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

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Abstract

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.

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Courtesy of L. Descroix, unpublished

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References

  • 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 2015:110–112

    Google Scholar 

  • Arciniegas A, Prieto F, Brancheriau L, Lasaygues P (2014) Literature review of acoustic and ultrasonic tomography in standing trees. Trees 28:1559–1567

    Google Scholar 

  • Asner GP, Mascaro J (2014) Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric. Remote Sens Environ 140:614–624

    Google Scholar 

  • 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. https://doi.org/10.1073/pnas.0604093103

    Google Scholar 

  • 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. https://doi.org/10.1186/1750-0680-8-7

    Google Scholar 

  • 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. https://doi.org/10.1016/j.foreco.2017.11.047

    Google Scholar 

  • 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. https://doi.org/10.1111/gcb.13139

    Google Scholar 

  • Baccini A, Asner GP (2013) Improving pantropical forest carbon maps with airborne LiDAR sampling. Carbon Manag 4:591–600

    Google Scholar 

  • 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. https://doi.org/10.1038/nclimate1354

    Google Scholar 

  • Baker TR, Phillips OL, Malhi Y et al (2004) Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob Change Biol 10:545–562

    Google Scholar 

  • 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. https://doi.org/10.1111/j.1466-8238.2012.00778.x

    Google Scholar 

  • Barbier N, Couteron P (2015) Attenuating the bidirectional texture variation of satellite images of tropical forest canopies. Remote Sens Environ 171:245–260

    Google Scholar 

  • 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 115:167–179

    Google Scholar 

  • 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. https://doi.org/10.1890/13-1574.1

    Google Scholar 

  • 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:e0142146

    Google Scholar 

  • Bastin J-F, Barbier N, Réjou-Méchain M et al (2015b) Seeing Central African forests through their largest trees. Sci Rep 5:13156

    Google Scholar 

  • 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 7:127

    Google Scholar 

  • 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 184:82–97

    Google Scholar 

  • 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 209:141–147

    Google Scholar 

  • 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 206:156–173

    Google Scholar 

  • 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 156:322–334

    Google Scholar 

  • 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

    Google Scholar 

  • 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. https://doi.org/10.1111/gcb.13087

    Google Scholar 

  • Calders K, Newnham G, Burt A et al (2014) Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods Ecol Evol. https://doi.org/10.1111/2041-210x.12301

    Article  Google Scholar 

  • Calders K, Origo N, Burt A et al (2018) Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling. Remote Sens 10:933

    Google Scholar 

  • Cescatti A (1997) Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. I. Model structure and algorithms. Ecol Model 101:263–274

    Google Scholar 

  • 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. https://doi.org/10.1073/pnas.1202894110

    Google Scholar 

  • 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. https://doi.org/10.1016/j.foreco.2017.08.001

    Google Scholar 

  • 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 359:409–420

    Google Scholar 

  • 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. https://doi.org/10.1007/s00442-005-0100-x

    Google Scholar 

  • Chave J, Coomes D, Jansen S et al (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:351–366

    Google Scholar 

  • 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. https://doi.org/10.1111/gcb.12629

    Google Scholar 

  • 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 160:134–143

    Google Scholar 

  • Clark DA (2002) Are tropical forests an important carbon sink? Reanalysis of the long-term plot data. Ecol Appl 12:3–7. https://doi.org/10.1890/1051-0761(2002)012%5b0003:atfaic%5d2.0.co;2

    Google Scholar 

  • Clark D, Clark D (2000) Landscape-scale variation in forest structure and biomass in a tropical rain forest. For Ecol Manag 137:185–198. https://doi.org/10.1016/s0378-1127(99)00327-8

    Google Scholar 

  • Clark DB, Kellner JR (2012) Tropical forest biomass estimation and the fallacy of misplaced concreteness. J Veg Sci 23:1191–1196. https://doi.org/10.1111/j.1654-1103.2012.01471.x

    Google Scholar 

  • 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 

  • Condit R, Ashton PS, Baker P et al (2000) Spatial patterns in the distribution of tropical tree species. Science 288(5470):1414–1418

