Ground Data are Essential for Biomass Remote Sensing Missions

Abstract

Several remote sensing missions will soon produce detailed carbon maps over all terrestrial ecosystems. These missions are dependent on accurate and representative in situ datasets for the training of their algorithms and product validation. However, long-term ground-based forest-monitoring systems are limited, especially in the tropics, and to be useful for validation, such ground-based observation systems need to be regularly revisited and maintained at least over the lifetime of the planned missions. Here we propose a strategy for a coordinated and global network of in situ data that would benefit biomass remote sensing missions. We propose to build upon existing networks of long-term tropical forest monitoring. To produce accurate ground-based biomass estimates, strict data quality must be guaranteed to users. It is more rewarding to invest ground resources at sites where there currently is assurance of a long-term commitment locally and where a core set of data is already available. We call these ‘supersites’. Long-term funding for such an inter-agency endeavour remains an important challenge, and we here provide costing estimates to facilitate dialogue among stakeholders. One critical requirement is to ensure in situ data availability over the lifetime of remote sensing missions. To this end, consistent guidelines for supersite selection and management are proposed within the Forest Observation System, long-term funding should be assured, and principal investigators of the sites should be actively involved.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Anderson-Teixeira KJ, Davies SJ, Bennett AC, Gonzalez-Akre EB, Muller-Landau HC, Wright SJ et al (2015) CTFS-Forest GEO: a worldwide network monitoring forests in an era of global change. Glob Change Biol 21:528–549

    Article  Google Scholar 

  2. Ashton PS (1964) Ecological studies in the mixed dipterocarp forests of Brunei State. Clarendon Press, Oxford

    Google Scholar 

  3. Asner GP, Powell GV, Mascaro J, Knapp DE, Clark JK, Jacobson J et al (2010) High-resolution forest carbon stocks and emissions in the Amazon. Proc Natl Acad Sci 107:16738–16742

    Article  Google Scholar 

  4. Avitabile V, Herold M, Heuvelink GB, Lewis SL, Phillips OL, Asner GP et al (2016) An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol 22:1406–1420

    Article  Google Scholar 

  5. Baccini A, Goetz SJ, Walker WS, Laporte NT, Sun M, Sulla-Menashe D, Hackler J et al (2012) Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat Clim Change 2:182–185

    Article  Google Scholar 

  6. Brede B, Lau A, Bartholomeus HM, Kooistra L (2017) Comparing RIEGL RiCOPTER UAV LiDAR derived canopy height and DBH with terrestrial LiDAR. Sensors 17:2371

    Article  Google Scholar 

  7. Brown S, Lugo AE (1982) The storage and production of organic matter in tropical forests and their role in the global carbon cycle. Biotropica 14(3):161–187

    Article  Google Scholar 

  8. Calders K, Origo N, Burt A, Disney MI, Nightingale J, Raumonen P, Åkerblom M, Malhi Y, Lewis P (2018) Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling. Remote Sens 10:933

    Article  Google Scholar 

  9. Castilho CV, Magnusson WE, de Araújo RN, Luizao RC, Luizao FJ, Lima AP, Higuchi N (2006) Variation in aboveground tree live biomass in a central Amazonian Forest: effects of soil and topography. For Ecol Manag 234:85–96

    Article  Google Scholar 

  10. Chapman B, Kasischke ES (2018) Evaluation of above study region sites for future calibration and validation of Nisar Science requirements. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp 8279–8281

  11. Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol 20:3177–3190

    Article  Google Scholar 

  12. Chazdon RL, Broadbent EN, Rozendaal DM, Bongers F, Zambrano AMA, Aide TM et al (2016) Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci Adv 2(5):e1501639

    Article  Google Scholar 

  13. Clark DB, Clark DA (2000) Landscape-scale variation in forest structure and biomass in a tropical rain forest. For Ecol Manag 137:185–198

    Article  Google Scholar 

  14. Clark DB, Kellner JR (2012) Tropical forest biomass estimation and the fallacy of misplaced concreteness. J Veg Sci 23:1191–1196

    Article  Google Scholar 

  15. Condit R (1998) Tropical forest census plots: methods and results from Barro Colorado Island, Panama and a comparison with other plots. Springer, Berlin

    Google Scholar 

  16. Disney MI, Boni Vicari M, Calders K, Burt A, Lewis S, Raumonen P, Wilkes P (2018) Weighing trees with lasers: advances, challenges and opportunities. R Soc Interface Focus 8(2):20170048

    Article  Google Scholar 

  17. Drake JB, Dubayah RO, Clark DB, Knox RG, Blair JB, Hofton MA et al (2002) Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sens Environ 79:305–319

