Effect of HPLC binary mobile phase composition on the analysis of carbonyls

The relative performance of the binary mobile phase in the high-performance liquid chromatography analysis of carbonyl compounds (CCs) was tested using the liquid-phase standards containing 15 aldehyde/ketone-DNPH mixture. The Hichrome column was employed for the analysis of CCs at a flow rate of 1.5 mL min(?-?1). The binary mobile phases prepared using both acetonitrile/water (AW) and a possible alternative of methanol:water (MW) mixture were examined by their calibration results. The data derived from these two binary phases were then evaluated in terms of three key variables (i.e., resolution, relative sensitivity, and retention time). The relative water content (or the water to organic solvent ratio (W/A) or (W/M)) of the binary phase was found as the key variable for the performance. The results indicate that the optimal resolution of AW combination was attained consistently for most composition, while MW generally suffered from overpressure problem. The changes of water content in the AW mixture led to the changes of all three variables in the quantitative analysis of CCs. The obtained results confirm that the AW mixture should be the optimal elutant for the CC analysis, as other simple binary compositions like MW are limited in many respects.     

The evaluation of a low resolution fourier transform infrared (FTIR) gas analyser for monitoring of solvent emission rates under field conditions

The applicability of a low resolution (8 cm-1) Fourier transform infrared (FTIR) gas analyser with an absorption path length of 3 m was evaluated for the on-line monitoring of organic solvent mixture emissions in a flexographic ink manufacturing plant. The on-line monitoring revealed that the highest variations of solvent concentrations, up to three decades, occurred in the exhaust air. The FTIR analyser with a dynamic range of four decades covers well the concentration ranges typically found in the exhaust air and in the workroom air of ink manufacturing plants. The average emission rate of solvent mixture based on a sampling period of two days was 1.8 kg h-1 consisting of mainly ethanol (70%), ethyl acetate (15%) and propan-2-ol (11%). The detection limits of the analyser for the solvent compounds ranged from 0.3 to 4.3 mg m-3 and the measurement uncertainty was less than 10% in the concentration range of 8-15,000 mg m-3. These characteristics make the apparatus appropriate for most industrial hygiene applications. An FTIR spectrophotometer, equipped with an multipoint sampling unit, facilitates rapid identification of solvent components, real-time display of concentration data relevant to workroom air and environment monitoring as well as process control. Furthermore, the on-line concentration information enabled a rapid selection of representative sampling locations. The spectrophotometer is transportable, rugged and relatively simple to calibrate even in a hostile industrial environment.     

A docking-based receptor library of antibiotics and its novel application in predicting chronic mixture toxicity for environmental risk assessment

As organisms are typically exposed to chemical mixtures over long periods of time, chronic mixture toxicity is the best way to perform an environmental risk assessment (ERA). However, it is difficult to obtain the chronic mixture toxicity data due to the high expense and the complexity of the data acquisition method. Therefore, an approach was proposed in this study to predict chronic mixture toxicity. The acute (15 min exposure) and chronic (24 h exposure) toxicity of eight antibiotics and trimethoprim to Vibrio fischeri were determined in both single and binary mixtures. The results indicated that the risk quotients (RQs) of antibiotics should be based on the chronic mixture toxicity. To predict the chronic mixture toxicity, a docking-based receptor library of antibiotics and the receptor-library-based quantitative structure–activity relationship (QSAR) model were developed. Application of the developed QSAR model to the ERA of antibiotic mixtures demonstrated that there was a close affinity between RQs based on the observed chronic toxicity and the corresponding RQs based on the predicted data. The average coefficients of variations were 46.26 and 34.93 % and the determination coefficients (R ²) were 0.999 and 0.998 for the low concentration group and the high concentration group, respectively. This result convinced us that the receptor library would be a promising tool for predicting the chronic mixture toxicity of antibiotics and that it can be further applied in ERA.     

