Quantifying micro-environmental variation in tropical rainforest understory at landscape scale by combining airborne LiDAR scanning and a sensor network

  • Blaise Tymen
  • Grégoire Vincent
  • Elodie A. Courtois
  • Julien Heurtebize
  • Jean Dauzat
  • Isabelle Marechaux
  • Jérôme Chave
Original Paper


Key message

We combined aerial LiDAR and ground sensors to map the spatial variation in micro-environmental variables of the tropical forest understory. We show that these metrics depend on forest type and proximity to canopy gaps. Our study has implications for the study of natural forest regeneration.


Light impacts seedling dynamics and animals, either directly or through their effect on air temperature and relative humidity. However, the micro-environment of tropical forest understories is heterogeneous.


We explored whether aerial laser scanning (LiDAR) can describe short-scale micro-environmental variables. We also studied the determinants of their spatial and intra-annual variation.


We used a small-footprint LiDAR coverage combined with data obtained from 47 environmental sensors monitoring continuously understory light, moisture and temperature during 1 year over the area. We developed and tested two models relating micro-environmental conditions to LiDAR metrics.


We found that a volume-based model predicts empirical light fluxes better than a model based on the proportion of the LiDAR signal reaching the ground. Understory field sensors measured an average daily light flux between 2.9 and 4.7% of full sunlight. Relative seasonal variation was comparable in the understory and in clearings. In canopy gaps, light flux was 4.3 times higher, maximal temperature 15% higher and minimal relative humidity 25% lower than in the forest understory. We found consistent micro-environmental differences among forest types.


LiDAR coverage improves the fine-scale description of micro-environmental variables of tropical forest understories. This opens avenues for modelling the distribution and dynamics of animal and plant populations.


French Guiana Light Nouragues station Plant dynamics Temperature Relative humidity 



We thank Lætitia Proux, Guillaume Robert and Guilhem Sommeria-Klein for their help collecting data on the field. We thank Maxime Réjou-Méchain for his highly constructive criticisms on the manuscript.

Compliance with ethical standards


We gratefully acknowledge financial support from CNES (TOSCA programme), and from ‘Investissement d’Avenir’ grants managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10- LABX-25-01; TULIP: ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001).

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

13595_2017_628_MOESM1_ESM.docx (119 kb)
Fig. S1 (DOCX 119 kb)
13595_2017_628_MOESM2_ESM.docx (116 kb)
Fig. S2 (DOCX 115 kb)
13595_2017_628_MOESM3_ESM.docx (197 kb)
Fig. S3 (DOCX 196 kb)
13595_2017_628_MOESM4_ESM.docx (102 kb)
Fig. S4 (DOCX 101 kb)
13595_2017_628_MOESM5_ESM.docx (84 kb)
Fig. S5 (DOCX 83 kb)
13595_2017_628_MOESM6_ESM.docx (14 kb)
Table S1 (DOCX 14 kb)
13595_2017_628_MOESM7_ESM.docx (12 kb)
Table S2 (DOCX 12 kb)
13595_2017_628_MOESM8_ESM.docx (16 kb)
Table S3 (DOCX 16 kb)


