References - VU

References
Aan, A., Hallik, L. E. A., & Kull, O. (2006). Photon flux partitioning among species along a
productivity gradient of an herbaceous plant community. Journal of Ecology, 94(6), 1143–
1155.
Abràmoff, M. D., Magalhães, P. J., & Ram, S. J. (2004). Image processing with ImageJ.
Biophotonics International, 11(7), 36–42.
Ackerly, D., & Cornwell, W. (2007). A trait-based approach to community assembly:
Partitioning of species trait values into within- and among-community components.
Ecology Letters, 10(2), 135–145.
Aptroot, A., Natuurmonumenten, & Landschap, N. &. (2009). Flora- en vegetatiekartering van
Kampina in 2009. Natuurmonumenten reports (p. 41). ’s-Graveland, NL:
Natuurmonumenten.
Asner, G. (1998). Biophysical and biochemical sources of variability in canopy reflectance.
Remote Sensing of Environment, 64(3), 234–253.
Asner, G., & Heidebrecht, K. B. (2002). Spectral unmixing of vegetation, soil and dry carbon
cover in arid regions: comparing multispectral and hyperspectral observations.
International Journal of Remote Sensing, 23(19), 3939–3958.
Asner, G., & Martin, R. E. (2008). Spectral and chemical analysis of tropical forests: Scaling
from leaf to canopy levels. Remote Sensing of Environment, 112(10), 3958–3970.
Asner, G., & Martin, R. E. (2009). Airborne spectranomics: Mapping canopy chemical and
taxonomic diversity in tropical forests. Frontiers in Ecology and the Environment, 7(5),
269–276.
Asner, G., & Martin, R. E. (2011). Canopy phylogenetic, chemical and spectral assembly in a
lowland Amazonian forest. New Phytologist, 189(4), 999–1012.
Asner, G., Martin, R. E., Knapp, D. E., Tupayachi, R., Anderson, C., Carranza, L., Martinez,
P., Houcheime, M., Sinca, F., & Weiss, P. (2011). Spectroscopy of canopy chemicals in
humid tropical forests. Remote Sensing of Environment, 115(12), 3587–3598.
Asner, G., & Vitousek, P. (2005). Remote analysis of biological invasion and biogeochemical
change. Proceedings of the National Academy of Sciences of the United States of America,
102(12), 4383–4386.
Baraloto, C., Timothy Paine, C. E., Poorter, L., Beauchene, J., Bonal, D., Domenach, A.,
Hérault, B., Patiño, S., Roggy, J., & Chave, J. (2010). Decoupled leaf and stem economics
in rain forest trees. Ecology Letters, 13(11), 1338–1347.
Baret, F., & Vanderbilt, V. (1994). Use of spectral analogy to evaluate canopy reflectance
sensitivity to leaf optical properties. Remote Sensing of Environment, 48, 253–260.
Bartholomeus, H., Epema, G., & Schaepman, M. E. (2007). Determining iron content in
Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging
spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 9(2),
194–203.
141
Bartholomeus, H., Kooistra, L., Stevens, A., van Leeuwen, M., van Wesemael, B., Ben-Dor,
E., & Tychon, B. (2011). Soil Organic Carbon mapping of partially vegetated agricultural
fields with imaging spectroscopy. International Journal of Applied Earth Observation and
Geoinformation, 13(1), 81–88.
Bartholomeus, R. (2010). Moisture Matters: Climate-proof and process-based relationships between
water, oxygen and vegetation. VU University.
Bartholomeus, R., Witte, J., van Bodegom, P. M., Dam, J., Becker, P., & Aerts, R. (2012).
Process-based proxy of oxygen stress surpasses indirect ones in predicting vegetation
characteristics. Ecohydrology, 5(6), 746–758.
Bartholomeus, R., Witte, J., van Bodegom, P. M., van Dam, J., & Aerts, R. (2008). Critical soil
conditions for oxygen stress to plant roots: Substituting the Feddes-function by a
process-based model. Journal of Hydrology, 360(1), 147–165.
Bastiaanssen, W., Noordman, E., Pelgrum, H., Davids, G., Thoreson, B., & Allen, R. (2005).
SEBAL model with remotely sensed data to improve water-resources management
under actual field conditions. Journal of Irrigation and Drainage Engineering, 131(1), 85–93.
Bell, D., Menges, C., Ahmad, W., & Van Zyl, J. J. (2001). The application of dielectric
retrieval algorithms for mapping soil salinity in a tropical coastal environment using
airborne polarimetric SAR. Remote Sensing of Environment, 75(3), 375–384.
Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., & Marani, M.
(2006). Mapping salt-marsh vegetation by multispectral and hyperspectral remote
sensing. Remote Sensing of Environment, 105(1), 54–67.
Berger, M., Moreno, J., Johannessen, J. A., Levelt, P. F., & Hanssen, R. F. (2012). ESA’s
sentinel missions in support of Earth system science. Remote Sensing of Environment, 120,
84–90.
Biesemans, J., Horsten, W., Verbeke, B., Vanderstraete, T., van der Linden, S., van Camp, N.,
& VITO. (2010). Image orthorectification and image mosaicing: algorithm theoretical base and
validation. VITO reports (p. 30). Mol, BE: VITO.
Biesemans, J., Sterckx, S., Knaeps, E., Vreys, K., Adriaensen, S., Hooyberghs, J., Meuleman,
K., Kempeneers, P., Deronde, B., & Everaerts, J. (2007). Image processing workflows for
airborne remote sensing. In Proceedings of the 5 th EARSeL Workshop on Imaging
Spectroscopy. Brugge, BE.
Buringh, P., Steur, G., & Vink, A. (1962). Some techniques and methods of soil survey in the
Netherlands. Netherlands Journal of Agricultural Science, 10, 157–178.
Cho, M. A., Debba, P., Mathieu, R., Naidoo, L., van Aardt, J. A. N., & Asner, G. (2010).
Improving discrimination of savanna tree species through a multiple-endmember
spectral angle mapper approach: Canopy-level analysis. IEEE Transactions on Geoscience
and Remote Sensing, 48(11), 4133–4142.
Cho, M. A., & Skidmore, A. K. (2006). A new technique for extracting the red edge position
from hyperspectral data: The linear extrapolation method. Remote Sensing of
Environment, 101(2), 181–193.
142
Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M. P., Vilar, L., Martínez, J.,
Martín, S., & Ibarra, P. (2010). Development of a framework for fire risk assessment
using remote sensing and geographic information system technologies. Ecological
Modelling, 221(1), 46–58.
Cirkel, D., Witte, J., Nijp, J., van Bodegom, P. M., & van der Zee, S. E. (2012). The influence of
spatiotemporal variability and adaptations to hypoxia on empirical relationships
between soil acidity and vegetation. Ecohydrology.
