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Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Hantaviruses more specifically cause thousands of human disease cases annually worldwide, while understanding and predicting human hantavirus epidemics pose numerous unsolved challenges. Nephropathia epidemica (NE) is a human infection caused by Puumala virus, which is naturally carried and shed by bank voles ( Myodes glareolus). The objective of this study was to develop a method that allows model-based predicting 3 months ahead of the occurrence of NE epidemics. Two data sets were utilized to develop and test the models. These data sets were concerned with NE cases in Finland and Belgium. In this study, we selected the most relevant inputs from all the available data for use in a dynamic linear regression (DLR) model. The number of NE cases in Finland were modelled using data from 1996 to 2008. The NE cases were predicted based on the time series data of average monthly air temperature (°C) and bank voles' trapping index using a DLR model. The bank voles' trapping index data were interpolated using a related dynamic harmonic regression model (DHR). Here, the DLR and DHR models used time-varying parameters. Both the DHR and DLR models were based on a unified state-space estimation framework. For the Belgium case, no time series of the bank voles' population dynamics were available. Several studies, however, have suggested that the population of bank voles is related to the variation in seed production of beech and oak trees in Northern Europe. Therefore, the NE occurrence pattern in Belgium was predicted based on a DLR model by using remotely sensed phenology parameters of broad-leaved forests, together with the oak and beech seed categories and average monthly air temperature (°C) using data from 2001 to 2009. Our results suggest that even without any knowledge about hantavirus dynamics in the host population, the time variation in NE outbreaks in Finland could be predicted 3 months ahead with a 34% mean relative prediction error (MRPE). This took into account solely the population dynamics of the carrier species (bank voles). The time series analysis also revealed that climate change, as represented by the vegetation index, changes in forest phenology derived from satellite images and directly measured air temperature, may affect the mechanics of NE transmission. NE outbreaks in Belgium were predicted 3 months ahead with a 40% MRPE, based only on the climatological and vegetation data, in this case, without any knowledge of the bank vole's population dynamics. In this research, we demonstrated that NE outbreaks can be predicted using climate and vegetation data or the bank vole's population dynamics, by using dynamic data-based models with time-varying parameters. Such a predictive modelling approach might be used as a step towards the development of new tools for the prevention of future NE outbreaks. [ABSTRACT FROM AUTHOR]

Lyme borreliosis (LB) and nephropathia epidemica (NE) are zoonoses resulting from two different transmission mechanisms and the action of two different pathogens: the bacterium Borrelia burgdorferi and the Puumala virus, respectively. The landscape configuration is known to influence the spatial spread of both diseases by affecting vector demography and human exposure to infection. Yet, the connections between landscape and disease have rarely been quantified, thereby hampering the exploitation of land cover data sources to segment areas in function of risk. This study implemented a data-driven approach to relate land cover metrics and an indicator of NE/LB risk at different scales of observation of the landscape. Our results showed the suitability of the modeling approach (r2  > 0.75,  ρ  < 0.001) and highlighted the relevance of the scale of observation in the set of landscape attributes found to influence disease risk as well as common and specific risk factors of NE and LB. [ABSTRACT FROM PUBLISHER]

Three-dimensional light interception by three uniform Populus canopies was studied using the Voxel-based Light Interception Model (VLIM) in combination with ground-based Light Detection and Ranging (LiDAR) measurements. As the VLIM was developed and validated in a virtual environment to ensure reference data availability, the objective was to test the consistency of the measurement and analysis protocol in real forest canopies. An automated pre-processing of raw LiDAR scans delivered high quality structure information, which was imported into a ray-tracing algorithm for modelling of light/canopy interactions. The low within-plot variability (mean standard deviation <6%) and significant differences between light interceptions modelled for the separate forest stands demonstrated the capacity of the procedure to repeatedly reproduce detailed representations of canopy/light interactions. [ABSTRACT FROM AUTHOR]

The value of hyperspectral imagery in agricultural management has been amply demonstrated. Despite this, image interpretation is often drastically impeded by changes in soil moisture content (SMC). Soil moisture variations dominate the spectral reflectance in the 350-2500-nm wavelength domain and affect the effectiveness of spectral indices used to monitor variations in soil and vegetation properties. Consequently, removing soil moisture effects in spectral images is critical and would provide a significant breakthrough for agricultural remote sensing. Yet, current available soil moisture reflectance models fail to properly address the moisture-induced reflectance changes occurring within soils of the same texture class. This very much limits the operational implementation of these models, particularly in agricultural fields where within-field variations in soil characteristics such as organic matter and clay content prevail. In this study, the effect of SMC on the reflectance in the 400-2500-nm spectral domain was studied for six sandy soils located in citrus orchards in the Western Cape Province, South Africa. In a series of experiments, novel insights into soil moisture reflectance modelling of sandy cultivated soils are provided. The wavelength and soil-specific variations in model parameters are mechanistically modelled and a general model for moist reflectance of cultivated sand soils is presented and successfully tested. Model comparison and validation demonstrate that the model fit of soil-type-specific models can be approximated ( R = 0.82; RRMSE = 0.14) while a significant increase in model fit compared with the traditional general models ( R = 0.59; RRMSE = 0.28) was achieved. [ABSTRACT FROM AUTHOR]

Novel and existing hyperspectral vegetation indices were evaluated in this study, with the aim of assessing their utility for accurate tracking of leaf spectral changes due to differences in biophysical indicators caused by apple scab. Novel indices were extracted from spectral profiles by means of narrow-waveband ratioing of all possible two-band combinations between 350 nm and 2500 nm at nanometer intervals (2 311 250 combinations) and all possible two-band derivative combinations. Narrow-waveband ratios consisting of wavelengths of approximately 1500 nm and 2250 nm, associated with water content, have proven to be the most appropriate for detecting apple scab at early developmental stages. Logistic regression c-values ranged from 0.80 to 0.88. At a more developed infection stage, vegetation indices such as R440/R690 and R695/R760 exhibited superior distinction between non-infected and infected leaves. Identified derivative indices were located in similar regions. It therefore was concluded that the most appropriate indices at early stages of infection are ratios of wavelengths situated at the water band slopes. The choice of appropriate indices and their discriminatory performances, however, depended on the phenological stage of the leaves. Hence, an undisturbed 20-day growth profile was examined to assess the effect of physiological changes on spectral variations at consecutive growth stages of leaves. Results suggested that an accurate distinction could be made between different leaf developmental stages using the 570 nm, 1460 nm, 1940 nm and 2400 nm wavelengths, and the red-edge inflection point. These results are useful to crop managers interested in an early warning system to aid proactive system management and steering. [ABSTRACT FROM AUTHOR]

The least squares error (LSE) technique is frequently used to estimate abundance fractions in linear spectral mixture analysis (LSMA). The LSE is typically equally weighted for all wavebands, assuming equally important effects. This is, however, not always the case and therefore traditional LSMA often results in suboptimal fraction estimates. This study presents a weighted LSMA approach that prioritises wavebands with minor or no negative effects on fraction estimates. Synthetic mixed pixel spectra compiled from in situ measured spectra of bare soil, citrus tree and weed canopies were used for validation. The results show markedly improved fraction estimates obtained for the weighted approach, with a mean absolute gain of 0.24 in R 2 and a mean absolute reduction in fraction abundance error of 0.06. [ABSTRACT FROM AUTHOR]

Hyperspectral canopy reflectance measurements are usually obtained under ambient solar illumination. In some specific cases, however, the use of artificial light is preferred. This raises issues, since artificial light is usually non-parallel and non-homogeneous over the area and height profile of the target. This study therefore attempts to address the characteristics of artificial illumination and the consequences for remote sensing. A low-cost experimental setup is proposed to counter the main issues of using artificial light in hyperspectral research. [ABSTRACT FROM AUTHOR]

This paper presents the results of estimation of leaf chlorophyll concentration through model inversion, from hyperspectral imagery of artificially treated orchard crops. The objectives were to examine model inversion robustness under changing viewing conditions, and the potential of multi-angle hyperspectral data to improve accuracy of chlorophyll estimation. The results were compared with leaf chlorophyll measurements from laboratory analysis and field spectroscopy. Two state-of-the-art canopy models were compared. The first is a turbid medium canopy reflectance model (MCRM) and the second is a 3D model (FLIGHT). Both were linked to the PROSPECT leaf model. A linear regression using a single band was also performed as a reference. The different techniques were able to detect nutrient deficiencies that caused stress from the hyperspectral data obtained from the airborne AHS sensor. However, quantitative chlorophyll retrieval was found largely dependent on viewing conditions for regression and the turbid medium model inversion. In contrast, the 3D model was successful for all observations. It offers a robust technique to extract chlorophyll quantitatively from airborne hyperspectral data. When multi-angular data were combined, the results for both the turbid medium and 3D model increased. Final RMSE values of 5.8 µg cm-2 (MCRM) and 4.7 µg cm-2 (FLIGHT) were obtained for chlorophyll retrieval on canopy level. [ABSTRACT FROM AUTHOR]

We evaluated several optical methods for in situ estimation of leaf area index (LAI) in a Belgian Scots pine (Pinus sylvestris L.) stand. The results obtained were compared with LAI determined from allometric relationships established in the same stand. We found high correlations between branch cross-sectional area, diameter at breast height (DBH) and basal area as dependent variables, and leaf mass, needle area and crown projection as independent variables. We then compared LAI estimated by allometry with LAI determined by three optical methods (LAI-2000, TRAC and digital hemispherical photography) both before and after corrections for blue light scattering, clumping and non-leafy material. Estimates of stand LAI of Scots pine ranged from 1.52 for hemispherical photography to 3.57 for the allometric estimate based on DBH. There was no significant difference (α = 0.01) between the allometric LAI estimates and the optical LAI values corrected for blue light scattering, clumping and interception by non-leafy material. However, we observed high sensitivity of the optical LAI estimates to the various conversion factors, particularly to the clumping factor, indicating the need for caution when correcting LAI measured by optical methods. [ABSTRACT FROM AUTHOR]

Sustainable forest management requires accurate and up-to-date information, which can nowadays be obtained using digital earth observation technology. This paper introduces a modified change vector analysis (mCVA) approach and conceptually contrasts it against traditional CVA. The results of a comparative study between this change detection algorithm and three other widely used change detection algorithms: standardized differencing, ratioing and selective principal component analysis are summarized. Landsat Thematic Mappper (TM) imagery and detailed change maps of a forested area in Northern Minnesota were used. Change indicators (vegetation indices) were grouped into three conceptually independent categories corresponding to soil, vegetation and moisture characteristics. Change periods of two, four and six years were considered. All change detection outputs were multidimensional and of a continuous nature, and could therefore be subjected to a supervised maximum likelihood algorithm using identical data training sets. Change extraction accuracies were determined by computing overall accuracy and Kappa coefficients of agreement against independent reference datasets. The mCVA outperformed the three other change detection methods in all cases, and we have shown that there is a clear advantage in running mCVA with three change indicator inputs where each input comes from a different change indicator category. Further validations with more detailed reference data are needed to improve this method and test its performance for other types of change events. [ABSTRACT FROM AUTHOR]

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Traditionally, the validation of a classified multispectral image only quantifies its correspondence to ground reference data containing thematic information generalized at the stand level, with stands represented as vector polygons. Little is known of the accuracy of such classifications at a scale below the stand. This study presents a methodology to assess classification accuracy at pixel level, i.e. sub-polygon, where the classification procedure is embedded in a change detection environment. A new type of reference data (Metatruth Image) was generated based on the integration of the outputs of various independent change detection procedures. The integration consisted of calculating for each pixel a probability distribution or pixel purity index for each change class by independent change detection procedures, defined by the number of times the pixel has been classified as a certain change class. First, the relationship between purity and accuracy was successfully validated. Next, the Metatruth Image was created based on 'high purity pixels'. Performing traditional accuracy assessment on the outputs of individual change detection procedures using the Metatruth Image as reference dataset, demonstrated that former outputs identified change events accurately at pixel level. As a consequence, traditional accuracy assessment at polygon level underestimates the true accuracy at pixel level of the change detection procedure in a systematic way with differences in kappa coefficients of agreement around 20%. [ABSTRACT FROM AUTHOR]

Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods. [ABSTRACT FROM AUTHOR]

This paper introduces the micrometeorological field campaigns known as OASIS (Observations At Several Interacting Scales) and then summarizes several companion studies that have used the OASIS dataset. Instrumented towers, aircraft and atmospheric sondes were used for measurements over three paired sites (crops and pastures), approximately equi-spaced along an 88-km transect in south-eastern New South Wales, Australia, during the austral springs of 1994 and 1995. Measurements included standard meteorological data and the fluxes of solar and net radiation, sensible heat, water vapour and the greenhouse gases CO2, N2O, CH4. Descriptions of the site, and the spatial and temporal variations of climate fields and fluxes, are presented. There were strong contrasts in fluxes and surface conductances, evaporation ratios and water use efficiencies between the 1994 drought year and the normal rainfall year of 1995. Despite greater incoming solar radiation in 1994 associated with less cloud cover, net radiation was lower than in 1995 because of greater outgoing thermal radiation caused by higher surface temperatures. In 1994 daily sensible heat fluxes were about 50% higher and evaporation rates about half those for 1995. Rainfall in the three-month growing season prior to the field campaigns was the key determinant of leaf area index, surface conductances and the fluxes of sensible and latent heat and CO2. Antecedent rainfall distribution also controlled variation in fluxes and surface properties along the transect within each year. There was a net loss of CO2 to the atmosphere at the drier central sites in 1994, and a net uptake at the wetter north-eastern sites. Both sites recorded uptake of CO2 in 1995, but values were lower at the central site than at the north–east site due to the strong rainfall gradient along the transect in the three months prior to each field campaign. Differences in fluxes between crops and pastures at each site were smaller than between sites. [ABSTRACT FROM AUTHOR]

Two methods are examined for combining measurements from instrumented aircraft and towers to estimate regional scale evapotranspiration. Aircraft data provided spatially averaged values of properties of the surface, the evaporative fraction and maximum stomatal conductance. These quantities are less sensitive to meteorological conditions than the turbulent fluxes of heat and water vapour themselves. The methods allowed aircraft data collected over several days to be averaged and thus to reduce the random error associated with the temporal under-sampling inherent in aircraft measurements. Evaporative fraction is estimated directly from the aircraft data, while maximum stomatal conductance is estimated by coupling the Penman–Monteith equation to a simple model relating surface conductance to the incoming shortwave radiation and specific humidity saturation deficit. The spatial averages of evaporative fraction and maximum stomatal conductance can then be used with routine tower data to estimate the regional scale evapotranspiration. Data from aircraft flights and six ground based sites during the OASIS field campaign in south–east New South Wales in 1995 have been used to check the methods. Both the evaporative fraction and the maximum stomatal conductance derived from the aircraft data give information on the spatial variability of the surface energy budget at scales from 10 to 100 km. Daily averaged latent heat fluxes estimated using these methods for the OASIS study region agree with the available observations in quasi-stationary conditions or in weakly non-stationary conditions when the data from several aircraft flights are averaged to reduce the impact of short term imbalances in the surface energy budget. [ABSTRACT FROM AUTHOR]

AbstractIn systems of interconnected ponds or lakes, the dispersal of zooplankton may be mediated by the active population component, with rivulets and overflows functioning as dispersal pathways. Using a landscape-based approach, we modelled the effective geographical distance among a set of interconnected ponds (De Maten, Genk, Belgium) in a Geographic Information System (GIS) environment. The first model (the Landscape Model; LM) corrects for the presence of direct connections among ponds and was based on the existing landscape structure (i.e. network of connecting elements among ponds, travelling distance and direction of the current). A second model (the Flow Rate Model; FRM) also incorporated field data on flow rates in the connecting elements as the driving force for the passive dispersal of the active zooplankton population component. Finally, the third model (the Dispersal Rate Model; DRM) incorporated field data on zooplankton dispersal rates. An analysis of the pattern of genetic differentiation among Daphnia ambigua populations inhabiting 10 ponds in the pond complex reveals that the effective geographical distance as modelled by the flow rate and the dispersal rate model provide a better approximation of the true rates of genetic exchange among populations than mere Euclidean geographical distances or the landscape model that takes into account solely the presence of physical connections. [ABSTRACT FROM AUTHOR]

