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Abstract

As the main player in the terrestrial biosphere, forests are of great significance in keeping the global carbon balance. Forests are also the biggest carbon sink in terrestrial ecosystems, accounting for at least 86% of the global vegetation carbon pool.

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References

  • Balzter H, Baker J, Hallikainen M et al (2002) Retrieval of timber volume and snow water equivalent over a Finnish boreal forest from airborne polarimetric synthetic aperture radar. Int J Remote Sens 23:3185–3208

    Google Scholar 

  • Blanc L, Echard M, Herault B et al (2009) Dynamics of aboveground carbon stocks in a selectively logged tropical forest. Ecol Appl 19:1397–1404

    Google Scholar 

  • Brown SL, Schroeder PE (1999) Spatial patterns of aboveground production and mortality of woody biomass from eastern US forests. Ecol Appl 9:968–980

    Google Scholar 

  • Canadell JG, Le Quere C, Raupach MR et al (2007) Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc Natl Acad Sci 104:18866–18870

    Google Scholar 

  • Cao M, Woodward FI (1998) Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their response to climate change. Glob Change Biol 4:185–198

    Google Scholar 

  • Cloude SR, Papathanassiou KP (1998) Polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 36:1551–1565

    Google Scholar 

  • Cloude SR, Papathanassiou KP (2003) Three-stage retrieval process for polarimetric SAR interferometry. IEE Proc Radar Sonar Navig 150:125–134

    Google Scholar 

  • Denman KL, Brasseur G, Chidthaisong A et al (2007) Couplings between changes in the climate system and biogeochemistry. In: Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

    Google Scholar 

  • Dubayah R, Drake J (2000) Lidar Remote sensing for forestry. J Forest 98:44–46

    Google Scholar 

  • Dubois P, Champion I, Paillou P (2004) Potential of low frequency SAR for vegetation characterization and sub-surface mapping: a case study from RAMSES data. Final report, ESA Contract, 18501/04/NL/CB

    Google Scholar 

  • Durrieu S, Cherchali S, Costeraste J et al (2013) Preliminary studies for a vegetation LaDAR/LiDAR space mission in France. In: IEEE IGRSS’2013, pp 4332–4335

    Google Scholar 

  • Englharta S, Keuck V, Siegert F (2011) Aboveground biomass retrieval in tropical forests—the potential of combined X- and L-band SAR data use. Remote Sens Environ 115:1260–1271

    Google Scholar 

  • Flynn T, Tabb M, Carande R (2002) Direct estimation of vegetation parameters from convariance data in Polarimetric SAR Interferometry. In: Proceedings of IEEE geoscience and remote sensing symposium 2002. IEEE, Toronto, pp 1908–1910

    Google Scholar 

  • Food and Agriculture Organization (2010) 2010 main report on forest resources evaluation. Collected Works on Forestry of Food and Agriculture Organization

    Google Scholar 

  • Food and Agriculture Organization (2011) State of the world’s forests 2011. UNFAO, Rome

    Google Scholar 

  • Forest Resource Management Division of the State Forestry Bureau (2010) Seventh national survey of forest resources. For Resources Management, pp 1–8

    Google Scholar 

  • Fung AK, Ulaby FT (1978) A scatter model for leafy vegetation. IEEE Trans Geosci Electron GE-16:281–185

    Google Scholar 

  • Garestier F, Le Toan T (2010) Forest modeling for height retrieval using single-baseline InSAR/Pol-InSAR data. IEEE Trans Geosci Remote Sens 48:1528–1539

    Google Scholar 

  • Garestier F, Dubois-Fernandez PC, Champion I (2008) Forest height retrieval using high-resolution P-band Pol-InSAR data. IEEE Trans Geosci Remote Sens 46:3544–3559

    Google Scholar 

  • Hajnsek I, Scheiber R, Ulander L et al (2008) BIOSAR 2007: technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2007 experiment. Final report, ESA Contract No.: 20755/07/NL/CB

    Google Scholar 

  • Hajnsek I, Kugler F, Lee SK et al (2009) Tropical-forest-parameter estimation by means of Pol-InSAR: the INDREX-II campaign. IEEE Trans Geosci Remote Sens 47:481–493

    Google Scholar 

  • Hoekman DH (1985) Radar backscattering of forest stand. Int J Remote Sens 6:325–343

