基于GIS的湟水河(西宁段)流域土壤侵蚀危险性评价

以湟水河流域（西宁段）为研究区,根据研究区地理与生态特点,选择降雨侵蚀力、坡度、土壤可蚀性、植被覆盖度作为土壤侵蚀危险性的评价指标,在ArcGIS支持下生成单因子危险性分布图,在此基础上基于ArcGIS的空间叠加分析功能,对土壤侵蚀危险性进行综合评价。结果表明:研究区域内土壤侵蚀中度和高度危险区域占大部分,达到71.4%;轻度危险区域和不敏感区域较少,分别为1.37%和27.08%;极危险区面积仅占0.15%。但是,对土壤侵蚀危险性较高的地方主要集中在人口密度较大的区域,主要为环西宁市区和湟水河河谷区域。     

基于GIS和RS的巢湖流域水土流失评估

基于地理信息系统(GIS)和遥感技术(RS),提取了巢湖流域地表覆盖、水土保持措施、坡度坡长、土壤可蚀性、降雨侵蚀力5个主要影响水土流失的因子,并运用修正的通用土壤侵蚀模型(revised univer-sal soil loss equation,RUSLE)估算土壤侵蚀量,生成水土流失等级分布图,从而完成对巢湖流域水土流失现状和空间分布特征的评估分析.结果表明,巢湖流域水土流失主要为微度侵蚀和轻度侵蚀,分别占流域总面积的93.87％和6.04％.此外,坡度和植被覆盖是影响流域土壤侵蚀的主要因素.研究结果可为巢湖流域水土流失治理及决策提供科学参考.     

基于GIS和RUSLE的高山峡谷区土壤侵蚀研究——以云南省泸水县为例

[目的]研究区域土壤侵蚀,揭示水土流失的空间分异规律,为区域水土保持和生态农业建设提供理论指导依据。[方法]应用GIS和RUSLE模型对云南省泸水县的土壤侵蚀进行研究。RUSLE模型中的因子包括降雨侵蚀力、土壤可蚀性、坡度坡长因子、植被覆盖和水土保持措施因子,运用GIS空间分析模块,获取泸水县土壤侵蚀模数空间分布图,根据SL 190-2007的分级标准进行土壤侵蚀强度分级,并分析该区土壤侵蚀强度空间分布格局。[结果](1)从各强度侵蚀面积上看,泸水县2014年土壤侵蚀以微度侵蚀为主,占总面积的86.86%,但从平均土壤侵蚀模数看,土壤侵蚀量为4.24×10~6 t,平均侵蚀模数为1 373.1t/(km~2·a),土壤侵蚀强度属于轻度侵蚀;(2)土壤侵蚀较严重区与未利用地、耕地空间分布基本一致,在坡度25°~50°的范围内,侵蚀面积占总侵蚀面积的75%,并且在该坡度段上的耕地面积占总耕地的63%,剧烈侵蚀集中分布在未利用地上,中度以上剧烈以下强度侵蚀集中分布在该坡度段上的耕地上,说明该坡耕地、未利用地对土壤侵蚀的贡献最大,要加强对未利用地的生态治理。[结论]坡度大,陡坡垦殖和未利用地的不合理利用是该区土壤侵蚀加重的主要原因,坡度在25°以上的地区不适宜耕种,应优化农业产业结构如实施退耕还林还草等措施,才能有效的保持水土。     

澜沧江中下游流域土壤侵蚀时空演变特征

为了探究土壤侵蚀演变机制,以澜沧江中下游流域为研究区域,利用改进的土壤流失方程(RUSLE)模型,开展流域内土壤侵蚀时空演变特征研究,引入随机森林算法探讨了流域内土壤侵蚀因子的相对重要程度。结果表明:澜沧江中下游流域2005—2015年土壤侵蚀量为0～1.89万t/(km²·a),平均土壤侵蚀模数为0.252万t/(km²·a),中下游子流域平均土壤侵蚀模数处于较低风险以上和中风险侵蚀以下级别。自2005年以后,澜沧江中下游流域土壤侵蚀空间分布特征呈现中度侵蚀风险区域扩张,高度和低度侵蚀风险收缩的趋势。随机森林算法结果发现植被覆盖管理因子和地形因子是影响澜沧江中下游流域土壤侵蚀的主要因素,土壤可蚀性因子、降雨侵蚀因子和水土保持措施因子的相对重要程度偏低,均未超过20%。可见,土壤侵蚀的时空异质性主要是由于植被覆盖和地形影响改变了局部气候而导致的。     

