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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UAV-based multi-angular measurements for improved crop parameter retrieval
Roosjen, Peter P.J. - \ 2017
Wageningen University. Promotor(en): Martin Herold, co-promotor(en): Jan Clevers; Harm Bartholomeus. - Wageningen : Wageningen University - ISBN 9789463436717 - 133
reflectance - anisotropy - crops - soil water content - drones - remote sensing - reflectiefactor - anisotropie - gewassen - bodemwatergehalte

Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.

Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications
Gevaert, C. ; Suomalainen, J.M. ; Tang, J. ; Kooistra, L. - \ 2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2015)6. - ISSN 1939-1404 - p. 3140 - 3146.
leaf chlorophyll concentration - remote-sensing data - vegetation indexes - data fusion - reflectance - variability - prediction - landsat
Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral–temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.
The Earth Observation Data for Habitat Monitoring (EODHaM) System
Lucas, R.M. ; Blonda, P. ; Bunting, P. ; Jones, G. ; Inglada, J. ; Arias-Maldonado, M. ; Kosmidou, V. ; Petrou, Z. ; Manakos, I. ; Adamo, M. ; Charnock, R. ; Tarantino, C. ; Mücher, C.A. ; Kramer, H. ; Jongman, R.H.G. ; Honrado, J. ; Mairota, P. - \ 2015
International Journal of applied Earth Observation and Geoinformation 37 (2015). - ISSN 0303-2434 - p. 17 - 28.
remotely-sensed data - categories ghc - file format - vegetation - satellite - classifications - biodiversity - reflectance - phenology - software
To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.
Predicting leaf traits of herbaceous species from their spectral characteristics
Roelofsen, H.D. ; Bodegom, P.M. van; Kooistra, L. ; Witte, J.P.M. - \ 2014
Ecology and Evolution 4 (2014)6. - ISSN 2045-7758 - p. 706 - 719.
optical-properties - conifer needles - plant traits - squares regression - tropical forests - canopy structure - carbon gain - wide-range - nitrogen - reflectance
Trait predictions from leaf spectral properties are mainly applied to tree species, while herbaceous systems received little attention in this topic. Whether similar trait–spectrum relations can be derived for herbaceous plants that differ strongly in growing strategy and environmental constraints is therefore unknown. We used partial least squares regression to relate key traits to leaf spectra (reflectance, transmittance, and absorbance) for 35 herbaceous species, sampled from a wide range of environmental conditions. Specific Leaf Area and nutrient-related traits (N and P content) were poorly predicted from any spectrum, although N prediction improved when expressed on a per area basis (mg/m2 leaf surface) instead of mass basis (mg/g dry matter). Leaf dry matter content was moderately to good correlated with spectra. We explain our results by the range of environmental constraints encountered by herbaceous species; both N and P limitations as well as a range of light and water availabilities occurred. This weakened the relation between the measured response traits and the leaf constituents that are truly responsible for leaf spectral behavior. Indeed, N predictions improve considering solely upper or under canopy species. Therefore, trait predictions in herbaceous systems should focus on traits relating to dry matter content and the true, underlying drivers of spectral properties.
Assessing the effects of subtropical forest fragmentation on leaf nitrogen distribution using remote sensing data
Cho, M.A. ; Ramoelo, A. ; Debba, P. ; Mutanga, O. ; Mathieu, R. ; Deventer, H. van; Ndlovu, N. - \ 2013
Landscape Ecology 28 (2013)8. - ISSN 0921-2973 - p. 1479 - 1491.
red-edge - tropical deforestation - spatial heterogeneity - chlorophyll content - multispectral data - eucalyptus leaves - land-use - landscape - reflectance - savanna
Subtropical forest loss resulting from conversion of forest to other land-cover types such as grassland, secondary forest, subsistence crop farms and small forest patches affects leaf nitrogen (N) stocks in the landscape. This study explores the utility of new remote sensing tools to model the spatial distribution of leaf N concentration in a forested landscape undergoing deforestation in KwaZulu-Natal, South Africa. Leaf N was mapped using models developed from RapidEye imagery; a relatively new space-borne multispectral sensor. RapidEye consists of five spectral bands in the visible to near infra-red (NIR) and has a spatial resolution of 5 m. MERIS terrestrial chlorophyll index derived from the RapidEye explained 50 % of the variance in leaf N across different land-cover types with a model standard error of prediction of 29 % (i.e. of the observed mean leaf N) when assessed on an independent test data. The results showed that indigenous forest fragmentation leads to significant losses in leaf N as most of the land-cover types (e.g. grasslands and subsistence farmlands) resulting from forest degradation showed lower leaf N when compared to the original indigenous forest. Further analysis of the spatial variation of leaf N revealed an autocorrelation distance of about 50 m for leaf N in the fragmented landscape, a scale corresponding to the average dimension of subsistence fields (2,781 m2) in the region. The availability of new multispectral sensors such as RapidEye thus, moves remote sensing closer to widespread monitoring of the effect of tropical forest degradation on leaf N distribution.
