the projection of Gauss-Boaga Zone ). Despite innovations in irrigation methods and farming systems, badland formation continues to persist. Due to the high energy relief in the area, the characteristic climate conditions, the vegetation type, and the anthropogenic influences on the area, Basilicata (a larger region in which Alianello lies) has undergone extreme levels of erosion (Oliver, 1993). For the above-mentioned reason, the Alianello area is taken as a very good example for studying erosion on a detailed level. 3. Data and methods 3.1 Data The data used in this study contain both preexisting and post processed data. The pre-existing data consist of: (1) field surveyed soil geophysical data for derivation of the K factor in the RUSLE model, (2) stereoscopic aerial photograph mainly for the visual interpretation and geomorphologic features extraction by digitization, (3) DEM (Digital Elevation Model) data with a resolution of 15 m, (4) ASTER (Advanced Spaceborne Thermal Emission and Reflection) image for NDVI derivation, and (5) rainfall data from nearly 40 weather stations. The first step in creation of the soil erosion map is to define its characteristics, such as timescale, spatial resolution, geo-reference system, and input factors. All of the available data are processed to a spatial resolution of 15 m with the same geo-reference system of Gauss-Boaga Zone 2 in order to facilitate the calculation of soil erosion by models. Based on the above-mentioned data, several thematic maps are produced, i.e., land cover map, slope angle, flow direction, flow sink, and accumulation map. Additional spatial data include daily and annual rainfall data during the period at stations scattering through the areas surrounding the Alianello watershed, soil maps (containing soil texture), topographic maps, and NDVI image derived from the ASTER image with a pixel size of 15 m. Note that conservation practices for farming are actually assumed to be constant here. 3.2 Methods As elucidated by Lane et al. (1988), erosion models can be classified into three types: processbased (physical-based) and empirical erosion, as well as conceptual models, which lie somewhere between the process-based and purely empirical models. Al-

4 NO.1 GUO Jianping, NIU Tao, Pooyan RAHIMY, et al. 101 though the development of process-based erosion models are becoming one of current research focuses, the standard model for soil erosion assessment is the empirically based USLE and its revised versions, which are expected to be active. Among the available models for assessing soil erosion, RUSLE (Renard et al., 1997) has been widely applied to various environments but is not free from criticism despite refinements and revisions (Van der Knijff et al., 1999, 2002; Grimm et al., 2003). Furthermore, RUSLE model relates management and environmental factors directly to soil loss or sedimentary yields through statistical methods. Considering the variety of space-borne and in-situ measurements available for this study, we first perform the visual interpretation of soil erosion from satellite and aerial images and then build up an erosion inventory. Based on the interpretation results, complemented with automatic derivation of factors affecting soil erosion, the soil susceptibility will be assessed in more detail using the RUSLE model, which takes the following simple product form: A = R K C S L P, (1) where A is the average annual potential soil erosion in tons per acre per year, R indicates the average rainfall erosivity, S and L represent slope gradient and length, respectively, the product of L and S means the average topographical parameter, K represents soil erodibility, C represents the average land cover and management factor, and P represents the average conservation practice factor. All factors R, K, C, S, L, and P are unitless. Another factor for soils is called T value in ton ha 1 yr 1, which stands for Tolerable Soil Loss. It is not directly used in RUSLE equation, but is often used along with RUSLE for conservation planning. As an attempt to replace the product of S and L in Eq. (1), we utilize the Sediment Transport Index (STI), which is also supposed to play a key role in the effect of topography on erosion as well (Moore et al., 1993). STI = (A f /22.13) 0.6 (sin(arctan(slope/100)))/ , (2) where A f indicates the catchment area, reflecting the erosive power of the overland flow. The modified RUSLE model is operated at an annual timescale and covers the whole area of Alianello (7.34 km 2 ). On this spatial scale, erosion processes are mostly controlled by soil type, land use, and climate patterns in relation to topography (Auzet et al., 2002). On a side note, the RUSLE model only emphasizes rill and inter-rill erosion but does not consider the sedimentation, which means the RUSLE results merely show the potential erosion. To derive the A value in Eq. (1) for each pixel in our study area, each factor should be calculated by combining the corresponding model and the visual image interpretation techniques. A summary of the soil erosion assessment strategies is given in Fig. 2. It should be emphasized here that the direct classification of soil erosion from the interpreted geomorphology is based on the erosion features in each geomorphologic unit. These features include (but are not limited to) badlands, dissected areas with gullies, and sheet or rill erosions. Three classes are given for the soil erosion on the basis of the loss estimations from the modified RUSLE model: low, moderate, and high. These classes are interpreted according to differences in soil tone and texture. Finally, we perform a statistical validation of the model (error matrix) through arranging the obtained linear channel volumes into three classes, allowing their comparison with modeled erosion classes using the adapted RUSLE. 4. Results and discussion 4.1 Geomorphologic characteristics Based on the visual image interpretation from Google image acquired by IKONOS (see Fig. 1 caption) in 2006, we obtain the geomorphic map and land cover map (due to the limitation of space, the maps are omitted here). The geomorphology over the study area of Alianello is generally hilly. Clearly, we can recognize badlands with erosion in process from the geomorphic map. It is noticeable that the northern Alianello is on the moderately steep slopes with moderately severe badlands development. In these hilly