Mapping soil erosion risk in Rondônia,Brazilian Amazonia: using RUSLE,remote sensing and GIS

This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover‐management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall–runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0·2, LS values of less than 2·5, and C values of less than 0·25. A soil erosion risk map with five classes (very low, low, medium, medium‐high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. Copyright © 2004 John Wiley & Sons, Ltd.     

Evaluation and mapping of soil erosion susceptibility: an example from Kenya

Abstract. The erosion susceptibility of the Erosion Research Farm at Kabete Campus was mapped using a qualitative parametric method. A grid soil survey of the 4 ha farm was combined with a map of slope gradients, slope segments being delineated by breaks in slope. Rainfall erosivity and soil erodibility were also measured. Areas with the greatest erosion susceptibility according to this method were those occupying convex slope positions and slopes of more than 30%. Field observations and soil loss measurements generally supported the erosion susceptibility rating map produced by this method. The soil and erosion susceptibility maps were useful for planning erosion control measures and for selecting suitable sites for runoff plot experiments.     

A FUZZY RULE BASE APPROACH FOR DEVELOPING A SOIL PROTECTION INDEX MAP: A CASE STUDY in the UPPER AWASH BASIN,ETHIOPIAN HIGHLANDS

Land use practices and vegetation cover distribution are considered to be the most important dynamic factors that influence the land degradation or the soil erosion of a region. In this study, a Soil Protection Index (SPI) is defined as a function of land use practices and intensity of vegetation cover. This index is used to map the relative degree of protection of topsoil from being eroded by external effects such as rainfall and overland flow. A fuzzy rule‐based model integrated within ArcGIS® has been set‐up and tested with the aim to develop SPI maps. The amount of vegetation cover distribution, that is, Normalized Difference Vegetation Index as proxy parameter and Land Use–Land Cover map are chosen as fuzzy input parameters for the SPI as the desired system output. The approach was tested in the Upper Awash basin in Ethiopia. The output SPI map was qualitatively evaluated against the expert‐defined land degradation risk class, and it was found that locations that are mapped with ‘low and very low’ SPI classes at different time periods of the year have a high potential land degradation risk. Furthermore, socio‐economic data (‘population and livestock densities’) and environmental parameters (‘altitude and soil erodibility’) for the region are used to correlate with the SPI map as an indirect method of evaluation. It is found that population and livestock density explained 68 per cent of the spatial distribution pattern of predicted SPI and an adjusted R‐squared value of 0·681 (p < 0·05) was obtained. It was also found that the SPI distribution over the region for two different time periods, that is, January and July 2001, correlated positively (R² = 0·41 and R² = 0·51) with the soil erodibility of the region. The transferability and applicability of the model for different environmental settings or landscapes were tested by mapping the SPI of Italy. This SPI map of Italy was compared with the soil erosion map of Italy produced by the European Soil Bureau. It can be concluded that the SPI map reflects the potential land degradation risk distribution of the case‐study region. Results show that a fuzzy rule‐based model can provide useful preliminary information even without detailed and precise data information for developing appropriate strategies for land degradation assessment vital for sustainable land use management. Copyright © 2012 John Wiley & Sons, Ltd.     

