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Miscellanea Geographica

Regional Studies on Development

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CiteScore 2016: 0.40

SCImago Journal Rank (SJR) 2016: 0.227
Source Normalized Impact per Paper (SNIP) 2016: 0.404

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2084-6118
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Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park

Jan Jelének / Lucie Kupková / Bogdan Zagajewski / Stanislav Březina / Adrian Ochytra
  • College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences University of Warsaw Department of Geoinformatics and Remote Sensing Faculty of Geography and Regional Studies University of Warsaw
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/ Adriana Marcinkowska
Published Online: 2014-06-17 | DOI: https://doi.org/10.2478/mgrsd-2014-0014

Abstract

The paper deals with the evaluation of mountain meadow vegetation condition using in-situ measurements of the fraction of Accumulated Photosynthetically Active Radiation (fAPAR) and Leaf Area Index (LAI). The study analyses the relationship between these parameters and spectral properties of meadow vegetation and selected invasive species with the goal of finding out vegetation indices for the detection of fAPAR and LAI. The developed vegetation indices were applied on hyperspectral data from an APEX (Airborne Prism Experiment) sensor in the area of interest in the Krkonoše National Park. The results of index development on the level of the field data were quite good. The maximal sensitivity expressed by the coefficient of determination for LAI was R2 = 0.56 and R2 = 0.79 for fAPAR. However, the sensitivity of all the indices developed at the image level was quite low. The output values of in-situ measurements confirmed the condition of invasive species as better than that of the valuable original meadow vegetation, which is a serious problem for national park management.

Keywords: LAI; fAPAR; hyperspectral data; meadow vegetation; invasive species; the Krkonoše National Park

References

  • 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.Google Scholar

  • ČÚZK 2013, Geoportal ČÚZK. Přístup k mapovým produktům a službám resortu. Available from: <http://geoportal.cuzk.cz>. [9 September 2013] Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Web of ScienceGoogle Scholar

  • 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.Web of ScienceGoogle Scholar

  • 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.Google Scholar

  • 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.Web of ScienceGoogle Scholar

  • Myneni, RB & Williams, DL 1994, ‘On the relationship between FAPAR and NDVI’, Remote Sensing of Environment, vol. 49, pp. 200-211.Google Scholar

  • 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.Web of ScienceGoogle Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Web of ScienceGoogle Scholar

  • 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.Web of ScienceGoogle Scholar

  • 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.Google Scholar

  • 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.Web of ScienceGoogle Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Web of ScienceGoogle Scholar

  • Zvára, K 2003, Biostatistika, Karolinum, Praha. Google Scholar

About the article

Received: 2013-10-10

Accepted: 2014-03-01

Published Online: 2014-06-17

Published in Print: 2014-06-01


Citation Information: Miscellanea Geographica, ISSN (Online) 2084-6118, DOI: https://doi.org/10.2478/mgrsd-2014-0014.

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© by Jan Jelének. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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