污灌农田土壤镉污染状况及分布特征研究

对沈阳郊区某河沿岸部分乡镇的污灌农田土壤中重金属全镉含量进行了分析,评价了土壤镉污染状况,并探讨了该河沿岸土壤中镉的沿程分布特征、横向分布特征和垂向分布特征.结果表明,农田土壤重金属镉含量范围为0.15～8.23mg/kg,均值为1.75mg/kg.用土壤环境质量标准二级标准值对土壤中的全镉含量进行评价,平均镉污染指数为5.95,为重度污染;用土壤背景值标准评价,平均镉污染指数为5.95,超过当地背景值水平8.39倍,污灌已造成该地区重金属镉污染,且污染程度十分严重.该河渠从上游到下游,沿岸土壤镉含量呈降低趋势;横向分布上,距离该河渠越远,镉含量有逐渐减少的趋势;垂向分布上,表层土壤镉含量最高.     

水体中泥沙对藻类光谱特征位置的影响分析

自然水体叶绿素a浓度的遥感反演中,泥沙的存在影响着反演精度.如何消除这种影响是提高叶绿素a遥感反演精度的关键,而了解泥沙对藻类光谱特征的影响是消除影响的前提.文章在人工控制条件下获取了不同泥沙浓度下藻类光谱曲线,通过分析光谱曲线特征位置的漂移和数值变化,总结泥沙对藻类光谱的影响并提出了消除影响的可能性.     

去除叶绿素a影响的泥沙反演模型

通过遥感方法反演自然水体中泥沙浓度时,大量藻类的存在影响泥沙的反演精度,如何消除这种影响是提高水体中泥沙反演精度的关键.文章通过对一定叶绿素a浓度下不同浓度泥沙的光谱曲线研究,分析光谱曲线特征位置的漂移和数值变化,寻找去除叶绿素a影响的光谱范围和特征位置,通过相关性分析,建立多个模型,并从中选取最佳模型.     

重庆市清水溪底泥中镉污染的潜在生态风险评价

采用地累积指数和Hakanson潜在生态风险指数法,通过分析清水溪18个采样点底泥中典型重金属镉的含量,定量确定了清水溪底泥中重金属镉的污染程度和潜在生态风险程度。结果表明,清水溪流域镉污染比较严重,上游比下游污染严重,而且在新桥到高滩岩段受到重金属镉的中强度污染,对周围环境存在极高的镉生态风险。在清水溪干流上采样点底泥中镉的质量浓度范围在0.38~1.48mg/kg之间,平均值0.88mg/kg,各支流底泥中镉的质量浓度范围为0.51~1.08mg/kg。对于清水溪各支流镉的污染程度与潜在生态风险程度由高到低的排序为杨家沟>金竹沟>芭蕉沟>关井沟>石碾盘沟。     

植物大气污染响应高光谱监测实例研究

选择某钢铁企业绿化树种桂花当年生叶片作为供试样本,测试叶片光谱反射率之后分别测试叶液pH值、叶片含硫量、叶绿素含量、叶片含水量,研究生长在S02 污染环境下桂花叶片光谱的变化以及相应的部分生理生化指标的变化.研究表明,污染较严重生产区采集的叶片光谱反射率和红边斜率均较生活区低.叶片含硫量随大气SO2 浓度的增减而相应地变化,叶片叶绿素含量、叶液pH值、叶片含水量的变化规律与叶片含硫量和大气SO2浓度的变化规律相反.     

铜陵市义安区蔬菜产地镉污染调查与评价

通过在铜陵市义安区5个乡镇点对点采集蔬菜及土壤样品,分析其中镉含量,并运用土壤及农产品综合质量影响指数(IICQ)对蔬菜产地镉污染状况作评价。结果表明,义安区蔬菜产地土壤镉平均值为0.56 mg/kg,高于背景值。32.5%的土壤样品镉质量比高于风险筛选值而低于风险管制值,7.5%的样品镉质量比高于风险管制值,集中在D乡镇。8种蔬菜样品的IICQ范围在0.01～7.34之间,D乡镇的污染程度最严重。蔬菜镉富集系数存在差异性,叶菜类较块茎类对镉的吸收能力更强,蔬菜产地蔬菜样品整体符合安全食用标准。     