Trait estimation in herbaceous plant assemblages from in situ canopy spectra
Roelofsen, H.D. ; Bodegom, P.M. van; Kooistra, L. ; Witte, J.M. - \ 2013
Remote Sensing 5 (2013)12. - ISSN 2072-4292 - p. 6323 - 6345.
least-squares regression - hyperspectral data - economics spectrum - vegetation indexes - indicator values - nitrogen-content - national-park - chlorophyll - reflectance - model
Estimating plant traits in herbaceous plant assemblages from spectral reflectance data requires aggregation of small scale trait variations to a canopy mean value that is ecologically meaningful and corresponds to the trait content that affects the canopy spectral signal. We investigated estimation capacities of plant traits in a herbaceous setting and how different trait-aggregation methods influence estimation accuracies. Canopy reflectance of 40 herbaceous plant assemblages was measured in situ and biomass was analysed for N, P and C concentration, chlorophyll, lignin, phenol, tannin and specific water concentration, expressed on a mass basis (mg·g-1). Using Specific Leaf Area (SLA) and Leaf Area Index (LAI), traits were aggregated to two additional expressions: mass per leaf surface (mg·m-2) and mass per canopy surface (mg·m-2). All traits were related to reflectance using partial least squares regression. Accuracy of trait estimation varied between traits but was mainly influenced by the trait expression. Chlorophyll and traits expressed on canopy surface were least accurately estimated. Results are attributed to damping or enhancement of the trait signal upon conversion from mass based trait values to leaf and canopy surface expressions. A priori determination of the most appropriate trait expression is viable by considering plant growing strategies
Shrimp pond effluent dominates foliar nitrogen in disturbed mangroves as mapped using hyperspectral imagery
Fauzi, A. ; Skidmore, A.K. ; Gils, H. ; Schlerf, M. ; Heitkonig, I.M.A. - \ 2013
Marine Pollution Bulletin 76 (2013)1-2. - ISSN 0025-326X - p. 42 - 51.
leaf-area index - species discrimination - absorption features - chlorophyll content - squares regression - vegetation indexes - avicennia-marina - canopy nitrogen - reflectance - forest
Conversion of mangroves to shrimp ponds creates fragmentation and eutrophication. Detection of the spatial variation of foliar nitrogen is essential for understanding the effect of eutrophication on mangroves. We aim (i) to estimate nitrogen variability across mangrove landscapes of the Mahakam delta using airborne hyperspectral remote sensing (HyMap) and (ii) to investigate links between the variation of foliar nitrogen mapped and local environmental variables. In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping. The variation of foliar nitrogen concentration in mangroves was significantly influenced by the local environment: (1) position of mangroves (seaward/landward), (2) distance to the shrimp ponds, and (3) predominant mangrove species. The findings suggest that anthropogenic disturbances, in this case shrimp ponds, influence nitrogen variation in mangroves. Mangroves closer to the shrimp ponds had higher foliar nitrogen concentrations.
Assessing water stress of desert Tamarugo trees using in situ data and very high spatial resolution remote sensing
Chávez Oyanadel, R.O. ; Clevers, J.G.P.W. ; Herold, M. ; Acevedo, E. ; Ortiz, M. - \ 2013
Remote Sensing 5 (2013)10. - ISSN 2072-4292 - p. 5064 - 5088.