崇礼清水河流域土壤侵蚀空间格局及其影响因素研究

基于DEM数字高程模型并结合RUSLE模型应用GIS、GeoDa、GS+等软件分析了河北省张家口市崇礼区25年间土壤侵蚀空间格局演变及影响因素。结果表明:(1)1990—2015年,研究区中部、西部和西南部土壤侵蚀较严重,土壤侵蚀强度以轻度、中度为主,土壤侵蚀量呈先减少后增加的趋势。(2)1990—2000年,土壤侵蚀强度转变以轻度侵蚀转入为主,土壤侵蚀状况减轻;2000—2010年,土壤侵蚀由微度侵蚀转为高级别侵蚀,侵蚀程度呈严重趋势;2010—2015年,总体表现为微度侵蚀、轻度侵蚀转向高级别侵蚀,但侵蚀增加面积有所减少,侵蚀状况稍有改善。(3)土壤侵蚀Moran’s I0,空间分布呈正相关性,表现为聚集状态,以高高型聚集为主,主要集中在崇礼区中西部和西南部。土壤侵蚀模数符合指数模型和球状模型,R2为0.943~0.979。变程A先由3 870m减小到860m再增加至1 470m,表明1990—2015年土壤侵蚀变化先快后慢,空间相关性分布范围由小变大,空间异质性呈先增强后减弱趋势。分形维数(FD)介于1.922~1.971,在区域较小空间尺度下,土壤侵蚀空间异质性主要是由植被覆盖、土地利用类型、水土保持措施等随机因子引起的。(4)土壤侵蚀影响因子中前3个主成分贡献率占到89.215 0%。在第1主成分载荷中,植被覆盖因子向量投影长度最大,为0.976 4。在第2,3主成分载荷中,水土保持措施因子、土壤可蚀性因子向量投影长度较大。因此,崇礼区土壤侵蚀影响因素大小依次为植被覆盖因子(C)、水土保持措施因子(P)、土壤可蚀性因子(K)、降雨侵蚀力因子(R)、坡长坡度因子(LS)。研究结果可为崇礼区清水河流域水土综合治理和可持续发展提供理论依据。     

Erodibility of arable soils in Georgia during the period of storm runoff

The erodibility of arable soils in Georgia varies from 1.0 to 2.9 t/ha per unit of the rainfall erosivity index. The well-structured brown forest and yellow-brown soils with a high humus content are the most resistant to erosion. The soils in the dry areas of Georgia (gray-cinnamon and cinnamon soils) are the most susceptible to erosion. The first map of the soil erodibility was composed that illustrates the spatial distribution pattern of this parameter in the Georgia territory.     

基于CSLE模型的巴基斯坦土壤侵蚀评价

通过制作土壤侵蚀图,分析土壤侵蚀主控因子,为巴基斯坦水土流失与保护提供合理的科学依据及治理参考。以土壤侵蚀抽样调查单元数据和土壤侵蚀因子数据为数据源,基于CSLE模型分别以空间插值法和地图代数法定量计算巴基斯坦水蚀区土壤侵蚀图,以空间插值结果为参照对地图代数计算结果做直方图匹配得到巴基斯坦水蚀速率图;采用水利部SL 190—2007标准对巴基斯坦风蚀强度进行了定性评价;使用分类决策树分析土壤侵蚀的主控因子。结果表明,空间插值法制图具有空间预测的准确性,地图代数法制图可以表现良好的局地变异特征;直方图匹配土壤侵蚀图兼具这2种方法的优点,土壤水蚀速率平均值为972.9 t/(km²·a),水蚀区土壤侵蚀比较严重,风蚀区以剧烈风蚀和极强烈风蚀为主,大部分地区生物措施因子是影响土壤侵蚀的主控因子,耕作区和山区的主控因子分别是R因子和LS因子。     

