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Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park Cover

Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park

Miscellanea Geographica
Volume 18 (2014): Issue 2 (June 2014)
By: Jan Jelének,  Lucie Kupková,  Bogdan Zagajewski,  Stanislav Březina,  Adrian Ochytra and  Adriana Marcinkowska  
Open Access
|Jun 2014

References

  1. Brown, LA, Chen, JM, Leblanc, SG & Cihlar, J 2000, ‘Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis’, Remote Sensing of Environment, vol. 71, no. 1, pp. 16-25.10.1016/S0034-4257(99)00035-8
    Search in Google Scholar
  2. ČÚZK 2013, Geoportal ČÚZK. Přístup k mapovým produktům a službám resortu. Available from: <http://geoportal.cuzk.cz>. [9 September 2013]
    Search in Google Scholar
  3. 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, vol. 63, no. 4, pp. 409-426.10.1016/j.isprsjprs.2008.01.001
    Search in Google Scholar
  4. Darvishzadeh, R, Skidmore, A, Schlerf, M & Atzberger, C 2008, ‘Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland’, Remote Sensing of Environment, vol. 112, no. 5, pp. 2592-2604.10.1016/j.rse.2007.12.003
    Search in Google Scholar
  5. Fensholt, R, Sandholt, I & Rasmussen, MS 2004, ‘Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements’, Remote Sensing of Environment, vol. 91, no. 3-4, pp. 490-507.10.1016/j.rse.2004.04.009
    Search in Google Scholar
  6. Fernandes, R, Butson, C, Leblanc, S & Latifovic, R 2003, ‘Landsat-5 TM and Landsat-7 ETM based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data’, Canadian Journal of Remote Sensing, vol. 29, no. 2, pp. 241-258.10.5589/m02-092
    Search in Google Scholar
  7. Gong, P, Pu, R, Biging, GS & Larrieu, MR 2003, ‘Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data’, IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6, pp. 1355-1362.10.1109/TGRS.2003.812910
    Search in Google Scholar
  8. Gonsamo, A & Pellikka, P 2012, ‘The sensitivity based estimation of leaf area index from spectral vegetation indices’, ISPRS journal of photogrammetry and remote sensing: official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), vol. 67, no. 4, pp. 15-25.10.1016/j.isprsjprs.2012.03.009
    Search in Google Scholar
  9. Haboudane, D 2004, ‘Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture’, Remote Sensing of Environment, vol. 90, no. 3, pp. 337-352.10.1016/j.rse.2003.12.013
    Search in Google Scholar
  10. Klimek, S, Richtergenkemmermann, A, Hofmann, M & Isselstein, J 2007, ‘Plant species richness and composition in managed grasslands: The relative importance of field management and environmental factors’, Biological Conservation, vol. 134, no. 4, pp. 559-570.10.1016/j.biocon.2006.09.007
    Search in Google Scholar
  11. Malenovský, Z, Homolová, L, Zurita-Milla, R, Lukeš, P, Kaplan, V, Hanuš, J, Gastellu-Etchegorry, J.-P. & Schaepman, ME 2013, ‘Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer’, Remote Sensing of Environment, vol. 131, pp. 85-102.10.1016/j.rse.2012.12.015
    Search in Google Scholar
  12. Mutanga, O, Skidmore, AK & Prins, HHT 2004, ‘Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features’, Remote Sensing of Environment, vol. 89, no. 3, pp. 393-408.10.1016/j.rse.2003.11.001
    Search in Google Scholar
  13. Mutanga, O & Skidmore, AK 2007, ‘Red edge shift and biochemical content in grass canopies’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 62, no. 1, pp. 34-42.10.1016/j.isprsjprs.2007.02.001
    Search in Google Scholar
  14. Myneni, RB & Williams, DL 1994, ‘On the relationship between FAPAR and NDVI’, Remote Sensing of Environment, vol. 49, pp. 200-211.10.1016/0034-4257(94)90016-7