oriented image-analysis - northern chile - red edge - chlorophyll concentration - canopy - plants - soil - reflectance - vegetation - photoinhibition
The hyper-arid Atacama Desert is one of the most extreme environments for life and only few species have evolved to survive its aridness. One such species is the tree Prosopis tamarugo Phil. Because Tamarugo completely depends on groundwater, it is being threatened by the high water demand from the Chilean mining industry and the human consumption. In this paper, we identified the most important biophysical variables to assess the water status of Tamarugo trees and tested the potential of WorldView2 satellite images to retrieve these variables. We propose green canopy fraction (GCF) and green drip line leaf area index (DLLAIgreen) as best variables and a value of 0.25 GCF as a critical threshold for Tamarugo survival. Using the WorldView2 spectral bands and an object-based image analysis, we showed that the NDVI and the Red-edge Chlorophyll Index (CIRed-edge) have good potential to retrieve GCF and DLLAIgreen. The NDVI performed best for DLLAIgreen (RMSE = 0.4) while the CIRed-edge was best for GCF (RMSE = 0.1). However, both indices were affected by Tamarugo leaf movements (leaves avoid facing direct solar radiation at the hottest time of the day). Thus, monitoring systems based on these indices should consider the time of the day and the season of the year at which the satellite images are acquired.
Detection of hydrocarbons in clay soils: A laboratory experiment using spectroscopy in the mid- and thermal infrared
Meijde, M. van der; Knox, N. ; Cundill, S.L. ; Noomen, M.F. ; Werff, H.M.A. van der; Hecker, C. - \ 2013
International Journal of applied Earth Observation and Geoinformation 23 (2013). - ISSN 0303-2434 - p. 384 - 388.
pipeline leakage - natural-gas - reflectance - airborne - spectrometer - regression
Remote sensing has been used for direct and indirect detection of hydrocarbons. Most studies so far focused on indirect detection in vegetated areas. We investigated in this research the possibility of detecting hydrocarbons in bare soil through spectral analysis of laboratory samples in the short wave and thermal infrared regions. Soil/oil mixtures were spectrally measured in the laboratory. Analysis of spectra showed development of hydrocarbon absorption features as soils became progressively more contaminated. The future application of these results airborne seems to be a challenge as present and future sensors only cover the diagnostic regions to a limited extent
Changes in plant defense chemistry (pyrrolizidine alkaloids) revealed through high-resolution spectroscopy
Almeida De Carvalho, S. ; Macel, M. ; Schlerf, M. ; Moghaddam, F.E. ; Mulder, P.P.J. ; Skidmore, A.K. ; Putten, W.H. van der - \ 2013
ISPRS Journal of Photogrammetry and Remote Sensing 80 (2013). - ISSN 0924-2716 - p. 51 - 60.
near-infrared spectroscopy - senecio-jacobaea - red edge - nitrogen - leaf - reflectance - forest - regression - vegetation - prediction
Plant toxic biochemicals play an important role in defense against natural enemies and often are toxic to humans and livestock. Hyperspectral reflectance is an established method for primary chemical detection and could be further used to determine plant toxicity in the field. In order to make a first step for pyrrolizidine alkaloids detection (toxic defense compound against mammals and many insects) we studied how such spectral data can estimate plant defense chemistry under controlled conditions. In a greenhouse, we grew three related plant species that defend against generalist herbivores through pyrrolizidine alkaloids: Jacobaea vulgaris, Jacobaea erucifolia and Senecio inaequidens, and analyzed the relation between spectral measurements and chemical concentrations using multivariate statistics. Nutrient addition enhanced tertiary-amine pyrrolizidine alkaloids contents of J. vulgaris and J. erucifolia and decreased N-oxide contents in S. inaequidens and J. vulgaris. Pyrrolizidine alkaloids could be predicted with a moderate accuracy. Pyrrolizidine alkaloid forms tertiary-amines and epoxides were predicted with 63% and 56% of the variation explained, respectively. The most relevant spectral regions selected for prediction were associated with electron transitions and CH, OH, and NH bonds in the 1530 and 2100 nm regions. Given the relatively low concentration in pyrrolizidine alkaloids concentration (in the order of mg g-1) and resultant predictions, it is promising that pyrrolizidine alkaloids interact with incident light. Further studies should be considered to determine if such a non-destructive method may predict changes in PA concentration in relation to plant natural enemies. Spectroscopy may be used to study plant defenses in intact plant tissues, and may provide managers of toxic plants, food industry and multitrophic-interaction researchers with faster and larger monitoring possibilities
Differentiation of plant age in grasses using remote sensing
Knox, N. ; Skidmore, A.K. ; Werff, H.M.A. van der; Groen, T.A. ; Boer, W.F. de; Prins, H.H.T. ; Kohi, E. ; Peel, M. - \ 2013
International Journal of applied Earth Observation and Geoinformation 24 (2013)10. - ISSN 0303-2434 - p. 54 - 62.
