Quantifying micro-environmental variation in tropical rainforest understory at landscape scale by combining airborne LiDAR scanning and a sensor network
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.
KeywordsFrench Guiana Light Nouragues station Plant dynamics Temperature Relative humidity
- Baskin CC, Baskin JM (2001) Seeds: ecology, biogeography, and evolution of dormancy and germination. ElsevierGoogle Scholar
- 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
- 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
- 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
- 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
- Den Dulk JA (1989) The interpretation of remote sensing: a feasibility study. Landbouwuniversiteit te WageningenGoogle Scholar
- GRASS Development Team (2012) Geographic Resources Analysis Support System (GRASS) Software. Open Source Geospatial Foundation ProjectGoogle Scholar
- 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
- Insenburg M., LAStools—efficient LiDAR processing software (version 160921, academic) obtained from http://rapidlasso.com/LAStools
- 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
- 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
- Lee, David W. 1987. “The Spectral Distribution of Radiation in Two Neotropical Rainforests.” Biotropica 19 (2): 161–66. doi:10.2307/2388739
- Monteith J, Unsworth M (2013) Principles of environmental physics: plants, animals, and the atmosphere. Academic PressGoogle Scholar
- Mücke W, Hollaus M, et al., (2011) Modelling light conditions in forests using airborne laser scanning data.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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