    Search in Google Scholar
  15. Ramoelo, A, Skidmore, AK, Schlerf, M, Mathieu, R & Heitkönig, IMA 2011, ‘Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 4, pp. 408-417.10.1016/j.isprsjprs.2011.01.008
    Search in Google Scholar
  16. Roberts, DA, Roth, KL & Perroy, RKL 2012, ‘Hyperspectral Vegetation Indices’ in Hyperspectral remote sensing of vegetation, eds PS Thenkabail, JG Lyon & A Huete, CRC Press, Boca Raton, pp. 309-327.
    Search in Google Scholar
  17. Sampson, PH, Zarco-Tejada, PJ, Mohammed, GH, Miller, JR & Noland, TL 2003, ‘Hyperspectral remote sensing of forest condition: Estimating chlorophyll content in tolerant hardwoods’, Forest Science, vol. 49, no. 3, pp. 381-391.
    Search in Google Scholar
  18. Si, Y, Schlerf, M, Zurita-Milla, R, Skidmore, AK & Wang, T 2012, ‘Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model’, Remote Sensing of Environment, vol. 121, pp. 415-425.10.1016/j.rse.2012.02.011
    Search in Google Scholar
  19. Skidmore, AK, Ferwerda, JG, Mutanga, O, van Wieren, SE, Peel, M, Grant, RC, Prins, HHT, Balcik, FB & Venus, V 2010, ‘Forage quality of savannas Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery’, Remote Sensing of Environment, vol. 114, no. 1, pp. 64-72.10.1016/j.rse.2009.08.010
    Search in Google Scholar
  20. Thenkabail, PS, Smith, RB & De Pauw, E 2000, ‘Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics’, Remote Sensing of Environment, vol. 71, no. 2, pp. 158-182.10.1016/S0034-4257(99)00067-X
    Search in Google Scholar
  21. Tian, YC, Yao, X, Yang, J, Cao, WX, Hannaway, DB & Zhu, Y 2011, ‘Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance’, Field Crops Research, vol. 120, no. 2, pp. 299-310.10.1016/j.fcr.2010.11.002
    Search in Google Scholar
  22. Wang, F, Huang, J, Tang Y & Wang, X 2007, ‘New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice’, Rice Science, vol. 14, no. 3, pp. 195-203.10.1016/S1672-6308(07)60027-4
    Search in Google Scholar
  23. Zagajewski, B & Jarocinska, A 2009, ‘Analysis of plant condition of the Bystrzanka catchment’, in Proceedings of the 28th EARSeL Symposium, IOS Press, Millpress Science Publishers, pp. 498-504.
    Search in Google Scholar
  24. Zarco-Tejada, PJ, Miller, JR, Noland, TL, Mohammed, GH & Sampson, PH 2001, ‘Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data’, IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 7, pp. 1491-1507.10.1109/36.934080
    Search in Google Scholar
  25. Zheng, G & Moskal, LM 2009, ‘Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors’, Sensors, vol. 9, no. 4, pp. 2719-2745.10.3390/s90402719334879222574042
    Search in Google Scholar
  26. Zvára, K 2003, Biostatistika, Karolinum, Praha.
    Search in Google Scholar
DOI: https://doi.org/10.2478/mgrsd-2014-0014 | Journal eISSN: 2084-6118 | Journal ISSN: 0867-6046
Journal RSS Feed
Language: English
Page range: 15 - 22
Submitted on: Oct 10, 2013
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Accepted on: Mar 1, 2014
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Published on: Jun 17, 2014
Published by: Faculty of Geography and Regional Studies, University of Warsaw
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
LAI,
fAPAR,
hyperspectral data,
meadow vegetation,
invasive species,
the Krkonoše National Park
Related subjects:
Geosciences,
Geography,
Geosciences, other

© 2014 Jan Jelének, Lucie Kupková, Bogdan Zagajewski, Stanislav Březina, Adrian Ochytra, Adriana Marcinkowska, published by Faculty of Geography and Regional Studies, University of Warsaw
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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