Clark, M. L., Roberts, D., & Clark, D. B. (2005). Hyperspectral discrimination of tropical rain
forest tree species at leaf to crown scales. Remote Sensing of Environment, 96(3), 375–398.
Clevers, J. G. P. W., & Kooistra, L. (2012). Using hyperspectral remote sensing data for
retrieving canopy chlorophyll and nitrogen content. IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing, 5(2), 574–583.
Clevers, J. G. P. W., Kooistra, L., & Schaepman, M. E. (2010). Estimating canopy water
content using hyperspectral remote sensing data. International Journal of Applied Earth
Observation and Geoinformation, 12(2), 119–125.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological
Measurement, 20(1), 37–46.
Cornelissen, J., Lavorel, S., Garnier, E., Diaz, S., Buchmann, N., Gurvich, D., Reich, P., ter
Steege, H., Morgan, H., van der Heijden, M., Pausas, J., & Poorter, H. (2003). A
handbook of protocols for standardised and easy measurement of plant functional traits
worldwide. Australian Journal of Botany, 51(4), 335–380.
Cornwell, W., Schwilk, D., & Ackerly, D. (2006). A trait-based test for habitat filtering:
convex hull volume. Ecology, 87(6), 1465–1471.
Cousins, S. A. O., & Lindborg, R. (2004). Assessing changes in plant distribution patterns indicator species versus plant functional types. Ecological Indicators, 4(1), 17–27.
Curran, P. (1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30, 271–
287.
Curran, P., Dungan, J., & Macler, B. (1992). Reflectance spectroscopy of fresh whole leaves
for the estimation of chemical concentration. Remote Sensing of Environment, 39, 153–166.
Damgaard, C., Strandberg, M., Kristiansen, S. M., Nielsen, K. E., & Bak, J. L. (2014). Is Erica
tetralix abundance on wet heathlands controlled by nitrogen deposition or soil
acidification? Environmental Pollution, 184, 1–8.
Damoiseaux, J. H., & Teunissen van Manen, T. C. (1984). Bodemkaart van Nederland, 1 : 50
000 51 West EINDHOVEN. (H. L. Kanters, Ed.)Bodemkaart van Nederland 1 : 50 000.
Wageningen, the Netherlands: Stichting voor Bodemkartering.
Darvishzadeh, R., Skidmore, A., Schlerf, M., & Atzberger, C. (2007). Inversion of a radiative
transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous
grassland. Remote Sensing of Environment, 112(5), 2592–2604.
Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Corsi, F., & Cho, M. (2008). LAI
and chlorophyll estimation for a heterogeneous grassland using hyperspectral
measurements. ISPRS Journal of Photogrammetry and Remote Sensing, 63(4), 409–426.
143
Daughtry, C., Biehl, L., & Ranson, K. (1989). A new technique to measure the spectral
properties of conifer needles. Remote Sensing of Environment, 27(1), 81–91.
Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E.
(2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance.
Remote Sensing of Environment, 74(2), 229–239.
De Bello, F., Lepš, J., & Sebastià, M. (2006). Variations in species and functional plant
diversity along climatic and grazing gradients. Ecography, 29(6), 801–810.
De Bello, F., Thuiller, W., Leps, J., Choler, P., Clement, J., Macek, P., Sebastia, M., & Lavorel,
S. (2009). Partitioning of functional diversity reveals the scale and extent of trait
convergence and divergence. Journal of Vegetation Science, 20(3), 475–486.
De Haan, J., Hovenier, J., Kokke, J., & Van Stokkom, H. (1991). Removal of atmospheric
influences on satellite-borne imagery: a radiative transfer approach. Remote Sensing of
Environment, 37(1), 1–21.
De Lange, W., Prinsen, G., Hoogewoud, J., Veldhuizen, A., Verkaik, J., Oude Essink, G., van
Walsum, P., Delsman, J., Hunink, J., Massop, Ht., & Kroon, T. (n.d.). The Netherlands
Hydrological Instrument: An operational, multi-scale, multi-model system for
consensus-based, integrated water management and policy analysis. Environmental
Modelling & Software.
De Vries, F. (1999). Karakterisering van Nederlandse gronden naar fysisch-chemische kenmerken (p.
41). Wageningen, the Netherlands: DLO-Staring Centrum.
Dengler, J., Chytrý, M., & Ewald, J. (2008). Phytosociology. In S. E. Jørgensen & B. D. Fath
(Eds.), Encyclopedia of ecology (Vol. 4, pp. 2767–2779). Oxford: Elsevier B.V.
Dennison, P. E., & Roberts, D. (2003). The effects of vegetation phenology on endmember
selection and species mapping in southern California chaparral. Remote Sensing of
Environment, 87(2), 295–309.
Dı́az, S., & Cabido, M. (2001). Vive la difference: plant functional diversity matters to
ecosystem processes. Trends in Ecology & Evolution, 16(11), 646–655.
Diekmann, M. (2002). Species indicator values as an important tool in applied plant ecology
- A review. Basic and Applied Ecology, 4(6), 493–506.
Dirkse, G., & Kruijsen, B. (1993). Indeling in ecologische groepen van Nederlandse blad-en
levermossen. Gorteria, 19, 1–29.
Dobben, H., & Slim, P. (2012). Past and future plant diversity of a coastal wetland driven by
soil subsidence and climate change. Climatic Change, 110(3), 597–618.
Dorigo, W., Richter, R., Baret, F., Bamler, R., & Wagner, W. (2009). Enhanced automated
canopy characterization from hyperspectral data by a novel two step radiative transfer
model inversion approach. Remote Sensing, 1(4), 1139–1170.
Doughty, C., Asner, G., & Martin, R. E. (2011). Predicting tropical plant physiology from leaf
and canopy spectroscopy. Oecologia, 165(2), 289–299.
Douma, J., Aerts, R., Witte, J., Bekker, R., Kunzmann, D., Metselaar, K., & van Bodegom, P.
M. (2012). A combination of functionally different plant traits provides a means to
144
quantitatively predict a broad range of species assemblages in NW Europe. Ecography,
35(4), 364–373.
Douma, J., Bardin, V., Bartholomeus, R., & van Bodegom, P. M. (2012). Quantifying the
functional responses of vegetation to drought and oxygen stress in temperate
ecosystems. Functional Ecology, 26, 1355–1365.
Douma, J., Witte, J., Aerts, R., Bartholomeus, R., Ordoñez, J. C., Olde Venterink, H., Wassen,
M. J., & van Bodegom, P. M. (2012). Towards a functional basis for predicting
vegetation patterns; incorporating plant traits in habitat distribution models. Ecography,
35(4), 294–305.