5 102 ACTA METEOROLOGICA SINICA VOL.27 Fig. 2. Strategies for the soil erosion assessment by combining visual image interpretation and RUSLE model technique. areas, one distinctive morphological environment can be identified: back slopes and valleys covered by forest and tree plantation and the face slopes mostly by shrubs and grass. In the middle part of the study area, longitudinal highlands with steep highly eroded face slopes (associated with badland development) consist of biancane and macro pediment, while in the back slopes, the area is on moderate steep slopes and covered by mixed shrubs and grass. Towards the south, the hilly badlands are continuing with different kinds of slopes, including steep eroded face slopes and agriculture fields of grasslands in the back slopes. To the southeast, sharp hilly badlands with calanchi show up, and it is possible that the erosion process of rills and gullies occurs. 4.2 Factor map The following subsections discuss factor estimations for use in the RUSLE model of this study Rain erosivity The R factor in the RUSLE model can be represented in the form of rain erosivity, which indicates the driving force of sheet and rill erosion by rainfall and runoff. We work out the R values from in-situ rainfall amount and intensity observations. In this study, the datasets of daily rainfall amount are obtained from the surrounding 40 weather stations, which are separated about 13 to 65 km apart. The data are first aggregated into annual rainfall amounts for the calculation of factor of rain erosivity in the RUSLE model. The most straightforward approach is to derive rainfall value directly from the collocated elevation through a nonlinear regression. Unfortunately, the coefficient of determination between elevation and rainfall amount is as low as The moving average interpolation method, therefore, is undertaken. To simplify the procedure, several attempts have been made to directly derive the R factor based on the empirical relationship between R and rainfall amount (Renard and Freimund, 1994; Hu et al., 2000). The equation developed by Renard and Freimund (1994) is taken for the estimation of R factor in this study as

6 NO.1 GUO Jianping, NIU Tao, Pooyan RAHIMY, et al. 103 follows: Pa 1.61, P a 850 mm; R = P a Pa 2.0, P a > 850 mm, (3) where P a denotes annual rainfall amount in mm. Based on Eq. (3), we derive the R factor map. As shown in Fig. 3a, the R value increases gradually from east to west K factor K factor, provided in point format, can be estimated from ground surveyed measurements. We interpolate the point value of K over continuous area using the moving average method (Fig. 3b). It can be found that the K factor shows almost the same spatial pattern as the R value. This is possibly due to the fact that the K factor represents both susceptibility of soil to erosion and the amount and rate of runoff (Jones et al., 1996), which are closely associated with rainfall STI index As mentioned above, as a function of DEM and flow accumulation, STI is used to account for the effect of topography. Regarding how water and mass fluxes are considered for the mass balance for each pixel, a simple explanation was given by Burrough and McDonnell (1998). Flow accumulation, depending on the drainage pattern of a terrain, quantifies how much water flows through each point of the terrain if water naturally drains into outlets. To estimate flow accumulation for every cell in a grid-based DEM, the flow direction is required to be calculated, which determines the natural drainage direction for every cell of a terrain. Based on the flow direction results, the flow accumulation function counts the total number of pixels that will flow into outlets. Afterwards, flow accumulation is used in the computation of STI, which is in turn applied to model soil erosion through RUSLE C and P factors To represent better the effect of land use and erosion conservation practice, RUSLE relies on C factor to consider the effect of cropping and management, while on P factor for support practices (Renard et al., 1997). Both C and P factors are related to the land use identified by land cover types. To estimate C factor, NDVI should be first derived from satellite images. Attempts have been made to explore the relationship between C and NDVI, including the linear equation of Van der Knijff et al. (1999). Accounting for the limitation of a linear model under certain circumstances, we employ the following equation to estimate C factor, which is strongly associated with land cover. C =0.227 e ( NDVI). (4) The NDVI map from ASTER with 15-m resolution and the C factor map are given in Fig. 4a, with most NDVI values between 0.16 and 0.64, whereas C generally is less than 0.1. Fig. 3. (a) R factor map derived from rainfall measurements at the surrounding weather stations and (b) K factor map derived from ground surveyed measurements at the discrete stations.