  1. Ackerly DD, Bazzaz FA (1995) Seedling crown orientation and interception of diffuse radiation in tropical forest gaps. Ecology 76:1134–1146. doi: 10.2307/1940921 CrossRefGoogle Scholar
  2. Balzter H, Braun PW, Köhler W (1998) Cellular automata models for vegetation dynamics. Ecol Model 107:113–125CrossRefGoogle Scholar
  3. Baraloto C, Goldberg DE (2004) Microhabitat associations and seedling bank dynamics in a neotropical forest. Oecologia 141:701–712. doi: 10.1007/s00442-004-1691-3 CrossRefPubMedGoogle Scholar
  4. Baskin CC, Baskin JM (2001) Seeds: ecology, biogeography, and evolution of dormancy and germination. ElsevierGoogle Scholar
  5. Bode CA, Limm MP, Power ME, Finlay JC (2014) Subcanopy solar radiation model: predicting solar radiation across a heavily vegetated landscape using LiDAR and GIS solar radiation models. Remote Sens Environ 154:387–397. doi: 10.1016/j.rse.2014.01.028 CrossRefGoogle Scholar
  6. Bone, R. A., D. W. Lee, and J. M. Norman. 1985. “Epidermal Cells Functioning as Lenses in Leaves of Tropical Rain-Forest Shade Plants.” Applied Optics 24 (10): 1408. doi: 10.1364/AO.24.001408
  7. Bongers, Frans, Peter J. van der Meer, and Marc Théry. 2001. “Scales of Ambient Light Variation.” In Nouragues, edited by Frans Bongers, Pierre Charles-Dominique, Pierre-Michel Forget, and Marc Théry, 19–30. Monographiae Biologicae 80. Springer Netherlands. http://link.springer.com/chapter/10.1007/978-94-015-9821-7_3
  8. Brokaw NVL (1982) The definition of treefall gap and its effect on measures of forest dynamics. Biotropica 14:158–160. doi: 10.2307/2387750 CrossRefGoogle Scholar
  9. Calders K, Schenkels T, Bartholomeus H et al (2015) Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements. Agric For Meteorol 203:158–168. doi: 10.1016/j.agrformet.2015.01.009 CrossRefGoogle Scholar
  10. Canham CD, Finzi AC, Pacala SW, Burbank DH (1994) Causes and consequences of resource heterogeneity in forests: interspecific variation in light transmission by canopy trees. Can J For Res 24:337–349. doi: 10.1139/x94-046 CrossRefGoogle Scholar
  11. Capers RS, Chazdon RL (2004) Rapid assessment of understory light availability in a wet tropical forest. Agric For Meteorol 123:177–185. doi: 10.1016/j.agrformet.2003.12.009 CrossRefGoogle Scholar
  12. Chazdon RL, Fetcher N (1984) Photosynthetic light environments in a lowland tropical rain forest in Costa Rica. J Ecol 72:553–564. doi: 10.2307/2260066 CrossRefGoogle Scholar
  13. Chazdon RL, Pearcy RW (1991) The importance of sunflecks for forest understory plants. Bioscience 41:760–766. doi: 10.2307/1311725 CrossRefGoogle Scholar
  14. Dalling, J. W., and S. P. Hubbell. 2002. “Seed Size, Growth Rate and Gap Microsite Conditions as Determinants of Recruitment Success for Pioneer Species.” Journal of Ecology 90 (3): 557–68. doi: 10.1046/j.1365-2745.2002.00695.x
  15. Dauzat J, Franck N, Vaast P, et al., (2007) Using virtual plants for upscaling carbon assimilation from the leaf to the canopy level. Application to coffee agroforestry systems. In: 21st International Conference on Coffee Science, Montpellier, France, 11–15 September, 2006. Association Scientifique Internationale du Café (ASIC), pp 1037–1044Google Scholar
  16. 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. doi: 10.1016/S0168-1923(01)00236-2 CrossRefGoogle Scholar
  17. Den Dulk JA (1989) The interpretation of remote sensing: a feasibility study. Landbouwuniversiteit te WageningenGoogle Scholar
  18. Dirzo R, Horvitz CC, Quevedo H, Lopez MA (1992) The effects of gap size and age on the understorey herb community of a tropical Mexican rain forest. J Ecol 80:809–822. doi: 10.2307/2260868 CrossRefGoogle Scholar
