Surveys in Geophysics

, Volume 40, Issue 4, pp 881–911 | Cite as

Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them

  • Maxime Réjou-MéchainEmail author
  • Nicolas Barbier
  • Pierre Couteron
  • Pierre Ploton
  • Grégoire Vincent
  • Martin Herold
  • Stéphane Mermoz
  • Sassan Saatchi
  • Jérôme Chave
  • Florian de Boissieu
  • Jean-Baptiste Féret
  • Stéphane Momo Takoudjou
  • Raphaël Pélissier


Forest biomass monitoring is at the core of the research agenda due to the critical importance of forest dynamics in the carbon cycle. However, forest biomass is never directly measured; thus, upscaling it from trees to stand or larger scales (e.g., countries, regions) relies on a series of statistical models that may propagate large errors. Here, we review the main steps usually adopted in forest aboveground biomass mapping, highlighting the major challenges and perspectives. We show that there is room for improvement along the scaling-up chain from field data collection to satellite-based large-scale mapping, which should lead to the adoption of effective practices to better control the propagation of errors. We specifically illustrate how the increasing use of emerging technologies to collect massive amounts of high-quality data may significantly improve the accuracy of forest carbon maps. Furthermore, we discuss how sources of spatially structured biases that directly propagate into remote sensing models need to be better identified and accounted for when extrapolating forest carbon estimates, e.g., through a stratification design. We finally discuss the increasing realism of 3D simulated stands, which, through radiative transfer modelling, may contribute to a better understanding of remote sensing signals and open avenues for the direct calibration of large-scale products, thereby circumventing several current difficulties.


Biomass Calibration Carbon Error propagation Field data Modelling 



We gratefully thank the organizers of the Workshop held at ISSI Bern in November 2017 that was at the origin of this Special Issue. This review has been conducted under the project 3DForMod funded by ERA-GAS (ANR-17-EGAS-0002-01, NWO-3DForMod-5160957540) and has also benefited from the “Investissement d’Avenir” programs managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01).


  1. Antin C, Grau E, Vincent G et al (2015) From leave scale to tree scale: which structural parameters influence a simulated full-waveform large-footprint LiDAR signal? SilviLaser 2015:110–112Google Scholar
  2. Arciniegas A, Prieto F, Brancheriau L, Lasaygues P (2014) Literature review of acoustic and ultrasonic tomography in standing trees. Trees 28:1559–1567Google Scholar
  3. Asner GP, Mascaro J (2014) Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric. Remote Sens Environ 140:614–624Google Scholar
  4. Asner GP, Broadbent EN, Oliveira PJC et al (2006) Condition and fate of logged forests in the Brazilian Amazon. Proc Natl Acad Sci 103:12947–12950. Google Scholar
  5. Asner GP, Mascaro J, Anderson C et al (2013) High-fidelity national carbon mapping for resource management and REDD+. Carbon Balance Manag 8:1–14. Google Scholar
  6. Avitabile V, Camia A (2018) An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots. For Ecol Manag 409:489–498. Google Scholar
  7. Avitabile V, Herold M, Heuvelink GBM et al (2016) An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol 22:1406–1420. Google Scholar
  8. Baccini A, Asner GP (2013) Improving pantropical forest carbon maps with airborne LiDAR sampling. Carbon Manag 4:591–600Google Scholar
  9. Baccini A, Goetz SJ, Walker WS et al (2012) Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat Clim Change 2:182–185. Google Scholar
  10. Baker TR, Phillips OL, Malhi Y et al (2004) Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob Change Biol 10:545–562Google Scholar
  11. Banin L, Feldpausch TR, Phillips OL et al (2012) What controls tropical forest architecture? Testing environmental, structural and floristic drivers. Glob Ecol Biogeogr 21:1179–1190. Google Scholar
  12. Barbier N, Couteron P (2015) Attenuating the bidirectional texture variation of satellite images of tropical forest canopies. Remote Sens Environ 171:245–260Google Scholar
  13. Barbier N, Proisy C, Véga C et al (2011) Bidirectional texture function of high resolution optical images of tropical forest: an approach using LiDAR hillshade simulations. Remote Sens Environ 115:167–179Google Scholar
