Vegetation monitoring in South-Varanger, Norway – species composition of ground vegetation and its relation to environmental variables and pollution impact

The area along the Norwegian-Russian border is threatened by air pollution from emission sources on the Kola Peninsula. A permanent network of 78 systematically chosen monitoring sites has been established in eastern Finnmark, Norway. Species abundance data from the ground vegetation have been recorded from 1320 systematically chosen permanent plots inside 66 of these sites, using frequency in subplots and visual estimates of percentage cover. Environmental variables were obtained for the whole site. Multivariate data analysis has been used to describe the variation in the species composition and to study its relation to environmental variables and pollution impact. The analyses show that much of the variation in the species composition, based on average species abundance at the sites, is well explained by different soil and climatic conditions. However, estimated SO₂ deposition, Ni, and Cu in the soil, and Ni in Cladina tissue have also been found to be statistically significantly correlated with the variation in the species data, but they explain only a minor part of the variation. The pollution impact over several years may have lead to a reduced lichen cover in the bottom-layer vegetation. Further development in an either negative or positive direction can be detected by re-investigations of the monitoring sites.     

3D Numerical Model of the Thermal Interaction Between Sediment–Water–Atmosphere

This paper describes a three-dimensional numerical model of heat flux using finite-difference approximation for the simulation, prediction, and visualization of sediment, water, and air temperature, applied in Villa del Mar saltmarsh, Bahía Blanca Estuary, Argentina. To make this computation, we develop an open-source software tool called Hemera 1.0 which is characterized by having little complexity and low hardware requirements. The model considers three heat transfer processes: diffusion, convection, and radiation, using bulk aerodynamic formulas as boundary conditions between the interfaces. The aforementioned model was applied for the month of January 2009. The model reproduced adequately the physical processes of the heat balance and showed an adequate response to changes in the boundary conditions. In addition, according to the model design, meteorological and oceanographic data and some soil properties are the only data input used for modeling. It is easily adaptable to other environments such us lakes and reservoirs, among others, in order to carry out similar studies.     

Implications of climate change for evaporation from bare soils in a Mediterranean environment

The purpose of this study was to predict quantitative changes in evaporation from bare soils in the Mediterranean climate region of Turkey in response to the projections of a regional climate model developed in Japan (hereafter RCM). Daily RCM data for the estimation of reference evapotranspiration (ET _r) and soil evaporation were obtained for the periods of 1994–2003 and 2070–2079. Potential evaporation (E _p) from bare soils was calculated using the Penman–Monteith equation with a surface resistance of zero. Simulation of actual soil evaporation (E _a) was carried out using Aydin model (Aydin et al., Ecological Modelling 182:91–105, 2005) combined with Aydin and Uygur (2006, A model for estimating soil water potential of bare fields. In Proceedings of the 18th International Soil Meeting (ISM) on Soils Sustaining Life on Earth, Managing Soil and Technology, Sanliurfa, 477–480pp.) model of predicting soil water potential at the top surface layer of a bare soil, after performances of Aydin model (R ² = 94.0%) and Aydin and Uygur model (R ² = 97.6) were tested. The latter model is based on the relations among potential soil evaporation, hydraulic diffusivity, and soil wetness, with some simplified assumptions. Input parameters of the model are simple and easily obtainable such as climatic parameters used to compute the potential soil evaporation, average diffusivity for the drying soil, and volumetric water content at field capacity. The combination of Aydin and Aydin and Uygur models appeared to be useful in estimating water potential of soils and E _a from bare soils, with only a few parameters. Unlike ET _r and E _p projected to increase by 92 and 69 mm (equivalent to 8.0 and 7.3% increases) due to the elevated evaporative demand of the atmosphere, respectively, E _a from bare soils is projected to reduce by 50 mm (equivalent to a 16.5% decrease) in response to a decrease in rainfall by 46% in the Mediterranean region of Turkey by the 2070s predicted by RCM, and consequently, to decreased soil wetness in the future.     