  19. Endler JA (1993) The color of light in forests and its implications. Ecol Monogr 63:2–27. doi: 10.2307/2937121 CrossRefGoogle Scholar
  20. Engelbrecht BMJ, Herz HM (2001) Evaluation of different methods to estimate understorey light conditions in tropical forests. J Trop Ecol 17:207–224. doi: 10.1017/S0266467401001146 CrossRefGoogle Scholar
  21. Gehrig-Downie C, Obregón A, Bendix J, Gradstein SR (2011) Epiphyte biomass and canopy microclimate in the tropical lowland cloud forest of French Guiana. Biotropica 43:591–596CrossRefGoogle Scholar
  22. Glaister P (2001) 85.13 least squares revisited. Math Gaz 85:104–107. doi: 10.2307/3620485 CrossRefGoogle Scholar
  23. Goetz SJ, Steinberg D, Betts MG et al (2010) LiDAR remote sensing variables predict breeding habitat of a Neotropical migrant bird. Ecology 91:1569–1576. doi: 10.1890/09-1670.1 CrossRefPubMedGoogle Scholar
  24. GRASS Development Team (2012) Geographic Resources Analysis Support System (GRASS) Software. Open Source Geospatial Foundation ProjectGoogle Scholar
  25. Heiskanen J, Korhonen L, Hietanen J, Pellikka PKE (2015) Use of airborne LiDAR for estimating canopy gap fraction and leaf area index of tropical montane forests. Int J Remote Sens 36:2569–2583. doi: 10.1080/01431161.2015.1041177 CrossRefGoogle Scholar
  26. Hofierka J, Suri M, others (2002) The solar radiation model for open source GIS: implementation and applications. In: Proceedings of the Open source GIS-GRASS users conference. pp 1–19Google Scholar
  27. Hubbell SP, Foster RB, O’Brien ST et al (1999) Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 283:554–557. doi: 10.1126/science.283.5401.554 CrossRefPubMedGoogle Scholar
  28. Hudson HJ (1968) The ecology of fungi on plant remains above the soil. New Phytol 67:837–874. doi: 10.1111/j.1469-8137.1968.tb06399.x CrossRefGoogle Scholar
  29. Insenburg M., LAStools—efficient LiDAR processing software (version 160921, academic) obtained from http://rapidlasso.com/LAStools
  30. Laurans M, Martin O, Nicolini E, Vincent G (2012) Functional traits and their plasticity predict tropical trees regeneration niche even among species with intermediate light requirements. Journal of Ecology 100:1440–1452. doi: 10.1111/j.1365-2745.2012.02007.x
  31. Le Galliard J-F, Guarini J-M, Gaill F (2012) Sensors for ecology: towards integrated knowledge of ecosystems. CNRS-[Institut écologie et environnement]Google Scholar
  32. Lee, David W. 1987. “The Spectral Distribution of Radiation in Two Neotropical Rainforests.” Biotropica 19 (2): 161–66. doi: 10.2307/2388739
  33. Lee H, Slatton KC, Roth BE, Cropper WP (2009) Prediction of forest canopy light interception using three-dimensional airborne LiDAR data. Int J Remote Sens 30:189–207. doi: 10.1080/01431160802261171 CrossRefGoogle Scholar
  34. Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) LiDAR remote sensing for ecosystem studies. Bioscience 52:19–30. doi: 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2 CrossRefGoogle Scholar
  35. Linnet K (1993) Evaluation of regression procedures for methods comparison studies. Clin Chem 39:424–432PubMedGoogle Scholar
  36. Long MH, Rheuban JE, Berg P, Zieman JC (2012) A comparison and correction of light intensity loggers to photosynthetically active radiation sensors. Limnol Oceanogr Methods 10:416–424. doi: 10.4319/lom.2012.10.416 CrossRefGoogle Scholar
  37. Marthews TR, Burslem DFRP, Paton SR et al (2008) Soil drying in a tropical forest: three distinct environments controlled by gap size. Ecol Model 216:369–384. doi: 10.1016/j.ecolmodel.2008.05.011 CrossRefGoogle Scholar
