共查询到18条相似文献，搜索用时 93 毫秒
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基于GIS技术的农用地非点源磷污染危险性评价——以长江流域为例 总被引：1，自引：0，他引：1
识别磷素流失的关键源区,对合理实施养分资源管理、指导平衡施肥、有目的治理来源于农业养分造成的污染和农业的可持续发展有着重要的意义,本文以地理信息系统(GIS)的空间分析功能和养分平衡计算等方法为依托,参照国际常用的磷指数法对长江流域主要农用地进行了农业非点源磷污染风险评价,得到以下结论:磷非点源污染危险性指数高和极高的农用地,分别占到了流域农用地的24.40%和0.49%;风险低和中区域分别占到了11.16%和63.95%。长江流域为农用地磷污染风险中等的流域。 相似文献
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《测绘与空间地理信息》2020,(6)
在已有研究的基础上,本文基于1990、1995、2000、2005、2010和2015年全国土地利用数据,利用输出系数模型对潘家口水库上游流域非点源污染氮与磷的输出风险进行评估和分析,为改善和进一步管理好潘家口水库的水质,找出应重点关注和管理的区域。 相似文献
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基于RS和GIS的农业土地利用污染分析 总被引：1，自引：0，他引：1
农业已被确认为地表和地下水最主要的非点源污染源，而土地利用方式又是影响非点源污染的关键性因素。大规模的土地利用与开发、化肥与农药用量的增加、规模养殖业的发展、生活垃圾的增加等，这些非点污染源严重威胁水体质量，进而影响到人们的生活。为了更好地预测和控制、管理非点源污染，必须研究其负荷定量化问题。本文从以上几方面入手，讨论了非点源污染与农业土地利用方式之间的关系，利用组件GIS技术，以网格为评价单元，实现非点源污染负荷的定量计算及可视化分析。文章最后以上海市松江区作为研究区域，对该区的污染情况进行了分析。 相似文献
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安徽省大别山区江子河小流域的水文模拟与分析 总被引：1，自引：0，他引：1
目前,许多发达国家的研究已经证实,农业非点源污染是导致水环境恶化的主要原因之一。土壤侵蚀与非点源污染是一对密不可分的共生现象,特别在农业非点源污染中,土壤侵蚀是主要的发生形式,是一种重要的非点源污染。本文主要应用目前使用较广泛的非点源污染模型—SWAT模型对淮河流域安徽省大别山区水土流失较严重的江子河小流域进行径流量和泥沙量的模拟,并得到该小流域径流量和泥沙流失量的空间分布图。 相似文献
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水系是具有高度结构化特征的复杂空间数据,在不同水文条件和地形环境下发育的水系形态可以表现为多种模式,如格状河系、羽毛状河系、平行状河系等,使得水系选取具有较大难度。探讨了河流分级(汇流区域特征)、河网结构层次化(河流分布的地理特征)与水系选取之间的关系,提出了基于流域的河流自动选取,从河流的局部重要性出发,考虑河流的分级主要基于两点:一是确定选取单元为流域;二是对流域内河流通过等级关系选取高等级河流,同等级间河流根据长度、密度、河流间距离等综合指标进行选取。 相似文献
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以四川省攀枝花地区为研究区域,选取高程、坡度、坡向、土地利用类型、地层岩性、道路交通、河流水系7个影响因子为评价指标,将上述指标作为栅格数据输入,并选择历史滑坡灾害点的影响因子数据作为样本,建立Logistic回归方程,进行回归方程和回归系数的显著性检验,最后利用回归方程对研究区滑坡危险性进行预测,编制滑坡危险性区划分布图。结果表明,逻辑回归方法得到的危险性分区图中,极高和高危险区包含了83%的已知滑坡灾害,攀枝花地区滑坡危险性较高的区域占到区域总面积的将近50%,主要分布在中部和南部人口比较集中的地区,与滑坡的发生受人类工程活动的影响比较吻合。 相似文献
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基于GIS的县域土壤重金属生态风险评价 总被引：2，自引：0，他引：2
为研究经济快速发展区农田土壤中重金属的含量及污染状况,本文以浙江省慈溪市为研究对象,研究土壤中铜、汞、镉、铅、砷、铬、锌七种重金属含量特征,并采用潜在生态危害指数法对其进行评价,并绘制生态风险危害指数分级图。结果表明:土壤中除汞元素含量较高外,其他各元素含量仅稍高于当地土壤背景值。七种元素的单因子污染指数Cfi值均属于中等的污染参数,综合污染指数Cd上限值已处于高污染指数的范围,但平均值为属于中等污染水平。从潜在生态风险评价结果来看,七种元素的单项潜在生态风险参数Eri的值也只有汞达到了强生态危害,七种元素综合潜在生态危害指数RI刚刚达到中等生态危害水平,说明该区农田土壤尚处于较低的生态风险状态。生态危害指数插值结果表明,慈溪市重金属元素的高风险区分布在中南部人类活动较为活跃、城乡工业较发达的区域,在今后的土地利用中,应高度重视人类活动对土壤重金属污染的影响。 相似文献
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汉江中下游农业面源污染动态监测信息系统的建立与初步应用 总被引：11，自引：0，他引：11
根据汉江中下游农业面源污染治理决策的需求,应用遥感与GIS一体化的方法,研究设计了汉江中下游农业面源污染动态监测信息系统的结构、功能及数据库的组成,并建立了可运行的汉江中下游农业面源污染动态监测信息系统,在系统支持下可有效地实现汉江中下游农业面源污染动态监测、最佳管理措施的选择以及区域农业面源污染综合治理规划.系统可以为地方政府相关部门决策提供依据,对有效的保护汉江流域水资源环境,控制汉江流域水污染有着重要意义. 相似文献
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白妙妮 《测绘与空间地理信息》2015,(9):186-188
近年来,土壤重金属污染日益严重,已造成农产品的质量安全问题,并危及人们的身体健康。因此,本文采用GIS技术与地统计学相结合的方法,应用ArcGIS软件对高陵县城镇化进展较快的通远镇农田土壤重金属进行空间分析与污染评价,为该区发展无公害蔬菜基地及国家级安全农产品生产示范区提供了科学的依据,为进一步做好城镇化工作提供有力的保障。 相似文献