Precision Measurements from Very-Large Scale Aerial Digital Imagery

Managers need measurements and resource managers need the length/width of a variety of items including that of animals, logs, streams, plant canopies, man-made objects, riparian habitat, vegetation patches and other things important in resource monitoring and land inspection. These types of measurements can now be easily and accurately obtained from very large scale aerial (VLSA) imagery having spatial resolutions as fine as 1 millimeter per pixel by using the three new software programs described here. VLSA images have small fields of view and are used for intermittent sampling across extensive landscapes. Pixel-coverage among images is influenced by small changes in airplane altitude above ground level (AGL) and orientation relative to the ground, as well as by changes in topography. These factors affect the object-to-camera distance used for image-resolution calculations. ‘ImageMeasurement’ offers a user-friendly interface for accounting for pixel-coverage variation among images by utilizing a database. ‘LaserLOG’ records and displays airplane altitude AGL measured from a high frequency laser rangefinder, and displays the vertical velocity. ‘Merge’ sorts through large amounts of data generated by LaserLOG and matches precise airplane altitudes with camera trigger times for input to the ImageMeasurement database. We discuss application of these tools, including error estimates. We found measurements from aerial images (collection resolution: 5–26 mm/pixel as projected on the ground) using ImageMeasurement, LaserLOG, and Merge, were accurate to centimeters with an error less than 10%. We recommend these software packages as a means for expanding the utility of aerial image data.     

Environmental variation between habitats and uptake of heavy metals by Urtica dioica

The observation from previous surveys, that Urtica dioica plants that had grown in metal contaminated soil in the floodplains of the former Rhine estuary in different habitats, but at comparable total soil metal concentrations, showed significant differences in tissue metal concentrations, led to the hypothesis that variation in other environmental characteristics than soil composition and chemical speciation of metals between habitats is also important in determining uptake and translocation of metals in plants. A field survey indicated that differences in root Cd, Cu and Zn concentrations might partly be explained by variation in speciation of metals in different habitats. However, shoot concentrations showed a different pattern that did not relate to variation in soil metal concentrations. In a habitat experiment Urtica dioica plants were grown in artificially contaminated soil in pots that were placed in the four habitats (grassland, pure reed, mixed reed, osier bed) that were also included in the field survey. After seven weeks the plants showed significant differences in Cu and Zn concentrations in roots and aboveground plant parts and in distribution of the metals in the plants between habitats. It was concluded that variation between habitats in environmental characteristics other than soil composition can explain as much variation in plants as can variation in soil metal concentrations and/or speciation. The implications for assessment of soil metal contamination and uptake by plants are discussed.     

Agricultural Chemicals in the Alluvial Aquifer of a Typical County of the Arkansas Delta

Statistical methods and a Geographic Information System (GIS) were used to investigate potential indicators of ground water vulnerability to agricultural chemical contamination in a representative area of the Mississippi River alluvial aquifer. A total of 47 wells were sampled for analysis of nitrate, phosphorus, potassium, and 13 pesticides commonly-used in the area. Ten soil and hydrogeologic variables and five ground water vulnerability indices were examined to explain the variations of chemical concentrations. The results showed that no individual soil or hydrogeologic variables or their linear combinations could explain more than 25% of the variation of the chemical concentrations. A quadratic response surface model with the values of confining unit thickness, slope, soil permeability, depth to ground water, and recharge rate accounted for 62% of the variation of nitrate, 43% of P, and 83% of K, suggesting that the interactions among soil and hydrogeologic variables were significant. Observed trends of decreasing nitrate and P concentrations with increasing well depth and/or depth to ground water seemed to correlate with carbonate equilibrium in the aquifer and more reduced environment with depth. In view of uncertainties involved, it was recognized that the limitations associated with input data resolution used in GIS and the formulation of leaching indices limited their use for predicting ground water vulnerability. Misuse of pesticides could be another factor that would complicate the relationships between pesticide concentrations and the vulnerability indices.     