Ecker, K., Waser, L. T., & Küchler, M. (2010). Contribution of multi-source remote sensing
data to predictive mapping of plant-indicator gradients within Swiss mire habitats.
Botanica Helvetica, 120(1), 29–42.
Ellenberg, H. (1950). Ackerunkraut-Gemeinschaften als Bodenzeiger. (pp. 1–14). StuttgartHohenheim.
Ellenberg, H. (1974). Zeigerwerte der Gefäβpflanzen Mitteleuropas. Scripta Geobotanica, 9, 1–
97.
Ellenberg, H. (1992). Zeigerwerte der Gefäßpflanzen (ohne Rubus). Zeigerwerte von Pflanzen
in Mitteleuropa, 18, 9–166.
Ellenberg, H., Weber, H. E., Düll, R., Witrth, V., Werner, W., & Paulißen, D. (1991).
Zeigerwerte von Pflanzen in Mitteleuropa. Scripta Geobotanica, 18, 1–97.
Elser, J., Fagan, W., Kerkhoff, A., Swenson, N., & Enquist, B. (2010). Biological stoichiometry
of plant production: metabolism, scaling and ecological response to global change. New
Phytologist, 186(3), 593–608.
Ertsen, A., Alkemade, J., & Wassen, M. J. (1998). Calibrating Ellenberg indicator values for
moisture, acidity, nutrient availability and salinity in the Netherlands. Plant Ecology,
135(1), 113–124.
Evans, J. R., & Poorter, H. (2001). Photosynthetic acclimation of plants to growth irradiance:
the relative importance of specific leaf area and nitrogen partitioning in maximizing
carbon gain. Plant, Cell & Environment, 24(8), 755–767.
Falster, D. S., & Westoby, M. (2005). Alternative height strategies among 45 dicot rain forest
species from tropical Queensland, Australia. Journal of Ecology, 93(3), 521–535.
Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N., & Zucca, C. (2009).
Identification of hyperspectral vegetation indices for Mediterranean pasture
characterization. International Journal of Applied Earth Observation and Geoinformation,
11(4), 233–243.
Feilhauer, H., Asner, G., Martin, R. E., & Schmidtlein, S. (2010). Brightness-normalized
partial least squares regression for hyperspectral data. Journal of Quantitative
Spectroscopy and Radiative Transfer, 111(12), 1947–1957.
Feilhauer, H., Faude, U., & Schmidtlein, S. (2011). Combining Isomap ordination and
imaging spectroscopy to map continuous floristic gradients in a heterogeneous
landscape. Remote Sensing of Environment, 115(10), 2513–2524.
145
Feilhauer, H., & Schmidtlein, S. (2009). Mapping continuous fields of forest alpha and beta
diversity. Applied Vegetation Science, 12(4), 429–439.
Feilhauer, H., & Schmidtlein, S. (2011). On variable relations between vegetation patterns
and canopy reflectance. Ecological Informatics, 6(2), 83–92.
Feldmeyer-Christe, E., Klaus, E., Kuchler, M., Graf, U., & Waser, L. T. (2007). Improving
predictive mapping in Swiss mire ecosystems through re-calibration of indicator values.
Applied Vegetation Science, 10(2), 183–192.
Ferwerda, J. G., & Skidmore, A. K. (2007). Can nutrient status of four woody plant species be
predicted using field spectrometry? ISPRS Journal of Photogrammetry and Remote Sensing,
62(6), 406–414.
Fliervoet, L. M., & Werger, M. J. A. (1984). Canopy structure and microclimate of two wet
grassland communities. New Phytologist, 96(1), 115–130.
Freschet, G., Cornelissen, J. H. C., Van Logtestijn, R. S. P., & Aerts, R. (2010). Evidence of the
“plant economics spectrum”in a subarctic flora. Journal of Ecology, 98(2), 362–373.
Freschet, G., & Dias, A. (2011). Global to community scale differences in the prevalence of
convergent over divergent leaf trait distributions in plant assemblages. Global Ecology
and Biogeography, 20, 755–765.
Fujita, Y., & van Bodegom, P. M. (2013). Towards a proper integration of hydrology in
predicting soil nitrogen mineralization rates along natural moisture gradients. Soil
Biology and Biochemistry, 58, 302–312.
Fujita, Y., van Bodegom, P. M., & Witte, J. (2013). Relationships between Nutrient-Related
Plant Traits and Combinations of Soil N and P Fertility Measures. PloS One, 8(12),
e83735.
Fukami, T., & Wardle, D. A. (2005). Long-term ecological dynamics: reciprocal insights from
natural and anthropogenic gradients. Proceedings of the Royal Society B: Biological Sciences,
272(1577), 2105–2115.
Garnier, E., Lavorel, S., Ansquer, P., Castro, H., Cruz, P., Dolezal, J., Eriksson, O., Fortunel,
C., Freitas, H., & Golodets, C. (2007). Assessing the effects of land-use change on plant
traits, communities and ecosystem functioning in grasslands: a standardized
methodology and lessons from an application to 11 European sites. Annals of Botany,
99(5), 967–985.
Goetz, A. (2009). Three decades of hyperspectral remote sensing of the Earth: A personal
view. Remote Sensing of Environment, 113, S5–S16.
Gómez, J. A., de Miguel, E., Gutiérrez de la Cámara, Ó., & Fernández-Renau, A. (2007).
STATUS OF THE INTA AHS SENSOR. In 5th EARSeL Workshop on Imaging Spectroscopy.
Brugge, BE.
Grootjans, A., & van Wirdum, G. (1996). Ecohydrology in The Netherlands: principles of an
application‐driven interdiscipline §. Acta Botanica Neerlandica, 45(4), 419–516.
Grossman, Y., Ustin, S. L., Jacquemoud, S., Sanderson, E., Schmuck, G., & Verdebout, J.
(1996). Critique of stepwise multiple linear regression for the extraction of leaf
146
biochemistry information from leaf reflectance data. Remote Sensing of Environment,
56(3), 182–193.
Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple
habitat models. Ecology Letters, 8(9), 993–1009.
Güsewell, S. (2004). N: P ratios in terrestrial plants: variation and functional significance.
New Phytologist, 164(2), 243–266.
Haaland, D. M., & Thomas, E. V. (1988). Partial least-squares methods for spectral analyses.
1. Relation to other quantitative calibration methods and the extraction of qualitative
information. Analytical Chemistry, 60(11), 1193–1202.
Hannerz, M., & Hånell, B. (1997). Effects on the flora in Norway spruce forests following
clearcutting and shelterwood cutting. Forest Ecology and Management, 90(1), 29–49.