Evaluation of the best compromise between the urban air quality monitoring resolution by diffusive sampling and resource requirements

The need to collect data representative of overall urban pollution is all-important in order to monitor the population exposure. High spatial resolution monitoring using diffusive samplers allows studying of the urban pollutant distribution, thus enabling deeper investigation of their generation and diffusion mechanisms. Nevertheless, such a monitoring campaign has a certain cost. In this study we point out how to find the best compromise between the number of necessary measurements and the affordable costs for monitoring campaigns. We also describe an innovative method for the proper design of a fixed urban monitoring network by means of preliminary high spatial resolution campaigns using diffusive samplers. Four European capital cities (Dublin, Madrid, Paris and Rome) were monitored six times, each time for seven days. Benzene, toluene, ethylbenzene, xylenes (BTEX) and NO(2) concentrations were measured at 146 sites in Dublin, 293 in Madrid, 339 in Paris and 290 in Rome. Multiscale grids have been drawn which ranged in mesh size from 500 m to 2 km. The statistical processing of data produced a twofold result: the creation of isoconcentration maps with geostatistical procedures, and an algorithm aimed at locating the minimum number of sampling sites where the fixed monitoring stations should be placed. Average urban levels estimated on the basis of these selected sites differ by less than 8% from those calculated on the whole populations of the sampled points. The aim of this work is to investigate how far the resolution of a monitoring campaign of urban pollution by diffusive sampling can be reduced, thus making the monitoring less expensive in terms of human and financial resources, while preserving the same quality of the results that could be achieved with a higher resolution. We found that there is no significant loss of information when the resolution of the monitoring grid for BTEX is lowered to a mesh size of 1.85 km, that is a sampling site each 3.4 km(2), and that the minimum number of sampling sites to be used is N = 0.29 A, where A is the urban surface to be monitored (in km(2)). As the spatial distribution of NO(2) is less sensitive to the distance from the emission source than that of BTEX, this relationship could be retained as a valid lower limit for the mesh grid size also for NO(2) monitoring.     

Sensitivity of a Land Change Model to Pixel Resolution and Precision of the Independent Variable

It is important to know how the results from a land change model vary based on both the pixel resolution of the maps and the precision of the independent variables because subjective decisions or default values frequently determine these two factors. This paper presents an approach to measure the variation in model accuracy that is triggered by alteration of the pixel resolution and the precision of the independent variable, which are bins of distance to previously built area for our case study. We illustrate the principles with an application of the Geomod land change model contained in the Idrisi GIS, applied to simulate the gain of built land in central Massachusetts, USA. Results reveal four general principles: (1) change in pixel resolution using the majority-takes-all rule can influence quantity error, (2) change in bin width of an independent variable does not influence the quantity error, (3) resolution and bin width interact so that bin width does not have an effect on error when bin widths are smaller than the pixel resolution, and (4) researchers are wise to examine the implications of their subjective decisions by plotting clearly how the resolution and bin size influence the mathematical relationships that the model uses. We have found no universal, hard, and fast rules that dictate how to decide on an appropriate pixel resolution and bin width, but our method demonstrates how these decisions can be influential. These insights can offer scientists guidance in how to prepare data in an appropriate manner.     

Effects of soil amendments on the bioavailability of heavy metals from zinc mine tailings

A study was conducted to test the effects of soil amendments on the bioavailability of heavy metals in a zinc mine tailings containing soil to plants, using the Indian mustard plant (Brassica juncea) as a test organism. Zinc mine tailing containing soil was amended with humus soil (HS) and phosphatic clay (PC). The zinc mine tailing containing soil (ZMTS) was characterized for heavy metals. It was mixed with PC and HS, and four mixtures were prepared. The first mixture contained ZMTS, and served as a control. The second mixture contained ZMTS and PC in the ratio of 1:1 (w/w). The third mixture contained ZMTS and HS in the ratio of 1:1(w/w). The fourth mixture containing ZMTS, PC and HS in the ratio of (2:1:1) (w/w). A slight increase in the bioavailability of Pb, Cu, Zn and Mn was noticed with increase in the incubation time from 14 to 42 days. The bioavailability of Pb, Cu, Zn and Mn from ZMTS alone in Brassica plant was in the range of 94-99% up to 42 days. Addition of PC and HS to the ZMTS soil reduced the bioavailabilities of Pb by (15%), of Cu by (20%), of Zn by (20%) and of Mn by (25%) in the mustard plant. The data showed that PC in the presence of HS had a high affinity for the heavy metals in the order of Pb, Cu, Zn and Mn.     

Mapping Turbidity in the Charles River, Boston Using a High-resolution Satellite

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).     