  14. Bastin J-F, Barbier N, Couteron P et al (2014) Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach. Ecol Appl 24:1984–2001. Google Scholar
  15. Bastin J-F, Fayolle A, Tarelkin Y et al (2015a) Wood specific gravity variations and biomass of central african tree species: the simple choice of the outer wood. PLoS ONE 10:e0142146Google Scholar
  16. Bastin J-F, Barbier N, Réjou-Méchain M et al (2015b) Seeing Central African forests through their largest trees. Sci Rep 5:13156Google Scholar
  17. Bauwens S, Bartholomeus H, Calders K, Lejeune P (2016) Forest inventory with terrestrial LiDAR: a comparison of static and hand-held mobile laser scanning. Forests 7:127Google Scholar
  18. Béland M, Baldocchi DD, Widlowski J-L et al (2014) On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR. Agric For Meteorol 184:82–97Google Scholar
  19. Blanchard E, Birnbaum P, Proisy C et al (2015) Prédire la structure des forêts tropicales humides calédoniennes: analyse texturale de la canopée sur des images Pléiades. Rev Fr Photogrammétrie Télédétection 209:141–147Google Scholar
  20. Bouvet A, Mermoz S, Le Toan T et al (2018) An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sens Environ 206:156–173Google Scholar
  21. Bouvier M, Durrieu S, Fournier RA, Renaud J-P (2015) Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sens Environ 156:322–334Google Scholar
  22. Brede B, Lau A, Bartholomeus HM, Kooistra L (2017) Comparing RIEGL RiCOPTER UAV LiDAR derived canopy height and DBH with terrestrial LiDAR. Sensors 17:2371Google Scholar
  23. Bustamante MMC, Roitman I, Aide TM et al (2016) Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. Glob Change Biol 22:92–109. Google Scholar
  24. Calders K, Newnham G, Burt A et al (2014) Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods Ecol Evol. Google Scholar
  25. Calders K, Origo N, Burt A et al (2018) Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling. Remote Sens 10:933Google Scholar
  26. Cescatti A (1997) Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. I. Model structure and algorithms. Ecol Model 101:263–274Google Scholar
  27. Chambers JQ, Negron-Juarez RI, Marra DM et al (2013) The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape. Proc Natl Acad Sci 110:3949–3954. Google Scholar
  28. Chanthorn W, Hartig F, Brockelman WY (2017) Structure and community composition in a tropical forest suggest a change of ecological processes during stand development. For Ecol Manag 404:100–107. Google Scholar
  29. Chave J, Condit R, Aguilar S et al (2004) Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc Lond Ser B-Biol Sci 359:409–420Google Scholar
  30. Chave J, Andalo C, Brown S et al (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99. Google Scholar
  31. Chave J, Coomes D, Jansen S et al (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:351–366Google Scholar
  32. Chave J, Réjou-Méchain M, Búrquez A et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol 20:3177–3190. Google Scholar
  33. Chen Q, Laurin GV, Valentini R (2015) Uncertainty of remotely sensed aboveground biomass over an African tropical forest: propagating errors from trees to plots to pixels. Remote Sens Environ 160:134–143Google Scholar
  34. Clark DA (2002) Are tropical forests an important carbon sink? Reanalysis of the long-term plot data. Ecol Appl 12:3–7.;2 Google Scholar
  35. Clark D, Clark D (2000) Landscape-scale variation in forest structure and biomass in a tropical rain forest. For Ecol Manag 137:185–198. Google Scholar
  36. Clark DB, Kellner JR (2012) Tropical forest biomass estimation and the fallacy of misplaced concreteness. J Veg Sci 23:1191–1196. Google Scholar
  37. Condit R (1998) Tropical forest census plots: methods and results from Barro Colorado Island, Panama and a comparison with other plots. Springer, BerlinGoogle Scholar
  38. Condit R, Ashton PS, Baker P et al (2000) Spatial patterns in the distribution of tropical tree species. Science 288(5470):1414–1418Google Scholar
  39. Condit R, Lao S, Singh A et al (2014) Data and database standards for permanent forest plots in a global network. For Ecol Manag 316:21–31Google Scholar
  40. Côté J-F, Fournier RA, Egli R (2011) An architectural model of trees to estimate forest structural attributes using terrestrial LiDAR. Environ Model Softw 26:761–777. Google Scholar
  41. Couteron P, Pelissier R, Nicolini EA, Paget D (2005) Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images. J Appl Ecol 42:1121–1128Google Scholar
