1 Department of Applied Geoinformatics and Cartography Faculty of Science Charles University in Prague
2 Department of Geoinformatics and Remote Sensing Faculty of Geography and Regional Studies University of Warsaw
3 The Krkonoše Mountains National Park administration
4 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
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.
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.
ČÚZK 2013, Geoportal ČÚZK. Přístup k mapovým produktům a službám resortu. Available from: . [9 September 2013]
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Myneni, RB & Williams, DL 1994, ‘On the relationship between FAPAR and NDVI’, Remote Sensing of Environment, vol. 49, pp. 200-211.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.