PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Modelowanie charakterystyk spektralnych heterogenicznych zbiorowisk trawiastych przy użyciu modelu transferu promieniowania

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
EN
Simulating spectrum for heterogeneous meadows using a Radiative Transfer Model
Języki publikacji
PL
Abstrakty
EN
Meadows are important ecosystems and should be protected. Also, in Poland organic agriculture and farming, where crops from meadows are used, is getting more popular. That is why meadows monitoring and predicting crops is important issue. Much information can be calculated from spectrum of plants and that is why remote sensing data are very useful tool. Two approaches are used to calculate biophysical variables: statistical and modelling. In statistical, values from field measurements have to be compared with images. In modelling, radiative transfer models are used. RTM are physical models based on the fundamental equation of radiative transfer. After all necessary adjustments, models can give the description of the canopy with fewer field measurements. In this paper model on leaf level was chosen. PROSPECT uses only five input variables: chlorophyll and carotenoid content, water content, dry matter and leaf structure parameter. Model is normally used to homogeneous canopy, like corn. In this paper, PROSPECT was used to simulate spectrum for heterogenic meadows using field measurements. Biophysical variables were collected during field measurements in the Bystrzanka catchment in the Low Beskid Mountains. In the same time more than 10 samples of spectrum were collected using ASD FieldSpec 3 FR and then averaged. The minimum size of polygon was 100m2. All input parameters for every polygon were included into the model and spectrum was modelled. Then spectrum was compared with measured samples of each polygon. In the end the vegetation indices were calculated using two kinds of spectrum and compared. All used vegetation indices are describing plant condition or crop monitoring: Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Photochemical Reflectance Index, Normalized Difference Nitrogen Index, Normalized Difference Lignin Index, Cellulose Absorption Index, Carotenoid Reflectance Index, Water Band Index and Moisture Stress Index. Researches shows, that it is possible to simulate spectra for heterogeneous meadows using PROSPECT. The average RMSE value for all polygons was 0,0346, which mean the spectra are well modelled. The biggest mistake was for near infrared range, where is the strongest influence of dry matter content. The differences between measured and modelled spectrum were also noticed on the part of visible light – 400-500nm. For most calculated vegetation indices values were similar for both kinds of spectra. Values of NDVI,WBI and NDLI were very close. The biggest differences were noticed form PRI and CRI.
Rocznik
Tom
Strony
29--42
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr.
Twórcy
  • Wydział Geografii i Studiów Regionalnych Uniwersytetu Warszawskiego
Bibliografia
  • 1. Blackburn G. A., Ferwerda J. G., 2008, Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis, Remote Sensing of Environment, nr 112, str. 1614-1632
  • 2. Ceccato P., Flasse S., Tarantola S., Jacquemoud S., Grégorie J-M., 2001, Detecing vegetation leaf water content using refl ectance on the optical domain, Remote Sensing of Environment, nr 77, str. 22-33.
  • 3. Dąbrowska-Zielińska K., Budzyńska M., Lewiński S., Hościło A., Bojanowski J., 2009, Application of remote and in situ information to the management of wetlands in Poland. Journal of Environmental Management, nr 90, str. 2261–2269
  • 4. Darvishadeh R., Skidmore A., Schlerf M., Atzberger C., 2008, Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in heterogeneous grassland, Remote Sensing of Environment, nr 112, str. 2592-2604
  • 5. Duke C., Guérif M., 1998, Crop Reflectance Estimate Errors from the SAIL Model Due to Spatial and Temporal Variability of Canopy and Soil Characteristics, Remote Sensing of Environment, nr 66, str. 286-297
  • 6. Feret J., Frençois C., Asner G. P., Gitelson A. A., Martin R. E., Bidel L. P. R., Ustin S., le Maire G., Jacquemoud S., 2008, PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments, Remote Sensing of Environment, nr 112, str. 3030-3043
  • 7. Gamon J. A., Peñuelas J., Field C. B., 1992, A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency, Remote Sensing of Environment, nr 41, str. 35−44