    Google Scholar 

  • Condit R, Lao S, Singh A et al (2014) Data and database standards for permanent forest plots in a global network. For Ecol Manag 316:21–31

    Google Scholar 

  • 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. https://doi.org/10.1016/j.envsoft.2010.12.008

    Google Scholar 

  • 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 42:1121–1128

    Google Scholar 

  • 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 109:143–160

    Google Scholar 

  • 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. https://doi.org/10.1016/j.foreco.2006.06.024

    Google Scholar 

  • 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 52:580–590

    Google Scholar 

  • De Reffye P, Houllier F, Blaise F et al (1995) A model simulating above-and below-ground tree architecture with agroforestry applications. Agrofor Syst 30:175–197

    Google Scholar 

  • 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 10:637

    Google Scholar 

  • 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. https://doi.org/10.1371/journal.pone.0076296

    Google Scholar 

  • Dickinson TA, Tanner EVJ (1978) Exploitation of hollow trunks by tropical trees. Biotropica 10:231–233. https://doi.org/10.2307/2387908

    Google Scholar 

  • Disney M (2018) Terrestrial LiDAR: a 3D revolution in how we look at trees. New Phytol. https://doi.org/10.1111/nph.15517

    Article  Google Scholar 

  • Egbert DD (1977) A practical method for correcting bidirectional reflectance variations. In: LARS symposia, p 203

  • 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. https://doi.org/10.1080/17550874.2013.772257

    Article  Google Scholar 

  • ESA (2012) Report for mission selection: biomass, ESA SP-1324/1 (3 volume series). European Space Agency Noordwijk, The Netherlands

  • 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 52:502–514

    Google Scholar 

  • 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. https://doi.org/10.1016/j.foreco.2013.05.036

    Google Scholar 

  • Feldpausch TR, Banin L, Phillips OL et al (2011) Height–diameter allometry of tropical forest trees. Biogeosciences 8:1081–1106

    Google Scholar 

  • Feldpausch TR, Lloyd J, Lewis SL et al (2012) Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9:3381–3403. https://doi.org/10.5194/bg-9-3381-2012

    Google Scholar 

  • Féret J-B, Asner GP (2014) Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol Appl 24:1289–1296

    Google Scholar 

  • Ferraz A, Saatchi S, Mallet C, Meyer V (2016) LiDAR detection of individual tree size in tropical forests. Remote Sens Environ 183:318–333

    Google Scholar 

  • Féret JB, Gitelson AA, Noble SD, Jacquemoud S (2017) PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens Environ 193:204–215

    Google Scholar 

  • Flores O, Coomes DA (2011) Estimating the wood density of species for carbon stock assessments. Methods Ecol Evol 2:214–220. https://doi.org/10.1111/j.2041-210x.2010.00068.x

    Google Scholar 

  • 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 214:65–90

    Google Scholar 

  • 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 115:636–649

    Google Scholar 

  • Fuller WA (1987) Measurement error models. Wiley, New York

    Google Scholar 

  • 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 74:27

    Google Scholar 

  • Gastellu-Etchegorry J-P, Demarez V, Pinel V, Zagolski F (1996) Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sens Environ 58:131–156

    Google Scholar 

  • 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 7:1667–1701

    Google Scholar 

  • 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 39:1036–1052

    Google Scholar 

  • 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. https://doi.org/10.1111/j.1654-1103.2012.01441.x

    Google Scholar 

  • 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 9:223–234

    Google Scholar 

  • Goodman RC, Phillips OL, Baker TR (2014) The importance of crown dimensions to improve tropical tree biomass estimates. Ecol Appl 24:680–698. https://doi.org/10.1890/13-0070.1

    Google Scholar 

  • 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. https://doi.org/10.1111/j.1365-2745.2011.01829.x

    Google Scholar 

  • 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 191:373–388

    Google Scholar 

  • Gregoire TG, Næsset E, McRoberts RE et al (2016) Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass. Remote Sens Environ 173:98–108