Uncertainty assessment of mapping mercury contaminated soils of a rapidly industrializing city in the Yangtze River Delta of China using sequential indicator co-simulation

Accurate characterization of heavy-metal contaminated areas and quantification of the uncertainties inherent in spatial prediction are crucial for risk assessment, soil remediation, and effective management recommendations. Topsoil samples (0–15 cm) (n = 547) were collected from the Zhangjiagang suburbs of China. The sequential indicator co-simulation (SIcS) method was applied for incorporating the soft data derived from soil organic matter (SOM) to simulate Hg concentrations, map Hg contaminated areas, and evaluate the associated uncertainties. High variability of Hg concentrations was observed in the study area. Total Hg concentrations varied from 0.004 to 1.510 mg kg⁻¹ and the coefficient of variation (CV) accounts for 70%. Distribution patterns of Hg were identified as higher Hg concentrations occurred mainly at the southern part of the study area and relatively lower concentrations were found in north. The Hg contaminated areas, identified using the Chinese Environmental Quality Standard for Soils critical values through SIcS, were limited and distributed in the south where the SOM concentration is high, soil pH is low, and paddy soils are the dominant soil types. The spatial correlations between Hg and SOM can be preserved by co-simulation and the realizations generated by SIcS represent the possible spatial patterns of Hg concentrations without a smoothing effect. Once the Hg concentration critical limit is given, SIcS can be used to map Hg contaminated areas and quantitatively assess the uncertainties inherent in the spatial prediction by setting a given critical probability and calculating the joint probability of the obtained areas.     

An air quality balance index estimating the total amount of air pollutants at ground level

A new index named Air Quality Balance Index (AQBI), which is able to characterise the amount of pollution level in a selected area, is proposed. This index is a function of the ratios between pollutant concentration values and their standards; it aims at identifying all situations in which there is a possible environmental risk even when several pollutants are below their limit values but air quality is reduced. AQBI is evaluated by using a high-resolution three-dimensional dispersion model: the air concentration for each substance is computed starting from detailed emissions sources: point, line and area emissions hourly modulated. This model is driven with accurate meteorological data from ground stations and remote sensing systems providing vertical profiles of temperature and wind; these data are integrated with wind and temperature profiles at higher altitudes obtained by a Local Area Model. The outputs of the dispersion model are compared with pollutant concentrations provided by measuring stations, in order to recalibrate emission data. A three-dimensional high resolution grid of AQBI data is evaluated for an industrial area close to Alessandria (Northern Italy), assessing air quality and environmental conditions. Performance of AQBI is compared with the Air Quality Index (AQI) developed by the U.S. Environmental Protection Agency. AQBI, computed taking into account all pollutants, is able to point out situations not evidenced by AQI, based on a preset limited number of substances; therefore, AQBI is a good tool for evaluating the air quality either in urban and in industrial areas. The AQBI values at ground level, in selected points, are in agreement with in situ observations.     

Estimation of suspended sediment concentration in rivers using acoustic methods

Acoustic Doppler current meters (ADV, ADCP, and ADP) are widely used in water systems to measure flow velocities and velocity profiles. Although these meters are designed for flow velocity measurements, they can also provide information defining the quantity of particulate matter in the water, after appropriate calibration. When an acoustic instrument is calibrated for a water system, no additional sensor is needed to measure suspended sediment concentration (SSC). This provides the simultaneous measurements of velocity and concentration required for most sediment transport studies. The performance of acoustic Doppler current meters for measuring SSC was investigated in different studies where signal-to-noise ratio (SNR) and suspended sediment concentration were related using different formulations. However, these studies were each limited to a single study site where neither the effect of particle size nor the effect of temperature was investigated. In this study, different parameters that affect the performance of an ADV for the prediction of SSC are investigated. In order to investigate the reliability of an ADV for SSC measurements in different environments, flow and SSC measurements were made in different streams located in the Aegean region of Turkey having different soil types. Soil samples were collected from all measuring stations and particle size analysis was conducted by mechanical means. Multivariate analysis was utilized to investigate the effect of soil type and water temperature on the measurements. Statistical analysis indicates that SNR readings ob tained from the ADV are affected by water temperature and particle size distribution of the soil, as expected, and a prediction model is presented relating SNR readings to SSC mea surements where both water temperature and sediment characteristics type are incorporated into the model. The coefficients of the suggested model were obtained using the multivariate anal ysis. Effect of high turbidity conditions on ADV performance was also investigated during and after rain events.     

