Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest

Rejou-Mechain Maxime, Tymen Blaise, Blanc Lilian, Fauset Sophie, Feldpausch Ted R., Monteagudo Abel, Phillips Oliver L., Richard Hélène, Chave Jérôme. 2015. Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest. Remote Sensing of Environment, 169 : 93-101.

Quartile : Outlier, Sujet : REMOTE SENSING / Quartile : Outlier, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q1, Sujet : ENVIRONMENTAL SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : In recent years, LiDAR technology has provided accurate forest aboveground biomass (AGB) maps in several forest ecosystems, including tropical forests. However, its ability to accurately map forest AGB changes in highbiomass tropical forests has seldom been investigated. Here, we assess the ability of repeated LiDAR acquisitions to map AGB stocks and changes in an old-growth Neotropical forest of French Guiana. Using two similar aerial small-footprint LiDAR campaigns over a four year interval, spanning ca. 20 km2, and concomitant ground sampling, we constructed a model relating median canopy height and AGB at a 0.25-ha and 1-ha resolution. This model had an error of 14% at a 1-ha resolution (RSE = 54.7 Mg ha−1) and of 23% at a 0.25-ha resolution (RSE= 86.5 Mg ha−1). This uncertainty is comparable with values previously reported in other tropical forests and confirms that aerial LiDAR is an efficient technology for AGB mapping in high-biomass tropical forests. Our map predicts a mean AGB of 340 Mg ha−1 within the landscape. We also created an AGB change map, and compared it with ground-based AGB change estimates. The correlation was weak but significant only at the 0.25-ha resolution. One interpretation is that large natural tree-fall gaps that drive AGB changes in a naturally regenerating forest can be picked up at fine spatial scale but are veiled at coarser spatial resolution. Overall, both field-based and LiDAR-based estimates did not reveal a detectable increase in AGB stock over the study period, a trend observed in almost all forest types of our study area. Small footprint LiDAR is a powerful tool to dissect the fine-scale variability of AGB and to detect the main ecological controls underpinning forest biomass variability both in space and time.

Mots-clés Agrovoc : forêt tropicale, biomasse, couverture végétale, carbone, télédétection, cartographie, écosystème forestier, production forestière, régénération naturelle, dynamique des populations, imagerie, modèle mathématique

Mots-clés géographiques Agrovoc : Guyane française, France

Classification Agris : K01 - Foresterie - Considérations générales
K10 - Production forestière
U30 - Méthodes de recherche

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

Auteurs et affiliations

• Rejou-Mechain Maxime, CNRS (FRA) - auteur correspondant
• Tymen Blaise, CNRS (FRA)
• Blanc Lilian, CIRAD-ES-UPR BSef (FRA)
• Fauset Sophie, University of Leeds (GBR)
• Feldpausch Ted R., University of Leeds (GBR)
• Monteagudo Abel, Jardin Botanico de Missouri (PER)
• Phillips Oliver L., University of Leeds (GBR)
• Richard Hélène, ONF (GUF)
• Chave Jérôme, CNRS (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/577213/)

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