    Google Scholar 

  • Huang Y (2002) Research of carbon budget of terrestrial and coastal ecosystems in China. Bull Chin Acad Sci 2:104–107

    Google Scholar 

  • Hussin YA, Reich RM, Hoffer RM (1991) Estimating slash pine biomass using radar backscatter. IEEE Trans Geosci Remote Sens 29:427–431

    Google Scholar 

  • IPCC (2008) 2007: Climate change 2007: overall report. IPCC, Switzerland, Geneva

    Google Scholar 

  • Karam MA, Fung AK, Antar YMM (1988) Electromagnetic wave scattering from some vegetation samples. IEEE Trans Geosci Remote Sens 26:799–808

    Google Scholar 

  • Kasischke ES, Melack JM, Dobson MC (1997) The use of imaging radar for ecological applications: a review. Remote Sens Environ 59:141–156

    Google Scholar 

  • Kimble JM, Heath LS, Birdsey RA et al (2002) The potential of U.S. forest soils to sequester carbon and mitigate the greenhouse effect. CRC/Lewis, Boca Raton, FL, p 429

    Google Scholar 

  • Kramer PJ (1981) Carbon dioxide concentration, photosynthesis, and dry matter production. Bioscience 31:29–33

    Google Scholar 

  • Lal R (2005) Forest soils and carbon sequestration. For Ecol Manage 220:242–258

    Google Scholar 

  • Lang RH, Sidhu JS (1983) Electromagnetic backscattering from a layer of vegetation: a discrete approach. IEEE Trans Geosci Remote Sens GE-21:62–71

    Google Scholar 

  • Le Toan T, Beaudoin A, Riom J (1992) Relating forest biomass to SAR data. IEEE Trans Geosci Remote Sens 30:403–411

    Google Scholar 

  • Le Toan T, Quegan S, Davidson M et al (2011) The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115:2850–2860

    Google Scholar 

  • Lee SK, Kugler F, Hajnsek I et al (2010) The potential and challenges of polarimetric SAR interferometry techniques for forest parameter estimation at P-band. In: 8th European conference on synthetic aperture radar. Offenbach, Berlin, pp 503–505

    Google Scholar 

  • Lefsky MA (2010) A global forest canopy height map from the moderate resolution imaging spectro-radiometer and the geoscience laser altimeter system. Geosphys Res Lett 37:L15401. https://doi.org/10.1029/2010GL043622

    Article  Google Scholar 

  • Lefsky MA, Harding DJ, Keller M et al (2005) Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32:L22S02

    Google Scholar 

  • Liao H, Zhu Y (2010) Global carbon cycle and centenary climate changes in China. Quat Sci 30:445–455

    Google Scholar 

  • Liseno A, Papathanassiou KP, Moreira A et al (2005) Analysis of forest-slab height retrieval from multibaseline SAR data. In: The 2005 international geoscience and remote sensing symposium, Seoul, South Korea, pp 2660–2663

    Google Scholar 

  • Means JE, Acker SA, Harding DJ et al (1999) Use of large-footprint scanning airborne LiDAR to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sens Environ 67:298–308

    Google Scholar 

  • Nelson R, Ranson KJ, Sun G et al (2009) Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sens Environ 113:691–701

    Google Scholar 

  • Nesset E (1997) Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J Photogram Remote Sens 52:49–56

    Google Scholar 

  • Neumann M, Ferro-Famil L, Reigber A (2010) Estimation of forest structure, ground, and canopy layer characteristics from multibaseline polarimetric interferometric SAR data. IEEE Trans Geosci Remote Sens 48:1086–1104

    Google Scholar 

  • Oliver LP, Sandra B (1998) Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282:439–442

    Google Scholar 

  • Pan Y, Birdsey RA, Fang J et al (2011) A large and persistent carbon sink in the world’s forests. Science 333:988–993

    Google Scholar 

  • Papathanassiou KP, Cloude SR (2001) Single base-line Polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 39:2352–2363

    Google Scholar 

  • Papathanassiou KP, Cloude SR, Krieger G, et al (2010) Polarimetric SAR interferometry (Pol-InSAR) for global forest biomass monitoring. In: 2010 IEEE international geoscience and remote sensing symposium, Hawaii, USA