    Article  Google Scholar 

  18. Dubois-Fernandez PC, Le Toan T, Daniel S, Oriot H, Chave J, Blanc L et al (2012) The TropiSAR airborne campaign in French Guiana: objectives, description, and observed temporal behavior of the backscatter signal. IEEE Trans Geosci Remote Sens 50:3228–3241

    Article  Google Scholar 

  19. Fridman J, Holm S, Nilsson M, Nilsson P, Ringvall AH, Ståhl G (2014) Adapting National Forest Inventories to changing requirements—the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fennica 48(3):29

    Article  Google Scholar 

  20. Frolking S, Palace MW, Clark DB, Chambers JQ, Shugart HH, Hurtt GC (2009) Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J Geophys Res Biogeosci 114(G2):222

    Article  Google Scholar 

  21. Gentry AH (1988) Changes in plant community diversity and floristic composition on environmental and geographical gradients. Ann Mo Bot Gard 75(1):1–34

    Article  Google Scholar 

  22. Golley FB (1993) A history of the ecosystem concept in ecology: more than the sum of the parts. Yale University Press, New Haven

    Google Scholar 

  23. Grassi G, House J, Dentener F, Federici S, den Elzen M, Penman J (2017) The key role of forests in meeting climate targets requires science for credible mitigation. Nat Clim Chang 7:220

    Article  Google Scholar 

  24. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SAA, Tyukavina A et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853

    Article  Google Scholar 

  25. IPCC (2018) Special report on global warming of 1.5 °C. https://www.ipcc.ch/sr15/

  26. Johnson MO, Galbraith D, Gloor M, De Deurwaerder H, Guimberteau M, Rammig A et al (2016) Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models. Glob Change Biol 22:3996–4013

    Article  Google Scholar 

  27. Keeling HC, Phillips OL (2007) The global relationship between forest productivity and biomass. Glob Ecol Biogeogr 16:618–631

    Article  Google Scholar 

  28. Labrière N, Tao S, Chave J, Scipal K, Le Toan T, Abernethy K et al (2018) In Situ reference datasets from the TropiSAR and AfriSAR campaigns in support of upcoming spaceborne biomass missions. IEEE J Sel Top Appl Earth Obs Remote Sens 99:1–11

    Google Scholar 

  29. Lutz JA, Furniss TJ, Johnson DJ, Davies SJ, Allen D, Alonso A et al (2018) Global importance of large-diameter trees. Glob Ecol Biogeogr 27(7):849–864

    Article  Google Scholar 

  30. Le Quéré C, Andrew RM, Friedlingstein P, Sitch S, Pongratz J, Manning AC et al (2018) Global carbon budget 2017. Earth Syst Sci Data 10:405–448

    Article  Google Scholar 

  31. Le Toan T, Quegan S, Davidson MWJ, Balzter H, Paillou P, Papathanassiou K et al (2011) The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115:2850–2860

    Article  Google Scholar 

  32. Lewis SL, Sonké B, Sunderland T, Begne SK, Lopez-Gonzalez G, Van Der Heijden GM et al (2013) Above-ground biomass and structure of 260 African tropical forests. Philos Trans R Soc B 368:20120295

    Article  Google Scholar 

  33. Malhi Y, Wood D, Baker TR, Wright J, Phillips OL, Cochrane T, Meir P et al (2004) The regional variation of aboveground live biomass in old-growth Amazonian forests. Glob Change Biol 12:1107–1138

    Article  Google Scholar 

  34. Martin AR, Doraisami M, Thomas SC (2018) Global patterns in wood carbon concentration across the world’s trees and forests. Nat Geosci 11(12):915

    Article  Google Scholar 

  35. Mitchard ET, Feldpausch TR, Brienen RJ, Lopez-Gonzalez G, Monteagudo A, Baker TR et al (2014) Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob Ecol Biogeogr 23:935–946

    Article  Google Scholar 

  36. Mokany K, Raison RJ, Prokushkin AS (2006) Critical analysis of root:shoot ratios in terrestrial biomes. Glob Change Biol 12:84–96

    Article  Google Scholar 

  37. NASA-ESA-Smithsonian Workshop on Calibration and Validation of Upcoming Satellite Missions on Forest Structure and Biomass, Washington DC, 2016. (https://nisar.jpl.nasa.gov/files/nisar/NISAR_Vegetation_Biomass_Workshop_Report.pdf)

  38. Nogueira EM, Nelson BW, Fearnside PM (2006) Volume and biomass of trees in central Amazonia: influence of irregularly shaped and hollow trunks. For Ecol Manag 227:14–21

    Article  Google Scholar 

  39. Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA et al (2011) A large and persistent carbon sink in the world’s forests. Science 1(2):1–3. https://doi.org/10.1126/science.1201609

