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

REMOTE SENSING OF ENVIRONMENT

卷:169

Article

Rejou-Mechain, Maxime^1,2  Tymen, Blaise¹  Blanc, Lilian³  Fauset, Sophie⁴  Feldpausch, Ted R.^4,5  Monteagudo, Abel⁶  Phillips, Oliver L.⁴  Richard, Helene⁷  Chave, Jerome¹ 

[1] Univ Toulouse 3, CNRS, UMR 5174, Lab Evolut & Diversite Biol, F-31062 Toulouse, France

[2] UMIFRE 21 USR 3330 CNRS MAEE, French Inst Pondicherry, Pondicherry, India

[3] UR Biens & Serv Ecosyst Forestiers, CIRAD ES, Embrapa Belam, Brazil

[4] Univ Leeds, Sch Geog, Leeds LS2 9JT, W Yorkshire, England

[5] Univ Exeter, Coll Life & Environm Sci, Geog, Exeter, Devon, England

[6] Jardin Bot Missouri, Oxapampa, Peru

[7] Serv Dev Sylvetude, Off Natl Forets Guyane, Reserve Montabo 97307, Cayenne, French Guiana

关键词: LiDAR;    Aboveground biomass;    Forest carbon;    Tropical forest;    Forest dynamic;   

DOI  :  10.1016/j.rse.2015.08.001

来源: Elsevier

【 摘 要 】

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 high-biomass 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 km(2), 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 = 865 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. (C) 2015 Elsevier Inc. All rights reserved.

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