    Google Scholar 

  • Ranson KJ, Sun G, Lang RH et al (1997) Mapping of boreal forest biomass from spaceborne synthetic aperture radar. J Geophys Res 102:599–629

    Google Scholar 

  • Richards JA (1990) Radar backscatter modeling of forests: a review of current trends. Int J Remote Sens 11:1299–1321

    Google Scholar 

  • Richards JA, Woodgate PW, Skidmore AK (1987) An explanation of enhanced radar backscattering from forest. Int J Remote Sens 8:1093–1100

    Google Scholar 

  • Rowland CS, Balzter H, Dawson TP et al (2003) Biomass estimation of Thetford forest from L-band SAR data: potential and limitations. IEEE, pp 2577–2579

    Google Scholar 

  • Sader SA (1987) Forest biomass, canopy structure, and species composition relationships with multipolarization L-band SAR data. Photogram Eng Remote Sens 53:193–202

    Google Scholar 

  • Sun G, Ranson KJ, Kimes DS et al (2008) Forest vertical structure from GLAS: an evaluation using LVIS and SRTM data. Remote Sens Environ 112:107–117

    Google Scholar 

  • Sun Q, Liu Y, Li Y et al (2013) Progress of research of forest carbon sink function. Environ Sci Manage 38:47–50

    Google Scholar 

  • Tabb M, Flynn T, Carande R (2004) Full maximum likelihood retrieval of PolInSAR scattering models. In: Proceedings of IEEE geoscience and remote sensing symposium 2004. IEEE, Alaska, pp 1232–1235

    Google Scholar 

  • Tebaldini S (2009) Algebraic synthesis of forest scenarios from multibaseline PolInSAR data. IEEE Trans Geosci Remote Sens 47:4132–4142

    Google Scholar 

  • Treuhaft RN, Siqueira PR (2000) Vertical structure of vegetated land surfaces from interferometric and polarimetric radar. Radio Sci 35:141–178

    Google Scholar 

  • Ulaby FT, Elachi C (1990) Radar polarimetry for geoscience applications. Artech House, Boston

    Google Scholar 

  • Ulaby FT, Moore RK, Fung AK (1986) Microwave remote sensing, active and passive. Vol. III: from theory to applications. Addison-Wesley, Reading, MA, pp 1065–2165

    Google Scholar 

  • Valentini R, Matteucci G, Dolman AJ et al (2000) Respiration as the main determinant of carbon balance in European forests. Nature 404:861–865

    Google Scholar 

  • Wang C, Glenn FN (2009) Integrating LiDAR intensity and elevation data for terrain characterization in a forested area. IEEE Geosc Remote Sens Lett 6:463–466

    Google Scholar 

  • Wang Y, Simonett DS, Imhoff MF (1990) A multiple-polarization layered radar model for Mangal forests. In: Proceedings of the IGARSS’90, vol 1, pp 487–490

    Google Scholar 

  • Wang C, Tang F, Li L et al (2013) Wavelet analysis for waveform decomposition of ICESAT/GLAS data and its application in tree height estimation. IEEE Geosci Remote Sens Lett 10:115–119

    Google Scholar 

  • Wegmifller U, Santoro M, Wiesmann A (2007) A novel methodology for parameter retrieval from multi-temporal data demonstrated for forest. IEEE, pp 1–6

    Google Scholar 

  • Wu ST, Sader SA (1987) Multipolarization SAR data for surface feature delineation and forest vegetation classification. IEEE Trans Geosci Remote Sens GRS-25:67

    Google Scholar 

  • Yamada H, Yamaguchi Y, Kim Y (2001) Polarimetric SAR interferometry for forest analysis based on the ESPRIT algorithm. IEICE Trans Electron E84- C(12):1917–1924

    Google Scholar 

  • Zheng D, Heath LS, Ducey MJ (2008) Identifying grain size dependent errors on global forest area estimates and carbon studies. Geophys Res Lett 35:L21403

    Google Scholar 

Download references

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Correspondence to Huadong Guo .

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Guo, H., Fu, W., Liu, G. (2019). Forest Biomass Satellite. In: Scientific Satellite and Moon-Based Earth Observation for Global Change. Springer, Singapore. https://doi.org/10.1007/978-981-13-8031-0_12

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