基于 GIS和USLE/RUSLE的小流域土壤侵蚀量研究

依据实地调查资料,建立了典型小流域地理数据库;应用采样分析数据结果及坡面单元法,确定了定量计算通用土壤流失方程RUSLE因子指标的方法.在地理信息系统ArcGIS支持下,根据USLE/RUSLE土壤侵蚀预测模型对数据库实施运算操作,预测了小流域土壤侵蚀量.结果表明:①流域总体土壤侵蚀为中度,治理难度仍很大;②坡耕地是流...     

中国土壤母质母岩图制图研究

1.制图的基本内容与重点对于基岩来讲,岩石性质对土壤物质组成的影响最为明显,故对基岩的分类应以岩性为主要依据,图中的主要表现内容应为岩性.对松散沉积物来讲,其岩性、成因、形成时代对土壤的物理、化学性质均有影响,因此在分类时就应兼顾这三个方面的因素.图中的主要表     

土壤侵蚀的环境磁学监测方法

依据土壤、沉积物磁性理论,河流或湖泊的悬移物和沉积物记载了其产地的磁信息,根据它们的磁性参数可以追踪其来源及分布范围;根据表土磁性增强现象,可以采用土壤剖面磁性变化方法来判断土壤侵蚀程度;另外,应用“磁性示踪剂”也是研究土壤侵蚀的有效手段。文章就这方面的国内外研究进展情况加以阐述,并对该方法的优缺点及发展前景提出看法。     

基于地形坡度的大别山区商城县土壤侵蚀研究

使用基于地形坡度的土壤侵蚀要素提取方法,选择大别山区商城县为研究区,利用TM/OLI和SPOT 5/GF-1数据得到植被覆盖度和土地利用类型图,并结合DEM数据生成的地形坡度因子,基于GIS/RS提取土壤侵蚀信息及其时空变化量。结果显示,研究区2011年和2016年的土壤侵蚀主要分布在南部中低山地区,以轻度侵蚀为主,分别占总侵蚀面积的81.13%和83.97%;土壤侵蚀主要发生在坡度为5°～25°的区域,且随着坡度增大土壤侵蚀越严重;耕地的土壤侵蚀最严重;2011—2016年间虽然大部分区域土壤侵蚀状况不变,但整体有所加重。     

现有大气监测系统的再优化

本文根据模糊聚类分析原理,建立了城市大气质量分类图,并以此为基础,提出了改进城市现有大气监测系统的再优化方法,本法的优点是克服了城市大气质量监测点设置数量以功能区多少及面积大小为主要依据的不足。     

基于RS和GIS的干旱荒漠区域土壤侵蚀动态监测与评价

为建立土壤侵蚀动态变化数据库，本文以土地利用数据、植被覆盖指数、最大风速等值线图和DEM数据为信息源，对干旱荒漠区新疆克拉玛依市2000年和2007年的土壤侵蚀状况进行了动态监测与评价。结果表明，受自然条件和人类活动影响，8年间克拉玛依市土壤侵蚀强度有所增加，变化区域主要集中在克拉玛依市中部平原区。该方法的应用实现了土壤侵蚀的定时定量评价。     

基于遥感和GIS的四川省草地动态变化分析

利用地理信息系统技术,在空间数据库平台的支持下,对四川省20世纪90年代中期到2000年间的草地动态变化特征、草地动态变化的背景特征及其空间分布特征进行了分析。研究表明,净增草地部分主要来源于耕地,净减草地部分的主要去向是林地。总体上,净增草地面积3812公顷,净增草地部分主要是在轻度土壤侵蚀区、中度土壤侵蚀区、剧烈土壤侵蚀区和强度土壤侵蚀区,坡度等级5、6和1的区域,环境等级5、6、4和1的区域;净减草地部分主要是在微度土壤侵蚀区和极强土壤侵蚀区,坡度等级3、2和4的区域,环境质量等级7和8的区域。净增草地的区域为川西北高原、丘陵和川西南山区,净增草地面积的前四个地州市是遂宁市、泸州市、巴中地区和广安市,净减少草地的区域为盆周山地和平原,净减少草地面积的前四个地州市是广元市、绵阳市、达川市和宜宾市。     

开封市生态功能区划方法与结果

根据开封市生态环境现状，利用不同的评价方法，对开封市的土壤侵蚀敏感性、生态功能重要性进行了评价．依据生态功能区划编制技术方案，得出了开封市生态功能区划结果．     

亚热带地区山地地形土壤环境背景值的分异规律——R型聚类分析

将山地坡上部26个剖面的表土层、心土层、底土层共115个样本,12种元素共1380个数据,利用计算机对变量进行R型聚类分析,用逐步形成分类系统的方法,使12个微量元素定量地按其亲疏程度进行分类,并形成聚类分析谱系图。结果表明:     

序贯数论优化法和侧影图进行河流优化布点

运用序贯数论优化法(SNTO)对监测点进行分类,同时根据分类结果,基于样本之间的相似性和差异性,构造一Rousseuw定义的函数并做侧影图,以确定各属类的最佳代表点,从而遴选出优化了的点位.     

