Abstract
Microwave radiometric tools of remote sensing are used to diagnose soil-plant formations, snow and ice covers, ocean surfaces, atmospheres and various natural-technogenic objects. In this light, it is necessary to solve the inverse radiometric tasks to estimate the parameters of the object to be diagnosed. In this case, knowledge of the attenuation characteristics of the electromagnetic waves undergoing propagation in the vegetation cover is important for solving the inverse tasks. It is also important for reliable radio communication. The attenuation of the electromagnetic waves of microwave-scale in the vegetation layer is a key factor in the study of radiation and the scattering of radio waves in the vegetation layers. In addition, data on the depending attenuation parameters on the frequency, the vegetation biomass and water content, as well as the structural characteristics of vegetation cover provide the basis for reconstructing the environment where radio waves are propagated.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Achard F, Hansen MC (eds) (2012) Global forest monitoring from Earth observation. CRC Press, London
Aires F, Prigent C, Rossow WB (2005) Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: 2. Global statistical relationships. J Geophys Res 110(D11103):1–14
Cernicharo J, Verger A, Camacho F (2013) Empirical and physical estimation of canopy water content from CHRIS/PROBA data. Remote Sens 5:5265–5284
Chiu T, Sarabandi K (2000) Electromagnetic scattering from short branching vegetation. IEEE Trans Geosci Remote Sens 38(2):911–925
Chukhlantsev AA (2006) Microwave radiometry of vegetation canopies. Springer, Berlin
Chukhlantsev AA, Shutko AM, Golovachev SP (2003) Attenuation of electromagnetic waves by vegetation canopies in the 100–1000 MHz frequency band (ISTC/IRE Technical report #2059-1)
Disney M, Lewis P, Saich P (2006) 3D modeling of forest canopy structure for remote sensing simulations in the optical and microwave domains. Remote Sens Environ 100:114–132
Duke S (2013) Seasons of the boreal forest biome. Rourke Educational Media, Vero Beach
Ferrazzoli P (1996) Passive microwave remote sensing of forests: a model investigation. IEEE Trans Geosci Remote Sens 34(2):433–443
Ferrazzoli P, Paloscia S, Pampaloni P, Schiavon G, Solimini D, Coppo P (1992) Sensitivity of microwave measurements to vegetation biomass and soil moisture content: a case study. IEEE Trans Geosci Remote Sens 30(4):750–756
Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Peńuelas J, Valentini R (1995) Relationships between NDVI, canopy structure, and photosynthesis in tree Californian vegetation types. Ecol Appl 5(1):28–41
Ghoraishi M, Takada J-I, Imai T (2013) Chapter 6: Radio wave propagation through vegetation. In: Zheng Y (ed) Wave propagation: theories and applications. InTech
Guha A, Jacobs JM, Jackson TJ, Cosh MN, Hsu E-C, Judge J (2003) Soil moisture mapping using ESTAR under dry conditions from the southern Greet Plains experiment (SGP99). IEEE Transaction on Geoscience and Remote Sensing 41(10):2392–2397
Hansen MC, DeFries RS, Townshend JRG, Sohlberg R, Dimiceli C, Carroll M (2002) Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sens Environ 83(1–2):303–319
Ishimaru A (2017) Electromagnetic wave propagation, radiation, and scattering: from fundamentals to applications. Wiley, Washington, DC
Johannesson P (2001) Wave propagation through vegetation at 3.1 GHz and 5.8 GHz. Lund Institute of Technology, Lund
Karam MA, Fung AK, Lang RH, Chauhan NS (1992) A microwave scattering model for layered vegetation. IEEE Trans Geosci Remote Sens 30(4):767–784
Keane RE, Reinhardt ED, Scott J, Gray K, Reardon J (2005) Estimating forest canopy bulk density using six indirect methods. Can J For Res 35:724–739
Kimmins JP (2004) Forest ecology: a foundation for sustainable forest management and environmental ethics in forestry. Prentice Hall, Upper Saddle River
Kondratyev KYA, Krapivin VF, Phillips GW (2003a) Arctic Basin pollution dynamics. In: Bobylev LP, Kondratyev KY, Johannessen OM (eds) Arctic environment variability in the context of global change. Springer/Praxis, Chichester, pp 309–362
Kondratyev KYA, Krapivin VF, Varotsos CA (2003b) Global carbon cycle and climate change. Springer/PRAXIS, Chichester
Krapivin VF, Shutko AM, Chukhlantsev AA, Golovachev SP, Phillips GW (2006) GIMS-based method vegetation microwave monitoring. Environ Model Softw 21:330–345
Krapivin VF, Varotsos CA, Soldatov VY (2015) New Ecoinformatics tools in environmental science: applications and decision-making. Springer, London, U.K., 903 pp
Krapivin VF, Varotsos CA, Marechek SV (2018) The dependence of the soil microwave attenuation on frequency and water content in different types of vegetation: an empirical model. Water Air Soil Pollut 229(110):1–10