difference water index - monitoring vegetation - nitrogen concentration - imaging spectroscopy - hyperspectral data - boreal regions - time-series - green-up - phenology - reflectance
Phenological or plant age classification across a landscape allows for examination of micro-topographical effects on plant growth, improvement in the accuracy of species discrimination, and will improve our understanding of the spatial variation in plant growth. In this paper six vegetation indices used in phenological studies (including the newly proposed PhIX index) were analysed for their ability to statistically differentiate grasses of different ages in the sequence of their development. Spectra of grasses of different ages were collected from a greenhouse study. These were used to determine if NDVI, NDWI, CAI, EVI, EVI2 and the newly proposed PhIX index could sequentially discriminate grasses of different ages, and subsequently classify grasses into their respective age category. The PhIX index was defined as: (An VNIR+ log(An SWIR2))/(An VNIR- log(An SWIR2)), where An VNIRand An SWIR2are the respective nor- malised areas under the continuum removed reflectance curve within the VNIR (500-800 nm) and SWIR2 (2000-2210 nm) regions. The PhIX index was found to produce the highest phenological classification accuracy (Overall Accuracy: 79%, and Kappa Accuracy: 75%) and similar to the NDVI, EVI and EVI2 indices it statistically sequentially separates out the developmental age classes. Discrimination between seedling and dormant age classes and the adult and flowering classes was problematic for most of the tested indices. Combining information from the visible near infrared (VNIR) and shortwave infrared region (SWIR) region into a single phenological index captures the phenological changes associated with plant pigments and the ligno-cellulose absorption feature, providing a robust method to discriminate the age classes of grasses. This work provides a valuable contribution into mapping spatial variation and monitoring plant growth across savanna and grassland ecosystems.
Simulation of Sentinel-3 images by four stream surface atmosphere radiative transfer modeling in the optical and thermal domains
Verhoef, W. ; Bach, H. - \ 2012
Remote Sensing of Environment 120 (2012). - ISSN 0034-4257 - p. 197 - 207.
reflectance - resolution - spectra - canopy - soil - tool
Simulation of future satellite images can be applied in order to validate the general mission concept and to test the performance of advanced multi-sensor algorithms for the retrieval of surface parameters. This paper describes the radiative transfer modeling part of a so-called Land Scene Generator (LSG) that was developed to simulate images of the sensors OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) on board of the Sentinel-3 mission. Features of this mission are its wide spectral coverage (optical and thermal domains) and its wide imaging swath, which imposes particular requirements on the simulator in dealing with atmospheric effects over both spectral domains and with angular effects caused by variations in surface bi-directional reflectance distribution function (BRDF) and atmospheric scattering. In the simulator, radiative transfer models for the combination vegetation-soil and for water are coupled to atmospheric parameters derived from MODTRAN runs in order to calculate top-of-atmosphere radiances. For this, four-stream radiative transfer theory is applied to allow simulation of BRDF effects, topography effects, adjacency effects, as well as its uniform application over the optical and thermal spectral domains.
Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 µm) to discriminate vegetation species
Ullah, S. ; Groen, T.A. ; Schlerf, M. ; Skidmore, A.K. ; Nieuwenhuis, W. ; Vaiphasa, C. - \ 2012
Sensors 12 (2012)7. - ISSN 1424-8220 - p. 8755 - 8769.
spectral discrimination - reflectance - spectroscopy - emissivity - imagery - leaves - identification - spectrometry - regression - plants
Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.
Identifying plant species using mid-wave infrared (2.5-6µm) and thermal infrared (8-14µm) emissivity spectra
Ullah, S. ; Schlerf, M. ; Skidmore, A.K. ; Hecker, C. - \ 2012
Remote Sensing of Environment 118 (2012)4. - ISSN 0034-4257 - p. 95 - 102.