  42. Dauzat J, Rapidel B, Berger A (2001) Simulation of leaf transpiration and sap flow in virtual plants: model description and application to a coffee plantation in Costa Rica. Agric For Meteorol 109:143–160Google Scholar
  43. de Castilho CV, Magnusson WE, de Araújo RNO et al (2006) Variation in aboveground tree live biomass in a central Amazonian forest: effects of soil and topography. For Ecol Manag 234:85–96. Google Scholar
  44. de Moura YM, Hilker T, Goncalves FG et al (2016) Scaling estimates of vegetation structure in Amazonian tropical forests using multi-angle MODIS observations. Int J Appl Earth Obs Geoinf 52:580–590Google Scholar
  45. De Reffye P, Houllier F, Blaise F et al (1995) A model simulating above-and below-ground tree architecture with agroforestry applications. Agrofor Syst 30:175–197Google Scholar
  46. de Souza Pereira FR, Kampel M, Gomes Soares ML et al (2018) Reducing uncertainty in mapping of mangrove aboveground biomass using airborne discrete return LiDAR data. Remote Sens 10:637Google Scholar
  47. Detto M, Muller-Landau HC, Mascaro J, Asner GP (2013) Hydrological networks and associated topographic variation as templates for the spatial organization of tropical forest vegetation. PLoS ONE 8:e76296. Google Scholar
  48. Dickinson TA, Tanner EVJ (1978) Exploitation of hollow trunks by tropical trees. Biotropica 10:231–233. Google Scholar
  49. Disney M (2018) Terrestrial LiDAR: a 3D revolution in how we look at trees. New Phytol. Google Scholar
  50. Egbert DD (1977) A practical method for correcting bidirectional reflectance variations. In: LARS symposia, p 203Google Scholar
  51. Emilio T, Quesada CA, Costa FRC et al (2013) Soil physical conditions limit palm and tree basal area in Amazonian forests. Plant Ecol Divers 10:1. Google Scholar
  52. ESA (2012) Report for mission selection: biomass, ESA SP-1324/1 (3 volume series). European Space Agency Noordwijk, The NetherlandsGoogle Scholar
  53. Fayad I, Baghdadi N, Guitet S et al (2016) Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data. Int J Appl Earth Obs Geoinf 52:502–514Google Scholar
  54. Fayolle A, Doucet J-L, Gillet J-F et al (2013) Tree allometry in Central Africa: testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks. For Ecol Manag 305:29–37. Google Scholar
  55. Feldpausch TR, Banin L, Phillips OL et al (2011) Height–diameter allometry of tropical forest trees. Biogeosciences 8:1081–1106Google Scholar
  56. Feldpausch TR, Lloyd J, Lewis SL et al (2012) Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9:3381–3403. Google Scholar
  57. Féret J-B, Asner GP (2014) Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol Appl 24:1289–1296Google Scholar
  58. Ferraz A, Saatchi S, Mallet C, Meyer V (2016) LiDAR detection of individual tree size in tropical forests. Remote Sens Environ 183:318–333Google Scholar
  59. Féret JB, Gitelson AA, Noble SD, Jacquemoud S (2017) PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens Environ 193:204–215Google Scholar
  60. Flores O, Coomes DA (2011) Estimating the wood density of species for carbon stock assessments. Methods Ecol Evol 2:214–220. Google Scholar
  61. Frazer GW, Wulder MA, Niemann KO (2005) Simulation and quantification of the fine-scale spatial pattern and heterogeneity of forest canopy structure: a lacunarity-based method designed for analysis of continuous canopy heights. For Ecol Manag 214:65–90Google Scholar
  62. Frazer GW, Magnussen S, Wulder MA, Niemann KO (2011) Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sens Environ 115:636–649Google Scholar
  63. Fuller WA (1987) Measurement error models. Wiley, New YorkGoogle Scholar
  64. Gao S, Wang X, Wiemann MC et al (2017) A critical analysis of methods for rapid and nondestructive determination of wood density in standing trees. Ann For Sci 74:27Google Scholar
  65. Gastellu-Etchegorry J-P, Demarez V, Pinel V, Zagolski F (1996) Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sens Environ 58:131–156Google Scholar
  66. Gastellu-Etchegorry J-P, Yin T, Lauret N et al (2015) Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LiDAR acquisitions of natural and urban landscapes. Remote Sens 7:1667–1701Google Scholar
  67. Gobakken T, Naesset E (2009) Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Can J For Res 39:1036–1052Google Scholar
