共查询到20条相似文献，搜索用时 281 毫秒
1.
Siruvani watershed with a surface area of 205.54 km2 (20,554 hectare), forming a part of the Western Ghats in Attapady valley, Kerala, was chosen for testing RUSLE methodology
in conjunction with remote sensing and GIS for soil loss prediction and identifying areas with high erosion potential. The
RUSLE factors (R, K, LS, C and P) were computed from local rainfall, topographic, soil classification and remote sensing data. This study proved that the
integration of soil erosion models with GIS and remote sensing is a simple and effective tool for mapping and quantifying
areas and rates of soil erosion for the development of better soil conservation plans. The resultant map of annual soil erosion
shows a maximum soil loss of 14.917 t h−1 year−1 and the computations suggest that about only 5.76% (1,184 hectares) of the area comes under the severe soil erosion zone
followed by the high-erosion zone (11.50% of the total area). The dominant high soil erosion areas are located in the central
and southern portion of the watershed and it is attributed to the shifting cultivation, and forest degradation along with
the combined effect of K, LS and C factor. The RUSLE model in combination with GIS and remote sensing techniques also enables the assessment of pixel based
soil erosion rate. 相似文献
2.
基于GIS技术对秦巴山区的宁强县土壤侵蚀特征进行研究.通过解译遥感影像得到研究区的土地利用现状和植被覆盖等数据,使用GIS技术对地形图数据处理得到DEM等数据,并用因子法计算研究区的土壤侵蚀模数,最终生成该区的土壤侵蚀模数图.研究结论可为该地区水土保持与土壤侵蚀防治提供科学依据. 相似文献
3.
4.
Probabilistic landslide susceptibility and factor effect analysis 总被引：18，自引：0，他引：18
The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the geographic information system (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat Thermatic Mapper (TM) satellite images; and the vegetation index value from SPOT HRV (High-Resolution Visible) satellite images. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors employing the probability–frequency ratio method using the all factors. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, all factors had relatively positive effects, except lithology, on the landslide susceptibility maps in the study area. 相似文献
5.
Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models 总被引：15，自引：10，他引：5
This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model. 相似文献
6.
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
7.
Djouher Saadoud Mohamed Saïd Guettouche Mohamed Hassani Francisco José Martin Peinado 《Arabian Journal of Geosciences》2017,10(16):363
Wind-erosion risk is a challenge that threatens land development in dry-land regions. Soil analysis, remote sensing, climatic, vegetal cover and topographic data were used in a geographic information system (GIS), using multi-criteria analysis (MCA) to map wind-erosion risk (Rwe) in Laghouat, Algeria. The approach was based on modelling the risk and incorporating topographic and climatic effects. The maps were coded according to their sensitivity to wind erosion and to their socio-economic potential, from low to very high. By overlapping the effects of these layers, qualitative maps were drawn to reflect the potential sensitivity to wind erosion per unit area. The results indicated that severe wind erosion affects mainly all the southern parts and some parts in the north of Laghouat, where wind-erosion hazard (Hwe) is very high in 43% of the total area, and which was affected mainly by natural parameters such as soil, topography and wind. The results also identified features vulnerable to Rwe. The product of the hazard and the stake maps indicated the potential risk areas that need preventive measures; this was more than half of the study area, making it essential to undertake environmental management and land-use planning. 相似文献
8.
Modeling runoff and soil erosion in a catchment area, using the GIS, in the Himalayan region, India 总被引：1，自引：0，他引：1
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
9.
Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India 总被引：2，自引：0，他引：2
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
10.
Soil degradation resulted from unreasonable land use and erosion has been a serious problem in the black soil region of northeastern
China. This paper seeks to understand the relationships between topsoil properties and topography and land use for land management
targeting at improving soil quality in this region. A total of 292 soil samples and 81 volumetric rings were taken from a
typical small watershed of the region in June 2005 for examining total carbon (TC), total nitrogen (TN), soil texture (classified
into gravel, sand, silt, and clay), and bulk density (ρ
b), respectively. Spatial variability of these soil properties was evaluated with classical statistics and geostatistics methods.