Inorganic chemical composition of native trees of the Atlantic Forest

The Atlantic Forest with its exuberant vegetation of high level of biodiversity is classified as one hotspot of the world. Chemical composition of leaves from native trees and underlying soils was evaluated by INAA. The predominant species Euterpe edulis, Bathysa meridionalis, Hyeronima alchorneoides, Marlierea tomentosa, Gomidesia flagellaris, and Gomidesia spectabilis belonging to the diverse plant families were studied. Euterpe edulis, the most abundant understory specie, presented the lowest element concentrations except for Zn. Some variation in chemical composition was noted, however, the chemical specificity of tree species can be more predominant than the soil variability for the obtained leaf concentrations. Factor values obtained through the Monte-Carlo assisted factor analysis were used for species discrimination. The results indicate that chemical investigation of native trees is a quite promising tool for biodiversity studies in the Atlantic Forest.     

Soil gas radon analysis in some areas of Northern Punjab, India

The radon concentration levels in soil samples from 39 locations of Northern Punjab are measured using AlphaGUARD (PQ 2000 PRO Model) of Genitron instruments, Germany. The radon concentration in soil varies from 0.3 to 35.8 kBq/l. The minimum value of radon is observed in Talwandi Choudhrian and is maximum for Nushera Dhala. The soil gas radon is correlated with soil temperature, pressure, and humidity to observe the effect of these parameters on radon release. The soil gas radon values in the study area are compared with that obtained in groundwater. The results are also compared with the available radon data for other parts of Punjab and Himachal Pradesh.     

A Method of Mercury Removal from Topsoil Using Low-Thermal Application

Mercury contamination in the environment is problematic due to the unusual physical properties and well-recognized toxicity of this common metal. The bioavailability of mercury depends strongly on its chemical speciation. Anthropogenic mercury and its compounds appear in soil as hot spots located close to industrial facilities that used or produced mercury. The nature of the chemical production process, transportation and disposal practices often determined the chemical composition and distribution of mercury in the surrounding soils. Current ex situ soil remediation methods are expensive, produce undesirable side effects to the environment and usually involve transportation of contaminated soil.In this project, sponsored by the U.S. Department of Energy, a low-cost, simple approach to removing mercury from soil was evaluated. The process uses low-temperature thermal desorption of volatile metallic mercury and its compounds, and subsequent vapor capture.The project consisted of laboratory and plot-scale experiments. The laboratory efforts evaluated theoretical calculations of mercury removal as a function of time and temperature. The plot-scale experiment was a practical application of the laboratory results. For both experiments, mercury-polluted soil was obtained from a chemical production facility located in southern Poland. In laboratory experiments, at temperature 373 K total mercury concentration decreased in soil by nearly 32%. In plot-scale experiments, at temperature 440 K, about 60–70% of total mercury was removed from the soil.At the end of the experiment, a test of soil biological activity was performed to check if the high temperature applied to the soil did not impair the soil growth properties. There was no negative effect of temperature found.     