黄土高原坡耕地的细沟侵蚀及其防治途径

陕北典型黄土丘陵沟壑区的定位观测、野外调查和室内一系列人工降雨试验资料,表明了细沟侵蚀量占坡面侵蚀量的70%。而细沟侵蚀的发生是在坡面股流的流程上,当径流侵蚀力增大到足以冲刷土块,形成小跌水,进而演化为细沟下切沟头时,细沟侵蚀就开始发生了。下切沟头的下切侵蚀和下切沟头间径流对沟底的冲刷、沟头的溯源侵蚀、沟壁的崩塌形成了断续细沟,而位于一条股流流程上多个断续沟头溯源侵蚀的连接就形成了连续细沟。在这个过程中。由于降雨径流侵蚀力和土壤抗侵蚀力在时空上的强弱对比关系,出现了细沟的分叉、合并及连通现象。所有这些过程不但促进了细沟的发展,而且也造成了严重的细沟侵蚀。降雨径流侵蚀力。土壤抗侵蚀性能,坡度、坡长、坡形和土地管理因素是影响细沟侵蚀的主要因素。因此。防治细沟侵蚀的关键是削弱降雨径流侵蚀力和提高土壤的抗侵蚀性能。而免耕留茬、覆盖、草粮带状间作、水平沟种植及土地的合理利用是防治细沟侵蚀行之有效的措施。     

坡面水蚀预报模型研究

基于对我国坡面水蚀预报模型研究成果的述评和考虑坡面土壤侵蚀特征，提出了我国坡面水蚀预报模型的基本形式，给出了模型中各参数，如降雨侵蚀力、坡度与坡长、浅沟侵蚀因子的计算公式，并对土壤可蚀性、作物和水保措施等因子的提取方法进行了讨论。     

黄土高原刺槐林地根系与枯落物对土壤侵蚀的影响

为探讨自然条件下黄土高原地区刺槐林地枯落物和根系对土壤侵蚀的影响。采用5个坡度(8.7%,17.6%,26.8%,36.4%和46.6%)及3种流量(0.5,1.0,2.0 L/s)分别在有枯落物覆盖、去除枯落物的植被和裸坡样地进行试验,探讨刺槐林地枯落物和根系对土壤侵蚀的影响。结果表明,枯落物和根系对土壤侵蚀有显著影响,当枯落物厚度超过3 cm、根系密度>0.5 kg/m³时,土壤侵蚀量减小程度趋于稳定。与裸坡相比,刺槐林地土壤侵蚀量减少约55%,且根系和枯落物对土壤侵蚀量减少的贡献率分别为66%和34%。此外,枯落物与根系可降低土壤可蚀性,增加土壤剪切力,进而增强土壤的抗蚀能力。与裸坡相比,有枯落物覆盖和去除枯落物覆盖的植被样地土壤可蚀性分别降低80%和66%,土壤剪切力分别提高285%和237%。研究结果为揭示森林植被的土壤侵蚀机制提供新的思路,对改善黄土高原的植被建设具有一定的指导意义。     

Losing ground? soil loss and erosion in the highlands of papua new guinea

First impressions suggest that the risk of soil loss through fluvial erosion from land under cultivation is considerable in the Southern Highlands Province of Papua New Guinea. the climate is very wet all year round, The terrain precipitous, and people regularly farm on steep slopes. the Wola-speaking people, who occupy a series of valleys in the centre of the province, and who practice a semi-shifting form of cultivation, are nonetheless off-hand about soil conservation and declare that erosion is not a serious problem. This paper assesses the status of their assertions by calculating potential soil loss rates. It applies the universal soil loss equation to data on rainfall erosivity, soil erodibility, slope length and steepness, vegetation cover and conservation measures, to compute likely runoff losses. the calculations suggest that, The steep slopes cultivated and wet climate notwithstanding, The local population's assessment of the dangers of erosion is realistic and not reckless. Although rainfall is high, it is rarely of an intensity sufficient to threaten serious soil erosion losses. the physical properties of the soils, which feature volcanic ash components and high organic matter levels, are such that they are particularly resistant to erosion. the staple crop of the region, sweet potato (Ipomoea batatas), also gives particularly good ground cover and protection when established, effectively shielding the soil from erosive rainfall.     