Hansen, P., & Schjoerring, J. (2003). Reflectance measurement of canopy biomass and
nitrogen status in wheat crops using normalized difference vegetation indices and
partial least squares regression. Remote Sensing of Environment, 86, 542–553.
Hantson, W., Kooistra, L., & Slim, P. (2012). Mapping invasive woody species in coastal
dunes in the Netherlands: a remote sensing approach using LIDAR and high resolution
aerial photographs. Applied Vegetation Science, 15(4), 536–547.
Harbaugh, A. W., Banta, E. R., Hill, M. C., & McDonald, M. G. (2000). MODFLOW 2000, The
U.S. geological survey modular ground water model user guide to modularization concepts and
the ground water flow process. Reston, Virginia: US Geological Survey.
Harrison, M. T., Edwards, E. J., Farquhar, G. D., Nicotra, A. B., & Evans, J. R. (2009).
Nitrogen in cell walls of sclerophyllous leaves accounts for little of the variation in
photosynthetic nitrogen‐use efficiency. Plant, Cell & Environment, 32(3), 259–270.
Hikosaka, K., & Shigeno, A. (2009). The role of Rubisco and cell walls in the interspecific
variation in photosynthetic capacity. Oecologia, 160(3), 443–451.
Hirose, T., & Werger, M. J. A. (1995). Canopy structure and photon flux partitioning among
species in a herbaceous plant community. Ecology, 76(2), 466–474.
Hoffmann, W. A., Schroeder, W., & Jackson, R. B. (2002). Positive feedbacks of fire, climate,
and vegetation and the conversion of tropical savanna. Geophysical Research Letters,
29(22), 2052.
Homolová, L., Malenovský, Z., Clevers, J., García-Santos, G., & Schaepman, M. E. (2013).
Review of optical-based remote sensing for plant trait mapping. Ecological Complexity,
15, 1–16.
Hunt Jr, E. R., & Rock, B. N. (1989). Detection of changes in leaf water content using nearand middle-infrared reflectances. Remote Sensing of Environment, 30(1), 43–54.
Im, J., Jensen, J. R., Jensen, R. R., Gladden, J., Waugh, J., & Serrato, M. (2012). Vegetation
cover analysis of hazardous waste sites in utah and arizona using hyperspectral remote
sensing. Remote Sensing, 4(2), 327–353.
Jacquemoud, S., & Baret, F. (1990). PROSPECT: A model of leaf optical properties spectra.
Remote Sensing of Environment, 34(2), 75–91.
147
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G., François, C.,
& Ustin, S. L. (2009). PROSPECT + SAIL models: A review of use for vegetation
characterization. Remote Sensing of Environment, 113(SUPPL. 1).
Jager, T. D. (2010). Toelichting bij de vegetatiekartering Ameland 2008. Op basis van false-colour
luchtfoto’s 1:5.000. RWS reports. Delft, NL: Rijkswaterstaat - DID - Afdeling GSMH.
Jager, T. D. (2012). Assessment of predicted association occurrence. (H. D. Roelofsen, Ed.).
Nieuwegein, NL: KWR Watercycle Research Institute.
Janssen, J. (2004). The use of sequential vegetation maps for monitoring in coastal areas.
Community Ecology, 5(1), 31–43.
Käfer, J., & Witte, J. (2004). Cover-weighted averaging of indicator values in vegetation
analyses. Journal of Vegetation Science, 15(5), 647–652.
Kaiser, T., Wehrhan, M., Werner, A., & Sommer, M. (2012). Regionalizing ecological
moisture levels and groundwater levels in grassland areas using thermal remote
sensing. Grassland Science, 58(1), 42–52.
Kalliola, R., & Syrjänen, K. (1991). To what extent are vegetation types visible in satellite
imagery? Annales Botanici Fennici, 28(1), 45–57.
Kattge, J., Díaz, S., & Lavorel, S. (2011). TRY–a global database of plant traits. Global Change
Biology, 17(9), 2905–2935.
Keddy, P. (1992). Assembly and response rules: two goals for predictive community
ecology. Journal of Vegetation Science, 3, 157–164.
Klapp, E. (1965). Grünlandvegetation und Standort nach Beispiel aus West-, Mittel-, und
Süddeutschland. … Beispielen aus West-, Mittel-, und Süddeutschland.- …. Berlin/Hamburg:
Paul Parey.
Klaus, V. H., Kleinebecker, T., Boch, S., Müller, J., Socher, S. A., Prati, D., Fischer, M., &
Hölzel, N. (2012). NIRS meets Ellenberg’s indicator values: Prediction of moisture and
nitrogen values of agricultural grassland vegetation by means of near-infrared spectral
characteristics. Ecological Indicators, 14(1), 82–86.
Kleinebecker, T., Klaus, V. H., & Hlzel, N. (2012). Reducing sample quantity and
maintaining high-prediction quality of grassland biomass properties with near infrared
reflectance spectroscopy. Journal of Near Infrared Spectroscopy, 19(6), 495.
Knapp, A. K., Beier, C., Briske, D. D., Classen, A. T., Luo, Y., Reichstein, M., Smith, M. D.,
Smith, S. D., Bell, J. E., Fay, P. A., Heisler, J. L., Leavitt, S. W., Sherry, R., Smith, B., &
Weng, E. (2008). Consequences of more extreme precipitation regimes for terrestrial
ecosystems. BioScience, 58(9), 811–821.
Knox, N. M., Skidmore, A. K., Prins, H. H. T., Asner, G. P., van der Werff, H., de Boer, W. F.,
van der Waal, C., de Knegt, H. J., Kohi, E. M., & Slotow, R. (2011). Dry season mapping
of savanna forage quality, using the hyperspectral Carnegie Airborne Observatory
sensor. Remote Sensing of Environment, 115(6), 1478–1488.
Knyazikhin, Y., Schull, M. A., Stenberg, P., Mõttus, M., Rautiainen, M., Yang, Y., Marshak,
A., Carmona, P. L., Kaufmann, R. K., Lewis, P., Disney, M. I., Vanderbilt, V., Davis, A.
B., Baret, F., Jacquemoud, S., Lyapustin, A., & Myneni, R. B. (2013). Hyperspectral
148
remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences,
110(3), E185–E192.
Koerselman, W., & Meuleman, A. F. (1996). The vegetation N: P ratio: a new tool to detect
the nature of nutrient limitation. Journal of Applied Ecology, 33(6), 1441–1450.
Kokaly, R. F., Despain, D. G., Clark, R. N., & Livo, K. E. (2003). Mapping vegetation in
Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote
Sensing of Environment, 84(3), 437–456.
Kraft, N., Valencia, R., & Ackerly, D. (2008). Functional traits and niche-based tree
community assembly in an Amazonian forest. Science, 322(5901), 580–582.
