With rapid economic development and the increasing demand for drinking water, a large amount of groundwater is exploited, resulting in a high F − content in groundwater, which is harmful to the environment and human body. In this study, 5,464 data points of fluoride in shallow groundwater were collected, and the F − content distribution, occurrence form and environmental impact of shallow groundwater were discussed. The results showed that 1) the F − content in shallow groundwater in China ranged from 0 to 60 mg/L, with a mean content of 0.90 mg/L; the lowest average F − content in shallow groundwater in Southwest China was 0.36 mg/L; South China (1.20 mg/L), Northeast China (1.25 mg/L) and Northwest China (1.25 mg/L) were considered high-fluoride areas, and North China (0.93 mg/L), East China (0.67 mg/L) and Central China (0.80 mg/L) were considered low-fluoride areas. The mean F − content in groundwater differed between provinces and cities. 2) The F − in shallow groundwater mainly occurred in ionic, complex ionic and organic fluoride molecular states. 3) The influence of a high F − content in shallow groundwater on the environment was mainly manifested in the increase in water F − concentration and soil F − and vegetable F − content. The influence of a high F − content on the human body was mainly manifested in an increase in urinary F − content in children, a high prevalence of dental fluorosis in children, an increase in skeletal fluorosis rate in adults with age, and an influence on cognitive function of older adults. These results provide a basis for F − pollution control and high-fluoride water treatment.

文章以长江中下游成矿带的南陵—宣城矿集区为研究区,分析Sentinel-2 卫星数据波段和研究区主要蚀变矿物光谱特征对应关系,在对植被、水体、建筑物等干扰信息去除的基础上,使用主成分分析(principal component analysis,PCA)法和波段比值法进行铁染异常、Al—OH异常、Mg—OH异常及碳酸盐异常信息提取,并通过与研究区已知矿点的叠加分析,与Landsat 8卫星遥感数据提取结果对比.结果表明:在植被覆盖度较高区域,Sentinel-2卫星数据具有更高的空间分辨率,受混合像元影响更小,对于高密度植被信息提取具有较大优势,在干扰信息去除的过程中保留的有效信息更多;从Sentinel-2、Landsat 8 卫星数据提取的蚀变异常信息与已知矿点的相关比率分别为 69.14%、47.43%,Sentinel-2卫星数据蚀变提取精度优于Landsat 8 卫星数据.研究结果可为植被覆盖度较高区域矿产资源遥感调查提供参考,为南陵—宣城矿集区找矿预测提供依据.

Fast and accurate tree species mapping using remote sensing technology is invaluable to forest sustainable management. However, due to the limitation of ecosystem complexity and the frequent cloudy weather, it remains challenging to obtain detailed knowledge about the varieties in subtropical areas. In this study, 18 scenes of Gaofen-6 (GF6) and 13 scenes of Sentinel-2 (S2) images were harmonized using linear regression models, and the identification abilities of 23 red-edge vegetation index (REVI) time-series and the normalized difference vegetation index (NDVI) time-series were evaluated using the random forest classifier (RF). The feature reduction methods were employed to boost the effectiveness of the input features, and the optimal red-edge combination consisting of spectral and textural features was built to map the dominant tree species precisely in Southeast China. Our results showed as follows: (1) GF6 and S2 could be harmonized with a mean coefficient of determination (R-2) greater than 0.94 and the root means squared error (RMSE) less than 0.02, which was helpful to improve the consistency between multi-source remote sensing data and subsequently derived products. (2) Most of the REVI time-series performed better than NDVI time-series, and the transformed chlorophyll absorption ratio indexes (TCARI(2)) got the highest overall accuracy of 86.28%, which could be further improved to 88.37% by adding 26 textural features. (3) The phenological characteristics from different seasons were conducive to tree species classification compared to the effect of different sensors, and the contribution of spring images played the most important role in RF, followed by autumn and winter, while the mid-summer images seemed to be negligible in the classifier. Overall, this study proposed an accurate and cost-efficient approach for tree species identification, which demonstrated good potential in providing a profound reference for subtropical forest landscape studying.

以中国东北赤峰市美林地区5种典型优势树种为研究对象,采用与当地森林植被生长期相对应的5景Senti-nel-2影像,借助支持向量机模型(SVM)与递归特征消除算法(RFE),根据可见光-近红外波段(VNIR)与不同红边谱段(RE)及红边指数(REVI)组合条件下的森林优势树种可分性测度及结果精度差异,探讨Sentinel-2影像不同红边谱段及其指数特征对区域优势树种遥感识别的影响.结果表明:Sentinel-2影像红边谱段的不同组合方式对不同生长期优势树种识别影响存在显著差异(P<0.05),其中VNIR+B5+B6为生长盛期的最佳组合方式,能够在VNIR基础上将生长盛期的识别精度均值提升约7.71％;叶全变色期是进行优势树种识别的最佳时期(P<0.05),该时期基于VNIR波段的识别精度均值达71.28％,在叠加红边波段B5+B6后提升至75.41％.此外,采用SVM-RFE算法构建适用于不同生长期的最佳REVI组合,其平均识别精度能够在全年5个生长期达到72.00％～84.31％,相比同时期基于RE+VNIR组合的最优识别结果平均提升了10.77％;在此基础上,构建适用于全生长期的优选植被指数PSRI+mSAVI+CIred-edge时间序列,可实现89.03％的平均识别精度,比单时相最佳REVI组合提升了4.72％～17.03％.研究证明Sentinel-2影像红边谱段及其衍生指数特征在区域森林优势树种识别中具有较高的应用价值,可为快速、准确地提取不同生长期森林植被信息提供技术方法参考.

地表温度是描述陆表过程和反映地表特征的重要参数.研究矿区地表温度的时空演变特征,对于理解煤矿开采活动对矿区生态环境的影响具有重要意义.淮南矿区是我国东部典型的高潜水位煤矿区,为了探究该区域地表温度的时空异质性与开采扰动的关系,利用2008—2018年5期Landsat影像进行地表温度的反演和下垫面信息提取,结合热力景观格局指数和城市热岛比例指数的计算,分析近十年来淮南矿区地表热环境的时空演变特征.结果表明:淮南矿区地表温度分布格局及其变化与下垫面结构及其变化密切相关.2008—2018年间,淮南矿区地表温度总体以中温区为主,其次是次低温和次高温区,高温区和低温区面积及其占比相对较低.由于城镇和矿井建设,以及煤矿开采产生的煤矸石堆积和开采沉陷积水,建设用地和水域面积不断增加,植被面积不断减少,相应的低温区、次高温和高温区增长,中温和次低温区减少,总体上城市热岛比例指数不断增加,热岛效应不断加剧;低温区斑块破碎度和复杂度不断减小,优势度不断增大.其他温度级斑块破碎度和复杂度,景观团聚程度和连通性,多样性和均匀度变化分为2008—2013、2013—2015和2015—2018三个阶段,其中2013—2015年人类干扰强度较大,斑块破碎度和复杂度增大,景观团聚程度和连通性,多样性和均匀度减小.研究成果为淮南矿区生态环境的恢复治理和城市产业规划提供了科学依据.