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1.
This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha−1 ya−1 in 2005, and the area of 47.5 km2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.  相似文献   

2.
Expansion of agricultural at the cost of forested land is a common cause of watershed degradation in the mountain zones of developing countries. Many studies have been conducted to demonstrate land use changes in such regions. However, current knowledge regarding the changes, driving forces and implications of such change within the context of watershed development is limited. This study analyses changes in spatial patterns of agricultural land use and their consequences for watershed degradation during the 1976–2000 period along an altitude gradient in a watershed in Nepal, by means of remote sensing, GIS and the universal soil loss equation. Estimated soil loss ranged from 589 to 620 t ha−1 y−1, while areas of extreme hazard severity (>100 t ha−1) increased from 9 to 14.5% from 1990 to 2000. Spatial distribution of soil loss in 2000 was characterized by 88% of total soil losses being from upland agricultural areas. The study determined that without considering other forms of land degradation, only water erosion was responsible for erosion of a substantial area in a short timeframe. Areas under upland cultivation are in an extremely vulnerable state, with these areas potentially no longer cultivable within a period of 6 years. As sustainability of the watershed is dependent on forests, continued depletion of forest resources will result in poor economic returns from agriculture for local people, together with loss of ecosystem services. Thus, in order to achieve the goal of watershed development, remaining forest lands must be kept under strict protection.  相似文献   

3.
Soil erosion modeling of a Himalayan watershed using RS and GIS   总被引：5，自引：1，他引：4  
Employing the remote sensing (RS) and geographical information system (GIS), an assessment of sediment yield from Dikrong river basin of Arunachal Pradesh (India) has been presented in this paper. For prediction of soil erosion, the Morgan-Morgan and Finney (MMF) model and the universal soil loss equation (USLE) have been utilized at a spatial grid scale of 100 m × 100 m, an operational unit. The average annual soil loss from the Dikrong river basin is estimated as 75.66 and 57.06 t ha−1 year−1 using MMF and USLE models, respectively. The watershed area falling under the identified very high, severe, and very severe zones of soil erosion need immediate attention for soil conservation.  相似文献   

4.
This study was aimed at predicting soil erosion risk in the Buyukcekmece Lake watershed located in the western part of Istanbul, Turkey, by using Revised Universal Soil Loss Equation (RUSLE) model in a GIS framework. The factors used in RUSLE were computed by using different data obtained or produced from meteorological station, soil surveys, topographic maps, and satellite images. The RUSLE factors were represented by raster layers in a GIS environment and then multiplied together to estimate the soil erosion rate in the study area using spatial analyst tool of ArcGIS 9.3. In the study, soil loss rate below 1 t/ha/year was defined as low erosion, while those >10 t/ha/year were defined as severe erosion. The values between low and severe erosion were further classified as slight, moderate, and high erosion areas. The study provided a reliable prediction of soil erosion rates and delineation of erosion-prone areas within the watershed. As the study revealed, soil erosion risk is low in more than half of the study area (54%) with soil loss <1 t/ha/year. Around one-fifth of the study area (19%) has slight erosion risk with values between 1 and 3 t/ha/year. Only 11% of the study area was found to be under high erosion risk with soil loss between 5 and 10 t/ha/year. The severe erosion risk is seen only in 5% of the study area with soil loss more than 10 t/ha/year. As the study revealed, nearly half of the Buyukcekmece Lake watershed requires implementation of effective soil conservation measures to reduce soil erosion risk.  相似文献   

5.
The present comparative study is multi-temporal in nature. The Revised Universal Soil Loss Equation (RUSLE), remote sensing, and GIS were used to model the soil loss estimation for soil conservation and vegetation rehabilitation in Nun Nadi watershed for the years 2000 and 2009. The estimated mean soil loss for the year 2000 and 2009 is 3,283.11 and 1,419.39 Mg?ha?1 year?1, respectively. The study finds that about 80 % area has low or least risk of erosion and about 7 % is exposed to high or very high risk which indicates the improvement in terms of soil loss if we compare the data of both the time periods. The findings show that the rainfall, LULC change, and elevation are the main responsible factors for the soil loss in Nun Nadi watershed. Conservation measures have been adopted; however, the problem still remains serious and demands urgent attention.  相似文献   

6.
Estimation of soil erosion using RUSLE in Caijiamiao watershed,China   总被引：4，自引：1，他引：3  
Jinghu Pan  Yan Wen 《Natural Hazards》2014,71(3):2187-2205
Soil erosion is a serious environmental and production problem in China. In particular, natural conditions and human impact have made the Chinese Loess Plateau particularly prone to intense soil erosion area. To decrease the risk on environmental impacts, there is an increasing demand for sound, and readily applicable techniques for soil conservation planning in this area. This work aims at the assessment of soil erosion and its spatial distribution in hilly Loess Plateau watershed (northwestern China) with a surface area of approximately 416.31 km2. This study was conducted at the Caijiamiao watershed to determine the erosion hazard in the area and target locations for appropriate initiation of conservation measures using the revised universal soil loss equation (RUSLE). The erosion factors of RUSLE were collected and processed through a geographic information system (GIS)-based approach. The soil erosion parameters were evaluated in different ways: The R-factor map was developed from the rainfall data, the K-factor map was obtained from the soil map, the C-factor map was generated based on Landsat-5 Thematic Mapper image and spectral mixture analysis, and a digital elevation model with a spatial resolution of 25 m was derived from topographic map at the scale of 1:50,000 to develop the LS-factor map. Support practice P factor was from terraces that exist on slopes where crops are grown. By integrating the six-factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the study area was obtained by the RUSLE model. The results showed that spatial average soil erosion at the watershed was 78.78 ton ha?1 year?1 in 2002 and 70.58 ton ha?1 year?1 in 2010, while the estimated sediment yield was found to be 327.96 × 104 and 293.85 × 104 ton, respectively. Soil erosion is serious, respectively, from 15 to 35 of slope degree, elevation area from 1,126 to 1,395 m, in the particular area of soil and water loss prevention. As far as land use is concerned, soil losses are highest in barren land and those in waste grassland areas are second. The results of the study provide useful information for decision maker and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a river watershed scale on a cell basis in Chinese Loess Plateau and for planning of conservation practices.  相似文献   

7.
Soil erosion is a growing problem in southern Greece and particularly in the island of Crete, the biggest Greek island with great agricultural activity. Soil erosion not only decreases agricultural productivity, but also reduces the water availability. In the current study, an effort to predict potential annual soil loss has been conducted. For the prediction, the Revised Universal Soil Loss Equation (RUSLE) has been adopted in a Geographical Information System framework. The RUSLE factors were calculated (in the form of raster layers) for the nine major watersheds which cover the northern part of the Chania Prefecture. The R-factor was calculated from monthly and annual precipitation data. The K-factor was estimated using soil maps available from the Soil Geographical Data Base of Europe at a scale of 1:1,000,000. The LS-factor was calculated from a 30-m digital elevation model. The C-factor was calculated using Remote Sensing techniques. The P-factor in absence of data was set to 1. The results show that an extended part of the area is undergoing severe erosion. The mean annual soil loss is predicted up to ∼200 (t/ha year−1) for some watersheds showing extended erosion and demanding the attention of local administrators.  相似文献   

8.
Remote sensing data and Geographical Information System (GIS) has been integrated with the weighted index overlay (WIO) method and E 30 model for the identification and delineation of soil erosion susceptibility zones and the assessment of rate of soil erosion in the mountainous sub-watershed of River Manimala in Kerala (India). Soil erosion is identified as the one of the most serious environmental problems in the human altered mountainous environment. The reliability of estimated soil erosion susceptibility and soil loss is based on how accurately the different factors were estimated or prepared. In the present analysis, factors that are considered to be influence the soil erosion are: land use/land cover, NDVI, landform, drainage density, drainage frequency, lineament frequency, slope, and relative relief. By the WIO analysis, the area is divided into zones representing low (33.30%), moderate (33.70%), and high (33%) erosion proneness. The annual soil erosion rate of the area under investigation was calculated by carefully determining its various parameters and erosion for each of the pixels were estimated individually. The spatial pattern thus created for the area indicates that the average annual rate of soil erosion in the area was ranging from 0.04 mm yr−1 to 61.80 mm yr−1. The high soil erosion probability and maximum erosion rate was observed in areas with high terrain alteration, high relief and slopes with the intensity and duration of heavy precipitation during the monsoons.  相似文献   

9.
A simplified regression model is here calibrated on the basis of rainfall data records of Sicily (southern Italy), in order to show the model reliability in assessing the R-factor of the Universal Soil Loss Equation and its revised version (RUSLE) and to provide an estimate of long-term rainfall erosivity at medium-regional scale. The proposed model is a rearrangement of a former simplified model, formulated for the Italian environment, grouping three easily available rainfall variables on various time scales, which has been shown to be more successful than others in reproducing the rainfall erosive power over different locations of Italy. A geostatistical interpolation procedure is then applied for generating the regional long-term erosivity map with associated standard error. Areas with severe erosive rainfalls (from 2,000 up to more than 6,000 MJ mm ha−1 h−1) are pointed out which will correspond to areas suffering from severe soil erosion. Solving the problem of calculating the R-factor value in the RUSLE equation by means of such a simplified model here formulated will allow to predict the related soil loss. Moreover, given the availability of long time-series of concerned rainfall data, it will be possible to analyse the variability of rainfall erosivity within the last 50 years, and to investigate the application of RUSLE or similar soil erosion models with forecasting purposes of soil erosion risk.  相似文献   

10.
Mapping of erosion risk areas is an important tool for the planning of natural resources management, allowing researchers to propose the modification of land use properly and implement more sustainable long-term management strategies. The objective of this study was to assess and identify critical sub-catchments for soil conservation management using the USLE, GIS, and remote sensing techniques. The Tapacurá catchment is one of the planning units for water resource management of the Recife Metropolitan Region. Maps of the erosivity (R), erodibility (K), slope (LS), cover-management (C), and support practice (P) factors were derived from the climate database, digital elevation model, and soil and land-use maps. In order to validate the simulation process, total sediment delivery ratio was estimated. The results showed a mean sediment delivery ratio (SDR) of around 11.5?% and a calculated mean sediment yield of 0.108?t?ha?1?year?1, which is close to the observed one, 0.169?t?ha?1?year?1. The obtained soil loss map could be considered as a useful tool for environmental monitoring and water resources management. The methodology applied showed acceptable precision and allowed the identification of the most susceptible areas to soil erosion by water, constituting an important predictive tool for soil and environmental management in this region, which is highly relevant for the prediction of varying development scenarios for Tapacurá catchment. This approach can be applied to other areas for simple and reliable identification of critical areas of soil erosion in catchments.  相似文献   

11.
The installation of a rural settlement complex in the watershed stream Indaiá has promoted changes in land-use and vegetation cover dynamics; however, the effects of intensive agriculture and cattle farming in rural settlements on soil loss rates are not well known. Predictive models implemented in geographic information systems have proven to be effective tools for estimating erosive processes. The erosion predictive model Revised Universal Soil Loss Equation (RUSLE) is a useful tool for analyzing, establishing and managing soil erosion. RUSLE has been widely used to estimate annual averages of soil loss, by both interrill and rill erosion, worldwide. Therefore, the aim of this work was to estimate the soil loss in the watershed stream Indaiá, using the RUSLE model and geoprocessing techniques. To estimate soil loss, the following factors were spatialized: erosivity (R), erodibility (K), topography (LS), land-use and management (C) and conservation practices (P); the annual soil loss values were calculated using the RUSLE model equation. The estimated value of soil loss in the hydrographic basin ranged from 0 to 4082.16 Mg ha?1 year?1 and had an average value of 47.81 Mg ha?1 year?1. These results have demonstrated that 68.16 % of the study area showed little or no soil loss based on the Food and Agriculture Organization’s (FAO 1980) classification. When comparing the average value of soil loss obtained using the RUSLE model with the Natural Potential for Erosion, a 16-fold reduction in soil was found, which highlighted the fact that vegetation cover (C factor) has a greater influence than other factors (R, K and LS) on soil loss prediction attenuation. These results lead to the conclusion that soil loss occurs by different methods in each settlement in the basin and that erosive processes modeled by geoprocessing have the potential to contribute to an orderly land management process.  相似文献   

12.
Water erosion is a serious and continuous environmental problem in many parts of the world. The need to quantify the amount of erosion, sediment delivery, and sediment yield in a spatially distributed form has become essential at the watershed scale and in the implementation of conservation efforts. In this study, an effort to predict potential annual soil loss and sediment yield is conducted by using the Revised Universal Soil Loss Equation (RUSLE) model with adaptation in a geographic information system (GIS). The rainfall erosivity, soil erosivity, slope length, steepness, plant cover, and management practice and conservation support practice factors are among the basic factors that are obtained from monthly and annual rainfall data, soil map of the region, 50-m digital elevation model, remote sensing (RS) techniques (with use of Normalized Difference Vegetation Index), and GIS, respectively. The Ilam dam watershed which is located southeast part of Ilam province in western Iran is considered as study area. The study indicates that the slope length and steepness of the RUSLE model are the most effective factors controlling soil erosion in the region. The mean annual soil loss and sediment yield are also predicted. Moreover, the results indicated that 45.25%, 12.18%, 12.44%, 10.79%, and 19.34% of the study area are under minimal, low, moderate, high, and extreme actual erosion risks, respectively. Since 30.13% of the region is under high and extreme erosion risk, adoption of suitable conservation measures seems to be inevitable. So, the RUSLE model integrated with RS and GIS techniques has a great potential for producing accurate and inexpensive erosion and sediment yield risk maps in Iran.  相似文献   

13.
This study is aimed at the evaluation of the hazard of soil erosion and its verification at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Precipitation, topographic, soil, and land use data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. Areas that had suffered soil erosion were analysed and mapped using the Universal Soil Loss Equation (USLE). The factors that influence soil erosion are rainfall erosivitiy (R) from the precipitation database, soil 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. The soil erosion map verified use of the landslide location data. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys.  相似文献   

14.
Soil erosion by water is a serious problem in southern Italy, particularly in Sicily which is one of the Italian administrative regions prone to desertification. Soil erosion not only affects soil quality, in terms of agricultural productivity, but also reduces the availability of water in reservoirs. This study was conducted in the Comunelli catchment in south-central Sicily, to predict potential annual soil loss using the revised universal soil loss equation (RUSLE) and to test the reliability of this methodology to predict reservoirs siltation. The RUSLE factors were calculated for the catchment using survey data and rain gauge measurement data. The R-factor was calculated from daily, monthly and annual precipitation data. The K-factor was calculated from soil samples collected in May and November 2004. The LS topographic factor was calculated from a 20 m digital elevation model. The C- and P-factors, in absence of detailed data, were set to 1. The results were compared with those obtained from another soil loss estimation method based on 137Cs and with the soil loss estimated from the sediment volume stored in the Comunelli reservoir between 1968 and 2004.  相似文献   

15.
This paper examines the soil loss spatial patterns in the Keiskamma catchment using the GIS-based Sediment Assessment Tool for Effective Erosion Control (SATEEC) to assess the soil erosion risk of the catchment. SATEEC estimates soil loss and sediment yield within river catchments using the Revised Universal Soil Loss Equation (RUSLE) and a spatially distributed sediment delivery ratio. Vegetation cover in protected areas has a significant effect in curtailing soil loss. The effect of rainfall was noted as two pronged, higher rainfall amounts received in the escarpment promote vegetation growth and vigour in the Amatole mountain range which in turn positively provides a protective cover to shield the soil from soil loss. The negative aspect of high rainfall is that it increases the rainfall erosivity. The Keiskamma catchment is predisposed to excessive rates of soil loss due to high soil erodibility, steep slopes, poor conservation practices and low vegetation cover. This soil erosion risk assessment shows that 35% of the catchment is prone to high to extremely high soil losses higher than 25 ton ha−1 year−1 whilst 65% still experience very low to moderate levels of soil loss of less than 25 ton ha−1 year−1. Object based classification highlighted the occurrence of enriched valley infill which flourishes in sediment laden ephemeral stream channels. This occurrence increases gully erosion due to overgrazing within ephemeral stream channels. Measures to curb further degradation in the catchment should thrive to strengthen the role of local institutions in controlling conservation practice.  相似文献   

16.
Remote sensing data and GIS techniques have been used to compute runoff and soil erosion in the catchment area along the NH-1A between Udhampur and Kud covering an area of approximately 181 km2. Different thematic layers, for example lithology, a landuse and landcover map, geomorphology, a slope map, and a soil-texture map, were generated from these input data. By use of the US Soil Conservation Service curve number method, estimated runoff potential was classified into five levels—very low, low, moderate, high, and very high. Data integration was performed by use of the weighting rating technique, a conventional qualitative method, to give a runoff potential index value. The runoff potential index values were used to delineate the runoff potential zones, namely low, moderate, high, and very high. Annual spatial soil loss estimation was computed using the Morgan–Morgan–Finney mathematical model in conjunction with remote sensing data and GIS techniques. Greater soil erosion was found to occur in the northwestern part of the catchment area. When average soil loss from the catchment area was calculated it was found that a maximum average soil loss of more than 20 t ha−1 occurred in 31 km2 of the catchment area.  相似文献   