Küchler, A. (1984). Ecological vegetation maps. Vegetatio, 55(1), 3–10.
Küchler, A., & Zonneveld, I. (1988). Vegetation mapping. (H. Lieth, Ed.)Handbook of vegetation
science (Vol. 10). Dordrecht, NL: Kluwer Academic Publishers.
Kull, O., & Aan, A. (1997). The relative share of graminoid and forb life‐forms in a natural
gradient of herb layer productivity. Ecography, 20(2), 146–154.
Kumar, L., Schmidt, K., Dury, S., & Skidmore, A. (2001). Imaging spectrometry and
vegetation science. In F. D. van der Meer & S. M. de Jong (Eds.), Imaging Spectroscopy:
Basic Principles and Prospective Applications (pp. 111–155). Dordrecht, NL: Kluwer
Academic Publishers.
Landolt, E. (1977). Okologische Zeigerwerte zur Schweizer Flora. Veröffentlichungen Des
Geobotanischen Institutes Der Eidgenössischen Technischen Hochschule, Stiftung Rübel,
Zürich, 64, 1–208.
Laughlin, D. C., Joshi, C., van Bodegom, P. M., Bastow, Z. A., & Fulé, P. Z. (2012). A
predictive model of community assembly that incorporates intraspecific trait variation.
Ecology Letters, 15(11), 1291–1299.
Lavorel, S., & Garnier, E. (2002). Predicting changes in community composition and
ecosystem functioning from plant traits: revisiting the Holy Grail. Functional Ecology,
16(5), 545–556.
Lavorel, S., Grigulis, K., Lamarque, P., Colace, M., Garden, D., Girel, J., Pellet, G., & Douzet,
R. (2011). Using plant functional traits to understand the landscape distribution of
multiple ecosystem services. Journal of Ecology, 99(1), 135–147.
Lee, D. W., & Graham, R. (1986). Leaf optical properties of rainforest sun and extreme shade
plants. American Journal of Botany, 73(8), 1100–1108.
Lloyd, J., Bloomfield, K., & Domingues, T. F. (2013). relevant foliar traits correlating better
on a mass vs an area basis: of ecophysiological relevance or just a case of mathematical
imperatives and statistical quicksand? New Phytologist, 199(2), 311–321.
Maarel, E., & Sykes, M. T. (1993). Small scale plant species turnover in a limestone grassland:
the carousel model and some comments on the niche concept. Journal of Vegetation
Science, 4(2), 179–188.
Makkar, H. P. S. (2003). Quantification of tannins in tree and shrub foliage: a laboratory manual.
Dordrecht, NL: Kluwer Academic Publishers.
149
Martin, M. E., Plourde, L. C., Ollinger, S. V, Smith, M.-L., & McNeil, B. E. (2008). A
generalizable method for remote sensing of canopy nitrogen across a wide range of
forest ecosystems. Remote Sensing of Environment, 112(9), 3511–3519.
Meinzer, O. E. (1927). Plants as indicators of ground water (Google eBoek). Washington: U.S.
Geological Survey - Department of the Interior.
Mercado, L. M., Patiño, S., Domingues, T. F., Fyllas, N. M., Weedon, G. P., Sitch, S.,
Quesada, C. A., Phillips, O. L., Aragão, L. E., & Malhi, Y. (2011). Variations in Amazon
forest productivity correlated with foliar nutrients and modelled rates of photosynthetic
carbon supply. Philosophical Transactions of the Royal Society B: Biological Sciences,
366(1582), 3316–3329.
Mesarch, M. A., Walter-Shea, E. A., Asner, G., Middleton, E. M., & Chan, S. S. (1999). A
revised measurement methodology for conifer needles spectral optical properties:
evaluating the influence of gaps between elements. Remote Sensing of Environment, 68(2),
177–192.
Mevik, B.-H., & Wehrens, R. (2007). The pls package: principal component and partial least
squares regression in R. Journal of Statistical Software, 18(2), 1–24.
Mirik, M., Norland, J. E., Crabtree, R. L., & Biondini, M. E. (2005). Hyperspectral one-meterresolution remote sensing in Yellowstone National Park, Wyoming: I. Forage nutritional
values. Rangeland Ecology & Management, 58(5), 452–458.
Morisette, J. T., Baret, F., Privette, J. L., Myneni, R. B., Nickeson, J. E., Garrigues, S.,
Shabanov, N. V, Weiss, M., Fernandes, R. A., & Leblanc, S. G. (2006). Validation of
global moderate-resolution LAI products: A framework proposed within the CEOS
land product validation subgroup. IEEE Transactions on Geoscience and Remote Sensing,
44(7), 1804–1817.
Mulder, V., De Bruin, S., Schaepman, M. E., & Mayr, T. (2011). The use of remote sensing in
soil and terrain mapping—A review. Geoderma, 162(1), 1–19.
Murphy, J., & Riley, J. P. (1962). A modified single solution method for the determination of
phosphate in natural waters. Analytica Chimica Acta, 27, 31–36.
Mutanga, O., Skidmore, A. K., & Prins, H. H. T. (2004). Predicting in situ pasture quality in
the Kruger National Park, South Africa, using continuum-removed absorption features.
Remote Sensing of Environment, 89(3), 393–408.
Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I—A
discussion of principles. Journal of Hydrology.
Niinemets, Ü. (2001). Global-scale climatic controls of leaf dry mass per area, density, and
thickness in trees and shrubs. Ecology, 82(2), 453–469.
Niinemets, Ü. (2010). A review of light interception in plant stands from leaf to canopy in
different plant functional types and in species with varying shade tolerance. Ecological
Research, 25(4), 693–714.
Niinemets, Ü., & Tenhunen, J. D. (1997). A model separating leaf structural and
physiological effects on carbon gain along light gradients for the shade‐tolerant species
Acer saccharum. Plant, Cell & Environment, 20(7), 845–866.
150
Noble, S. D., & Crowe, T. G. (2007). Sample holder and methodology for measuring the
reflectance and transmittance of narrow-leaf samples. Applied Optics, 46(22), 4968–4976.
Noda, H. M., Motohka, T., Murakami, K., Muraoka, H., & Nasahara, K. N. (2013). Accurate
measurement of optical properties of narrow leaves and conifer needles with a typical
integrating sphere and spectroradiometer. Plant, Cell & Environment.
Olde Venterink, H., & Wassen, M. J. (1997). A comparison of six models predicting
vegetation response to hydrological habitat change. Ecological Modelling, 101(2), 347–361.
Oldeland, J., Dorigo, W., Lieckfeld, L., Lucieer, A., & Jürgens, N. (2010). Combining
vegetation indices, constrained ordination and fuzzy classification for mapping seminatural vegetation units from hyperspectral imagery. Remote Sensing of Environment,
114(6), 1155–1166.