The results of classical statistics indicated that TC, TN, sand, silt, clay content, and ρ
b were moderate variables while gravel had great variability. Soil properties were mainly correlated to slope position, elevation
and land types. Geostatistical analyses showed that the spatial autocorrelation for TC, TN, and silt was weak, strong for
clay and moderate for and ρ
b sand, respectively. The spatial variations of soil properties are affected comprehensively by topographic factors, land use,
erosion, and erosion control in this watershed. Past erosion, however, is the most important component to induce change of
soil properties. In this small watershed, current soil and water conservation measures play an important role in controlling
soil loss. But the restoration of soil properties was unsatisfactory. Comparing with untilled soil of this region, TC, TN,
silt content are excessively low; whereas ρ
b, sand and clay content are excessively high; gravel appears at most sampling locations. It is necessary for improving soil
properties to protect forest and grassland and change cultivation system of farmlands. 相似文献
11.
This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS)
and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs
and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing
tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are
topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance
from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation
amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility
was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis
were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%.
The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis
in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard
map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing
infrastructure like transportation network. 相似文献
12.
区域土壤侵蚀定量研究的国内外进展 总被引：27，自引：0，他引：27
由于水土保持宏观决策的需要、土壤侵蚀学科自身的进步和全球变化研究的促进,过去的10多年来,国内外研究者对区域尺度土壤侵蚀研究给予了高度重视。已经开展的主要研究包括:全球和区域（包括国家尺度）土壤侵蚀调查、区域土壤侵蚀过程和尺度效应、区域土壤侵蚀因子和区域土壤侵蚀模型等。将区域土壤侵蚀作为现代陆地地表过程的一部分,充分考虑全球变化的影响,集成土壤侵蚀研究成果与遥感和GIS技术,开发分布式区域土壤侵蚀模型,成为区域土壤侵蚀定量评价研究的基本趋势。在对国内外区域土壤侵蚀定量评价研究评述的基础上,提出我国近期在区域土壤侵蚀方面研究的重点问题为：区域土壤侵蚀过程及其尺度效应的量化描述、区域土壤侵蚀模型开发、区域土壤侵蚀动态模拟与趋势预测、区域土壤侵蚀与全球变化关系研究和区域土壤侵蚀数据处理与管理方法。 相似文献
13.
Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia 总被引：20，自引：6，他引：14
This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information
system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial
photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed
into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope,
topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall,
land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed
using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined
by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation
weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model
suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the
other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to
validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation
results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas. 相似文献
14.
Soil erosion prediction at the basin scale using the revised universal soil loss equation (RUSLE) in a catchment of Sicily (southern Italy) 总被引：3，自引：0，他引：3
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.
A. Ugur Ozcan Gunay Erpul Mustafa Basaran H. Emrah Erdogan 《Environmental Geology》2008,53(8):1731-1741
The universal soil loss equation (USLE) is an erosion model to estimate average soil loss that would generally result from
splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting
soil losses and planning control practices by the effective integration of the GIS-based procedures to estimate the factor
values on a grid cell basis. This study was performed for five different lands uses of Indağı Mountain Pass, Cankırı to predict
the soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Of the USLE factors, rainfall-runoff
erosivity factor (USLE-R) and topographic factor (USLE-LS) were greatly involved in GIS. These were surfaced by correcting
USLE-R site-specifically using DEM and climatic data and by evaluating USLE-LS by the flow accumulation tool using DEM and
watershed delineation tool to consider the topographical and hydrological effects on the soil loss. The study assessed the
soil erodibility factor (USLE-K) by randomly sampled field properties by geostatistical analysis. Crop management factor for
different land-use/land cover type and land use (USLE-C) was assigned to the numerical values from crop and flora type, canopy
and density of five different land uses, which are plantation, recreational land, cropland, forest and grassland, by means
of reclassifying digital land use map available for the site. Support practice factor (USLE-P) was taken as a unit assuming
no erosion control practices. USLE/GIS technology together with the geostatistics combined these major erosion factors to
predict average soil loss per unit area per unit time. Resulting soil loss map revealed that spatial average soil loss in
terms of the land uses were 1.99, 1.29, 1.21, 1.20, 0.89 t ha−1 year−1 for the cropland, grassland, recreation, plantation and forest, respectively. Since the rate of soil formation was expected
to be so slow in Central Anatolia of Turkey and any soil loss of more than 1 ton ha−1 year−1 over 50–100 years was considered as irreversible for this region, soil erosion in the Indağı Mountain Pass, to the great
extent, attained the irreversible state, and these findings should be very useful to take mitigation measures in the site. 相似文献
16.
