A universal airborne LiDAR approach for tropical forest carbon mapping
A universal airborne LiDAR approach for tropical forest carbon mapping
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DOI：
10.1007/s00442-011-2165-z
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发表时间：
2012-04-01
期刊：
影响因子：
2.7
通讯作者：
van Breugel, Michiel
中科院分区：
文献类型：
--
作者：
Asner, Gregory P.;Mascaro, Joseph;van Breugel, Michiel
机载光探测和测距（LiDAR）正在迅速从示范技术转变为评估热带森林碳储量的关键工具。由于激光雷达能够穿透热带森林冠层并探测三维森林结构，它可能被证明是衡量和核算热带森林碳排放和吸收的国际战略的一个主要组成部分。然而，迄今为止，基本的生态信息，如高度-直径异速生长和林分水平木材密度尚未被机械地纳入区域和全球范围的森林碳测绘方法。更好地将这些结构模式纳入森林，可以减少用地面森林清查图校准空中数据所需的大量时间，目前需要彻底测量树木直径和高度，以及确定树木以估计木材密度。在这里，我们开发了一种新的方法，可以促进快速激光雷达校准与最少的现场数据。在四个热带地区（巴拿马，秘鲁，马达加斯加和夏威夷），我们能够预测地上碳密度估计在现场库存地块使用一个单一的通用激光雷达模型（r（2）= 0.80，RMSE = 27.6 Mg C ha（-1））。该模型在预测能力方面与当地校准模型相当，但依赖于给定区域的断面积和木材密度信息的有限输入，而不是传统的地块清单。通过这种方法，我们建议从根本上减少校准机载激光雷达数据所需的时间，从而增加高分辨率碳地图的输出，支持热带森林保护和气候减缓政策。
Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r (2) = 0.80, RMSE = 27.6 Mg C ha(-1)). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy.