Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Food Insecurity and Food Access in U.S. Metro Areas
Bonanno, A. ; Li, J. - \ 2015
Applied Economic Perspectives and Policy 37 (2015)2. - ISSN 2040-5790 - p. 177 - 204.
stamp program - wal-mart - neighborhood characteristics - united-states - competition - variables - models - participation - supercenters - endogeneity
Household food insecurity in the United States has reached its highest levels to date. As public and private initiatives have emerged to help improve diets by fostering access to food, the availability of more food stores may result in lower levels of food insecurity. In this article, we assess the relationship between adult food insecurity and food store density in metropolitan areas of the United States. We find that while small grocery/convenience stores show a mitigating effect on adult food insecurity across different samples of households, the effects of large supermarkets/grocery stores and supercenters vary. We also find that Supplemental Nutrition Assistance Program participation and food access can have a simultaneously beneficial effect in reducing adult food insecurity. Implications for policies aiming to improve food security by fostering access to food stores are discussed.
Determinants of maternal fetal attachment in women from a community-based sample
Maas, A.J.B.M. ; Vreeswijk, C.M.J.M. ; Braeken, J. ; Vingerhoets, A.J.J.M. ; Bakel, H.J.A. van - \ 2014
Journal of Reproductive and Infant Psychology 32 (2014)1. - ISSN 0264-6838 - p. 5 - 24.
prenatal attachment - antenatal attachment - child characteristics - personality - pregnancy - representations - temperament - variables - validity - behavior
BackgroundMaternal fetal attachment (MFA) has been found to be an important predictor for the developing relationship between mother and child. During the last decades, research on determinants of MFA has yielded inconclusive and even contradictory results. Until now, a process model in which multiple determinants of MFA are studied concurrently has been lacking. The present study evaluates a process model (based on Belsky's model of parenting) in which the specific contributions of parental, contextual, and expected child characteristics to MFA were examined.MethodsParticipants, 351 pregnant women from a community-based sample, completed questionnaires concerning their personality, attachment security, partner support, perceived stress, expected child temperament, and MFA at 26 weeks gestational age. Based on Belsky's model, a set of competing structural equation models were formulated and evaluated with path analysis.ResultsMFA was found to be multiply determined by parental, contextual, and expected child characteristics. These factors explained 19% of the variance in MFA. Pregnant women who were more extrovert, conscientious and agreeable reported having higher levels of MFA. In contrast, those women who perceived more stress and expected having an infant with a dull temperament reported lower levels of MFA.ConclusionThis study demonstrated that the theoretical framework of Belsky's model is applicable for explaining variations in the quality of the mother-fetus relationship in the pregnancy period. More knowledge of the determinants of MFA could help to identify mothers at risk for developing suboptimal feelings of attachment.
A Comparison of Three Approaches to Predict Phytoplankton Biomass in Gonghu Bay of Lake Taihu
Huang, J.C. ; Gao, J.F. ; Mooij, W.M. - \ 2014
Journal of Environmental Informatics 24 (2014)1. - ISSN 1726-2135 - p. 39 - 51.
artificial neural-network - water-resources applications - algal blooms - model description - dissolved-oxygen - climate-change - china - dynamics - variables - ecology
Algal blooms have caused severe problems in Lake Taihu, China. Early warning of phytoplankton accumulation can support decision-making against harmful algal bloom events. To investigate the performance of different models in forecasting high phytoplankton biomass, we developed a mechanistic, a regression and three artificial neural network (ANN) models to predict short-term (3 days) changes of phytoplankton biomass (expressed as chlorophyll-a concentration) in Gonghu Bay of Lake Taihu. We determined the input variables of the ANN models with a sensitivity analysis, and optimized their parameters with a trial-and-error approach. The sensitivity analysis revealed the effects of the input variables on phytoplankton biomass. To calibrate and validate the models, we collected two data sets of Lake Taihu in 2009: hourly-averaged data collected by an automatic monitoring system and field data with a sampling interval of twice a week. Although the sensitivity analysis results vary among the five models, there is a general consensus that phytoplankton changes are significantly affected by water temperature in Gonghu Bay. The ANN models obtained good model fit indicating their practical values in predicting non-linear phytoplankton dynamics for water management purpose. The mechanistic model predicted the phytoplankton distribution dynamically and described the variable interactions explicitly. The regression model is characterized by its easy development. This comparison study assists the modelers in selecting an approximate model for their specific purposes.
Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation
Dray, S. ; Choler, P. ; Dolédec, S. ; Peres-Neto, P.R. ; Thuiller, W. ; Pavoine, S. ; Braak, C.J.F. ter - \ 2014
Ecology 95 (2014). - ISSN 0012-9658 - p. 14 - 21.
co-inertia analysis - species traits - community ecology - plant - variables - linking
Assessing trait responses to environmental gradients requires the simultaneous analysis of the information contained in three tables: L (species distribution across samples), R (environmental characteristics of samples) and Q (species traits). Among the available methods, the so-called fourth-corner and RLQ methods are two appealing alternatives that provide a direct way to test and estimate trait-environment relationships. Both methods are based on the analysis of the fourth-corner matrix which crosses traits and environmental variables weighted by species abundances. However, they greatly differ in their outputs: RLQ is a multivariate technique that provides ordination scores to summarize the joint structure among the three tables, whereas the fourth-corner method mainly tests for individual trait-environment relationships (i.e. one trait and one environmental variable at a time). Here, we illustrate how the complementarity between these two methods can be exploited to promote new ecological knowledge and to improve the study of trait-environment relationships. After a short description of each method, we apply them to real ecological data to present their different outputs and provide hints about the gain resulting from their combined use. Read More: http://www.esajournals.org/doi/abs/10.1890/13-0196.1
Cross-validation of predictive models for deoxynivalenol in wheat at harvest
Camardo Leggieri, M. ; Fels-Klerx, H.J. van der; Battilani, P. - \ 2013
World Mycotoxin Journal 6 (2013)4. - ISSN 1875-0710 - p. 389 - 397.
fusarium head blight - small-grain cereals - winter-wheat - weather data - ear blight - mycotoxins - management - variables
To date, several models that predict deoxynivalenol (DON) in wheat at harvest are available. This study aimed to evaluate the performance of two of such models, including a mechanistic model developed in Italy and an empirical model developed in the Netherlands. To this end, field data collected in the periods 2002-2004 and 2009-2011 in Italy, and in the period 2001-2010 in the Netherlands were used. These historical data covered farm observations at 1,306 wheat fields, of which 155 in the Netherlands and 1,151 in Italy. A subset of 10% of the Italian data, derived by random sampling from the total Italian dataset, was used to validate both the Italian and the Dutch model. Additionally, the Italian mechanistic model was validated using the total Dutch dataset. Before validating the Dutch model, it was recalibrated using the remaining 90% of the Italian data. Results showed that predictions of both modelling approaches (mechanistic and empirical) for independent wheat fields were in accordance. Applying a threshold for DON concentration of 1,250 ?g/kg, the mechanistic DON model predicted 90% of the samples correctly. Results for cross-validation of the mechanistic DON model and the recalibrated empirical DON model showed that 93% of the samples were correctly predicted. In general, no more than 6% of underestimates were observed.
Characterizing regional soil mineral composition using spectroscopy and geostatistics
Mulder, V.L. ; Bruin, S. de; Weyermann, J. ; Kokaly, R.F. ; Schaepman, M.E. - \ 2013
Remote Sensing of Environment 139 (2013). - ISSN 0034-4257 - p. 415 - 429.
spatial prediction - usgs tetracorder - regression - vegetation - carbon - model - area - attributes - variograms - variables
This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD) was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We tested whether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was done with Fixed Rank Kriging (FRK) to represent the medium and long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 = 0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 = 0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale.
Sponge species composition, abundance, and cover in marine lakes and coastal mangroves in Berau, Indonesia
Becking, L.E. ; Cleary, D.F.R. ; Voogd, N.J. de - \ 2013
Marine Ecology Progress Series 481 (2013). - ISSN 0171-8630 - p. 105 - 120.
spermonde archipelago - neighbor matrices - east kalimantan - kakaban-island - beta diversity - porifera - assemblages - variability - variables - scale
We compared the species composition, abundance, and cover of sponges in 2 marine lakes (Kakaban Lake and Haji Buang Lake) and adjacent coastal mangroves on the islands of Kakaban and Maratua in the Berau region of Indonesia. We recorded a total of 115 sponge species, 33 of which were restricted to Kakaban Lake, 18 to Haji Buang Lake, and 30 to coastal mangroves on Maratua Island. Only 13 species were shared among all habitats. The 2 marine lakes are located 10 km apart, but their assemblages were more similar to each other than to the bay mangrove systems just 200 to 500 m away. Our results show that marine lakes represent a distinct habitat with significantly higher sponge cover and abundance as well as a markedly different species composition when compared with coastal mangroves. In both lake and coastal mangrove habitats there was a pronounced gradient in composition away from the shore with the primary difference between solid (root or rock) and soft substrate (mud or sand). Each substrate type harbored different sets of species in both lake and coastal mangrove habitats. There was no significant difference in sponge species composition, abundance, or cover between semi-permanent transects sampled in 2008 and 2009. We show for the first time that mangroves in the Indo-Pacific harbor a diverse array of sponge species and, further, that marine lakes harbor numerous unique species hitherto unknown to science.
Assessment time of the Welfare Quality protocol for dairy cattle
Vries, M. de; Engel, B. ; Uijl, I. ; Schaik, G. van; Dijkstra, T. ; Boer, I.J.M. de; Bokkers, E.A.M. - \ 2013
Animal Welfare 22 (2013)1. - ISSN 0962-7286 - p. 85 - 93.
lying behavior - cows - reliability - lameness - imputation - indicator - agreement - variables - system
The Welfare Quality® (WQ) protocols are increasingly used for assessing welfare of farm animals. These protocols are time consuming (about one day per farm) and, therefore, costly. Our aim was to assess the scope for reduction of on-farm assessment time of the WQ protocol for dairy cattle. Seven trained observers quantified animal-based indicators of the WQ protocol in 181 loose-housed and 13 tied Dutch dairy herds (herd size from 10 to 211 cows). Four assessment methods were used: avoidance distance at the feeding rack (ADF, 44 min); qualitative behaviour assessment (QBA, 25 min); behavioural observations (BO, 150 min); and clinical observations (CO, 132 min). To simulate reduction of on-farm assessment time, a set of WQ indicators belonging to one assessment method was omitted from the protocol. Observed values of omitted indicators were replaced by predictions based on WQ indicators of the remaining three assessment methods, resources checklist, and interview, thus mimicking the performance of the full WQ protocol. Agreement between predicted and observed values of WQ indicators, however, was low for ADF, moderate for QBA, slight to moderate for BO, and poor to moderate for CO. It was concluded that replacing animal-based WQ indicators by predictions based on remaining WQ indicators shows little scope for reduction of onfarm assessment time of the Welfare Quality® protocol for dairy cattle. Other ways to reduce on-farm assessment time of the WQ protocol for dairy cattle, such as the use of additional data or automated monitoring systems, should be investigated.
Spatio-temporal variation of wheat and silage maize water requirement using CGMS model
Sargordi, F. ; Farhadi Bansouleh, B.F. ; Sharifi, M.A. ; Keulen, H. van - \ 2013
International Journal of Plant Production 7 (2013)2. - ISSN 1735-6814 - p. 207 - 224.
north china plain - growth simulation - climate-change - crop - variables - impacts - yield - iran
The Crop Growth Monitoring System (CGMS) has been applied for spatial biophysical resource analysis of Borkhar & Meymeh district in Esfahan province, Iran. The potentially suitable area for agriculture in the district has been divided into 128 homogeneous land units in terms of soil (physical characteristics), weather and administrative unit. Crop parameters required in the WOFOST simulation model for winter wheat and silage maize, have been calibrated based on experimental data from the study area. The study area has been classified into three cropping calendar zones based on average annual temperature, altitude and latitude. For each zone, a sowing date has been defined for each crop as the starting point of crop growth simulation. Growth of these crops has been simulated for the potential situation in each land unit for 20 years of historical daily weather data. Daily potential evapotranspiration and irrigation requirements of each crop per land unit have been calculated in a postsimulation, on the basis of model outputs. Outputs of the model are crop yield (marketable yield and total biomass) and irrigation requirements per decade. Spatial and temporal variation in irrigation requirements has been analyzed. The temporal variation in crop water requirements is larger than the spati3al variation.
Exercise self-identity: interactions with social comparison and exercise behaviour
Verkooijen, K.T. ; Bruijn, G.J. de - \ 2013
Psychology, Health & Medicine 18 (2013)4. - ISSN 1354-8506 - p. 490 - 499.
vigorous physical-activity - planned behavior - reasoned action - metaanalysis - norms - variables
Possible interactions among exercise self-identity, social comparison and exercise behaviour were explored in a sample of 417 undergraduate students (Mean age¿=¿21.5, SD¿=¿3.0; 73% female). Two models were examined using self-report data; (1) a mediation model which proposed an association between social comparison and exercise behaviour mediated by exercise self-identity and (2) a moderation model proposing an association between exercise behaviour and self-identity moderated by social comparison. Results of the mediation analyses revealed partial mediation of the social comparison – exercise behaviour relationship by self-identity in females. Results of the moderation analyses revealed in males a significant interaction of social comparison with exercise behaviour in the prediction of self-identity – the positive association between exercise behaviour and exercise self-identity showed only significant among male students who believed to exercise equally much or less than peers. Possible explanations and implications for exercise promotion are discussed.
Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data
Mulder, V.L. ; Bruin, S. de; Schaepman, M.E. - \ 2013
International Journal of applied Earth Observation and Geoinformation 21 (2013). - ISSN 0303-2434 - p. 301 - 310.
design-based estimation - spatial prediction - classification tree - optimization - landscape - attributes - strategies - variables - desert - model
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin Hypercube Sampling (cLHS) to assess variability in soil properties at regional scale. The method optimizes the sampling scheme for a defined spatial population based on selected covariates, which are assumed to represent the variability of the target variables. The optimization also accounts for specific constraints and costs expressing the field sampling effort. The approach is demonstrated using a case study in Morocco, where a small but representative sample record had to be collected over a 15,000 km2 area within 2 weeks. The covariate space of the Latin Hypercube consisted of the first three principal components of ASTER imagery as well as elevation. Comparison of soil properties taken from the topsoil with the existing soil map, a geological map and lithological data showed that the sampling approach was successful in representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability within a short distances resulting in an overestimation of the variograms nugget and short distance variability. However, the exhaustive covariate data appeared to be spatially correlated which supports our premise that once the relation between spatially explicit remote sensing data and soil properties has been modelled, the latter can be spatially predicted based on the densely sampled remotely sensed data. Therefore, the LHS approach is considered as time and cost efficient for regional scale surveys that rely on remote sensing-based prediction of soil properties.
Sensitivity of APSIM/ORYZA model due to estimation errors in solar radiation
Heinemann, A.B. ; Oort, P.A.J. van; Simoes Fernandes, D. ; Maia, A.H.N. - \ 2012
Bragantia 71 (2012)4. - ISSN 0006-8705 - p. 572 - 582.
upland rice - new-zealand - simulation - temperature - systems - variables - brazil - apsim
Crop models are ideally suited to quantify existing climatic risks. However, they require historic climate data as input. While daily temperature and rainfall data are often available, the lack of observed solar radiation (Rs) data severely limits site-specific crop modelling. The objective of this study was to estimate Rs based on air temperature solar radiation models and to quantify the propagation of errors in simulated radiation on several APSIM/ORYZA crop model seasonal outputs, yield, biomass, leaf area (LAI) and total accumulated solar radiation (SRA) during the crop cycle. The accuracy of the 5 models for estimated daily solar radiation was similar, and it was not substantially different among sites. For water limited environments (no irrigation), crop model outputs yield, biomass and LAI was not sensitive for the uncertainties in radiation models studied here.