7 104 ACTA METEOROLOGICA SINICA VOL.27 Through further statistical analyses, we find that the area covered by agriculture fields or grasslands and mixture of shrubs and trees have the maximum C value (0.023) (Table 1). Because not enough field observations have been taken for land cover type and land use over the study area of Alianello, different P values corresponding to various land cover types have been taken from the results from Jung et al. (2004) for use in the RUSLE model. 4.3 Soil loss estimation The soil loss values over the Alianello area are calculated using Eq. (1). Figure 5 depicts the spatial pattern of soil loss. Three qualitative categories have been chosen for the output soil erosion classes: low, moderate, and high soil erosion susceptibility. From Fig. 5, it can be seen that the total annual soil loss in this region is less than 1400 ton ha 1 yr 1, except for some spikes with extremely high value of 7000 ton ha 1 yr 1 in the valleys with steep slopes. Table 2 shows that the very steep slope leads to the risk of soil erosion, resulting in loss of ton ha 1 yr 1 (35.20%), while flat slope is the least susceptible to soil erosion. Furthermore, it can be seen clearly from Table 2 that the steeper the slope, the more the soil loss. This holds true when no other factors like conservative practice takes effect. As for the land cover type (with respect to visual image interpretation), it is more complicated. Table 1 shows that mixed bare with shrubs and grass is the most susceptible ( ton ha 1 yr 1 ) to soil erosion in this region, followed by mixture of shrubs and trees. Meanwhile, the type of build-up is the most resistant to soil erosion, but bare land results in a soil loss as low as ton ha 1 yr 1, which can be attributed to the soil resistance to erosion or the gentle slope in these areas. To assess the accuracy of Fig. 4. (a) NDVI map from ASTER acquired in 2006 and (b) C factor map based on NDVI. Table 1. Average C factor and average annual soil loss for each land cover type Land cover type Average C Average soil loss Percentage factor (ton ha 1 yr 1 ) (%) Build-up Shrubs Bare land Mixture of shrubs and grass Trees Tree plantation Mixed bare with shrubs and grass Agriculture fields or grasslands Mixture of shrubs and trees

8 NO.1 GUO Jianping, NIU Tao, Pooyan RAHIMY, et al. 105 the produced table, validation with independent data is required, which can be obtained from field measurements, surveys, and high-resolution imagery. Unfortunately, the ground survey data are very limited in this study and this should be further investigated in the future. Fig. 5. Annual total soil loss over Alianello in 2006 from the modified RUSLE model. Table 2. Average annual soil loss area and the area located in the high erosion area for each slope type Slope angle range Average soil loss Area Slope type (degree) (ton ha 1 yr 1 )(percentage) (km 2 )(percentage) Flat (3.98%) 0.01(0.12%) Gentle (10.08%) 0.04(0.59%) Moderately steep (20.89%) 0.08(1.14%) Steep (29.85%) 0.30(4.12%) Very steep (35.20%) 3.00(40.9%) Table 3. Average annual soil loss for each geomorphologic type Geomorphologic type Average soil loss (ton ha 1 yr 1 ) Footslope Micro-footslopes (micro pediments) Non-dissected footslopes Dissected footslopes Other denudational slope Moderate steep slopes without badland development Moderate steep steep dissected slopes Moderate steep steep slopes with moderate to severe erosion Moderate steep steep highly dissected slopes Steep eroded escarpment caused by undercutting of the river with landslide activity Infilled valleys and footslopes Infilled valley bottoms and foot slope zone Re-incised infilled valley bottoms Face-slope Steep, highly eroded face slopes associated with badland development Steep eroded face slopes Back-slope Slightly dissected back slopes Highly dissected back slopes Back slopes with landslides Slightly dissected long back slopes

9 106 ACTA METEOROLOGICA SINICA VOL.27 Table 4. Area size and percentage for the high erosion area in terms of land cover type Land cover type Area (km 2 ) Percentage (%) Shrubs Build-up Agriculture fields or grasslands Tree plantation Trees Mixture of shrubs and grass Bare land Mixture of shrubs and trees Mixed bare with shrubs and grass Regarding the vulnerability of geomorphologic type to soil erosion given in Table 3, the type of moderate steep slope ranks the first, followed by foot slope, and back slope is the least vulnerable to erosion. Although the model can yield quantitative values of soil erosion, the impact of individual factors such as P, K, land cover, and topographic features (take STI here for example) can only be explained in a qualitative sense (De Jong and Riezebos, 1997). 4.4 Soil erosion susceptibility assessment To assess where the future soil erosion will more probably occur in this study area, we calculate the average annual soil loss mainly in terms of topographic characteristics of slope type, land cover, and geomorphologic type, respectively. Based on the soil loss, the susceptibility can be deduced accordingly. High erosion susceptibility areas indicate the high soil erosion loss areas, as demonstrated in Fig. 5. Although there are actually several possibilities in extracting them, the identified high erosion areas have been directly extracted from the erosion loss maps according to the scheme described in detail in Section 3.2. Namely, the erosion is classified as low (< 1000 ton ha 1 yr 1 ), moderate ( ton ha 1 yr 1 ), and high ( ton ha 1 yr 1 ). The high erosion susceptible area constitutes about 46.8% of the whole area and when classified by land cover type, 33% (2.427 km 2 ) is mixed bare with shrubs and grass, followed by 5.29% (0.389 km 2 ) of mixture of shrubs and trees, with shrubs having the lowest percentage of 0.06%. Table 4 shows the percentage for each land cover type in detail. In terms of slope type, Fig. 6a shows that very steep slope type accounts for a total of 40.90% of the study area belonging to the class of high susceptibility, whereas flat slope area only accounts for 0.12%, indicating the flat topography has a very limited effect on the erosion hazard. The details of percentage for each slope class are given in Table 2. As far as the geomorphologic types are concerned, Table 5 shows that the type of moderate steep steep slopes with moderate to severe erosion is most susceptible to high soil erosion, which comprises about km 2 (9.34%). In contrast, the least area located in the high erosion area is the type of re-incised infilled valley bottoms with km 2 (0.04%). Table 5. Area size and percentage of the high erosion area in terms of geomorphologic types Geomorphologic type Area (km 2 ) Percentage (%) Re-incised infilled valley bottoms Back slopes with landslides Infilled valley bottoms and foot slope zone Non-dissected footslopes Dissected footslopes Slightly dissected back slopes Infilled valleys and footslopes Slightly dissected long back slopes Moderate steep steep highly dissected slopes Moderate steep steep dissected slopes Moderate steep slopes without badland development Highly dissected back slopes Steep eroded face slopes Steep, highly eroded face slopes associated with badland development Moderate steep steep slopes with moderate to severe

10 NO.1 GUO Jianping, NIU Tao, Pooyan RAHIMY, et al Validation of soil erosion susceptibility The validation of the erosion susceptibility from the adapted RUSLE model is of great importance for this study. As Vrieling et al. (2006) and Kheir et al. (2006) described, validation can be accomplished through ground truth survey and confusion matrix. However, there are always difficulties in erosion validation using ground truth survey especially at locations with complex terrain and private lands where accessibility is quite limited. A quantitative validation of soil erosion by means of field measurements is still a great challenge. As we know, rills and gullies are the most distinct signs of erosion in the study area, whereby the visual interpretation from high resolution aerial photos to some extent can be thought as an alternative to validate the model result. The quantitative erosion loss computation from the modified RUSLE model is carried out, whereby the erosion susceptibility class map (Fig. 6a) is derived based on classification scheme described in Section 3.2. Considering the unavailability of groundbased measurements, the visually interpreted erosion results from remotely sensed satellite images acquired in 2006 are shown in Fig. 6b, as a means of validation against model results. The soil erosion susceptibility map (Fig. 6a) from the modified RUSLE model comprises the same classes (low, moderate, and high) as those from visual image interpretation (Fig. 6b). The high erosion class covers about 45% of the study area on the erosion map from the adapted RUSLE model but increases to an area of 56% in the map from visual interpretation. The increase of area in the map shown in Fig. 6b is equivalent to km 2 (10%). The comparison (in km 2 ) of classes in the two maps in Fig. 6 permits assessment of the effect of adapted RUSLE model on erosion mapping (Table 6) in some degree. In this contingency table, the sum of areas to which the classes of Figs. 6a and 6b correspond strictly (km 2 ; in boldface on the diagonal of the table) are divided by the total area of the study region (km 2 ). Table 6 shows a similarity degree between the two maps of 71.9%, reflecting the major effect played by the adapted RUSLE model in mapping the soil erosion in this study area. According to Fig. 6, there are some differences between the two erosion classification maps. Figure 6b shows that most of the area is hazard-prone to high erosion while Fig. 6a seems to show more details. Furthermore, the cross operation shows that there is also a little difference between the erosion modeled and the image interpreted maps. The difference can be caused by errors in image interpretation, lack of field work, and the subjective characteristics of the inventory map. Also, the soil factor used in the erosion model was produced by inadequate soil data, which may cause errors in the computation. The C and P factors were not rather accurate because the exact land cover type and land use practices must be studied for Fig. 6. Soil erosion susceptibility classifications over the Alianello area in 2006 from (a) modified RUSLE model and (b) visual image interpretation.