Improving Estimation of Specific Surface Area by Artificial Neural Network Ensembles Using Fractal and Particle Size Distribution Curve Parameters as Predictors

Specific surface area (SSA) is one of the principal soil properties used in modeling soil processes. In this study, artificial neural network (ANN) ensembles were evaluated to predict SSA. Complete soil particle-size distribution was estimated from sand, silt, and clay fractions using the model by Skaggs et al. and then the particle-size distribution curve parameters (PSDCPs) and fractal parameters were calculated. The PSDCPs were used to predict 20 particle-size classes for a soil sample’s particle size distribution. Fractal parameters were calculated by the model of Bird et al. In addition, total soil-specific surface area (TSS) was calculated using the above 20 size classes. Pedotransfer functions were developed for SSA and TSS using ANN ensembles from 63 pieces of SSA data taken from the literature. Fractal parameters, PSDCPs, and some other soil properties were used to predict SSA and TSS. Introducing fractal parameters and PSDCPs improved the SSA estimations by 12.5 and 11.1 %, respectively. The improvements were even better for TSS estimations (27.7 and 27.0 %, respectively). The use of fractal parameters as estimators described 44 and 92.8 % of the variation in SSA and TSS, respectively, while PSDCPs explained 42 and 6.6 % of the variation in SSA and TSS, respectively. The results suggested that fractal parameters and PSDCPs could be successfully used as predictors in ANN ensembles to predict SSA and TSS.     

Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability

Collection of standardized assessment and monitoring data is critically important for supporting policy and management at local to continental scales. Remote sensing techniques, including image interpretation, have shown promise for collecting plant community composition and ground cover data efficiently. More work needs to be done, however, evaluating whether these techniques are sufficiently feasible, cost-effective, and repeatable to be applied in large programs. The goal of this study was to design and test an image-interpretation approach for collecting plant community composition and ground cover data appropriate for local and continental-scale assessment and monitoring of grassland, shrubland, savanna, and pasture ecosystems. We developed a geographic information system image-interpretation tool that uses points classified by experts to calibrate observers, including point-by-point training and quantitative quality control limits. To test this approach, field data and high-resolution imagery (∼3 cm ground sampling distance) were collected concurrently at 54 plots located around the USA. Seven observers with little prior experience used the system to classify 300 points in each plot into ten cover types (grass, shrub, soil, etc.). Good agreement among observers was achieved, with little detectable bias and low variability among observers (coefficient of variation in most plots <0.5). There was a predictable relationship between field and image-interpreter data (R ² > 0.9), suggesting regression-based adjustments can be used to relate image and field data. This approach could extend the utility of expensive-to-collect field data by allowing it to serve as a validation data source for data collected via image interpretation.     

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the “Enter” method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685–3.65% and 0.9988–0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.     

A Simple Model for Urban Background Pollution

A simple urban background pollution model is presented. Contributions from the individual area sources, subdivided into a grid net of a resolution of 2km × 2km, are integrated along the wind direction path assuming linear dispersion with the distance to the receptor point. Horizontal dispersion is accounted for by averaging the calculated concentrations over a certain, wind speed dependent wind direction sector, centred on the average wind direction. Formation of the nitrogen dioxide due to oxidation of nitrogen monoxide by ozone is calculated using a simple chemical model based on assumption of a photochemical equilibrium on the time scale of the pollution transport across the city area. The rate of entrainment of fresh rural ozone is governed by this time scale. The model is suitable for calculations of urban background when the dominating source is the road traffic. For this source the emissions take place at ground level, and a good approximation is to treat the emissions as area sources, but with an initial vertical dispersion determined by the height of the buildings.     