Adapting the RUSLE and GIS to model soil erosion risk in a mountains karst watershed, Guizhou Province, China

Soil erosion is a serious environmental problem in Guizhou Province, which is located in the centre of the karst areas of southwestern China. Unfortunately, Guizhou Province suffers from a lack of financial resources to research, monitor and model soil erosion at large watershed. In order to assess the soil erosion risk, soil erosion modeling at the watershed scale are urgently needed to be undertaken. This study integrated the Revised Universal Soil Loss Equation (RUSLE) with a Geographic Information System (GIS) to estimate soil loss and identify the risk erosion areas in the Maotiao River watershed, which is a typical rural watershed in Guizhou Province. All factors used in the RUSLE were calculated for the watershed using local data. It was classified into five categories ranging from minimal risk to extreme erosion risk depending on the calculated soil erosion amount. The soil erosion map was linked to land use, elevation and slope maps to explore the relationship between soil erosion and environmental factors and identify the areas of soil erosion risk. The results can be used to advice the local government in prioritizing the areas of immediate erosion mitigation. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil erosion. It thus indicates that RUSLE-GIS model is a useful and efficient tool for evaluating and mapping soil erosion risk at a large watershed scale in Guizhou Province.     

Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed

Complex mountainous environments such as Himalayas are highly susceptibility to natural hazards particular those that are triggered by the action of water such as floods, soil erosion, mass movements and siltation of the hydro-electric power dams. Among all the natural hazards, soil erosion is the most implicit and the devastating hazard affecting the life and property of the millions of people living in these regions. Hence to review and devise strategies to reduce the adverse impacts of soil erosion is of utmost importance to the planners of watershed management programs in these regions. This paper demonstrates the use of satellite based remote sensing data coupled with the observational field data in a multi-criteria analytical (MCA) framework to estimate the soil erosion susceptibility of the sub-watersheds of the Rembiara basin falling in the western Himalaya, using geographical information system (GIS). In this paper, watershed morphometry and land cover are used as an inputs to the MCA framework to prioritize the sub-watersheds of this basin on the basis of their different susceptibilities to soil erosion. Methodology included the derivation of a set of drainage and land cover parameters that act as the indicators of erosion susceptibility. Further the output from the MCA resulted in the categorization of the sub-watersheds into low, medium, high and very high erosion susceptibility classes. A detailed prioritization map for the susceptible sub-watersheds based on the combined role of land cover and morphometry is finally presented. Besides, maps identifying the susceptible sub-watersheds based on morphometry and land cover only are also presented. The results of this study are part of the watershed management program in the study area and are directed to instigate appropriate measures to alleviate the soil erosion in the study area.     

Assessing soil quality indicator under different land use and soil erosion using multivariate statistical techniques

Soil degradation associated with soil erosion and land use is a critical problem in Iran and there is little or insufficient scientific information in assessing soil quality indicator. In this study, factor analysis (FA) and discriminant analysis (DA) were used to identify the most sensitive indicators of soil quality for evaluating land use and soil erosion within the Hiv catchment in Iran and subsequently compare soil quality assessment using expert opinion based on soil surface factors (SSF) form of Bureau of Land Management (BLM) method. Therefore, 19 soil physical, chemical, and biochemical properties were measured from 56 different sampling sites covering three land use/soil erosion categories (rangeland/surface erosion, orchard/surface erosion, and rangeland/stream bank erosion). FA identified four factors that explained for 82 % of the variation in soil properties. Three factors showed significant differences among the three land use/soil erosion categories. The results indicated that based upon backward-mode DA, dehydrogenase, silt, and manganese allowed more than 80 % of the samples to be correctly assigned to their land use and erosional status. Canonical scores of discriminant functions were significantly correlated to the six soil surface indices derived of BLM method. Stepwise linear regression revealed that soil surface indices: soil movement, surface litter, pedestalling, and sum of SSF were also positively related to the dehydrogenase and silt. This suggests that dehydrogenase and silt are most sensitive to land use and soil erosion.     

Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques

Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), vegetation cover (C), and support practice factor (P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient (r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha(-1) ya(-1) in 2005, and the area of 46.61 km(2) (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 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified.     