  68. Gomes ACS, Andrade A, Barreto-Silva JS et al (2013) Local plant species delimitation in a highly diverse Amazonian forest: do we all see the same species? J Veg Sci 24:70–79. Google Scholar
  69. Gonzalez de Tanago J, Lau A, Bartholomeus H et al (2018) Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods Ecol Evol 9:223–234Google Scholar
  70. Goodman RC, Phillips OL, Baker TR (2014) The importance of crown dimensions to improve tropical tree biomass estimates. Ecol Appl 24:680–698. Google Scholar
  71. Gourlet-Fleury S, Rossi V, Réjou-Méchain M et al (2011) Environmental filtering of dense-wooded species controls above-ground biomass stored in African moist forests. J Ecol 99:981–990. Google Scholar
  72. Grau E, Durrieu S, Fournier R et al (2017) Estimation of 3D vegetation density with terrestrial laser scanning data using voxels. A sensitivity analysis of influencing parameters. Remote Sens Environ 191:373–388Google Scholar
  73. Gregoire TG, Næsset E, McRoberts RE et al (2016) Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass. Remote Sens Environ 173:98–108Google Scholar
  74. Guitet S, Pélissier R, Brunaux O et al (2015) Geomorphological landscape features explain floristic patterns in French Guiana rainforest. Biodivers Conserv 24:1215–1237Google Scholar
  75. Guitet S, Sabatier D, Brunaux O et al (2018) Disturbance regimes drive the diversity of regional floristic pools across Guianan rainforest landscapes. Sci Rep 8:3872Google Scholar
  76. Hajj ME, Baghdadi N, Fayad I et al (2017) Interest of integrating spaceborne LiDAR data to improve the estimation of biomass in high biomass forested areas. Remote Sens 9:213Google Scholar
  77. Hansen MC, Potapov PV, Moore R et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853Google Scholar
  78. Henry M, Besnard A, Asante WA et al (2010) Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For Ecol Manag 260:1375–1388Google Scholar
  79. Herold M, Carter S, Avitabile V et al (2019) The role and need for space-based forest biomass-related measurements in environmental management and policy. Surv Geophys. Google Scholar
  80. Huang W, Swatantran A, Johnson K et al (2015) Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA. Carbon Balance Manag 10:19Google Scholar
  81. Hunter MO, Keller M, Victoria D, Morton DC (2013) Tree height and tropical forest biomass estimation. Biogeosciences 10:8385–8399Google Scholar
  82. Inglada J, Vadon H (2005) Fine registration of SPOT5 and Envisat/ASAR images and ortho-image production: a fully automatic approach. In: Proceedings 2005 IEEE international geoscience and remote sensing symposium, 2005. IGARSS'05. IEEE, Vol. 5, pp 3510–3512Google Scholar
  83. Ishimaru A (1978) Wave propagation and scattering in random media, vol 2. Academic press, New York, pp 336–393Google Scholar
  84. Johnson CE, Barton CC (2004) Where in the world are my field plots? Using GPS effectively in environmental field studies. Front Ecol Environ 2:475–482.;2 Google Scholar
  85. Jonckheere I, Nackaerts K, Muys B et al (2006) A fractal dimension-based modelling approach for studying the effect of leaf distribution on LAI retrieval in forest canopies. Ecol Model 197:179–195Google Scholar
  86. Jucker T, Asner GP, Dalponte M et al (2017a) A regional model for estimating the aboveground carbon density of Borneo’s tropical forests from airborne laser scanning. arXiv Prepr arXiv170509242Google Scholar
  87. Jucker T, Caspersen J, Chave J et al (2017b) Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Change Biol 23:177–190Google Scholar
  88. Jucker T, Asner GP, Dalponte M et al (2018a) Estimating aboveground carbon density and its uncertainty in Borneo’s structurally complex tropical forests using airborne laser scanning. Biogeosciences 15:3811–3830Google Scholar
  89. Jucker T, Bongalov B, Burslem DF et al (2018b) Topography shapes the structure, composition and function of tropical forest landscapes. Ecol Lett 21:989–1000Google Scholar
  90. Justice CO, Giglio L, Korontzi S et al (2002) The MODIS fire products. Remote Sens Environ 83:244–262Google Scholar
  91. Kearsley E, De Haulleville T, Hufkens K et al (2013) Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin. Nat Commun 4:2269Google Scholar
  92. Kellner JR, Asner GP (2009) Convergent structural responses of tropical forests to diverse disturbance regimes. Ecol Lett 12:887–897Google Scholar
  93. Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. Remote Sens Environ 114:2897–2910Google Scholar