  38. Monteith J, Unsworth M (2013) Principles of environmental physics: plants, animals, and the atmosphere. Academic PressGoogle Scholar
  39. Montgomery RA, Chazdon RL (2001) Forest structure, canopy architecture, and light transmittance in tropical wet forests. Ecology 82:2707–2718. doi: 10.1890/0012-9658(2001)082[2707:FSCAAL]2.0.CO;2 CrossRefGoogle Scholar
  40. Montgomery R, Chazdon R (2002) Light gradient partitioning by tropical tree seedlings in the absence of canopy gaps. Oecologia 131:165–174. doi: 10.1007/s00442-002-0872-1 CrossRefPubMedGoogle Scholar
  41. Mücke W, Hollaus M, et al., (2011) Modelling light conditions in forests using airborne laser scanning data.Google Scholar
  42. Newnham GJ, Armston JD, Calders K et al (2015) Terrestrial laser scanning for plot-scale forest measurement. Curr For Rep 1:239–251. doi: 10.1007/s40725-015-0025-5 Google Scholar
  43. Nicotra AB, Chazdon RL, Iriarte SVB (1999) Spatial heterogeneity of light and woody seedling regeneration in tropical wet forests. Ecology 80:1908–1926. doi: 10.1890/0012-9658(1999)080[1908:SHOLAW]2.0.CO;2 CrossRefGoogle Scholar
  44. Obregon A, Gehrig-Downie C, Gradstein SR et al (2011) Canopy level fog occurrence in a tropical lowland forest of French Guiana as a prerequisite for high epiphyte diversity. Agric For Meteorol 151:290–300. doi: 10.1016/j.agrformet.2010.11.003 CrossRefGoogle Scholar
  45. Palomaki MB, Chazdon RL, Arroyo JP, Letcher SG (2006) Juvenile tree growth in relation to light availability in second-growth tropical rain forests. J Trop Ecol 22:223–226. doi: 10.1017/S0266467405002968 CrossRefGoogle Scholar
  46. Parker GG, Lefsky MA, Harding DJ (2001) Light transmittance in forest canopies determined using airborne laser altimetry and in-canopy quantum measurements. Remote Sens Environ 76:298–309. doi: 10.1016/S0034-4257(00)00211-X CrossRefGoogle Scholar
  47. Peng S, Zhao C, Xu Z (2014) Modeling spatiotemporal patterns of understory light intensity using airborne laser scanner (LiDAR). ISPRS J Photogramm Remote Sens 97:195–203. doi: 10.1016/j.isprsjprs.2014.09.003 CrossRefGoogle Scholar
  48. Raumonen P, Kaasalainen M, Åkerblom M et al (2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5:491–520. doi: 10.3390/rs5020491 CrossRefGoogle Scholar
  49. Regan BC, Julliot C, Simmen B et al (2001) Fruits, foliage and the evolution of primate colour vision. Philos Trans Biol Sci 356:229–283. doi: 10.1098/rstb.2000.0773 CrossRefGoogle Scholar
  50. Réjou-Méchain M, Tymen B, Blanc L et al (2015) Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest. Remote Sens Environ 169:93–101. doi: 10.1016/j.rse.2015.08.001 CrossRefGoogle Scholar
  51. Remund J, Wald L, Lefevre M, et al (2003) Worldwide Linke turbidity information. In: ISES Solar World Congress 2003. International Solar Energy Society (ISES), Göteborg, Sweden, p 13 pGoogle Scholar
  52. Ross J, Sulev M (2000) Sources of errors in measurements of PAR. Agric For Meteorol 100:103–125. doi: 10.1016/S0168-1923(99)00144-6 CrossRefGoogle Scholar
  53. Sabatier D, Prévost M-F (1990) Variations du peuplement forestier a l’ echelle stationnelle: le cas de la station des Nouragues en Guyane Francaise.Google Scholar
  54. Salinas N, Malhi Y, Meir P et al (2011) The sensitivity of tropical leaf litter decomposition to temperature: results from a large-scale leaf translocation experiment along an elevation gradient in Peruvian forests. New Phytol 189:967–977. doi: 10.1111/j.1469-8137.2010.03521.x CrossRefPubMedGoogle Scholar
  55. Scanga, Sara E. 2014. “Population Dynamics in Canopy Gaps: Nonlinear Response to Variable Light Regimes by an Understory Plant.” Plant Ecology 215 (8): 927–35. doi: 10.1007/s11258-014-0344-9