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Basic principles of compilation and utilization of a multiscale series of evaluative-forecast maps depicting procedures for mitigating the negative environmental impacts of chemical pollution from pesticides are outlined. The accuracy of forecasts of the natural capacity of ecosystems in agricultural regions to purify themselves of pesticide residues can be checked using maps depicting different aspects of chemical pollution across different natural elements of the landscape. Translated from: Izvestiya Akademii Nauk, seriya geograficheskaya, 1994, No. 3, pp. 137-146. 相似文献
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In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R2 values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting. 相似文献
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An Integrated GIS Based Statistical Model to Compute Groundwater Vulnerability Index for Decision Maker in Agricultural Area 总被引：3，自引：0，他引：3
Aminreza Neshat Biswajeet Pradhan Helmi Zulhaidi Mohd Shafri 《Journal of the Indian Society of Remote Sensing》2014,42(4):777-788
The conservation areas in a plain are affected by the groundwater contamination from intense application of the fertilizers. The vulnerability of groundwater can be tested by using the DRASTIC model for the pollutants. The groundwater susceptibility to pollution in the various areas is mapped through DRASTIC model. However, the effects of pollution types and its characteristics are not considered, as this model is used without any modifications. This technique must be standardized for usage in the various aquifers and specific pollution types. The rates of DRASTIC parameters are corrected to obtain the potential for a more accurate analysis of the vulnerability pollution. The relationships between the parameters are identified with respect to the nitrate concentration in the groundwater by calculating the new rates. The methodology was applied to the selected area situated in the south eastern region of Iran at Kerman plain. Twenty-seven different locations were selected to test and analyse the nitrate concentration in the water from underground wells. The pollution in the aquifer was associated and correlated with the DRASTIC index by using the measured nitrate concentrations. The relationship between the index and the measured pollution in the Kerman plain was determined by applying the Wilcoxon rank-sum nonparametric statistical tests and the rates were calculated. It was found specifically in the agricultural areas that the modified DRASTIC model performed more efficiently than the traditional method for nonpoint source pollution, as indicated by the results. After modifications, the regression coefficients revealed that the relationship between the vulnerability index and the nitrate concentration was 77 %, while it was 37 % before the modifications were used. These statistics show that the modified DRASTIC performed far more efficiently than the original version. 相似文献