    Google Scholar 

  • Guitet S, Pélissier R, Brunaux O et al (2015) Geomorphological landscape features explain floristic patterns in French Guiana rainforest. Biodivers Conserv 24:1215–1237

    Google Scholar 

  • Guitet S, Sabatier D, Brunaux O et al (2018) Disturbance regimes drive the diversity of regional floristic pools across Guianan rainforest landscapes. Sci Rep 8:3872

    Google Scholar 

  • 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 9:213

    Google Scholar 

  • Hansen MC, Potapov PV, Moore R et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853

    Google Scholar 

  • 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 260:1375–1388

    Google Scholar 

  • 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. https://doi.org/10.1007/s10712-019-09510-6

    Article  Google Scholar 

  • 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 10:19

    Google Scholar 

  • Hunter MO, Keller M, Victoria D, Morton DC (2013) Tree height and tropical forest biomass estimation. Biogeosciences 10:8385–8399

    Google Scholar 

  • 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 3510–3512

  • Ishimaru A (1978) Wave propagation and scattering in random media, vol 2. Academic press, New York, pp 336–393

    Google Scholar 

  • 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. https://doi.org/10.1890/1540-9295(2004)002%5b0475:witwam%5d2.0.co;2

    Google Scholar 

  • 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 197:179–195

    Google Scholar 

  • 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 arXiv170509242

  • Jucker T, Caspersen J, Chave J et al (2017b) Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Change Biol 23:177–190

    Google Scholar 

  • 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 15:3811–3830

    Google Scholar 

  • Jucker T, Bongalov B, Burslem DF et al (2018b) Topography shapes the structure, composition and function of tropical forest landscapes. Ecol Lett 21:989–1000

    Google Scholar 

  • Justice CO, Giglio L, Korontzi S et al (2002) The MODIS fire products. Remote Sens Environ 83:244–262

    Google Scholar 

  • 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 4:2269

    Google Scholar 

  • Kellner JR, Asner GP (2009) Convergent structural responses of tropical forests to diverse disturbance regimes. Ecol Lett 12:887–897

    Google Scholar 

  • 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 114:2897–2910

    Google Scholar 

  • 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. https://doi.org/10.1016/s0378-1127(00)00460-6

    Google Scholar 

  • 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 37:369–379

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2016.10.023

    Google Scholar 

  • 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 99:1–11

    Google Scholar 

  • 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. https://doi.org/10.1002/rse2.3

    Google Scholar 

  • 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. https://doi.org/10.1111/2041-210x.12071

    Google Scholar 

  • Lau A, Bentley LP, Martius C et al (2018) Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling. Trees 32(5):1219–1231

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2011.03.020

    Google Scholar 

  • 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 52:19–30

    Google Scholar 

  • Leitold V, Morton DC, Longo M et al (2018) El Niño drought increased canopy turnover in Amazon forests. New Phytol 219:959–971

    Google Scholar 

  • 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 30:276–292

    Google Scholar 

  • 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 40:289–308

    Google Scholar 

  • 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 48:1058–1066

    Google Scholar 

  • 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 30:1639–1660

    Google Scholar 

  • Lopez-Gonzalez G, Lewis SL, Burkitt M, Phillips OL (2011) ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J Veg Sci 22:610–613

    Google Scholar 

  • 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 116:1–9

    Google Scholar 

  • 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 54:679–696

    Google Scholar 

  • 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 112:1–18

    Google Scholar 

  • 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. https://doi.org/10.1088/1748-9326/aae2bf

    Article  Google Scholar 

  • 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. https://doi.org/10.1073/pnas.1412999111

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2011.07.019

    Google Scholar 

  • 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. https://doi.org/10.1016/j.foreco.2011.07.028

    Google Scholar 

  • McRoberts RE, Westfall JA (2013) Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. For Sci 60(1):34–42

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2014.01.029

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2014.12.019

    Google Scholar 

  • 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 52:967–979

    Google Scholar 

  • 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 175:138–147

    Google Scholar 

  • Mitchard ETA (2018) The tropical forest carbon cycle and climate change. Nature 559:527–534. https://doi.org/10.1038/s41586-018-0300-2

