Skip to main content

Vegetation Screening Effect in Remote Sensing Monitoring

  • Chapter
  • First Online:
  • 391 Accesses

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   189.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Achard F, Hansen MC (eds) (2012) Global forest monitoring from Earth observation. CRC Press, London

    Google Scholar 

  • 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

    Google Scholar 

  • Cernicharo J, Verger A, Camacho F (2013) Empirical and physical estimation of canopy water content from CHRIS/PROBA data. Remote Sens 5:5265–5284

    Article  Google Scholar 

  • Chiu T, Sarabandi K (2000) Electromagnetic scattering from short branching vegetation. IEEE Trans Geosci Remote Sens 38(2):911–925

    Article  Google Scholar 

  • Chukhlantsev AA (2006) Microwave radiometry of vegetation canopies. Springer, Berlin

    Google Scholar 

  • 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)

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Duke S (2013) Seasons of the boreal forest biome. Rourke Educational Media, Vero Beach

    Google Scholar 

  • Ferrazzoli P (1996) Passive microwave remote sensing of forests: a model investigation. IEEE Trans Geosci Remote Sens 34(2):433–443

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ishimaru A (2017) Electromagnetic wave propagation, radiation, and scattering: from fundamentals to applications. Wiley, Washington, DC

    Book  Google Scholar 

  • Johannesson P (2001) Wave propagation through vegetation at 3.1 GHz and 5.8 GHz. Lund Institute of Technology, Lund

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Kimmins JP (2004) Forest ecology: a foundation for sustainable forest management and environmental ethics in forestry. Prentice Hall, Upper Saddle River

    Google Scholar 

  • 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

    Google Scholar 

  • Kondratyev KYA, Krapivin VF, Varotsos CA (2003b) Global carbon cycle and climate change. Springer/PRAXIS, Chichester

    Google Scholar 

  • Krapivin VF, Shutko AM, Chukhlantsev AA, Golovachev SP, Phillips GW (2006) GIMS-based method vegetation microwave monitoring. Environ Model Softw 21:330–345

    Article  Google Scholar 

  • Krapivin VF, Varotsos CA, Soldatov VY (2015) New Ecoinformatics tools in environmental science: applications and decision-making. Springer, London, U.K., 903 pp

    Book  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Liang S (2004) Quantitative remote sensing of land surfaces. Wileys, Hoboken

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Meng YS, Lee YH (2010) Investigations of foliage effect on modern wireless communication systems: a review. Prog Electromagn Res 105:313–332

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Pretzsch H (2014) Canopy space filling and tree canopy morphology in mixed-species stands compared with monocultures. For Ecol Manag 327:251–264

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Scaggs AK (ed) (2007) New research on forest ecology. Nova Science Publisher, New York

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Shugart HH, Leemans R, Bonan GB (1992) A systems analysis of the global boreal forest. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • 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

    Article  Google Scholar 

  • Smith WK, Hinckley TM, Roy J (eds) (1994) Ecophsiology of coniferous forests. Academic, New York

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Varotsos CA, Nitu C, Krapivin VF (2018) Global ecoinformatics: theory and applications. Matrix ROM, Bucharest

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

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

Publish with us

Policies and ethics