Repeatability of free jumping parameters on tests of different duration
Lewczuk, D. ; Ducro, B.J. - \ 2012
Livestock Science 146 (2012)1. - ISSN 1871-1413 - p. 22 - 28.
gait analysis - horses - reproducibility - consistency - locomotion - variables - trotters - back
The aim of this study was to compare the effect of the horse on various performance tests calculated for free jumping parameters. Free jumping parameters were measured on the basis of video image analysis. Three groups of horses were compared: 141 stallions on the 11-month test (744 jumps), 50 stallions on the 8-month test (301 jumps) and 43 stallions on the 100-day test (221 jumps). Linear measurements of taking off and landing distances, height of limb lifting above the obstacle, height of elevation of "bascule points" and position of the head were measured. The statistical model was comparable for all tests including the random effect of the horse, fixed effects of the test, height of the obstacle and successive number of the jump. Repeatability on the 11-month test was high for taking off, landing and bascule parameters and reached values from 0.50 to 0.60. Repeatability of heights of limb lifting in the 11-month test was lower, but at the same value of about 0.3 for front and hind limbs. In the test of the same duration but for horses selected as the best ones-the repeatability of limb lifting was higher for front limbs and almost the same for hind limbs. Repeatability of the parameters that characterised the length of the jump and the bascule of the horse was higher in the test for the best horses and reached 0.82 for landing and 0.66-0.77 for bascule points. Repeatability of almost all jumping parameters reached highest values in the group of the best horses with longer training.
Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey.
Bostan, P.A. ; Heuvelink, G.B.M. ; Akyurek, S.Z. - \ 2012
International Journal of applied Earth Observation and Geoinformation 19 (2012). - ISSN 0303-2434 - p. 115 - 126.
spatial interpolation - rainfall - prediction - elevation - variables - radar
Accurate mapping of the spatial distribution of annual precipitation is important for many applications in hydrology, climatology, agronomy, ecology and other environmental sciences. In this study, we compared five different statistical methods to predict spatially the average annual precipitation of Turkey using point observations of annual precipitation at meteorological stations and spatially exhaustive covariate data (i.e. elevation, aspect, surface roughness, distance to coast, land use and eco-region). The methods compared were multiple linear regression (MLR), ordinary kriging (OK), regression kriging (RK), universal kriging (UK), and geographically weighted regression (GWR). Average annual precipitation of Turkey from 1970 to 2006 was measured at 225 meteorological stations that are fairly uniformly distributed across the country, with a somewhat higher spatial density along the coastline. The observed annual precipitation varied between 255 mm and 2209 mm with an average of 628 mm. The annual precipitation was highest along the southern and northern coasts and low in the centre of the country, except for the area near the Van Lake, Keban and Ataturk Dams. To compare the performance of the interpolation techniques the total dataset was first randomly split in ten equally sized test datasets. Next, for each test data set the remaining 90% of the data comprised the training dataset. Each training dataset was then used to calibrate and apply the spatial prediction model. Predictions at the test dataset locations were compared with the observed test data. Validation was done by calculating the Root Mean Squared Error (RMSE), R-square and Standardized MSE (SMSE) values. According to these criteria, universal kriging is the most accurate with an RMSE of 178 mm, an R-square of 0.61 and an SMSE of 1.06, whilst multiple linear regression performed worst (RMSE of 222 mm, R-square of 0.39, and SMSE of 1.44). Ordinary kriging, UK using only elevation and geographically weighted regression are intermediate with RMSE values of 201 mm, 212 mm and 211 mm, and an R-square of 0.50, 0.44 and 0.45, respectively. The RK results are close to those of UK with an RMSE of 186 mm and R-square of 0.57. The spatial extrapolation performance of each method was also evaluated. This was done by predicting the annual precipitation in the eastern part of Turkey using observations from the western part. Results showed that MLR, GWR and RK performed best with little differences between these methods. The large prediction error variances confirmed that extrapolation is more difficult than interpolation. Whilst spatial extrapolation benefits most from covariate information as shown by an RMSE reduction of about 60 mm, in this study covariate information was also valuable for spatial interpolation because it reduced the RMSE with on average 30 mm.