空间关联随机森林模型结合Sentinel-2影像估算潍北地区裸土期土壤盐分

土壤盐分含量(SSC)是评价土地退化和肥力水平的重要指标,实现SSC状态和空间分异的快速准确监测对区域环境的优化管理极为关键。选取潍北平原为研究区,野外采集233处土壤样品并获取同时相Sentinel-2多光谱影像,进一步将特征光谱波段和构建的最优光谱指数作为输入自变量,测试得到的SSC实测值为因变量,最后将空间关联函数引入到随机森林中去建立基于空间关联随机森林算法的SSC遥感估算模型,完成区域尺度上的SSC反演估算与空间制图。结果表明：影像的B3、B8和B11是SSC的特征波段,通过波段比值变换能够增强卫星光谱信号对SSC的吸收响应,筛选得到的最优光谱指数分别为RI₃₄(波段3和波段4的反射率比值)、RI₇₁₁(波段7和波段11的反射率比值)、ND₆₁₁(波段6和波段11的反射率归一化值)和D₄₅(波段4和波段5的反射率差值);仅用特征波段或最优光谱指数来构建模型不能取得满意的SSC估算精度,空间关联随机森林模型的SSC估算精度要高于随机森林模型;在将上述特征波段和最优光谱指数共同输入空间关联随机...     

Field scale variability of cadmium and zinc in soil and barley

CaCl₂-extractable soil Cd and Zn contents have been suggested as a measure of bioavailability. To investigate the ability of this measure to reflect spatial patterns of Cd and Zn concentrations in barley (Hordeum vulgare L.) in an arable field, plant and soil samples were taken from a 0.5 ha area sandy soil contaminated with Cd and Zn. Cd and Zn contents in barley and yield were spatially variable. Yield was low, which may have been caused by Zn toxicity or atrazine turnover. For Cd, CaCl₂-extractable soil contents explained only 17% of the variation in Cd contents in grain, and for Zn no significant correlation was observed. Nevertheless, surface plots of CaCl₂-extractable soil contents and contents of barley grain illustrated their corresponding spatial patterns. Despite the poor linear correlation between CaCl₂-extractable soil-Cd and grain-Cd, a stochastic model for long term behaviour of Cd in field soils predicted observed variability in Cd contents of barley grain well from spatial variability of soil pH and organic matter content. The probabilistic model predicted behaviour of Cd in terms of probability, and was more appropriate than the deterministic approach.     

A soil-site evaluation index of productivity in intensively managed Pinus radiata (D. Don) plantations in South Australia

A limiting-factor, environmental model for radiata pine (Pinus radiata (D. Don)) has been developed using landform and soil morphological features that influence site productivity. The model focuses on soil and landscape constraints to productivity and predicts the native productivity of land and tree species. It permits the integration of land-use objectives for a catchment through forest management and use of silvicultural practices which increase productivity. The soil site evaluation index (SSEI) is an index of forest productivity found when silviculture extends only to the minimum amount of site disturbance needed to establish a plantation of radiata pine. The impacts of intensive silvicultural practices were deducted from the Site Quality productivity survey rating to calculate the unimproved yield class (uYC). We calculated SSEI by range standardising uYC values from 0 to 1. SSEI was correlated with the environmental factors in a regression tree model using readily available analytical software. The model accurately predicts unimproved forest productivity from observed soil horizon and land surface properties. The environmental constraints in low lying areas relate to waterlogging, soil sodicity and gravel content. In elevated areas, plant available water storage, rock weathering, landform, ironstone gravel and aspect are recognised factors for pine growth.     

Wheat yield functions for analysis of land-use change in China

CERES-Wheat, a dynamic process crop growth model, is specified and validated for eight sites in the major wheat-growing regions of China. Crop model results are then used to test the Mitscherlich-Baule and the quadratic functional forms for yield response to nitrogen fertilizer, irrigation water, temperature, and precipitation. The resulting functions are designed to be used in a linked biophysical-economic model of land-use and land-cover change in China. While both functions predict yield responses adequately, the Mitscherlich-Baule function is preferable to the quadratic function because its parameters are biologically and physically realistic. Variables explaining a significant proportion of simulated yield variance are nitrogen, irrigation water, and precipitation; temperature was a less significant component of yield variation within the range of observed year-to-year variability at the study sites. Crop model simulations with a generic soil with median characteristics of the eight sites compared to simulations with site-specific soils showed that agricultural soils in China have similar and, in general, low-to-moderate water-holding capacities and organic matter contents. The validated crop model is useful for simulating the range of conditions under which wheat is grown in China, and provides the means to estimate production functions when experimental field data are not available.