salt-marsh vegetation - hyperspectral data - biomass estimation - reflectance - discrimination - indexes - imagery - leaves - classification - spectroscopy
Plant species discrimination using remote sensing is generally limited by the similarity of their reflectance spectra in the visible, NIR and SWIR domains. Laboratory measured emissivity spectra in the mid infrared (MIR; 2.5µm-6µm) and the thermal infrared (TIR; 8µm-14µm) domain of different plant species, however, reveal significant differences. It is anticipated that with the advances in airborne and space borne hyperspectral thermal sensors, differentiation between plant species may improve. The laboratory emissivity spectra of thirteen common broad leaved species, comprising 3024 spectral bands in the MIR and TIR, were analyzed. For each wavelength the differences between the species were tested for significance using the one way analysis of variance (ANOVA) with the post-hoc Tukey HSD test. The emissivity spectra of the analyzed species were found to be statistically different at various wavebands. Subsequently, six spectral bands were selected (based on the histogram of separable pairs of species for each waveband) to quantify the separability between each species pair based on the Jefferies Matusita (JM) distance. Out of 78 combinations, 76 pairs had a significantly different JM distance. This means that careful selection of hyperspectral bands in the MIR and TIR (2.5µm-14µm) results in reliable species discrimination.
The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass
Basuki, T.M. ; Skidmore, A.K. ; Laake, P.E. van; Duren, I.C. van; Hussin, Y.A. - \ 2012
Geocarto International 27 (2012)4. - ISSN 1010-6049 - p. 329 - 345.
urban vegetation abundance - landsat tm data - aboveground biomass - satellite estimation - kyoto protocol - indexes - amazon - carbon - reflectance - information
A main limitation of pixel-based vegetation indices or reflectance values for estimating above-ground biomass is that they do not consider the mixed spectral components on the earth's surface covered by a pixel. In this research, we decomposed mixed reflectance in each pixel before developing models to achieve higher accuracy in above-ground biomass estimation. Spectral mixture analysis was applied to decompose the mixed spectral components of Landsat-7 ETM+ imagery into fractional images. Afterwards, regression models were developed by integrating training data and fraction images. The results showed that the spectral mixture analysis improved the accuracy of biomass estimation of Dipterocarp forests. When applied to the independent validation data set, the model based on the vegetation fraction reduced 5 – 16% the root mean square error compared to the models using a single band 4 or 5, multiple bands 4, 5, 7 and all non-thermal bands of Landsat ETM+.
Geleide N-bemesting voor aardappelen op basis van gewasreflectie-metingen : integratie van sensormetingen in een N-bijmestsysteem
Evert, F.K. van; Schans, D.A. van der; Malda, J.T. ; Berg, W. van den; Geel, W.C.A. van; Jukema, J.N. - \ 2011
Lelystad : Praktijkonderzoek Plant & Omgeving, Business Unit PPO-agv - 133
precisielandbouw - bemesting - stikstofgehalte - stikstof - reflectiefactor - sensors - aardappelen - veldproeven - akkerbouw - precision agriculture - fertilizer application - nitrogen content - nitrogen - reflectance - potatoes - field tests - arable farming
Voor aardappelen zijn er nog geen regels die een sensormeting om kunnen zetten naar een N-advies. Het doel van het in dit rapport beschreven onderzoek is (1) het ontwikkelen van een ijklijn om de N-inhoud van het gewas aan de hand van reflectiemetingen te kunnen bepalen, en (2) het ontwikkelen van adviesregels om de bijmestgift vast te stellen aan de hand van de gemeten N-inhoud van het gewas. Onder leiding van Remmie Booij werd in de periode 1996-2003 een op gewasreflectiemetingen gebaseerd N-bijmestsysteem voor aardappelen ontwikkeld. Dit systeem is nooit gedocumenteerd en is daarom opnieuw uit de originele onderzoeksgegevens afgeleid. De resultaten zijn (1) de N-inhoud van een aardappelgewas kan voldoende nauwkeurig bepaald worden met een meting van de gewasreflectie, (2) wachten met bijmesten tot de bodembedekking minimaal 90% bedraagt, resulteert niet in een lagere opbrengst terwijl wel N wordt bespaard, en (3) de grootte van de bijmestgift kan worden bepaald door een streefwaarde voor de N-inhoud te verminderen met de gemeten N-inhoud. In dit rapport wordt een praktische handleiding gegeven aan de hand waarvan de teler systeem-Booij kan uitvoeren.
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.