    Google Scholar 

  • 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 8:10

    Google Scholar 

  • 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. https://doi.org/10.1111/geb.12168

    Google Scholar 

  • Molto Q, Rossi V, Blanc L (2013) Error propagation in biomass estimation in tropical forests. Methods Ecol Evol 4:175–183. https://doi.org/10.1111/j.2041-210x.2012.00266.x

    Google Scholar 

  • 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 9:905–916

    Google Scholar 

  • 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 36:566–570

    Google Scholar 

  • 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. https://doi.org/10.1038/nature13006

    Google Scholar 

  • 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 44:685–691

    Google Scholar 

  • Myneni RB (1991) Modeling radiative transfer and photosynthesis in three-dimensional vegetation canopies. Agric For Meteorol 55:323–344

    Google Scholar 

  • Myneni RB, Ross J, Asrar G (1989) A review on the theory of photon transport in leaf canopies. Agric For Meteorol 45:1–153

    Google Scholar 

  • 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 37:987–999

    Google Scholar 

  • 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. https://doi.org/10.1016/j.foreco.2006.02.004

    Google Scholar 

  • North PR (1996) Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans Geosci Remote Sens 34:946–956

    Google Scholar 

  • 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 9:228

    Google Scholar 

  • Pearson TR, Brown S, Murray L, Sidman G (2017) Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag 12:3

    Google Scholar 

  • Phillips OL, Baker TR, Brienen R, Feldpausch TR (2009) Field manual for plot establishment and remeasurement. https://www.forestplots.net/upload/ManualsEnglish/RAINFOR_field_manual_EN.pdf

  • Ploton P, Pélissier R, Proisy C et al (2012) Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecol Appl 22:993–1003

    Google Scholar 

  • 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 237–245

    Google Scholar 

  • 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 13:1571–1585

    Google Scholar 

  • 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 200:140–153

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2007.01.009

    Google Scholar 

  • Puliti S, Ørka HO, Gobakken T, Næsset E (2015) Inventory of small forest areas using an unmanned aerial system. Remote Sens 7:9632–9654

    Google Scholar 

  • Raumonen P, Kaasalainen M, Åkerblom M et al (2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5:491–520

    Google Scholar 

  • 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 11:6827–6840

    Google Scholar 

  • 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 169:93–101

    Google Scholar 

  • 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 8:1163–1167

    Google Scholar 

  • 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 5:1001–1023

    Google Scholar 

  • Rocchini D, Luque S, Pettorelli N et al (2018) Measuring β-diversity by remote sensing: a challenge for biodiversity monitoring. Methods Ecol Evol 9:1787–1798

    Google Scholar 

  • 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), Manaus

    Google Scholar 

  • Rodriguez-Veiga P, Wheeler J, Louis V et al (2017) Quantifying forest biomass carbon stocks from space. Curr For Rep 3:1–18

    Google Scholar 

  • 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 85:101–112

    Google Scholar 

  • 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 8:20170038

    Google Scholar 

  • 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 3832–3835

  • 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 97:20455–20468

    Google Scholar 

  • Saatchi SS, Houghton RA, Alvalá DS et al (2007) Distribution of aboveground live biomass in the Amazon basin. Glob Change Biol 13:816–837. https://doi.org/10.1111/j.1365-2486.2007.01323.x

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2010.07.015

    Google Scholar 

  • 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. https://doi.org/10.1073/pnas.1019576108

    Google Scholar 

  • Saatchi S, Mascaro J, Xu L et al (2015) Seeing the forest beyond the trees. Glob Ecol Biogeogr 24:606–610

    Google Scholar 

  • 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. https://doi.org/10.1016/j.foreco.2018.04.054

    Google Scholar 

  • 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 18932

  • 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 158:255–266

    Google Scholar 

  • 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 1–4

  • 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. https://doi.org/10.1080/014311699211228

    Google Scholar 

  • Simard M, Pinto N, Fisher JB, Baccini A (2011) Mapping forest canopy height globally with spaceborne LiDAR. J Geophys Res Biogeosci 116:1–12