Kruopis N, Praks J, Arslan AN, Alasalmi H, Koskinen JT, Hallikainen MT (1999) Passive microwave measurements of snow-covered forest areas in EMAC’95. IEEE Trans Geosci Remote Sens 37:2699–2705
Lang MW, Purkis S, Klemas VV, Tiner RW (2015) Chapter 25: Promising developments and future challenges for remote sensing of wetlands. In: Tiner RW, Lang MW, Klemas VV (eds) Remote sensing of wetlands: applications and advances. CRC Press, Boca Raton, pp 533–544
Lewis P (2007) Canopy modeling as a tool in remote sensing research. In: Vos J, Marcelis LFM, de Visser PHB, Struik PC, Evers JB (eds) Functional structural plant modeling in crop production. Springer, Dordrecht, pp 219–229
Liang S (2004) Quantitative remote sensing of land surfaces. Wileys, Hoboken
Liang P, Moghaddam M, Pierce LE, Lucas RM (2005) Radar backscattering model for multilayer mixed-species forests. IEEE Trans Geosci Remote Sens 43(11):2612–2626
Meng YS, Lee YH (2010) Investigations of foliage effect on modern wireless communication systems: a review. Prog Electromagn Res 105:313–332
Mironov VL, Yakubov VP, Telpukhovskiy ED, Novil SN, Chukhlantsev AA (2005) Spectral study of microwave attenuation in a larch forest stand for oblique wave incidence. In: Proceedings of the Geoscience and Remote Sensing Symposium, 29–29 July 2005, Seoul, South Korea, pp 3204–3207
Pampaloni P, Paloscia S (1986) Microwave emission and plant water content: a comparison between field measurements and theory. IEEE Trans Geosci Remote Sens 24:900–905
Pretzsch H (2014) Canopy space filling and tree canopy morphology in mixed-species stands compared with monocultures. For Ecol Manag 327:251–264
Ranson KJ, Rock BN, Salas WA, Smith K, Williams DL (1992) Analysis of the dielectric properties of trunl wood in dominant conifer species from New England and Siberia. In: Proceedings of the international symposium on Geoscience and Remote Sensing, 26–29 May 1992, Houston, TX, USA, pp 1283–1285
Rogers NC, Seville A, Richter J, Ndzi D, Savage N, Caldeirinha RFS, Shukla AK, Al-Nuaimi MO, Craig K, Vilar E, Austin J (2002) A generic model of 1–60 GHz radio propagation through vegetation, Final Report. QinetiQ for the UK Radiocommunications Agency, Malvern Technology Centre, Malvern
Saleh K, Porté A, Guyon D, Ferrazzoli P, Wigneron J-P (2005) A forest geometric description of a maritime pine forest suitable for discrete microwave models. IEEE Trans Geosci Remote Sens 43(9):2024–2035
Savage N, Ndzi D, Seville A, Vilar E, Austin J (2003) Radio wave propagation through vegetation: factors influencing signal attenuation. Radio Sci 38(5):1088
Scaggs AK (ed) (2007) New research on forest ecology. Nova Science Publisher, New York
Schmugge TJ, Jackson TJ (1992) A dielectric model of the vegetation effects on the microwave emission from soils. IEEE Trans Geosci Remote Sens 30(4):757–760
Shabanov NV, Huang D, Yang W, Tan B, Yu K, Myneni RB, Ahl DE, Gower ST, Huete AR, Aragão LEOC, Shimabukuro YE (2005) Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests. IEEE Trans Geosci Remote Sens 43(8):1855–1865
Shugart HH, Leemans R, Bonan GB (1992) A systems analysis of the global boreal forest. Cambridge University Press, Cambridge
Sims DA, Gamon JA (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chrolophyll absorption features. Remote Sens Environ 84(4):526–537
Smith WK, Hinckley TM, Roy J (eds) (1994) Ecophsiology of coniferous forests. Academic, New York
Van de Griend AA, Wigneron JP (2004) The b-factor as a function of frequency and canopy type at H-polarization. IEEE Trans Geosci Remote Sens 42:786–794
Varotsos CA, Nitu C, Krapivin VF (2018) Global ecoinformatics: theory and applications. Matrix ROM, Bucharest
Xu D, Guo X (2014) Compare NDVI extracted from Landsat 8 imagery with that from Landsat 7 imagery. Am J Remote Sens 2(2):10–14
Yang H, Yang X, Heskel M, Sun A, Tang J (2017) Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest. Sci Rep 7:1267–1276
Zhan X, Sohlberg RA, Townshend JRG, DiMiceli C, Carroll ML, Eastman JC, Hansen MC, DeFries RS (2002) Detection of land cover changes using MODIS 250 m data. Remote Sens Environ 83(1–2):336–350
Zhu Z, Guo W (2017) Frequency, moisture content, and temperature dependent dielectric properties of potato starch related to drying with radio-frequency/microwave energy. Sci Rep 7. https://doi.org/10.1038/s41598-017-09197-y
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Varotsos, C.A., Krapivin, V.F. (2020). Vegetation Screening Effect in Remote Sensing Monitoring. In: Microwave Remote Sensing Tools in Environmental Science . Springer, Cham. https://doi.org/10.1007/978-3-030-45767-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-45767-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45766-2
Online ISBN: 978-3-030-45767-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)