11 108 ACTA METEOROLOGICA SINICA VOL.27 Table 6. Error matrix of the two erosion susceptibility maps Class of erosion Map from RUSLE model susceptibility Low Moderate High Total Low Map from visual image Moderate interpretation High Total Numbers were in unit of km 2. Similarity degree between the two maps is calculate as 5.28/7.34 = 71.9%. Boldface on the diagonal indicates the correct classification. each geomorphologic unit. All of these can contribute to the difference between the two erosion maps. 5. Summary In this paper, we employ the visual image interpretation technique from combination of stereo aerial photograph and satellite images to derive the geomorphologic characteristics and land cover over Alianello of southern Italy in Furthermore, the empirical model RUSLE for assessing soil erosion is used to investigate the soil erosion in this area. The RUSLE model is a quantitative erosion model that has fixed input parameters, which can be adapted to fit with available data accounting for locally important processes and factors. We applied STI instead of the combination of L and S to calculate the soil erosion. Apart from this, the soil erosion map is produced from visual image interpretation as a way of validation against the RUSLE model output. The results show that the similarity degree between the two erosion maps from different methods amounts up to 71.9%, indicating that the adapted model is a useful tool for assessing spatial variations in erosion patterns for the study area where the terrain is quite rugged. The estimation of soil loss using the modified RUSLE model has a large variance, ranging from 10 to 7000 ton 1 ha 1 yr 1. Furthermore, it is observed that the soil loss is closely associated with the degree of terrain slopes. Besides, other factors such as land cover influence the soil loss greatly as well. On the basis of the maps produced from the adapted RUSLE model, it has become possible to assess the spatial patterns in soil erosion and to identify high erosion areas. Above all, the model could be good at prediction of the soil erosion. The modeled erosion classification gives a quite realistic erosion prediction in comparison to the image interpreted erosion classification. Also, the susceptibility and loss mapping reported in this paper can be adapted to other countries which need an erosion assessment for the identification of high erosion areas and priority action areas. Acknowledgments. The authors wish to thank Dr. C. J. Cees van Westen and Doctorandus N. C. Kingma with International Institute for Geo- Information Science and Earth Observation (ITC), University of Twente, the Netherlands for their kind suggestions and comments on the susceptibility assessment. This work was mainly finished at ITC, the Netherlands. REFERENCES Auzet, A. V., J. Poesen, and C. Valentin, 2002: Soil patterns as a key controlling factor of soil erosion by water. Catena, 46, Basso, F., E. Bove, M. Del Prete, et al., 1996: The Agri Basin, Basilicata, Italy. Atlas of Mediterranean Environments in Europe: The Desertification Context. John Wiley and Sons, Inc., London, Burrough, P. A., and R. A. McDonnell, 1998: Principles of Geographic Information Systems. Oxford University Press, New York, 356 pp. De Jong, S. M., and H. T. Riezebos, 1997: SEMMED: A distributed approach to soil erosion modelling. The 16th EARSel Symposium, Malta, May 1996, Grimm, M., R. J. A. Jones, and L. Montanarella, 2003: Soil Erosion Risk in Italy: A Revised USLE Approach. European Soil Bureau Research Report No. 11, EUR EN. Office for Official Publications of the European Communities, Luxembourg, 26. Hu, Q., C. J. Gantzer, P. K. Jung, et al., 2000: Rainfall erosivity in the Republic of Korea. J. Soil Water

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