  94. Ketterings QM, Coe R, van Noordwijk M et al (2001) Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For Ecol Manag 146:199–209. Google Scholar
  95. Kleinn C (2017) The renaissance of National Forest Inventories (NFIs) in the context of the international conventions—a discussion paper on context, background and justification of NFIs. Pesqui Florest Bras 37:369–379Google Scholar
  96. Kükenbrink D, Schneider FD, Leiterer R et al (2017) Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm. Remote Sens Environ 194:424–436. Google Scholar
  97. Labriere N, Tao S, Chave J 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 99:1–11Google Scholar
  98. Lagomasino D, Fatoyinbo T, Lee S-K, Simard M (2015) High-resolution forest canopy height estimation in an African blue carbon ecosystem. Remote Sens Ecol Conserv 1:51–60. Google Scholar
  99. Larjavaara M, Muller-Landau HC (2013) Measuring tree height: a quantitative comparison of two common field methods in a moist tropical forest. Methods Ecol Evol 4:793–801. Google Scholar
  100. Lau A, Bentley LP, Martius C et al (2018) Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling. Trees 32(5):1219–1231Google Scholar
  101. Le Toan T, Quegan S, Davidson MWJ 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
  102. Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) LiDAR remote sensing for ecosystem studies LiDAR, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. Bioscience 52:19–30Google Scholar
  103. Leitold V, Morton DC, Longo M et al (2018) El Niño drought increased canopy turnover in Amazon forests. New Phytol 219:959–971Google Scholar
  104. Li X, Strahler AH (1992) Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing. IEEE Trans Geosci Remote Sens 30:276–292Google Scholar
  105. Lindenmayer DB, Cunningham RB, Tanton MT et al (1991) Characteristics of hollow-bearing trees occupied by arboreal marsupials in the montane ash forests of the Central Highlands of Victoria, south-east Australia. For Ecol Manag 40:289–308Google Scholar
  106. Liu J-Y, Zheng Z, Xu X et al (2018) Abundance and distribution of cavity trees and the effect of topography on cavity presence in a tropical rainforest, southwestern China. Can J For Res 48:1058–1066Google Scholar
  107. Longo M, Keller M, dos-Santos MN et al (2016) Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob Biogeochem Cycles 30:1639–1660Google Scholar
  108. Lopez-Gonzalez G, Lewis SL, Burkitt M, Phillips OL (2011) a web application and research tool to manage and analyse tropical forest plot data. J Veg Sci 22:610–613Google Scholar
  109. Lyapustin A, Martonchik J, Wang Y et al (2011) Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables. J Geophys Res Atmos 116:1–9Google Scholar
  110. Ma L, Zheng G, Eitel JU et al (2016) Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial LiDAR point cloud data of forest canopies. IEEE Trans Geosci Remote Sens 54:679–696Google Scholar
  111. Malenovskỳ Z, Martin E, Homolová L et al (2008) Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution. Remote Sens Environ 112:1–18Google Scholar
  112. Marra RE, Brazee NJ, Fraver S (2018) Estimating carbon loss due to internal decay in living trees using tomography: implications for forest carbon budgets. Environ Res Lett. Google Scholar
  113. Marvin DC, Asner GP, Knapp DE et al (2014) Amazonian landscapes and the bias in field studies of forest structure and biomass. Proc Natl Acad Sci 111:E5224–E5232. Google Scholar
  114. Mascaro J, Detto M, Asner GP, Muller-Landau HC (2011) Evaluating uncertainty in mapping forest carbon with airborne LiDAR. Remote Sens Environ 115:3770–3774. Google Scholar
  115. McEwan RW, Lin Y-C, Sun I-F et al (2011) Topographic and biotic regulation of aboveground carbon storage in subtropical broad-leaved forests of Taiwan. For Ecol Manag 262:1817–1825. Google Scholar
  116. McRoberts RE, Westfall JA (2013) Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. For Sci 60(1):34–42Google Scholar
  117. Mermoz S, Le Toan T, Villard L et al (2014) Biomass assessment in the Cameroon savanna using ALOS PALSAR data. Remote Sens Environ 155:109–119. Google Scholar
  118. Mermoz S, Réjou-Méchain M, Villard L et al (2015) Decrease of L-band SAR backscatter with biomass of dense forests. Remote Sens Environ 15:307–317. Google Scholar
  119. Minh DHT, Le Toan T, Rocca F et al (2014) Relating P-band synthetic aperture radar tomography to tropical forest biomass. IEEE Trans Geosci Remote Sens 52:967–979Google Scholar