Identification of erosional and inundation hazard zones in Ken?CBetwa river linking area, India, using remote sensing and GIS

Ken-Betwa river link is one of the pilot projects of the Inter Linking of Rivers program of Government of India in Bundelkhand Region. It will connect the Ken and Betwa rivers through a system of dams, reservoirs, and canals to provide storage for excess rainfall during the monsoon season and avoid floods. The main objective of this study is to identify erosional and inundation prone zones of Ken-Betwa river linking site in India using remote sensing and geographic information system tools. In this study, Landsat Thematic Mapper data of year 2005, digital elevation model from the Shuttle Radar Topographic Mission, and other ancillary data were analyzed to create various thematic maps viz. geomorphology, land use/land cover, NDVI, geology, soil, drainage density, elevation, slope, and rainfall. The integrated thematic maps were used for hazard zonation. This is based on categorizing the different hydrological and geomorphological processes influencing the inundation and erosion intensity. Result shows that the southern part of the study area which lies in Panna district of Madhya Pradesh, India, is more vulnerable than the other areas.     

Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete

Due to inappropriate agricultural management practices, soil erosion is becoming one of the most dangerous forms of soil degradation in many olive farming areas in the Mediterranean region, leading to significant decrease of soil fertility and yield. In order to prevent further soil degradation, proper measures are necessary to be locally implemented. In this perspective, an increase in the spatial accuracy of remote sensing datasets and advanced image analysis are significant tools necessary and efficient for mapping soil erosion risk on a fine scale. In this study, the Revised Universal Soil Loss Equation (RUSLE) was implemented in the spatial domain using GIS, while a very high resolution satellite image, namely a QuickBird image, was used for deriving cover management (C) and support practice (P) factors, in order to map the risk of soil erosion in Kolymvari, a typical olive farming area in the island of Crete, Greece. The results comprised a risk map of soil erosion when P factor was taken uniform (conventional approach) and a risk map when P factor was quantified site-specifically using object-oriented image analysis. The results showed that the QuickBird image was necessary in order to achieve site-specificity of the P factor and therefore to support fine scale mapping of soil erosion risk in an olive cultivation area, such as the one of Kolymvari in Crete. Increasing the accuracy of the QB image classification will further improve the resulted soil erosion mapping.     

Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin,Turkey

Land use and land cover (LULC) changes affect several natural environmental factors, including soil erosion, hydrological balance, biodiversity, and the climate, which ultimately impact societal well-being. Therefore, LULC changes are an important aspect of land management. One method used to analyze LULC changes is the mathematical modeling approach. In this study, Cellular Automata and Markov Chain (CA-MC) models were used to predict the LULC changes in the Seyhan Basin in Turkey that are likely to occur by 2036. Satellite multispectral imagery acquired in the years 1995, 2006, and 2016 were classified using the object-based classification method and used as the input data for the CA-MC model. Subsequently, the post-classification comparison technique was used to determine the parameters of the model to be simulated. The Markov Chain analyses and the multi-criteria evaluation (MCE) method were used to produce a transition probability matrix and land suitability maps, respectively. The model was validated using the Kappa index, which reached an overall level of 77%. Finally, the LULC changes were mapped for the year 2036 based on transition rules and a transition area matrix. The LULC prediction for the year 2036 showed a 50% increase in the built-up area class and a 7% decrease in the open spaces class compared to the LULC status of the reference year 2016. About an 8% increase in agricultural land is also likely to occur in 2036. About a 4% increase in shrub land and a 5% decrease in forest areas are also predicted.     

Properties and erosional response of soils in a degraded ecosystem in Crete (Greece)

To study desertification processes relating to soil erosion, a climatological and altitudinal gradient from south to north was selected in Crete (Greece) and four locations were selected along the gradient. At the locations precipitation ranged from 1400 mm/year at the highest location to 400 mm/year at the lowest. All locations are affected by the actual land use: intensive grazing, small controlled fires, and abandoned agricultural terraces. Representative soil profiles were described in the field and analyzed in the laboratory, and rainfall simulation experiments in the field measured soil erosion over different soil surfaces and land uses. Data on physical and chemical properties were obtained from the soil profiles and soil hydrology, and erosion data were obtained from the rainfall simulation experiments. Soil aggregation was studied with samples taken from the soil in the rainfall simulation plots and special attention being paid to the aggregate size distribution and the water-stable microaggregation. The interaction between climatological conditions and land use seems to be the main factor controlling soil erosion. This paper describes how the expected erosion along the gradient (from the most humid to the driest site) can be affected and disturbed by specific processes derived from land use.     

Use of USLE/GIS Methodology for Predicting Soil Loss in a Semiarid Agricultural Watershed

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 in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.