基于GIS的广东省水土流失潜在危险度评价

以GIS为支撑,选取整个广东省为研究区域,基于对自然界潜在水土流失强度的客观认识,综合考虑区域内坡度、坡长、土壤、降雨及地质等因子,通过空间叠加分析,对区域水土流失潜在危险度进行了度量和评价,生成了广东省水土流失潜在危险度分布图。分析结果表明,广东省水土流失潜在危险分布区以无险、轻险型区为主,其面积占全省面积的80%以上,主要分布在平原区;重险型区域面积占该省面积的18.3%,主要分布在东部、南部、北部地势较高,降雨量较大的山丘区,区内植被一旦遭到破坏,极易发生流失,是水土保持工作中的重点预防保护区域。     

Erodibility of agricultural soils on the Loess Plateau of China

Soil erodibility is thought of as the ease with which soil is detached by splash during rainfall or by surface flow. Soil erodibility is an important factor in determining the rate of soil loss. In the universal soil loss equation (USLE) and the revised universal soil loss equation (RUSLE), soil erodibility is represented by an erodibility factor (K). The K factor was defined as the mean rate of soil loss per unit rainfall erosivity index from unit runoff plots. Although high rate of soil loss from the Loess Plateau in China is well known and widely documented, it is remarkable that there is little systematic attempt to develop and validate an erodibility index for soils on the Loess Plateu for erosion prediction. Field experimental data from four sites on the Loess Plateau were analyzed to determine the K factor for USLE/RUSLE and to compare with another erodibility index based on soil loss and runoff commonly used for the region. The data set consists of event erosivity index, runoff, and soil loss for 17 runoff plots with slope ranging from 8.7 to 60.1%. Results indicate that the K factor for USLE/RULSE is more appropriate for agricultural soils on the Loess Plateau than the erodibility index developed locally. Values of the K factor for loessial soils range from 0.0096 to 0.0269 t h/(MJ mm). The spatial distribution of the K value in the study area follows a simple pattern showing high values in areas with low clay content. For the four sites investigated, the K factor was significantly related to the clay content, (K=0.031−0.0013 Cl, r²=0.75), where Cl is the clay content in percent. The measured values of the K factor are systematically lower than the nomograph-based estimates by a factor of 3.3–8.4. This implies that use of the nomograph method to estimate soil erodibility would considerably over-predict the rate of soil loss, and local relationship between soil property and the K factor is required for soil erosion prediction for the region.     

Regional modeling of wind erosion in the North West and South West of Iran

About two-thirds of the Iran’s area is located in the arid and semiarid region. Lack of soil moisture and vegetation is poor in most areas can lead to soil erosion caused by wind. So that the annual suffered severe damage to large areas of rich soils. Modeling studies of wind erosion in Iran is very low and incomplete. Therefore, this study aimed to wind erosion modeling, taking into three factors: wind speed, vegetation and soil types have been done. Wind erosion sensitivity was modeled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available datasets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.     

Assessment of soil erosion risk in England and Wales

Abstract. Information on rainfall erosivity, soil erodibility and land capability is combined to produce a map of England and Wales showing areas with a risk of soil erosion at rates above the soil loss tolerance level. About 20 500 km² or 37% of the arable area is at risk. Given the shallow soils and current rates of erosion, sustained use of this area for cereal, sugar beet and vegetable production beyond the first quarter of the next century is threatened. A further 4000 km² is at risk in non-arable areas, mainly associated with blanket peat in the uplands and with coastal sand dunes.     

基于空间主成分分析的湖北省土壤侵蚀敏感性评价

以湖北省为研究区,利用GIS技术将土壤侵蚀与地形坡度、海拔、植被、降水量、土壤类型和土地利用类型等环境背景因素进行叠加分析,计算不同环境背景条件下的土壤侵蚀综合指数,分析土壤侵蚀与这些因素间的关系。在此基础上,运用空间主成分分析法(SPCA)评价不同环境背景下的土壤侵蚀敏感性程度,揭示研究区土壤侵蚀风险的空间分布特征。结果表明,研究区30.6%的地区属于土壤侵蚀非敏感区,55.8%的地区属于轻度和中度敏感区,13.6%的地区属于高度敏感区。土壤侵蚀敏感性高的地区主要位于相对高程150—500 m、坡度8°—15°、植被覆盖度低、土质疏松、土地利用以坡耕旱地为主的地带,这些地区是水土保持综合治理的重点区域。