高光谱遥感在土壤重金属污染监测中的应用

综述了高光谱遥感直接监测土壤重金属污染及利用植被重金属胁迫光谱数据间接监测重金属污染区域的各种方法,分析了反演模型存在的问题,提出了引入人工智能技术、高维矩阵和数据挖掘算法,以及寻求更好的数据同化模型等提高反演精度的后续研究方向。     

基于实测光谱的矿业开发集中区土壤元素含量反演

采用ASD FieldSpec 3型便携式地物光谱仪对矿集区的296个土壤样本进行高光谱反射率测定,通过土壤光谱特征分析,提取污染元素潜在光谱特征,并与土样化学测试结果进行相关分析,得到As、Cd和Zn的光谱特征参数,并从Aster影像对应的特征波段上实现该3种元素含量的反演。结果表明:As、Cd和Zn预测值的相对误差平均值分别为0.116、0.106和0.088,该方法可为大面积的土壤环境质量调查提供技术参考。     

银川地区菜田土壤和蔬菜中有害元素含量及污染程度评价

对银川地区菜地土壤和蔬菜中有害元素进行调查并作出初步评价.表明该地区菜田土壤中氟、铅、汞有一定程度污染,镉污染也已露头;一定比例的蔬菜中,受到轻污染的元素有:铜、锌、铅、镉、砷和氟;汞在28.3％～53.2％的大白菜和青萝卜中受到中度及重度污染.     

基于分形求和模型的克拉玛依市空气质量评价

以克拉玛依市4个区2012年的大气自动监测数据为样本，基于分形求和模型，分析大气污染物的分布特征，利用分维数确定污染物浓度分布的随机程度，计算 SO2、NO2、PM10的大气环境背景值与标准值，确定适合于评价区域的ORAQI指数计算公式，并与 API指数作对比。ORAQI指数计算结果显示，克拉玛依市全年环境空气质量基本呈现“U”字形变化，春夏季大气质量好于秋冬季，全年空气质量有明显的季节变化，4个区中克拉玛依区空气质量相对较差，乌尔禾区空气质量最好。相对于 API指数的均匀分布结果，ORAQI指数具有更好的次要污染物体现能力，可以综合体现所评价的各项污染因子的贡献。     

Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.     

Novel hyperspectral reflectance models for estimating black-soil organic matter in Northeast China

This paper presents a novel method for estimating black-soil organic matter (SOM) in the black-soil zone of northeast China from hyperspectral reflectance models. Traditional black-soil property measurements are relatively slow, but the pressures of agricultural production and environmental protection require a quick method to collect black-soil organic matter content. SOM estimation using soil hyperspectral reflectance models can meet this requirement, based on the spectral characteristics of black-soil in Northeast China. On the basis of the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The concepts of curvature and ratio indices are also applied to compare and test the stability and accuracy of data modeling. The results show that the response of black-soil spectral reflectance from 400-1,100 nm to organic matter content is more marked than that from 1,100-2,500 nm. Specifically, the main response range of black-soil organic matter is between 620-810 nm, with a maximal spectral response at 710 nm. By comparing different models, we found that the normalized first derivate model is optimal for estimating SOM content, with a determination coefficient of 0.93 and root mean squared errors (RMSE) of 0.18%.     

Research on the relationship between the fractional coverage of the submerged plant Vallisneria spiralis and observed spectral parameters

The present paper discusses the relationship between the coverage fraction of submerged plants and the observed spectral characteristics. The purpose of this paper is to validate a remote sensing technology to monitor the change in the plant composition of a water body. In the current study, the reflectance spectra of the submerged plant Vallisneria spiralis at different fraction coverages of the wetland in Hangzhou Bay were measured. The relationships between the fraction coverage of V. spiralis and simulated Quickbird normalized difference vegetation index (NDVI), red edge, and other spectral characteristic parameters were established. The results showed that the spectral reflectance characteristics of submerged plant V. spiralis were mainly in the visible light (490–650 nm) and near infrared (700–900 nm). The rate of change of the blue band curve and simulated Quickbird NDVI showed a higher correlation with the V. spiralis coverage, so estimation models of the fraction coverage were constructed using these parameters. The estimated fraction coverage of V. spiralis with different models were validated with ground data, and the accuracy of estimation models was assessed. The most suitable estimated fraction coverage of V. spiralis was obtained using the rate of change of the blue band curve and simulated Quickbird NDVI. The present work demonstrated a method to monitor the distribution and dynamical variation of submerged plants at the large scale.     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Evaluating soil quality under a long-term integrated tillage–water–nutrient experiment with intensive rice–wheat rotation in a semi-arid Inceptisol,India