Ordoñez, J. C., van Bodegom, P. M., Witte, J., Wright, I., Reich, P., & Aerts, R. (2009). A
global study of relationships between leaf traits, climate and soil measures of nutrient
fertility. Global Ecology and Biogeography, 18(2), 137–149.
Osnas, J., Lichstein, J., Reich, P., & Pacala, S. (2013). Global leaf trait relationships: mass,
area, and the leaf economics spectrum. Science, 340(6133), 741–744.
Ozinga, W., Schaminée, J., Bekker, R., Bonn, S., Poschlod, P., Tackenberg, O., Bakker, J., &
Groenendael, J. (2005). Predictability of plant species composition from environmental
conditions is constrained by dispersal limitation. Oikos, 108(3), 555–561.
Panciera, R., Walker, J. P., Kalma, J. D., Kim, E. J., Saleh, K., & Wigneron, J. (2009).
Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm.
Remote Sensing of Environment, 113(2), 435–444.
Pasolli, L., Melgani, F., & Blanzieri, E. (2010). Gaussian process regression for estimating
chlorophyll concentration in subsurface waters from remote sensing data. IEEE
Geoscience and Remote Sensing Letters, 7(3), 464–468.
Peduzzi, A., Wynne, R. H., Thomas, V. A., Nelson, R. F., Reis, J. J., & Sanford, M. (2012).
Combined use of airborne lidar and DBInSAR data to estimate LAI in temperate mixed
forests. Remote Sensing, 4(6), 1758–1780.
Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P.,
Bret-Harte, M., Cornwell, W., Craine, J., Gurvich, D., Urcelay, C., Veneklaas, E., Reich,
P., Poorter, L., Wright, I., Ray, P., Enrico, L., … Cornelissen, J. (2013). New handbook for
standardised measurement of plant functional traits worldwide. Australian Journal of
Botany, 61(3), 167.
Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfil, D. J. (2011). Exploring remotely sensed
technologies for monitoring wheat potassium and phosphorus using field spectroscopy.
Field Crops Research, 121(1), 125–135.
Poorter, H., Lambers, H., & Evans, J. R. (2013). Trait correlation networks: a whole-plant
perspective on the recently criticized leaf economic spectrum. New Phytologist.
Poorter, H., & Villar, R. (1997). The fate of acquired carbon in plants: chemical composition
and construction costs. In Plant resource allocation (pp. 39–72). San Diego, USA:
Academic Press.
151
Poorter, L., Oberbauer, S. F., & Clark, D. B. (1995). Leaf optical properties along a vertical
gradient in a tropical rain forest canopy in Costa Rica. American Journal of Botany, 82(10),
1257–1263.
Porra, R. J. (2002). The chequered history of the development and use of simultaneous
equations for the accurate determination of chlorophylls a and b. Photosynthesis
Research, 73(1), 149–156.
Porra, R. J., Thompson, W. A., & Kriedemann, P. E. (1989). Determination of accurate
extinction coefficients and simultaneous equations for assaying chlorophylls a and b
extracted with four different solvents: verification of the concentration of chlorophyll
standards by atomic absorption spectroscopy. Biochimica et Biophysica Acta (BBA)Bioenergetics, 975(3), 384–394.
Prentice, I., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A., & Solomon, A. M.
(1992). Special paper: a global biome model based on plant physiology and dominance,
soil properties and climate. Journal of Biogeography, 19(2), 117–134.
R Core Team. (2013). R: a language and environment for statistical computing. Vienna,
Austria: R foundation for statistical computing.
Ramoelo, A., Skidmore, A., Cho, M., Mathieu, R., Heitkönig, I., Dudeni-Tlhone, N., Schlerf,
M., & Prins, H. (2013). Non-linear partial least square regression increases the
estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and
environmental data. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 27–40.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning.
Adaptative computation and machine learning series (pp. 63–71). MIT Press.
Reich, P., Wright, I., & Lusk, C. (2007). Predicting leaf physiology from simple plant and
climate attributes: a global GLOPNET analysis. Ecological Applications, 17(7), 1982–1988.
Rivera, J. P., Verrelst, J., Leonenko, G., & Moreno, J. (2013). Multiple Cost Functions and
Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI
through Inversion of the PROSAIL Model. Remote Sensing, 5(7), 3280–3304.
Rocchini, D., Foody, G. M., Nagendra, H., Ricotta, C., Anand, M., He, K. S., Amici, V.,
Kleinschmit, B., Förster, M., & Schmidtlein, S. (2012). Uncertainty in ecosystem
mapping by remote sensing. Computers & Geosciences, 50, 128–135.
Röder, A., Kuemmerle, T., Hill, J., Papanastasis, V. P., & Tsiourlis, G. M. (2007). Adaptation
of a grazing gradient concept to heterogeneous Mediterranean rangelands using cost
surface modelling. Ecological Modelling, 204(3), 387–398.
Roelofsen, H. D., Kooistra, L., van Bodegom, P. M., Verrelst, J., Krol, J., & Witte, J. (2014).
Mapping a priori defined plant associations using remotely sensed vegetation
characteristics. Remote Sensing of Environment, 140, 639–651.
Roelofsen, H. D., van Bodegom, P. M., Kooistra, L., & Witte, J. (2013). Trait estimation in
herbaceous plant assemblages from in situ canopy spectra. Remote Sensing, 5(12), 6323–
6345.
152
Roth, K. L., Dennison, P. E., & Roberts, D. (2012). Comparing endmember selection
techniques for accurate mapping of plant species and land cover using imaging
spectrometer data. Remote Sensing of Environment, 127, 139–152.
Runhaar, J., Jalink, M., Hunneman, H., Witte, J., & Hennekens, S. (2009). Ecologische vereisten
habitattypen. Nieuwegein, NL.
Runhaar, J., Landuyt, W. Van, & Groen, C. (2004). Herziening van de indeling in ecologische
soortengroepen voor Nederland en Vlaanderen. Gorteria, 30, 12–26.
Runhaar, J., Witte, J., & Verburg, P. (1997). Ground-water level, moisture supply, and
vegetation in the Netherlands. Wetlands, 17(4), 528–538.
Sanders, M., Dirkse, G., & Slim, P. (2004). Objectifying thematic, spatial and temporal
aspects of vegetation mapping for monitoring. Community Ecology, 5(1), 81–91.
Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified
least squares procedures. Analytical Chemistry, 36(8), 1627–1639.
Schaffers, A. P., & Sykora, K. V. (2000). Reliability of Ellenberg indicator values for moisture,
nitrogen and soil reaction: A comparison with field measurements. Journal of Vegetation
Science, 11(2), 225–244.