    Google Scholar 

  • 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 35:3331–3349

    Google Scholar 

  • 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 14:2015–2039

    Google Scholar 

  • Solberg S, May J, Bogren W et al (2018) Interferometric SAR DEMs for forest change in Uganda 2000–2012. Remote Sens 10:228

    Google Scholar 

  • Steininger MK (2000) Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. Int J Remote Sens 21:1139–1157

    Google Scholar 

  • 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 29:3343–3364

    Google Scholar 

  • Sullivan MJ, Lewis SL, Hubau W et al (2018) Field methods for sampling tree height for tropical forest biomass estimation. Methods Ecol Evol 9:1179–1189

    Google Scholar 

  • Sun G, Ranson KJ (2000) Modeling LiDAR returns from forest canopies. IEEE Trans Geosci Remote Sens 38:2617–2626

    Google Scholar 

  • 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 95:516–519

    Google Scholar 

  • 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. https://doi.org/10.1007/s00468-019-01826-5

    Google Scholar 

  • 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:e0176871

    Google Scholar 

  • 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 74:32

    Google Scholar 

  • Verhoef W (1985) Earth observation modeling based on layer scattering matrices. Remote Sens Environ 17:165–178

    Google Scholar 

  • 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 22:572–583

    Google Scholar 

  • 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 Sabatier

  • 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 8:214–223

    Google Scholar 

  • Vincent G, Caron F, Sabatier D, Blanc L (2012a) LiDAR shows that higher forests have more slender trees. Bois For Trop 314:51–56

    Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2012.06.019

    Google Scholar 

  • 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 175:439–443

    Google Scholar 

  • 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 198:254–266

    Google Scholar 

  • 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 29:551–561

    Google Scholar 

  • 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. https://doi.org/10.1002/jgrd.50497

    Google Scholar 

  • Williamson GB, Wiemann MC (2010) Measuring wood specific gravity… correctly. Am J Bot 97:519–524

    Google Scholar 

  • Wu X, Liu H, Li X et al (2018) Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob Change Biol 24:504–516

    Google Scholar 

  • Xu L, Saatchi SS, Yang Y et al (2016) Performance of non-parametric algorithms for spatial mapping of tropical forest structure. Carbon Balance Manag 11:18

    Google Scholar 

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

    Google Scholar 

  • 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. https://doi.org/10.5061/dryad.234

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.rse.2012.10.017

    Google Scholar 

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Acknowledgements

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).

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Authors and Affiliations

  1. AMAP, IRD, CNRS, CIRAD, INRA, Univ Montpellier, Montpellier Cedex 05, France

    Maxime Réjou-Méchain, Nicolas Barbier, Pierre Couteron, Pierre Ploton, Grégoire Vincent, Stéphane Momo Takoudjou & Raphaël Pélissier

  2. Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB, Wageningen, The Netherlands

    Martin Herold

  3. CESBIO, CNES/CNRS, IRD/UPS, Université de Toulouse, Toulouse, France

    Stéphane Mermoz

  4. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA

    Sassan Saatchi

  5. CNRS, ENFA; UMR5174 EDB (Laboratoire Evolution et Diversité Biologique), Université Paul Sabatier, 118 Route de Narbonne, 31062, Toulouse, France

    Jérôme Chave

  6. TETIS, Irstea, AgroParisTech, CIRAD, CNRS, University of Montpellier, 34000, Montpellier, France

    Florian de Boissieu & Jean-Baptiste Féret

  7. Plant Systematic and Ecology Laboratory, Higher Teacher’s Training College, University of Yaoundé I, P.O. Box 047, Yaoundé, Cameroon

    Stéphane Momo Takoudjou

  8. GlobEO, Avenue Saint-Exupery, Toulouse, France

    Stéphane Mermoz

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  1. Maxime Réjou-Méchain
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Réjou-Méchain, M., Barbier, N., Couteron, P. et al. Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them. Surv Geophys 40, 881–911 (2019). https://doi.org/10.1007/s10712-019-09532-0

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