Long-term sustainability and a declining trend in productivity of rice–wheat rotation in the Indo-Gangetic plain, often direct towards the changes in soil quality parameters. Soil quality is decided through few sensitive soil physical, chemical and biological indicators as it cannot be measured directly. The present investigation was carried out to develop a valid soil quality index through some chosen indicators under long-term influences of tillage, water and nutrient-management practices in a rice–wheat cropping system. The experiment consisted of two tillage treatments, three irrigation treatments, and nine nutrient management treatments for both rice and wheat, was continued for 8 years. The index was developed using expert-opinion based conceptual framework model. After harvest of rice, the CFSQI-P (productivity) was higher under puddled situation, whereas CFSQI-EP (environmental protection) was more under non-puddled condition and 3-days of drainage was found promising for all the indices. No-tillage practice always showed higher soil quality index. The treatments either receiving full organics (100 % N) or 25 % substitution of fertilizer N with organics showed higher soil quality indices. Puddling, irrigation after 3 days of drainage and substitution of 25 % recommended fertilizer N dose with FYM in rice could be practiced for maintaining or enhancing soil quality. No-tillage, two irrigations, and domestic sewage sludge in wheat can safely be recommended for achieving higher soil quality.     

基于无人机高光谱遥感在太湖蓝藻水华监测中的一次应用

采用机载高光谱视频相机,在4个季节对太湖蓝藻进行7次、18个架次的有效拍摄。对拍摄到的高光谱影像进行辐射定标、几何拼接等预处理后,提取不同浓度蓝藻和水草等其他物体的高光谱数据,发现不同浓度的蓝藻光谱在680 nm后表现出较大差异。采用主成分分析（PCA）对高光谱数据降维后,结合k-近邻（kNN）分类算法,可实现对蓝藻的精准定位。定性识别结果经光谱预处理后,采用连续投影算法（SPA）进行特征波段提取,发现蓝藻光谱的季节差异主要表现在450 nm~570 nm和760 nm~910 nm波段。     

A landscape approach to quantifying land cover changes in Yulin, Northwest China

In this study we quantified land cover changes in the arid region of Yulin City, Northwest China between 1985 and 2000 using remote sensing and GIS in conjunction with landscape modeling. Land covers were mapped into 20 categories from multitemporal Landsat TM images. Five landscape indices were calculated from these maps at the land cover patches level. It was found that fallow land decreased by 125,148 ha while grassland and woodland increased by 107,975 and 17,157 ha, respectively. Landscape heterogeneity, dominance and fractal dimension changed little during the 15-year period while landscape became more fragmented, with an index rising from 0.56 to 0.58. The major factors responsible for these changes are identified as the change in the government policy on preserving the environment, continued growth in mining, and urbanization.     

Canopy assessment of biochemical features by ground-based hyperspectral data for an invasive species, giant reed (Arundo donax)

This study explored the potential use of hyperspectral data in the non-destructive assessment of chlorophyll, carbon, and nitrogen content of giant reed at the canopy level. We found that pseudoabsorption and derivatives of original hyperspectral data were able to describe the relationship between spectral data and measured biochemical characteristics. Based on correlogram analyses of ground-based hyperspectral data, we found that derivatives of pseudoabsorption were the best predictors of chlorophyll, carbon, and nitrogen content of giant reed canopies. Within the visible region, spectral data significantly correlated with chlorophyll content at both 461 nm and 693 nm wavelengths. Within the near-infrared region, carbon levels correlated with hyperspectral data at five causal wavelengths: 1038 nm, 1945 nm, 1132 nm, 1525 nm, and 1704 nm. The best spectral wavelength for estimating nitrogen content was 1542 nm. Such relationships between nutrient content and spectral data were best represented by exponential functions in most situations.     

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.