Schaminée, J., Hennekens, S., & Ozinga, W. (2012). The Dutch national vegetation database.
In J. Dengler (Ed.), Vegetation databases for the 21st century (Vol. 4, pp. 201–209). BEE,
Biocentre Klein Flottbek and Botanical Garden.
Schaminée, J., Stortelder, A., & Weeda, E. (1996). De Vegetatie van Nederland. Deel 3.
Plantengemeenschappen van grasslanden, zomen en droge heiden. Uppsala, SE: Opulus Press.
Schaminée, J., Stortelder, A., & Westhoff, V. (1995). De Vegetatie van Nederland. Deel 1.
inleiding tot de plantensociologie–grondslagen, methoden en toepassingen. Uppsala, SE:
Opulus Press.
Schaminée, J., Weeda, E., & Westhoff, V. (1995a). De Vegetatie van Nederland. Deel 2.
Plantengemeenschappen van wateren, moerassen en natte heiden. Uppsala, SE: Opulus Press.
Schaminée, J., Weeda, E., & Westhoff, V. (1995b). De Vegetatie van Nederland. Deel 4.
Plantengemeenschappen van de kust en van binnenlandse pioniermilieus. Uppsala, SE: Opulus
Press.
Scherrer, D., & Körner, C. (2011). Topographically controlled thermal-habitat differentiation
buffers alpine plant diversity against climate warming. Journal of Biogeography, 38(2),
406–416.
Schimper, A. F. W. (1898). Pflanzen-geographie auf physiologischer Grundlage (p. 876). G.
Fischer.
Schmidt, K., & Skidmore, A. (2003). Spectral discrimination of vegetation types in a coastal
wetland. Remote Sensing of Environment, 85(1), 92–108.
Schmidt, K., Skidmore, A., Kloosterman, E., Van Oosten, H., Kumar, L., & Janssen, J. (2004).
Mapping coastal vegetation using an expert system and hyperspectral imagery.
Photogrammetric Engineering and Remote Sensing, 70(6), 703–715.
153
Schmidtlein, S. (2005). Imaging spectroscopy as a tool for mapping Ellenberg indicator
values. Journal of Applied Ecology, 42(5), 966–974.
Schmidtlein, S., Feilhauer, H., & Bruelheide, H. (2011). Mapping plant strategy types using
remote sensing. Journal of Vegetation Science, 23(3), 395–405.
Schmidtlein, S., & Sassin, J. (2004). Mapping of continuous floristic gradients in grasslands
using hyperspectral imagery. Remote Sensing of Environment, 92(1), 126–138.
Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigment content and spectral
reflectance across a wide range of species, leaf structures and developmental stages.
Remote Sensing of Environment, 81(2), 337–354.
Skidmore, A. K., Ferwerda, J. G., Mutanga, O., Van Wieren, S. E., Peel, M., Grant, R. C.,
Prins, H. H. T., Balcik, F. B., & Venus, V. (2010). Forage quality of savannas—
simultaneously mapping foliar protein and polyphenols for trees and grass using
hyperspectral imagery. Remote Sensing of Environment, 114(1), 64–72.
Slim, P., Heuvelink, G., Kuipers, H., Dirkse, G., & van Dobben, H. (2005). Vegetatiemonitoring
en geostatistische vegetatiekartering duinvalleien Ameland-Oost. Monitoring effecten van
bodemdaling op Ameland-Oost. Wageningen, NL: Alterra.
Soudzilovskaia, N. (2013). Functional traits predict relationship between plant abundance
dynamic and long-term climate warming. Proceedings of the National Academy of Sciences,
110(45), 18180–18184.
Staatsbosbeheer. (2013). Jaarstukken 2012 (p. 74). Driebergen, the Netherlands.
Sterner, R. W., & Elser, J. (2002). Ecological stoichiometry: the biology of elements from molecules
to the biosphere (p. 584). Princeton University Press.
Steur, G., & Heijink, W. (1991). Bodemkaart van Nederland, schaal 1:50.000 Algemene begrippen
en indelingen (4th ed.). Wageningen, the Netherlands: Staring Centrum.
Stortelder, A., Schaminée, J., & Hommel, P. (1999). De vegetatie van Nederland. Deel 5.
Plantengemeenschappen van ruigten, struwelen en bossen. Uppsala, SE: Opulus Press.
Svoray, T., Perevolotsky, A., & Atkinson, P. (2013). Ecological sustainability in rangelands:
the contribution of remote sensing. International Journal of Remote Sensing, 1–27.
Swenson, N., & Weiser, M. (2010). Plant geography upon the basis of functional traits: an
example from eastern North American trees. Ecology, 91(8), 2234–2241.
Thomas, V., Treitz, P., Jelinski, D., Miller, J., Lafleur, P., & McCaughey, J. H. (2003). Image
classification of a northern peatland complex using spectral and plant community data.
Remote Sensing of Environment, 84(1), 83–99.
Thuiller, W., Araújo, M., & Lavorel, S. (2004). Do we need land-cover data to model species
distributions in Europe? Journal of Biogeography, 31(3), 353–361.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring
vegetation. Remote Sensing of Environment, 8(2), 127–150.
Tüxen, R. (1954). Pflanzengesellschaften und Grundwasser-Ganglinien. Angewandte
Pflanzensoziologie, 8, 64–97.
154
Underwood, E., Ustin, S. L., & DiPietro, D. (2003). Mapping nonnative plants using
hyperspectral imagery. Remote Sensing of Environment, 86(2), 150–161.
Ustin, S. L. (2013). Remote sensing of canopy chemistry. Proceedings of the National Academy
of Sciences, 110(3), 804–805.
Ustin, S. L., & Gamon, J. A. (2010). Remote sensing of plant functional types. New Phytologist,
186(4), 795–816.
Ustin, S. L., Schaepman, M. E., Gitelson, A., Jacquemoud, S., Asner, G., Gamon, J. A., &
Zarco-Tejada, P. J. (2009). Retrieval of foliar information about plant pigment systems
from high resolution spectroscopy. Remote Sensing of Environment, 113, S67–S77.
Van Bodegom, P. M., Douma, J., & Verheijen, L. (2013). A fully traits-based approach to
modeling global vegetation distribution. Proceedings of the National Academy of Sciences.
Van Raam, J., & Maier, E. (1993). Overzicht van de Nederlandse kranswieren. Gorteria, 18,
111–116.
Van Walsum, P., & Veldhuizen, A. (2011). Integration of models using shared state
variables: Implementation in the regional hydrologic modelling system SIMGRO.
Journal of Hydrology, 409(1–2), 363–370.