Efficiency comparison of conventional and digital soil mapping for updating soil maps
Kempen, B. ; Brus, D.J. ; Stoorvogel, J.J. ; Heuvelink, G.B.M. ; Vries, F. de - \ 2012
Soil Science Society of America Journal 76 (2012)6. - ISSN 0361-5995 - p. 2097 - 2115.
model-based geostatistics - spatial interpolation - peat soils - information - prediction - uncertainty - variables - knowledge - regression - science
This study compared the efficiency of geostatistical digital soil mapping (DSM) with conventional soil mapping (CSM) for updating soil class and property maps of a cultivated peatland in the Netherlands. For digital soil class mapping, the generalized linear geostatistical model was used. Digital mapping of the soil organic matter (SOM) content and peat thickness was done by universal kriging. The conventional soil class map was created by free survey, while the property maps were created with the representative profile description (RPD) and map unit means (MUM) methods. For each method, we computed the effort invested in the mapping in terms of the sampling and cost densities. The accuracies of the created soil maps were estimated from independent probability sample data. The results showed that for DSM, the cost density could be reduced by a factor of three compared with CSM without compromising accuracy. The map purity of both maps was around 55%. For conventional soil property mapping, the MUM maps were more accurate than the RPD maps. For SOM, CSM-MUM (RMSE 7.5%) performed better than DSM (RMSE 12.1%), although accuracy differences were not significant. For peat thickness, DSM (RMSE 23.3 cm) performed slightly better than CSM-MUM (RMSE 24.9 cm). Despite the differences in accuracy being small, the digital soil property maps were produced more efficiently. The cost density was a factor of 3.5 smaller. We conclude that for updating conventional soil maps in the Dutch peatlands, geostatistical DSM can be more efficient, although not necessarily more accurate, than CSM.
On the Bivariate Kummer-Beta Type IV Distribution
Jacobs, R. ; Bekker, A. ; Human, S.W. - \ 2012
Communications in Statistics. Part A, Theory and Methods 41 (2012)18. - ISSN 0361-0926 - p. 3339 - 3354.
product - variables - quotient - moments
In this article, the non central bivariate Kummer-beta Type IV distribution is introduced and derived via the Laplace transform of the non central bivariate beta distribution by Gupta et al. (2011 ). We focus on and discuss the central bivariate Kummer-beta Type IV distribution; this distribution is a special case of the non central bivariate Kummer-beta Type IV distribution and extends the popular Jones’ bivariate beta distribution. The probability density functions of the product and the ratio of the components of the central bivariate Kummer-beta Type IV distribution are also derived and we provide tabulations of the associated lower percentage points as well as some upper percentage points that are useful in reliability.
Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images
Hengl, T. ; Heuvelink, G.B.M. ; Percec Tadic, M. ; Pebesma, E.J. - \ 2012
Theoretical and Applied Climatology 107 (2012)1-2. - ISSN 0177-798X - p. 265 - 277.
external drift - interpolation - space - precipitation - models - variables - rainfall - example - region - mexico
A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10–fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images—are anticipated.