Assessing the wildlife habitat value of New England salt marshes: I. Model and application

We developed an assessment model to quantify the wildlife habitat value of New England salt marshes based on marsh characteristics and the presence of habitat types that influence habitat use by terrestrial wildlife. Applying the model to 12 salt marshes located in Narragansett Bay, RI resulted in assessment scores that ranged over a factor of 1.5 from lowest to highest. Pre-classifying the results based on marsh size and morphology helped to compare assessment scores between marshes, and demonstrated that even the lower ranking marshes had substantial habitat value. Stepwise multiple regression analysis of assessment scores and model components demonstrated that salt marsh morphology, the degree of anthropogenic modification, and salt marsh vegetative heterogeneity were significant variables and accounted for 91.3% of the variability in component scores. Our results suggest that targeting these components for restoration may lead to improved assessment scores for our study marshes. We also examined the use of lower resolution remote sensing data in the assessment in order to minimize the time and effort required to complete the model. Scores obtained using smaller-scale, lower resolution data were significantly lower than those obtained using larger-scale, higher resolution data (df = 11; t = 2.2; p < 0.001). The difference was significantly positively correlated with the portion of the assessment score that could be attributed to trees, pools, and pannes and marsh size (r (2) =0.50, F = 4.6, p = 0.04), and could indicate a bias against smaller, more heterogeneous marshes. We conclude that potential differences need to be weighed against the time benefit of using this type of data, bearing in mind the marsh size and the goals of the assessment. Overall, our assessment can provide information to aid in prioritizing marshes for protection and restoration, identify marshes that may harbor significant biodiversity, or help monitor changes in habitat value over time.     

Satellite detection of land-use change and effects on regional forest aboveground biomass estimates

We used remote-sensing-driven models to detect land-cover change effects on forest aboveground biomass (AGB) density (Mg.ha(-1), dry weight) and total AGB (Tg) in Minnesota, Wisconsin, and Michigan USA, between the years 1992-2001, and conducted an evaluation of the approach. Inputs included remotely-sensed 1992 reflectance data and land-cover map (University of Maryland) from Advanced Very High Resolution Radiometer (AVHRR) and 2001 products from Moderate Resolution Imaging Spectroradiometer (MODIS) at 1-km resolution for the region; and 30-m resolution land-cover maps from the National Land Cover Data (NLCD) for a subarea to conduct nine simulations to address our questions. Sensitivity analysis showed that (1) AVHRR data tended to underestimate AGB density by 11%, on average, compared to that estimated using MODIS data; (2) regional mean AGB density increased slightly from 124 (1992) to 126 Mg ha(-1) (2001) by 1.6%; (3) a substantial decrease in total forest AGB across the region was detected, from 2,507 (1992) to 1,961 Tg (2001), an annual rate of -2.4%; and (4) in the subarea, while NLCD-based estimates suggested a 26% decrease in total AGB from 1992 to 2001, AVHRR/MODIS-based estimates indicated a 36% increase. The major source of uncertainty in change detection of total forest AGB over large areas was due to area differences from using land-cover maps produced by different sources. Scaling up 30-m land-cover map to 1-km resolution caused a mean difference of 8% (in absolute value) in forest area estimates at the county-level ranging from 0 to 17% within a 95% confidence interval.     

Emission database for global atmospheric research (Edgar)

Atmospheric chemistry and climate modellers require gridded global emissions data as input into their models. To meet this urgent need a global emissions source database called EDGAR is being developed by TNO and RIVM to estimate for 1990, on a regional and on a grid basis, annual emissions of greenhouse gases (CO₂, CH₄, N₂O, CO, NO_x, non-methane VOC, SO_x), of NH₃, and of ozone depleting compounds (halocarbons) from all known sources. The aim is to establish at due levels of spatial, temporal and source aggregation the emissions from both anthropogenic and biogenic sources: a complete set of data required to estimate the total source strength of the various gases with a 1×1 ° resolution (altitude resolution of 1 km) and a temporal resolution of a month, supplemented by diurnal variation, as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). In this way EDGAR will meet the requirements of present and future developments in the field of atmospheric modelling. The data comprise demographic data, social and economic factors, land use distributions and emission factors (with due emphasis on the uncertainty). As understanding in this field is still changing, due attention is paid to flexibility regarding the disaggregation of sources, spatial and temporal resolution and species. The objective and methodology chosen for the construction of the database and the structural design of the database system are presented, as well as the type and sources of data and the approach used for data collection. As an example, the construction of the N₂O inventory is discussed.     