  120. Minh DHT, Le Toan T, Rocca F et al (2016) SAR tomography for the retrieval of forest biomass and height: cross-validation at two tropical forest sites in French Guiana. Remote Sens Environ 175:138–147Google Scholar
  121. Mitchard ETA (2018) The tropical forest carbon cycle and climate change. Nature 559:527–534. Google Scholar
  122. Mitchard ET, Saatchi SS, Baccini A et al (2013) Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manage 8:10Google Scholar
  123. Mitchard ETA, Feldpausch TR, Brienen RJW et al (2014) Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob Ecol Biogeogr 23:935–946. Google Scholar
  124. Molto Q, Rossi V, Blanc L (2013) Error propagation in biomass estimation in tropical forests. Methods Ecol Evol 4:175–183. Google Scholar
  125. Momo Takoudjou S, Ploton P, Sonké B et al (2018) Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: a comparison with traditional destructive approach. Methods Ecol Evol 9:905–916Google Scholar
  126. Morsdorf F, Eck C, Zgraggen C et al (2017) UAV-based LiDAR acquisition for the derivation of high-resolution forest and ground information. Lead Edge 36:566–570Google Scholar
  127. Morton DC, Nagol J, Carabajal CC et al (2014) Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506:221–224. Google Scholar
  128. Moundounga Mavouroulou Q, Ngomanda A, Engone Obiang NL et al (2014) How to improve allometric equations to estimate forest biomass stocks? Some hints from a central African forest. Can J For Res 44:685–691Google Scholar
  129. Myneni RB (1991) Modeling radiative transfer and photosynthesis in three-dimensional vegetation canopies. Agric For Meteorol 55:323–344Google Scholar
  130. Myneni RB, Ross J, Asrar G (1989) A review on the theory of photon transport in leaf canopies. Agric For Meteorol 45:1–153Google Scholar
  131. Ni W, Li X, Woodcock CE et al (1999) An analytical hybrid GORT model for bidirectional reflectance over discontinuous plant canopies. IEEE Trans Geosci Remote Sens 37:987–999Google Scholar
  132. 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. Google Scholar
  133. North PR (1996) Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans Geosci Remote Sens 34:946–956Google Scholar
  134. Pargal S, Fararoda R, Rajashekar G et al (2017) Inverting aboveground biomass–canopy texture relationships in a landscape of Forest mosaic in the Western Ghats of India using very high resolution Cartosat imagery. Remote Sens 9:228Google Scholar
  135. Pearson TR, Brown S, Murray L, Sidman G (2017) Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag 12:3Google Scholar
  136. Phillips OL, Baker TR, Brienen R, Feldpausch TR (2009) Field manual for plot establishment and remeasurement.
  137. Ploton P, Pélissier R, Proisy C et al (2012) Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecol Appl 22:993–1003Google Scholar
  138. Ploton P, Pélissier R, Barbier N et al (2013) Canopy texture analysis for large-scale assessments of tropical forest stand structure and biomass. In: Devy S, Ganesh T, Lowman MD (eds) Treetops at risk. Springer, Berlin, pp 237–245Google Scholar
  139. Ploton P, Barbier N, Momo ST et al (2016) Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries. Biogeosciences 13:1571–1585Google Scholar
  140. Ploton P, Barbier N, Couteron P et al (2017) Toward a general tropical forest biomass prediction model from very high resolution optical satellite images. Remote Sens Environ 200:140–153Google Scholar
  141. Proisy C, Couteron P, Fromard F (2007) Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sens Environ 109:379–392. Google Scholar
  142. Puliti S, Ørka HO, Gobakken T, Næsset E (2015) Inventory of small forest areas using an unmanned aerial system. Remote Sens 7:9632–9654Google Scholar
  143. Raumonen P, Kaasalainen M, Åkerblom M et al (2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5:491–520Google Scholar
  144. Réjou-Méchain M, Muller-Landau HC, Detto M et al (2014) Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks. Biogeosciences 11:6827–6840Google Scholar
  145. Réjou-Méchain M, Tymen B, Blanc L et al (2015) Using repeated small-footprint LiDAR maps to infer spatial variation and dynamics of a high-biomass neotropical forest. Remote Sens Environ 169:93–101Google Scholar
  146. Réjou-Méchain M, Tanguy A, Piponiot C et al (2017) BIOMASS: an r package for estimating above-ground biomass and its uncertainty in tropical forests. Methods Ecol Evol 8:1163–1167Google Scholar