  • 8. Gitelson A. A., Zur Y., Chivkunova O. B., Merzlyak M. N., 2002, Assessing carotenoid content in plant leaves with reflectance spectroscopy, Photochemistry and Photobiology, nr 75, str. 272−281
  • 9. Gitelson A., Merzlyak M. N., 1994, Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation, Journal of Plant Physiology, nr 143, str. 286−292
  • 10. Haboudane D., Miller J. R., Tremblay N., Zarco-Tejada P., Dextraze L., 2002, Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sensing of Environment, nr 81, str. 416-426
  • 11. Jacquemoud S., Bacour C., Poilvé H., Frangi J. P., 2000, Comparison of Four Radiative Transfer Models to Simulate Plant Canopies Refl ectance: Direct and Inverse Mode, Remote Sensing of Environment, nr 74, str. 471-481
  • 12. Jacquemoud S., Baret F., 1990, PROSPECT: A Model of Leaf Optical properties Spectra, Remote Sensing of Environment, nr 34, str. 75-91
  • 13. Jacquemoud S., Ustin S. L., Verdebout J., Schmuck G., Anderoli G., Hosgood B., 1996, Estimating Leaf Biochemistry Using the PROSPECT Leaf Optical Properties Model, Remote Sensing of Environment, nr 56, str. 194-202
  • 14. Jacquemoud S., Verhoef W., Baret F., Bacour C., Zarco-Tejada P., Asner G., Frençois C., Ustin S., 2009, PROSPEC +SAIL models: A review of use for vegetation characterization, Remote Sensing of Environment, nr 113, str. 556-566
  • 15. Jarocińska A., Zagajewski B., 2009, Remote sensing tools for analysis of vegetation condition in extensively used agricultural areas. W: Ben-Dor E. [red.] Proceedings of the 6th EARSeL Imaging Spectroscopy SIG Workshop ‘Imaging Spectroscopy: Innovative Tool for Scientific and Commercial Environmental Applications’, ISPRS technical commission VII. Tel Aviv, Israel, March 16-18, 2009. Tel Aviv, str. 1-6
  • 16. Jensen J. R., 1983, Biophysical Remote sensing – Review Article, Annals of the Associations of American Geographers, t. 73, nr 1, 111-132
  • 17. Koetz B., Sun G., Morsdorf F., Ranon K. J., Kneubühler M., Itten K., Allgöwer B., 2007, Fusion of imaging spectrometer and LIDAR data over radiative transfer models for forest canopy characterization, Remote Sensing of Environment, nr 106, str. 449-459
  • 18. Kumar L., Schmidt K., Dury S., Skidmore A., 2006, Imaging spectrometry and vegetation science, W: van der Meer F. D., de Jong S. M. [red.], Imaging Spectrometry. Basic principles and Prospective Applications, wyd. Springer, Holandia, str. 111-155
  • 19. Nagler P. L., Inoueb Y., Glenn E. P., Russ A. L., Daughtry C.S.T., 2003, Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes, Remote Sensing of Environment, nr 87, str. 10–325
  • 20. Peñuelas J., Pinol J., Ogaya R., Filella I., 1997, Estimation of plant water concentration by the reflectance water index WI (R900/R970), International Journal of Remote Sensing, nr 18, str. 2863– 2868
  • 21. Rock B. N., Williams D. L., Vogehnann J. E., 1985, Field and airborne spectral characterization of suspected acid deposition damage in red spruce (Picea rubens) from Vermont. Machine Processing of Remotely Sensed Data Symposium, Purdue University, Lafayette, IN, str. 71-81
  • 22. Rouse, J.W., Jr., R.H. Haas, J.A. Schell, and D.W. Deering, 1973, Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, Prog. Rep. RSC 1978-1, Remote Sensing Center, Texas A&M Univ., College Station, nr. E73-106393, 93. (NTIS No. E73-106393
  • 23. Serrano L., Peñuelas J., Ustin S. L., 2002, Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals, Remote Sensing of Environment, nr 81, str. 355– 364
  • 24. Tretyn A., 2007, Podstawy strukturalno-funkcjonalne komórki roślinnej; W: Kopcewicz J., Lewak S., [red.], Fizjologia roślin, Wydawnictwo Naukowe PWN, Warszawa, str. 22-87
  • 25. Verhoef W., Bach H., 2003a, Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models, Remote Sensing of Environment, nr 87, str. 23-41
  • 26. Verhoef W., Bach H., 2003b, Remote sensing data assimilation using coupled radiative transfer models, Physics and Chemistry of the Earth, nr 28, str. 3–13
  • 27. Yilmaz M. T., Hunt Jr R., Jackson T. J., 2008, Remote sensing of vegetation water content from equivalent water thickness using satellite imagery, Remote Sensing of Environment, nr 112, str. 2514–2522
  • 28. Zarco-Tejada P. J., Rueda C. A., Ustin S. L., 2003, Water content estimation in vegetation with MODIS reflectance data and model inversion methods, Remote Sensing of Environment, nr 85, str. 109-124.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-64a7a884-f157-40d6-9160-1d2ffef56a72
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.