Monitoring the changing position of coastlines using aerial and satellite image data: an example from the eastern coast of Trabzon, Turkey

Coastline mapping and coastline change detection are critical issues for safe navigation, coastal resource management, coastal environmental protection, and sustainable coastal development and planning. Changes in the shape of coastline may fundamentally affect the environment of the coastal zone. This may be caused by natural processes and/or human activities. Over the past 30 years, the coastal sites in Turkey have been under an intensive restraint associated with a population press due to the internal and external touristic demand. In addition, urbanization on the filled up areas, settlements, and the highways constructed to overcome the traffic problems and the other applications in the coastal region clearly confirm an intensive restraint. Aerial photos with medium spatial resolution and high resolution satellite imagery are ideal data sources for mapping coastal land use and monitoring their changes for a large area. This study introduces an efficient method to monitor coastline and coastal land use changes using time series aerial photos (1973 and 2002) and satellite imagery (2005) covering the same geographical area. Results show the effectiveness of the use of digital photogrammetry and remote sensing data on monitoring large area of coastal land use status. This study also showed that over 161 ha areas were filled up in the research area and along the coastal land 12.2 ha of coastal erosion is determined for the period of 1973 to 2005. Consequently, monitoring of coastal land use is thus necessary for coastal area planning in order to protecting the coastal areas from climate changes and other coastal processes.     

The sensitivity of mussel-watch programmes to detect temporal trends in the bio-availability of metals. A case study of the Saronicos Gulf of Greece

Statistical power analysis has been used to determine the sensitivity of the mussel-watch programme in Hellenic seas, with particular focus placed on the data obtained for metal content in mussels from the Saronicos Gulf during the period 1985-1997. Sample collection was carried out quarterly. Soft tissues of 60-100 similarly sized mussels (Mytilus galloprovincialis) were pooled into six composite samples and analysed by Atomic Absorption Spectroscopy (AAS). The study concludes that the resolution of monitoring programmes employed thus far is sufficiently high to detect temporal trends related to changes in contaminant levels caused either by drastic environmental control (such as those imposed on point-source pollutants) or by sudden increases in contaminant load. However, the detection of more subtle trends, which may be expected in the near future as a result of global pollution-control policies, is likely to require the adoption of much more sensitive programmes. In addition, the review of up-to-date knowledge on metal biology in mussels suggests that factors related to growth and reproduction should be taken into account to remarkably increase the resolution of mussel-watch programmes. Indicative maximum values for components of error to be complied with during routine monitoring are suggested.     

A dynamic statistical experiment for atmospheric interactions

Interactions among atmospheric parameters exist at different scales. The pristine approach for observational or model data analysis involves changing the input parameters one at a time (OAT) and studying the effect on the system. Limitations of this approach for atmospheric applications are discussed. A fractional factorial (FF) based study is evolved and a methodology is outlined involving dynamic graphical analysis. Observational data from the FIFE and HAPEX‐MOBILHY experiments are utilized with a vegetation and soil moisture scheme dynamically coupled in a planetary boundary layer model to demonstrate the robustness of this approach. Both low‐resolution and high‐resolution designs are considered. Various aspects of the vegetation‐atmosphere interactions are delineated. Results obtained from the interaction‐based FF approach differ considerably from the earlier OAT‐type studies.     

唐山市钢铁行业大气污染物排放清单建立

以唐山市钢铁企业为研究对象,在收集已有排放源数据的基础上,基于排放因子和活动水平数据采用排放因子法估算了唐山市钢铁企业多个大气污染物的排放量,得到了符合空气质量模型要求的污染源输入数据,建立钢铁行业和主要防控因子污染源数据库。结合唐山市各县区的环境空气质量状况,利用GIS技术,将不同污染因子的排放进行空间分布,最终形成准确完善的多尺度、高时空分辨率大气污染源排放清单。研究介绍了符合中国特色的区域高分辨率大气排放源清单建立的方法体系,为京津冀地区区域大气污染联防联控及2020年大气污染物区域削减计划工作提供数据支撑。     

Mapping the spatio-temporal distribution of key vegetation cover properties in lowland river reaches,using digital photography

The presence of vegetation in stream ecosystems is highly dynamic in both space and time. A digital photography technique is developed to map aquatic vegetation cover at species level, which has a very high spatial and a flexible temporal resolution. A digital single-lens reflex (DSLR) camera mounted on a handheld telescopic pole is used. The low-altitude (5 m) orthogonal aerial images have a low spectral resolution (red-green-blue), high spatial resolution (～1.9 pixels cm^?2, ～1.3 cm length) and flexible temporal resolution (monthly). The method is successfully applied in two lowland rivers to quantify four key properties of vegetated rivers: vegetation cover, patch size distribution, biomass and hydraulic resistance. The main advantages are that the method is (i) suitable for continuous and discontinuous vegetation covers, (ii) of very high spatial and flexible temporal resolution, (iii) relatively fast compared to conventional ground survey methods, (iv) non-destructive and (v) relatively cheap and easy to use, and (vi) the software is widely available and similar open source alternatives exist. The study area should be less than 10 m wide, and the prevailing light conditions and water turbidity levels should be sufficient to look into the water. Further improvements of the image processing are expected in the automatic delineation and classification of the vegetation patches.     

GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed,Ethiopia

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources. Land cover changes in Lake Hawassa Watershed, Southern Ethiopia, were investigated using Landsat MSS image data of 1973, and Landsat TM images of 1985, 1995, and 2011, covering a period of nearly four decades. Each image was partitioned in a GIS environment, and classified using an unsupervised algorithm followed by a supervised classification method. A hybrid approach was employed in order to reduce spectral confusion due to high variability of land cover. Classification of satellite image data was performed integrating field data, aerial photographs, topographical maps, medium resolution satellite image (SPOT 20 m), and visual image interpretation. The image data were classified into nine land cover types: water, built-up, cropland, woody vegetation, forest, grassland, swamp, bare land, and scrub. The overall accuracy of the LULC maps ranged from 82.5 to 85.0 %. The achieved accuracies were reasonable, and the observed classification errors were attributable to coarse spatial resolution and pixels containing a mixture of cover types. Land cover change statistics were extracted and tabulated using the ERDAS Imagine software. The results indicated an increase in built-up area, cropland, and bare land areas, and a reduction in the six other land cover classes. Predominant land cover is cropland changing from 43.6 % in 1973 to 56.4 % in 2011. A significant portion of land cover was converted into cropland. Woody vegetation and forest cover which occupied 21.0 and 10.3 % in 1973, respectively, diminished to 13.6 and 5.6 % in 2011. The change in water body was very peculiar in that the area of Lake Hawassa increased from 91.9 km² in 1973 to 95.2 km² in 2011, while that of Lake Cheleleka whose area was 11.3 km² in 1973 totally vanished in 2011 and transformed into mud-flat and grass dominated swamp. The “change and no change” analysis revealed that more than one third (548.0 km²) of the total area was exposed to change between 1973 and 2011. This study was useful in identifying the major land cover changes, and the analysis pursued provided a valuable insight into the ongoing changes in the area under investigation.     

Single and mixture effects of pesticides and a degradation product on fluvial biofilms

The Morcille River located in the Beaujolais vineyard area (Eastern France) is subjected to strong vine-growing pressure leading to the contamination by a range of herbicides and fungicides of the surrounding freshwater environment. Particularly high concentrations of norflurazon, desmethyl norflurazon and tebuconazole were recorded in spring 2010 at the downstream site of the river. Despite their occurrence in rivers, scarce toxicity data are available for these products, in particular in the case of desmethyl norflurazon (main norflurazon degradation product). Furthermore, the toxicity data are generally available only for single compounds and are issued from single species toxicity tests, leading to a lack of ecological relevance. Consequently, this study was undertaken to evaluate the toxic effects of norflurazon, desmethyl norflurazon and tebuconazole singly and in a ternary mixture on fluvial biofilm. Toxicity tests were performed in microplates for 48 h. Photosynthetic endpoints were measured using pulse amplitude-modulated fluorometry; diatom densities and taxonomic composition were determined. After 48 h of exposure, significant effects on optimal quantum yield (F _v/F _m) for desmethyl norflurazon and mixture were observed.     

The Spatial-Scale Effect of an Atmospheric Environmental Impact Assessment in Regional Strategic Environmental Assessment (R-SEA)

When evaluating the atmospheric environment in regional strategic environment assessment (R-SEA), the variation and choice of the spatial scale have a substantial influence on the conclusions of the assessment. In this study, we used numerical simulation to investigate the spatial-scale effect. Two varying spatial extents and two varying spatial details of pollutant emission data (emission inventories in this case) were provided for numerical modeling, and output distributions of atmospheric pollutants at different air pollution levels were compared. The results show that the resolution and spatial range of data collection do indeed influence the atmospheric prediction and assessment results in R-SEA. The spatial-scale effect is more significant under the air pollution condition than under excellent and good air quality conditions. A comparison of varying spatial extents of emission inventory shows that narrowing the prediction area to a local scale is more conducive to identifying the impact of local pollution sources. A comparison of varying spatial details of emission inventory indicates that a higher resolution is favorable for identifying local high concentrations of pollutants and their locations.     

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.