Vermulst, J., Kroon, T., & De Lange, W. (1998). Modelling the hydrology of the Netherlands
on a nation wide scale. In H. Wheater & C. Kirby (Eds.), Hydrology in a changing
environment (p. 710). John Wiley and Sons Ltd.
Verrelst, J., Alonso, L., Camps-Valls, G., Delegido, J., & Moreno, J. (2012). Retrieval of
vegetation biophysical parameters using Gaussian process techniques. IEEE
Transactions on Geoscience and Remote Sensing, 50(5), 1832–1843.
Verrelst, J., Geerling, G. W., Sykora, K. V, & Clevers, J. (2009). Mapping of aggregated
floodplain plant communities using image fusion of CASI and LiDAR data. International
Journal of Applied Earth Observation and Geoinformation, 11(1), 83–94.
Verrelst, J., Muñoz, J., Alonso, L., Delegido, J., Rivera, J. P., Camps-Valls, G., & Moreno, J.
(2012). Machine learning regression algorithms for biophysical parameter retrieval:
Opportunities for Sentinel-2 and -3. Remote Sensing of Environment, 118, 127–139.
Verrelst, J., Romijn, E., & Kooistra, L. (2012). Mapping Vegetation Density in a
Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data.
Remote Sensing, 4(9), 2866–2889.
Vervoort, R. W., & Van der Zee, S. E. (2008). Simulating the effect of capillary flux on the soil
water balance in a stochastic ecohydrological framework. Water Resources Research,
44(8).
Violle, C., Navas, M.-L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., & Garnier, E. (2007).
Let the concept of trait be functional! Oikos, 116(5), 882–892.
Von Asmuth, J. R., Maas, K., Knotters, M., Bierkens, M. F., Bakker, M., Olsthoorn, T. N.,
Cirkel, D., Leunk, I., Schaars, F., & Von Asmuth, D. C. (2012). Software for
hydrogeologic time series analysis, interfacing data with physical insight. Environmental
Modelling & Software, 38, 178–190.
155
Wallace, O. C., Qi, J., Heilma, P., & Marsett, R. C. (2003). Remote sensing for cover change
assessment in southeast Arizona. Journal of Range Management, 56, 402–409.
Wamelink, G., Joosten, V., Van Dobben, H., & Berendse, F. (2002). Validity of Ellenberg
indicator values judged from physico-chemical field measurements. Journal of Vegetation
Science, 13(2), 269–278.
Wamelink, G., van Dobben, H., & Berendse, F. (2009). Vegetation succession as affected by
decreasing nitrogen deposition, soil characteristics and site management: A modelling
approach. Forest Ecology and Management, 258(8), 1762–1773.
Weber, H. E., Moravec, J., & Theurillat, J. P. (2000). International code of phytosociological
nomenclature. Journal of Vegetation Science, 11(5), 739–768.
Welles, J. M., & Norman, J. M. (1991). Instrument for indirect measurement of canopy
architecture. Agronomy Journal, 83(5), 818–825.
Westhoff, V., & van Oosten, M. F. (1991). De plantengroei van de Waddeneilanden (1st ed., p.
416). Utrecht, NL: Koninklijke Nederlanse Natuurhistorische Vereniging.
Westhoff, V., & Westra, R. (1981). Wilde planten. Utrecht, NL: Vereniging tot Behoud van
Natuurmonumenten in Nederland.
Wigneron, J.-P., Kerr, Y. H., Waldteufel, P., Saleh, K., Escorihuela, M.-J., Richaume, P.,
Ferrazzoli, P., De Rosnay, P., Gurney, R., & Calvet, J.-C. (2007). L-band Microwave
Emission of the Biosphere (L-MEB) Model: Description and calibration against
experimental data sets over crop fields. Remote Sensing of Environment, 107(4), 639–655.
Witte, J., Bartholomeus, R., Douma, J., Runhaar, J., & van Bodegom, P. M. (2010). De
vegetatiemodule van Probe-2. Nieuwegein, NL.
Witte, J., Bartholomeus, R., van Bodegom, P. M., Ek, R. Van, Fujita, Y., & Runhaar, J. (n.d.). A
probabilistic eco-hydrological model to predict the effects of climate change on natural
vegetation at a regional scale. Landscape Ecology.
Witte, J., De Haan, M., Raterman, B., & Aggenbach, C. (2006). PROBE - Versie 1: effecten van
grondwaterbeheer, atmosferische depositie, maaien en plaggen.
Witte, J., Meuleman, A. F., van der Schaaf, S., & Raterman, B. (2004). Eco-hydrology and
biodiversity. In R. Feddes, G. de Rooij, & J. van Dam (Eds.), Unsaturated zone modelling:
Progress, challenges and applications (pp. 301–329). Springer.
Witte, J., Runhaar, J., & Ek, R. Van. (2008). Ecohydrological modelling for managing scarce
water resources in a groundwaterdominated temperate system. In D. Harper, J.
Zalewski, M., E., & N. Pacini (Eds.), Ecohydrology: Processes, Models and Case Studies (pp.
88–111). CABI Publishing, Oxfordshire, UK.
Witte, J., Runhaar, J., Ek, R. Van, van der Hoek, D., Bartholomeus, R., Batelaan, O., van
Bodegom, P. M., Wassen, M. J., & van der Zee, S. E. (2012). An ecohydrological sketch of
climate change impacts on water and natural ecosystems for the Netherlands: bridging
the gap between science and society. Hydrology and Earth System Sciences, 9(5), 6311–
6344.
156
Witte, J., Wójcik, R., Torfs, P., De Haan, M., & Hennekens, S. (2007). Bayesian classification
of vegetation types with Gaussian mixture density fitting to indicator values. Journal of
Vegetation Science, 18(4), 605–612.
Wójcik, R., & Torfs, P. (2003). PARDENS: an experimental program for Parzen density fitting.
WUR reports. Wageningen: Wageningen University. Environmental sciences, subdepartment water resources.
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics.
Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.
Wright, I., Reich, P., Westoby, M., Ackerly, D., Baruch, Z., Bongers, F., Cavender-Bares, J.,
Chapin, T., Cornelissen, J., & Diemer, M. (2004). The worldwide leaf economics
spectrum. Nature, 428(6985), 821–827.
Xiao, Q., Ustin, S. L., & McPherson, E. G. (2004). Using AVIRIS data and multiple-masking
techniques to map urban forest tree species. International Journal of Remote Sensing,
25(24), 5637–5654.
Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based
detailed vegetation classification with airborne high spatial resolution remote sensing
imagery. Photogrammetric Engineering and Remote Sensing, 72(7), 799.
Zelený, D., & Schaffers, A. P. (2012). Too good to be true: pitfalls of using mean Ellenberg
indicator values in vegetation analyses. Journal of Vegetation Science, 23(3), 419–431.
157