  147. Robinson C, Saatchi S, Neumann M, Gillespie T (2013) Impacts of spatial variability on aboveground biomass estimation from L-band radar in a temperate forest. Remote Sens 5:1001–1023Google Scholar
  148. Rocchini D, Luque S, Pettorelli N et al (2018) Measuring β-diversity by remote sensing: a challenge for biodiversity monitoring. Methods Ecol Evol 9:1787–1798Google Scholar
  149. Rodrigues WA, Valle RC (1964) Ocorrência de troncos ocos em mata de baixio da regiao de Manaus, 16th edn. Publicacao. Botanica - Instituto Nacional de Pesquisa da Amazonia (Brazil), ManausGoogle Scholar
  150. Rodriguez-Veiga P, Wheeler J, Louis V et al (2017) Quantifying forest biomass carbon stocks from space. Curr For Rep 3:1–18Google Scholar
  151. Romijn E, De Sy V, Herold M et al (2018) Independent data for transparent monitoring of greenhouse gas emissions from the land use sector—what do stakeholders think and need? Environ Sci Policy 85:101–112Google Scholar
  152. Roşca S, Suomalainen J, Bartholomeus H, Herold M (2018) Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests. Interface Focus 8:20170038Google Scholar
  153. Rosen P, Hensley S, Shaffer S et al (2017) The NASA-ISRO SAR (NISAR) mission dual-band radar instrument preliminary design. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS), pp 3832–3835Google Scholar
  154. Roujean J-L, Leroy M, Deschamps P-Y (1992) A bidirectional reflectance model of the Earth’s surface for the correction of remote sensing data. J Geophys Res Atmos 97:20455–20468Google Scholar
  155. Saatchi SS, Houghton RA, Alvalá DS et al (2007) Distribution of aboveground live biomass in the Amazon basin. Glob Change Biol 13:816–837. Google Scholar
  156. Saatchi S, Marlier M, Chazdon RL et al (2011a) Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass. Remote Sens Environ 115:2836–2849. Google Scholar
  157. Saatchi SS, Harris NL, Brown S et al (2011b) Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci 108:9899–9904. Google Scholar
  158. Saatchi S, Mascaro J, Xu L et al (2015) Seeing the forest beyond the trees. Glob Ecol Biogeogr 24:606–610Google Scholar
  159. Sagang LBT, Momo ST, Libalah MB et al (2018) Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data. For Ecol Manag 424:519–528. Google Scholar
  160. Santoro M, Cartus O, Mermoz S et al (2018) A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations. In: EGU general assembly conference abstracts, p 18932Google Scholar
  161. Schlund M, von Poncet F, Kuntz S et al (2015) TanDEM-X data for aboveground biomass retrieval in a tropical peat swamp forest. Remote Sens Environ 158:255–266Google Scholar
  162. Schneider FD, Yin T, Gastellu-Etchegorry J et al (2014) At-sensor radiance simulation for airborne imaging spectroscopy. In: 2014 6th workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS), pp 1–4Google Scholar
  163. Sigrist P, Coppin P, Hermy M (1999) Impact of forest canopy on quality and accuracy of GPS measurements. Int J Remote Sens 20:3595–3610. Google Scholar
  164. Simard M, Pinto N, Fisher JB, Baccini A (2011) Mapping forest canopy height globally with spaceborne LiDAR. J Geophys Res Biogeosci 116:1–12Google Scholar
  165. Singh M, Malhi Y, Bhagwat S (2014) Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery. Int J Remote Sens 35:3331–3349Google Scholar
  166. Sitch S, Huntingford C, Gedney N et al (2008) Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Glob Change Biol 14:2015–2039Google Scholar
  167. Solberg S, May J, Bogren W et al (2018) Interferometric SAR DEMs for forest change in Uganda 2000–2012. Remote Sens 10:228Google Scholar
  168. Steininger MK (2000) Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. Int J Remote Sens 21:1139–1157Google Scholar
  169. St-Onge B, Vega C, Fournier RA, Hu Y (2008) Mapping canopy height using a combination of digital stereo-photogrammetry and LiDAR. Int J Remote Sens 29:3343–3364Google Scholar
  170. Sullivan MJ, Lewis SL, Hubau W et al (2018) Field methods for sampling tree height for tropical forest biomass estimation. Methods Ecol Evol 9:1179–1189Google Scholar
  171. Sun G, Ranson KJ (2000) Modeling LiDAR returns from forest canopies. IEEE Trans Geosci Remote Sens 38:2617–2626Google Scholar
  172. Swenson NG, Enquist BJ (2008) The relationship between stem and branch wood specific gravity and the ability of each measure to predict leaf area. Am J Bot 95:516–519Google Scholar