    Google Scholar 

  40. Paul KI, Roxburgh SH, Chave J, England JR, Zerihun A, Specht A et al (2016) Testing the generality of above-ground biomass allometry across plant functional types at the continent scale. Glob Change Biol 22:2106–2124

    Article  Google Scholar 

  41. Paul KI, Larmour J, Specht A, Zerihun A, Ritson P, Roxburgh SH et al (2019) Testing the generality of below-ground biomass allometry across plant functional types. For Ecol Manag 432:102–114

    Article  Google Scholar 

  42. Phillips J, Duque A, Scott C, Wayson C, Galindo G, Cabrera E et al (2016) Live aboveground carbon stocks in natural forests of Colombia. For Ecol Manag 374:119–128

    Article  Google Scholar 

  43. Réjou-Méchain M, Muller-Landau HC, Detto M, Thomas SC, Toan TL, Saatchi SS et al (2014) Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks. Biogeosciences 11:5711

    Article  Google Scholar 

  44. Réjou-Méchain M, Tanguy A, Piponiot C, Chave J, Hérault B (2017) Biomass: an r package for estimating above-ground biomass and its uncertainty in tropical forests. Methods Ecol Evol 8:1163–1167

    Article  Google Scholar 

  45. Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ET, Salas W, Zutta BR et al (2011a) Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci USA 108:9899–9904

    Article  Google Scholar 

  46. Saatchi S, Marlier M, Chazdon RL, Clark DB, Russell AE (2011b) Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass. Remote Sens Environ 115:2836–2849

    Article  Google Scholar 

  47. Schimel D, Pavlick R, Fisher JB, Asner GP, Saatchi S, Townsend P et al (2015) Observing terrestrial ecosystems and the carbon cycle from space. Glob Change Biol 21:1762–1776

    Article  Google Scholar 

  48. Shugart HH, Saatchi S, Hall FG (2010) Importance of structure and its measurement in quantifying function of forest ecosystems. J Geophys Res Biogeosci. https://doi.org/10.1029/2009JG000993

    Google Scholar 

  49. Sillett SC, Van Pelt R, Carroll AL, Campbell-Spickler J, Coonen EJ, Iberle B (2019) Allometric equations for Sequoia sempervirens in forests of different ages. For Ecol Manag 433:349–363

    Article  Google Scholar 

  50. Sist P, Rutishauser E, Peña-Claros M, Shenkin A, Hérault B, Blanc L et al (2015) The Tropical managed Forests Observatory: a research network addressing the future of tropical logged forests. Appl Veg Sci 18:171–174

    Article  Google Scholar 

  51. Smith WB (2002) Forest inventory and analysis: a national inventory and monitoring program. Environ Pollut 116:S233–S242

    Article  Google Scholar 

  52. Stegen JC, Swenson NG, Enquist BJ, White EP, Phillips OL, Jørgensen PM, Weiser MD, Mendoza AM, Vargas PN (2011) Variation in above-ground forest biomass across broad climatic gradients. Glob Ecol Biogeogr 20:744–754

    Article  Google Scholar 

  53. Sullivan MJ, Talbot J, Lewis SL, Phillips OL, Qie L, Begne SK et al (2017) Diversity and carbon storage across the tropical forest biome. Scientific Rep 7:39102

    Article  Google Scholar 

  54. Turner W, Rondinini C, Pettorelli N, Mora B, Leidner AK, Szantoi Z et al (2015) Free and open-access satellite data are key to biodiversity conservation. Biol Cons 182:173–176

    Article  Google Scholar 

  55. Xu L, Saatchi SS, Shapiro A, Meyer V, Ferraz A, Yang Y et al (2017) Spatial distribution of carbon stored in forests of the Democratic Republic of Congo. Sci Rep 7:15030

    Article  Google Scholar 

Download references

Acknowledgements

We thank the organizers of the ISSI ESA Meeting held in Bern, Switzerland, in November 2017 for stimulating discussions and for their invitation to submit this paper. We gratefully acknowledge funding by ‘Investissement d’Avenir’ programs managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01; TULIP, ref. ANR-10-LABX-41), from CNES and from ESA (IFBN Project 4000114425/15/NL/FF/gp, as part of the BIOMASS mission program).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jérôme Chave.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chave, J., Davies, S.J., Phillips, O.L. et al. Ground Data are Essential for Biomass Remote Sensing Missions. Surv Geophys 40, 863–880 (2019). https://doi.org/10.1007/s10712-019-09528-w

Download citation

Keywords

  • Biomass
  • Calibration
  • Forest
  • In situ data
  • Validation