Dataset - Adviesregel PPL 2010
Evert, F.K. van; Schans, D.A. van der; Geel, W.C.A. van; Slabbekoorn, J.J. ; Booij, R. ; Jukema, J.N. ; Meurs, E.J.J. ; Uenk, D. - \ 2011
precision agriculture - fertilizer application - nitrogen content - nitrogen - reflectance - sensors - potatoes - field tests - arable farming
This dataset contains experimental data from a number of field experiments with potato in The Netherlands (Van Evert et al., 2011). The data are presented as an SQL dump of a PostgreSQL database (version 8.4.4). An outline of the entity-relationship diagram of the database is given in an accompanying file. Dutch is required to understand some of the column names, because time pressure has prevented translating all column names to English. Views are presented without much comment. You may or may not find them helpful. The database was used in this form by us to perform our analyses and to prepare our publications. The information presented here represents a good-faith effort to preserve and transfer the experimental data from a number of experiments. We realize that the documentation of the meaning of tables and columns could have been better, but this is as complete as we could make it with the available resources. The following researchers and assistants were each involved in one or more of the experiments: * Frits K. van Evert * David A. van der Schans * Willem C.A. van Geel * Hanja Slabbekoorn * Remmie Booij * Jan Nammen Jukema * Bert Meurs * Dik Uenk References: Van Evert F.K., Van der Schans D.A., Malda J.T., Van den Berg W., Van Geel W.C.A., Jukema J.N. (2011) Geleide N-bemesting voor aardappelen op basis van gewasreflectie-metingen: Integratie van sensormetingen in een N-bijmestsysteem. PPO Rapport 423. WUR-PPO, Lelystad.
Dry season mapping of savanna forage quality, using the hyperspectral Carnegie
Knox, N. ; Skidmore, A.K. ; Prins, H.H.T. ; Asner, P. ; Werff, H.M.A. van der; Boer, W.F. de; Waal, C. van der; Knegt, H.J. de; Kohi, E. ; Slotow, R. ; Grant, R.C. - \ 2011
Remote Sensing of Environment 115 (2011)6. - ISSN 0034-4257 - p. 1478 - 1488.
kruger-national-park - african savannas - neural-networks - south-africa - absorption features - leaf biochemistry - mineral-nutrition - grass quality - nitrogen - reflectance
Forage quality within an African savanna depends upon limiting nutrients (nitrogen and phosphorus) and nutrients that constrain the intake rates (non-digestible fibre) of herbivores. These forage quality nutrients are particularly crucial in the dry season when concentrations of limiting nutrients decline and non-digestible fibres increase. Using artificial neural networks we test the ability of a new imaging spectrometer (CAO Alpha sensor), both alone and in combination with ancillary data, to map quantities of grass forage nutrients in the early dry season within an African savanna. Respectively 65%, 57% and 41%, of the variance in fibre, phosphorus and nitrogen concentrations were explained. We found that all grass forage nutrients show response to fire and soil. Principal component analysis, not only reduced image dimensionality, but was a useful method for removing cross-track illumination effects in the CAO imagery. To further improve the mapping of forage nutrients in the dry season we suggest that spectra within the shortwave infrared (SWIR) region, or additional relevant ancillary data, are required.
Estimating specific inherent optical properties of tropical coastal waters using bio-optical model inversion and in situ measurements: case of the Berau estuary, East Kalimantan, Indonesia
Ambarwulan, W. ; Salama, M.S. ; Mannaerts, C.M. ; Verhoef, W. - \ 2011
Hydrobiologia 658 (2011)1. - ISSN 0018-8158 - p. 197 - 211.
dissolved organic-matter - ocean color - scattering coefficients - absorption - chlorophyll - phytoplankton - seawifs - reflectance - throughflow - variability
Specific inherent optical properties (SIOP) of the Berau coastal waters were derived from in situ measurements and inversion of an ocean color model. Field measurements of water-leaving reflectance, total suspended matter (TSM), and chlorophyll a (Chl a) concentrations were carried out during the 2007 dry season. The highest values for SIOP were found in the turbid waters, decreasing in value when moving toward offshore waters. The specific backscattering coefficient of TSM varied by an order of magnitude and ranged from 0.003 m2 g-1, for clear open ocean waters, to 0.020 m2 g-1, for turbid waters. On the other hand, the specific absorption coefficient of Chl a was relatively constant over the whole study area and ranged from 0.022 m2 mg-1, for the turbid shallow estuary waters, to 0.027 m2 mg-1, for deeper shelf edge ocean waters. The spectral slope of colored dissolved organic matter light absorption was also derived with values ranging from 0.015 to 0.011 nm-1. These original derived values of SIOP in the Berau estuary form a corner stone for future estimation of TSM and Chl a concentration from remote sensing data in tropical equatorial waters
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