  173. Tarelkin Y, Hufkens K, Hahn S et al (2019) Wood anatomy variability under contrasted environmental conditions of common deciduous and evergreen species from central African forests. Trees Struct Funct 33:893–909. Google Scholar
  174. Trochta J, Krŭček M, Vrška T, Král K (2017) 3D Forest: an application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS ONE 12:e0176871Google Scholar
  175. Tymen B, Vincent G, Courtois EA et al (2017) Quantifying micro-environmental variation in tropical rainforest understory at landscape scale by combining airborne LiDAR scanning and a sensor network. Ann For Sci 74:32Google Scholar
  176. Verhoef W (1985) Earth observation modeling based on layer scattering matrices. Remote Sens Environ 17:165–178Google Scholar
  177. Vieilledent G, Vaudry R, Andriamanohisoa SF et al (2012) A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models. Ecol Appl 22:572–583Google Scholar
  178. Villard L (2009) Forward and inverse modeling of synthetic aperture radar in the bistatic configuration: applications in forest remote sensing. Ph.D. thesis, ONERAISAE-Universite Paul SabatierGoogle Scholar
  179. Villard L, Le Toan T (2015) Relating P-band SAR intensity to biomass for tropical dense forests in hilly terrain: γ 0 or t 0? IEEE J Sel Top Appl Earth Obs Remote Sens 8:214–223Google Scholar
  180. Vincent G, Caron F, Sabatier D, Blanc L (2012a) LiDAR shows that higher forests have more slender trees. Bois For Trop 314:51–56Google Scholar
  181. Vincent G, Sabatier D, Blanc L et al (2012b) Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Remote Sens Environ 125:23–33. Google Scholar
  182. Vincent G, Sabatier D, Rutishauser E (2014) Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape. Oecologia 175:439–443Google Scholar
  183. Vincent G, Antin C, Laurans M et al (2017) Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor. Remote Sens Environ 198:254–266Google Scholar
  184. Wassenberg M, Chiu H-S, Guo W, Spiecker H (2015) Analysis of wood density profiles of tree stems: incorporating vertical variations to optimize wood sampling strategies for density and biomass estimations. Trees 29:551–561Google Scholar
  185. Widlowski J-L, Pinty B, Lopatka M et al (2013) The fourth radiation transfer model intercomparison (RAMI-IV): proficiency testing of canopy reflectance models with ISO-13528. J Geophys Res Atmos 118:6869–6890. Google Scholar
  186. Williamson GB, Wiemann MC (2010) Measuring wood specific gravity… correctly. Am J Bot 97:519–524Google Scholar
  187. Wu X, Liu H, Li X et al (2018) Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob Change Biol 24:504–516Google Scholar
  188. Xu L, Saatchi SS, Yang Y et al (2016) Performance of non-parametric algorithms for spatial mapping of tropical forest structure. Carbon Balance Manag 11:18Google Scholar
  189. Xu L, Saatchi SS, Shapiro A et al (2017) Spatial distribution of carbon stored in forests of the Democratic Republic of Congo. Sci Rep 7:15030Google Scholar
  190. Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC, Chave J (2009) Data from: towards a worldwide wood economics spectrum. Dryad Digit Repos. Google Scholar
  191. Zolkos SG, Goetz SJ, Dubayah R (2013) A meta-analysis of terrestrial aboveground biomass estimation using LiDAR remote sensing. Remote Sens Environ 128:289–298. Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Maxime Réjou-Méchain
    • 1
    Email author
  • Nicolas Barbier
    • 1
  • Pierre Couteron
    • 1
  • Pierre Ploton
    • 1
  • Grégoire Vincent
    • 1
  • Martin Herold
    • 2
  • Stéphane Mermoz
    • 3
    • 8
  • Sassan Saatchi
    • 4
  • Jérôme Chave
    • 5
  • Florian de Boissieu
    • 6
  • Jean-Baptiste Féret
    • 6
  • Stéphane Momo Takoudjou
    • 1
    • 7
  • Raphaël Pélissier
    • 1
  1. 1.AMAP, IRD, CNRS, CIRAD, INRAUniv MontpellierMontpellier Cedex 05France
  2. 2.Laboratory of Geo-Information Science and Remote SensingWageningen University and ResearchWageningenThe Netherlands
  3. 3.CESBIO, CNES/CNRS, IRD/UPSUniversité de ToulouseToulouseFrance
  4. 4.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  5. 5.CNRS, ENFA; UMR5174 EDB (Laboratoire Evolution et Diversité Biologique)Université Paul SabatierToulouseFrance
  6. 6.TETIS, Irstea, AgroParisTech, CIRAD, CNRSUniversity of MontpellierMontpellierFrance
  7. 7.Plant Systematic and Ecology Laboratory, Higher Teacher’s Training CollegeUniversity of Yaoundé IYaoundéCameroon
  8. 8.GlobEOToulouseFrance

Personalised recommendations