17.
Undulating landscapes of Chhotanagpur plateau of the Indian state of Jharkhand suffer from soil erosion vulnerability of varying degrees. An investigation was undertaken in some sections of the Upper Subarnarekha River Basin falling within this state. An empirical equation known as Universal Soil Loss Equation (USLE) was utilized for estimating the soil loss. Analysis of remote sensing satellite data, digital elevation model (DEM) and geographical information system (GIS)–based geospatial approach together with USLE led to the soil erosion assessment. Erosion vulnerability assessment was performed by analyzing raster grids of topography acquired from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM data. LANDSAT TM and ETM+ satellite data of March 2001 and March 2011 were used for inferring the land use–land cover characteristics of the watershed for these years, respectively. USLE equation was computed within the GIS framework to derive annual soil erosion rates and also the areas with varying degrees of erosion vulnerability. Erosion vulnerability units thus identified covered five severity classes of erosion ranging from very low (0–5 ton ha?1 yr?1) to very severe (> 40 ton ha?1 yr?1). Results indicated an overall increase of erosion in the year 2011 as compared to the erosion computed for the year 2001. Maximum soil erosion rate during the year 2001 was found up to 40 ton ha?1 yr?1, whereas this went up to 49.80 ton ha?1 yr?1 for the year 2011. Factors for the increase in overall erosion could be variation in rainfall, decrease in vegetation or protective land covers and most important but not limited to the increase in built-up or impervious areas as well.  相似文献   

18.
Due to the existence of fragile karst geo-ecological environments, such as environments with extremely poor soil cover, low soil-forming velocity, and fragmentized terrain and physiognomy, as well as inappropriate and intensive land use, soil erosion is a serious problem in Guizhou Province, which is located in the centre of the karst areas of southwestern China; evaluation of soil loss and spatial distribution for conservation planning is urgently needed. This study integrated the revised universal soil loss equation (RUSLE) with a GIS to assess soil loss and identify risk erosion areas in the Maotiao River watershed of Guizhou. Current land use/cover and management practices were evaluated to determine their effects on average annual soil loss and future soil conservation practices were discussed. Data used to generate the RUSLE factors included a Landsat Thematic Mapper image (land cover), digitized topographic and soil maps, and precipitation data. The results of the study compare well with the other studies and local data, and provide useful information for decision makers and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a larger watershed scale in Guizhou.  相似文献   

19.
Studying spatial and temporal variation of soil loss is of great importance because of global environmental concerns. Understanding the spatial distribution of soil erosion and deposition in the high-cold steppe is important for designing soil and water conservation measures. Measured 137Cs losses (Bq m−2) from long-term high altitude (4,000 m above sea level) watershed plots on the Qinghai–Tibet plateau and derived soil erosion estimates (Mg ha−1 year−1) were significantly correlated to directly measured soil losses from the same plots, over the same period (1963–2005). The local reference inventory was estimated to be 2,468 Bq m−2. The result of analyzing 137Cs distribution and its intensity in the soil profiles in this area shows similarities to 137Cs distribution in other areas. 137Cs is basically distributed in the topsoil layer of 0–0.3 m. Soil erosions vary greatly in the entire sampled area, ranging from 5.5 to 23 Mg ha−1 year−1, with an average of 16.5 Mg ha−1 year−1 which is a moderate rate of erosion.  相似文献   

20.
Gediz Basin is one of the regions where intense agricultural activities take place in Western Turkey. Erosion and soil degradation have long been causing serious problems to cultivated fields in the basin. This work describes the application of two different 137Cs models for estimating soil erosion rates in cultivated sites of the region. Soil samples were collected from five distinct cultivated regions subject to soil erosion. The variations of 137Cs concentrations with depth in soil profiles were investigated. Soil loss rates were calculated from 137Cs inventories of the samples using both proportional model (PM) and simplified mass balance model (SMBM). When PM was used, erosion and deposition rates varied from −15 to −28 t ha−1 year−1 and from +5 to +41 t ha−1 year−1, respectively; they varied from −16 to −33 t ha−1 year−1 and from +5 to +55 t ha−1 year−1 with SMBM. A good agreement was observed between the results of two models up to 30 t ha−1 year−1 soil loss and gain in the study area. Ulukent, a small representative agricultural field, was selected to compare the present data of 137Cs techniques with the results obtained by universal soil loss equation (USLE) applied in the area before.  相似文献   

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