In this paper, remote sensing, geographic information systems (GIS) and fieldwork techniques were combined to study the groundwater conditions in Vaigai basin, Tamilnadu. Several digital image processing techniques, including standard color composites, intensity–hue–saturation transformation and decorrelation stretch were applied to map rock types. Remote sensing data were interpreted to produce lithological and lineament maps such as geology, geomorphology, soil hydrological group, land use/land cover and drainage map were prepared and analyzed using GIS Arc Map GIS Raster Calculator module as geomorphology?×?12?+?drainage?×?9?+?lineament?×?5?+?geology?×?8?+?land use?×?2?+?relief?×?4. The final cumulative map generated by applying the above equation had weight values ranging from 0.315 to 4.515. The overall results demonstrate that the use of remote sensing and GIS provide potentially powerful tools to study groundwater resources and design a suitable exploration plan, the thematic maps for the study area. 相似文献
17.
Bago River is an important river in Myanmar. Although shorter than other rivers, it has its own river system, and people along
the river rely heavily on it for their daily lives. The upper part of the watershed has changed rapidly from closed forest
to open forest land in the 1990s. Since the recent degradation of the forest environment, annual flooding has become worse
during the rainy season in Bago City. This paper aims at determining soil conservation prioritization of watershed based on
soil loss due to erosion and morphometric analysis in the Bago Watershed by integrating remote sensing and geographic information
system (GIS) techniques. In this study, soil erosion of the Bago watershed was determined using the Universal Soil Loss Equation.
Such factormaps as rainfall, soil erodibility, slope length gradient, and crop management were compiled as input parameters
for the modeling; and the soil loss from 26 sub-watersheds were estimated. Then, the soil erosion maps of the Bago watershed
for 2005 were developed. The resulting Soil Loss Tolerance Map could be utilized in developing watershed management planning,
forestry management planning, etc. 相似文献
18.
Ali Mahmoud Mahmoud Mohamed Shendi Biswajeet Pradhan Fatma Attia 《Central European Journal of Geosciences》2009,1(3):347-367
The North-Western Coast of Egypt (NWCE) represents one of the high priority regions for future development in the country. El-Hammam area is located in the NWCE with an area of 94752 acres and is one of the main challenging regions for sustaianble development. In this study, we have used remote sensing and soil data in combination with GIS tools, for land use sustainable analysis (SLU) in El-Hammam area. The SLU was established based on various factors such as: land capability and suitability, water resources availability, economic return from water and financial return from land and water. A physiographic soil map for the study area was prepared using remote sensing and GIS. Multiple field surveys were carried out for collecting information on various soil map units (SMUs) and their profiles. Laboratory analysis for the collected samples was performed, and then the soil properties were stored as attributes in a geographical soil database linked with the SMUs. Furthermore, land capability assessment was done to define the suitable areas for agricultural production using a capability model built in ALES software. Results indicate that the area currently lacks high capability and moderate capability classes. By improving the soil properties, the soil can attain potential capability; and 55630 acres will become marginally capable. The assessment of soil physical suitability for different land use types (LUTs) were analysed in ALES software, in order to generate the most suitable areas. The results from the land suitability analysis indicated that, 17114 acres are moderately suitable for wheat and sorghum; whereas 15823 acres are moderately suitable for barley and 12752 acres are moderately suitable for maize, olive and figs. Finally, the SLU was investigated based on two scenarios; (1) the most SLU under the conditions of shortage of irrigation water: clover, barley and sorghum against figs, as the irrigation requirements for barley and sorghum are low; (2) the most sustainable land use in the conditions of irrigation availability will be wheat and maize against figs and guava. From the results it is quite evident that GIS combined with modeling approaches are powerful tools for decision making in the study area. 相似文献
19.
利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害(英文) 总被引：3，自引：0，他引：3
Biswajeet Pradhan Saro Lee 《地学前缘》2007,14(6):143-152
用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。 相似文献
20.
An assessment of soil erosion probability and erosion rate in a tropical mountainous watershed using remote sensing and GIS 总被引：1，自引：1，他引：0
H. Vijith M. Suma V. B. Rekha C. Shiju P. G. Rejith 《Arabian Journal of Geosciences》2012,5(4):797-805
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