A geostatistical approach to data harmonization - Application to radioactivity exposure data
Baume, O. ; Skoien, J.O. ; Heuvelink, G.B.M. ; Pebesma, E.J. ; Melles, S.J. - \ 2011
International Journal of applied Earth Observation and Geoinformation 13 (2011)3. - ISSN 0303-2434 - p. 409 - 419.
statistics - variables - networks - germany - need
Environmental issues such as air, groundwater pollution and climate change are frequently studied at spatial scales that cross boundaries between political and administrative regions. It is common for different administrations to employ different data collection methods. If these differences are not taken into account in spatial interpolation procedures then biases may appear and cause unrealistic results. The resulting maps may show misleading patterns and lead to wrong interpretations. Also, errors will propagate when these maps are used as input to environmental process models. In this paper we present and apply a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data sources. The associated best linear unbiased estimation and prediction (BLUE and BLUP) equations are presented and it is shown that these lead to harmonized maps from which estimated biases are removed. The methodology is illustrated with an example of country bias removal in a radioactivity exposure assessment for four European countries. The application also addresses multicollinearity problems in data harmonization, which arise when both artificial bias factors and natural drifts are present and cannot easily be distinguished. Solutions for handling multicollinearity are suggested and directions for further investigations proposed.
Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study
Laurent, V.C.E. ; Verhoef, W. ; Clevers, J.G.P.W. ; Schaepman, M.E. - \ 2011
Remote Sensing of Environment 115 (2011)10. - ISSN 0034-4257 - p. 2603 - 2612.
leaf-area index - radiative-transfer models - remote-sensing data - vegetation structure - misr data - reflectance - retrieval - variables - products - prospect
Since the launch of sensors with angular observation capabilities, such as CHRIS and MISR, the additional potential of multi-angular observations for vegetation structural and biochemical variables has been widely recognised. Various methods have been successfully implemented to estimate forest biochemical and biophysical variables from atmospherically-corrected multi-angular data, but the use of physically based radiative transfer (RT) models is still limited. Because both canopy and atmosphere have an anisotropic behaviour, it is important to understand the multi-angular signal measured by the sensor at the top of the atmosphere (TOA). Coupled canopy–atmosphere RT models allow linking surface variables directly to the TOA radiance measured by the sensor and are therefore very interesting tools to use for estimating forest variables from multi-angular data. We investigated the potential of TOA multi-angular radiance data for estimating forest variables by inverting a coupled canopy–atmosphere physical RT model. The case study focussed on three Norway spruce stands located at the Bily Kriz experimental site (Czech Republic), for which multi-angular CHRIS and field data were acquired in September 2006. The soil–leaf–canopy RT model SLC and the atmospheric model MODTRAN4 were coupled using a method allowing to make full use of the four canopy angular reflectance components provided by SLC. The TOA radiance simulations were in good agreement with the spectral and angular signatures measured by CHRIS. Singular value decompositions of the Jacobian matrices showed that the dimensionality of the variable estimation problem increased from 3 to 6 when increasing the number of observation angles from 1 to 4. The model inversion was conducted for two cases: 4 and 7 variables. The most influential parameters were chosen as free variables in the look-up tables, namely: vertical crown cover (Cv), fraction of bark material (fB), needle chlorophyll content (needleCab), needle dry matter content (needleCdm) for the 4-variable case, and additionally, tree shape factor (Zeta), dissociation factor (D), and needle brown pigments content (needleCs) in the 7-variable case. All angular combinations were tested, and the best estimates were obtained with combinations using two or three angles, depending on the number of variables and on the stand used. Overall, this case study showed that, although making use of its full potential is still a challenge, TOA multi-angular radiance data do have a higher potential for variable estimation than mono-angular data.
Estimating canopy water content using hyperspectral remote sensing data
Clevers, J.G.P.W. ; Kooistra, L. ; Schaepman, M.E. - \ 2010
International Journal of applied Earth Observation and Geoinformation 12 (2010)2. - ISSN 0303-2434 - p. 119 - 125.
leaf optical-properties - radiative-transfer models - imaging spectrometer data - dynamic vegetation model - reflectance data - indexes - retrieval - information - variables - products
Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow. PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015–1050 nm spectral interval and CWC (R2 = 0.97). For 8 plots at the floodplain site the spectral derivative over the 1015–1050 nm interval obtained with an ASD FieldSpec spectroradiometer yielded an R2 of 0.51 with CWC. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015–1050 nm interval with CWC. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC
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