  56. Smith AP, Hogan KP, Idol JR (1992) Spatial and temporal patterns oflLight and canopy structure in a lowland tropical moist forest. Biotropica 24:503–511. doi: 10.2307/2389012 CrossRefGoogle Scholar
  57. Stark SC, Enquist BJ, Saleska SR et al (2015) Linking canopy leaf area and light environments with tree size distributions to explain Amazon forest demography. Ecol Lett 18:636–645. doi: 10.1111/ele.12440 CrossRefPubMedGoogle Scholar
  58. Stark SC, Leitold V, Wu JL et al (2012) Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecol Lett 15:1406–1414. doi: 10.1111/j.1461-0248.2012.01864.x CrossRefPubMedGoogle Scholar
  59. Svenning J-C (2001) On the role of microenvironmental heterogeneity in the ecology and diversification of neotropical rain-forest palms (Arecaceae). Bot Rev 67:1–53CrossRefGoogle Scholar
  60. Tang H, Dubayah R, Swatantran A et al (2012) Retrieval of vertical LAI profiles over tropical rain forests using waveform LiDAR at La Selva, Costa Rica. Remote Sens Environ 124:242–250. doi: 10.1016/j.rse.2012.05.005 CrossRefGoogle Scholar
  61. Tinoco-Ojanguren C, Pearcy RW (1995) A comparison of light quality and quantity effects on the growth and steady-state and dynamic photosynthetic characteristics of three tropical tree species. Funct Ecol 9:222–230. doi: 10.2307/2390568 CrossRefGoogle Scholar
  62. Tymen B, Réjou-Méchain M, Dalling JW et al (2016) Evidence for arrested succession in a liana-infested Amazonian forest. J Ecol 104:149–159. doi: 10.1111/1365-2745.12504 CrossRefGoogle Scholar
  63. Valladares F (2003) Light heterogeneity and plants: from ecophysiology to species coexistence and biodiversity. In: Progress in botany. Springer, Heidelberg, pp 439–471CrossRefGoogle Scholar
  64. van der Meer PJ, Bongers F (1996) Patterns of tree-fall and branch-fall in a tropical rain forest in French Guiana. J Ecol 84:19–29. doi: 10.2307/2261696 CrossRefGoogle Scholar
  65. Vincent G, Molino J-F, Marescot L, Barkaoui K, Sabatier D, Freycon V, Roelens J-B (2011) The relative importance of dispersal limitation and habitat preference in shaping spatial distribution of saplings in a tropical moist forest: a case study along a combination of hydromorphic and canopy disturbance gradients. Annals of Forest Science 68:357–370. doi: 10.1007/s13595-011-0024-z
  66. Vincent G, Antin C, Dauzat J et al (2015) Mapping plant area index of tropical forest by LiDAR: calibrating ALS with TLS. Proc SilviLaser 2015:146–148Google Scholar
  67. Wagner W, Ullrich A, Ducic V et al (2006) Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J Photogramm Remote Sens 60:100–112. doi: 10.1016/j.isprsjprs.2005.12.001 CrossRefGoogle Scholar
  68. Willis CG, Baskin CC, Baskin JM et al (2014) The evolution of seed dormancy: environmental cues, evolutionary hubs, and diversification of the seed plants. New Phytol 203:300–309. doi: 10.1111/nph.12782 CrossRefPubMedGoogle Scholar

Copyright information

© INRA and Springer-Verlag France 2017

Authors and Affiliations

  • Blaise Tymen
    • 1
  • Grégoire Vincent
    • 2
  • Elodie A. Courtois
    • 3
    • 4
  • Julien Heurtebize
    • 2
  • Jean Dauzat
    • 2
  • Isabelle Marechaux
    • 1
  • Jérôme Chave
    • 1
  1. 1.Laboratoire Evolution et Diversité Biologique UMR 5174, CNRSUniversité Paul SabatierToulouseFrance
  2. 2.IRD, UMR AMAP, TA A-51/PS1MontpellierFrance
  3. 3.Laboratoire Ecologie, évolution, interactions des systèmes amazoniens (LEEISA)Université de Guyane, CNRS, IFREMERCayenneFrance
  4. 4.Department of Biology, Centre of Excellence PLECO (Plant and Vegetation Ecology)University of AntwerpWilrijkBelgium

Personalised recommendations