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1.
以浙江省80个重要湿地为研究对象,综合考虑景观、生物多样性及社会发展等多种因素,构建湿地生态状况评价指标体系。采用层次分析法(AHP)和德尔菲法,对13个评价指标进行分级和赋值,计算每处重要湿地生态状况的综合得分,定量表征湿地的生态状况。结果表明:浙江省生态状况评级为“好”的湿地有20个,评级为“中”的湿地有36个,评级为“差”的湿地有24个,分别占浙江省重要湿地总数的25%、45%和30%。浙江省重要湿地生态状况总体良好,但部分湿地存在水质污染、富营养化和外来物种入侵等问题。 相似文献
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Characteristics of the main inorganic nitrogen accumulation in surface water and groundwater of wetland succession zones 下载免费PDF全文
Based on the observation of a complete hydrological year from June 2014 to May 2015, the temporal and spatial variations of the main inorganic nitrogen(MIN, referring to NO_3~--N, NO_2~--N, NH_4~+-N) in surface water and groundwater of the Li River and the Yuan River wetland succession zones are analyzed. The Li River and the Yuan River are located in agricultural and non-agricultural areas, and this study focus on the influence of surface water level and groundwater depth and precipitation on nitrogen pollution. The results show that NO_3~-N in surface water accounts for 70%-90% of MIN, but it does not exceed the limit of national drinking water surface water standard. Groundwater is seriously polluted by H_4~+-N. Based on the groundwater quality standard of H_4~+-N, the groundwater quality in the Li River exceeds Class III water standard throughout the year, and the exceeding months' proportion of Yuan River reaches 58.3%. Compared with the Yuan River, MIN in groundwater of the Li River shows significant temporal and spatial variations owing to the influence of agricultural fertilization. The correlation between the concentrations of MIN and surface water level is poor, while the fitting effect of quadratic correlation between H_4~+-N concentration and groundwater depth is the best(R~2=0.9384), NO_3~-N is the next(R~2=0.5128), NO_2~--N is the worst(R~2=0.2798). The equation of meteoric water line is δD =7.83δ~(18) O+12.21, indicating that both surface water and groundwater come from atmospheric precipitation. Surface infiltration is the main cause of groundwater H_4~+-N pollution. Rainfall infiltration in non-fertilization seasons reduces groundwater nitrogen pollution, while rainfall leaching farming and fertilization aggravate groundwater nitrogen pollution. 相似文献
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为了揭示黑龙江哈尔滨白渔泡国家湿地公园沼泽、林地和农田土壤物理、化学和生物性质的差异,于2018年7月25日~8月2日,在湿地公园内,在天然芦苇(Phragmites australis)沼泽、林地、旱田和水田中设置采样地,采集不同深度(0~10 cm、10~20 cm和20~30 cm)的土壤样品,测定土壤样品的物理、化学和生物指标。研究结果表明,白渔泡国家湿地公园不同采样地土壤指标存在差异;与天然芦苇沼泽土壤相比,其它采样地土壤的含水量明显偏低,土壤全氮、全磷、碱解氮和有机质含量都明显偏小,水田土壤速效磷含量偏大;天然芦苇沼泽土壤脲酶、硝酸还原酶、纤维素酶、蛋白酶和β-葡萄糖苷酶活性都高于林地和农田土壤,水田0~10 cm和10~20 cm深度土壤的硝酸还原酶活性显著高于旱田和林地;与天然芦苇沼泽土壤相比,旱田土壤小于0.25 mm的小团聚体含量偏大,而其它采样地土壤的各粒级团聚体的比例变化较小,水田土壤团聚体平均重量直径比天然芦苇沼泽和旱田土壤低。 相似文献
4.
近30年长江下游升金湖湿地不同季节景观生态风险时空分析 总被引:3,自引:0,他引:3
以安徽省升金湖湿地为研究对象,使用1989年、1996年、2003年、2010年和2017年四季Landsat系列遥感数据,构建景观生态风险评价模型,计算不同季节景观生态风险指数,分析风险空间分布及其变化特征,并使用Pearson相关系数分析季节间、季节与年度间景观生态风险相关性.结果显示:(1)不同季节景观生态风险指数有显著差异,生态风险从高到低依次为夏季、冬季、秋季和春季,夏、冬季风险指数平均高出春、秋季37.03%.(2) 1989—2017年升金湖湿地景观生态风险指数明显增加,湖区内泥滩、草滩等重要景观类型极易受人类活动影响,逐渐由中风险、较高风险区转变成较高风险、高风险区,且人造表面与草滩面积与较高风险和高风险区面积呈现出一定的协同变化特征.总体上,升金湖湿地以较低景观生态风险和中景观生态风险为主,较高景观生态风险与高景观生态风险主要位于上、下湖区.(3)季节间景观生态风险相关性最高的为秋季与冬季;年度生态风险与冬季生态风险高度相关.因此,近30年升金湖不同季节湿地景观生态风险时空演变趋势体现了该湿地景观格局变化对景观生态系统干扰的压力响应,且秋季与冬季湖区湿地需引起高度重视. 相似文献
5.
基于鄱阳湖南矶山湿地土壤及藜蒿Cd、As污染风险评价,利用盆栽实验研究水分条件对区域土壤理化性质、砷镉形态及其上生长的藜蒿(Artemisia selengensis)重金属富集能力的影响.结果表明:土壤Cd含量超标,存在生态风险,藜蒿茎中As含量达食品污染限量标准,存在食用风险;藜蒿对Cd、As的富集能力受土壤水分环境影响,水分增加会造成土壤pH与有机质含量上升,抑制藜蒿富集Cd的同时促进富集As.线性回归计算显示土壤有效态As含量与藜蒿As富集量呈显著正相关,可以用来评价藜蒿As富集情况,而土壤有效态Cd含量与藜蒿Cd富集量间无显著相关性.结合区域土壤Cd、As污染情况,适宜控制水分为缺水或旱湿交替条件,可以降低藜蒿等湿地植物中Cd的富集量;适宜控制水分为淹水条件,可以减少土壤Cd、As有效态含量. 相似文献
6.
Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively. 相似文献
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