'Landscape' refers explicitly to spatial structures of outdoor environments and the role of these structures in nature and society. Since repetitive land coverages of Earth observation systems such as SPOT (Systéme Probatoire pour l'Observation de la Terre) and Landsat provide a wealth of landscape images from local to continental scale, one would expect a more systematic use of these data by landscape researchers and planners than is currently the case. This paper states that the divergence between the central goals, vocabularies and methods in the distinct fields of landscape research, landscape policy and remote sensing may be prohibiting a systematic implementation. Examples are given in which the methodological steps necessary to bridge the gaps between these fields are explained. These are based on experiences in remote sensing data analysis for landscape ecological and landscape planning projects. The coverage over extensive areas, the grid format and the multi-temporal facility remain major assets of remote sensing. At continental scales, the coverage of remote sensing images in Europe is less complete than expected given the length of operation of both SPOT and Landsat Thematic Mapper (TM). Integration with spatial datasets of different origin (soil maps, road maps, etc.) is necessary to cover basic information needs. For landscape planners and researchers in general, specific qualities of spatial data are more relevant than their remote sensing origin. This is an important fact in stimulating the use of Earth observation data in landscape research. The mere presence of satellite images and their derived products (such as land cover datasets) is a 'push' factor both for landscape research and policy and the implementation of scientific knowledge and models into planning and policy. The task of 'intermediators' should be recognized against classical, if not mythical, 'data providers' and 'end users' commonly identified in Earth observation. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS) technology has become an essential tool in the Earth observation field for georeferencing, classification and accuracy assessment activities. While it was developed to be employed primarily in the open, many users nowadays operate GPS receivers in less favourable conditions. We investigated the impact of varying types and degrees of forest cover overstorey on GPS data collection, accuracy and precision. While the occurrence of canopy overhead may degrade the positional precision by one order of magnitude, it is the presence of the foliage itself (leaf-on condition) that plays a major role in the signal reception and the positional accuracy. The relationship is inverse, with increasing relative canopy closure resulting in decreasing logging efficiency and accuracy. We also found that under larger forest canopy the Position Dilution of Precision (PDOP) values may improve signal reception without deteriorating accuracy. Moreover, the relationship between degree of canopy closure and the accuracy RMSE follows an exponential pattern with small increases in relative canopy closure leading to huge increases in positional error. Averaging proved to be a powerful way to diminish residual positional error under forest cover, 300 fixes being proposed as a good number for GPS data acquisition under mature canopy conditions. Ultimately, we judged that PDOP is not as good an indicator for positional accuracy under forest canopy as is universally acclaimed. [ABSTRACT FROM AUTHOR]

The presence or absence of 75 forest-core plant species in 156 forest fragments in Western Belgium was surveyed. We performed a nestedness analysis both at the total species pool level and at the individualspecies level. Community composition of these plant species seemed to exhibit a highly significant nested subset pattern. We tested the classic hypotheses concerning the ecological mechanisms generating nestedness, i.e. stochastic species extinction and differential colonization. Neither patch area nor patch isolation, however, adequately explained the species nestedness pattern, hence our conclusion that these variables are of little use in predicting the ultimate plant species composition of a forest fragment. We then derived a complex synthetic habitat diversity measure for each forest fragment and ranked the patches accordingly. The resulting plant species occurrence patterns approximated the nestedness pattern very closely. Hence we suggest that nested plant species assemblages result in the first place from nested habitats, and that species diversity in forest fragments is determined primarily by habitat diversity and is predictable on the basisthereof. Nested species subsets are not generated by differences in minimal sustainable population sizes between species but by differences in habitat specialisation. We conclude that forest plant species extinctions in Western Belgium were probably not caused by patch-area-dependent stochastic extinction processes resulting from shrinking forest size (i.e. species relaxation), but in contrast must have their origin in deterministic processes caused by overall habitat deterioration. We also learned from our dataset that a significantly positive species-area relationship and significant species nestedness do not necessary imply that one large forest fragment supports more species than several small patches of the same total size. This result is alsoassociated with habitat diversity and the intrinsic high sensitiveness [ABSTRACT FROM AUTHOR]