Weighing trees with lasers: reducing uncertainty in tropical forest biomass and allometry

用激光称重树木：减少热带森林生物量和异速生长的不确定性

• 批准号: NE/N00373X/1
• 负责人: Mathias Disney
• 金额: $ 64.34万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FN00373X%2F1
• 关键词: Weighing; trees; lasers; reducing; uncertainty

Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.We hope to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.A key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.

准确测量树木的体积和结构使我们能够计算树木中储存的地上碳总量（C），这是一个非常重要的属性。树木在光合作用过程中从大气中去除二氧化碳，并可以将这些碳储存几十年甚至几个世纪，直到树木死亡，其中一些通过分解释放回大气中。热带森林储存了大约一半的地上陆地碳，但由于森林砍伐和退化，以及降雨和温度模式的变化，热带森林面临着特别的风险。令人惊讶的是，我们的知识热带森林C股票是相当差，这些股票的错误是大的和不确定的。这种不确定性影响了对毁林、退化和土地使用变化造成的二氧化碳排放量的估计。我们将通过部署一个新的，NERC资助的地面激光扫描仪（TLS）来扫描三大洲热带森林中的1000棵树来解决陆地碳循环中的这一重大不确定性：亚马逊，刚果盆地和东南亚。激光数据将使我们能够非破坏性地测量3D树木体积和生物量，其误差在目前最佳估计值的百分之几以内，这些估计值是通过破坏性收获和称重得出的。目前，很大的不确定性是因为称量一棵树是非常困难的：热带树木可能超过50米高，重达100吨或更多。收获也排除了随着时间的推移重新访问树木来衡量变化。在实践中，一个小样本的树木已被收获和称重有关的直径和高度的容易测量的参数，使用经验的“异速生长”（大小质量）的关系。然后利用这些关系将在更大范围内进行的直径和高度测量转化为生物量估计数。异速生长法也是从遥感测量推断大尺度（泛热带）生物量的唯一方法。不幸的是，支持全球异速生长关系的采伐树木样本在地理上是有限的，并且包含很少的大树。目前估计的热带森林碳储量从卫星和地面数据，所有这些非常有限的异速生长样本的基础上，分歧显着的大小和模式，导致激烈的辩论，为什么这应该是。我们希望解决这一争论，因为我们的激光雷达衍生的生物量估计是完全独立的异速生长和公正的树木大小。我们将“权衡”更多的树木比目前包括在所有全球泛热带异速生长和量化的异速生长模型的不确定性。我们还将测试异速生长模型中关于树形和木材密度的假设。我们的测量还将回答有关热带森林结构特征的地理差异的基本问题。我们的数据对于测试遥感生物量的新估计至关重要;英国领导的ESA BIOMASS RADAR和NASA GEDI激光任务都将依靠森林高度和生物量之间的异速生长关系来估计泛热带C库存。我们的工作将通过与首席科学家的长期合作为这两项任务提供信息。更广泛地说，我们将收集的大量树木测量数据将引起热带生态学、林业、生物多样性、遥感和C制图等领域研究人员的极大兴趣。该项目的一个关键目标是通过公开我们的数据和工具，确保我们的结果得到最广泛的使用。我们将与合作伙伴共同探索商业发展的途径，并为政府政策提供投入，特别是与森林管理和C测绘和减缓有关的政策。最后，我们将通过一系列外联活动，包括在亚马逊地区的一所学校和英国学校之间建立联系，提高人们对热带森林科学、保护和政策问题的认识，使人们能够了解我们的工作。

项目成果

Supplementary Material from New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

补充材料来自直接收获和地面激光雷达对大型热带树木质量和结构的新见解

• DOI: 10.6084/m9.figshare.13685849
• 发表时间: 2021
• 作者: Burt A

Assessment of Bias in Pan-Tropical Biomass Predictions

• DOI: 10.3389/ffgc.2020.00012
• 发表时间: 2020-02-20
• 期刊: FRONTIERS IN FORESTS AND GLOBAL CHANGE
• 影响因子: 3.2
• 作者: Burt, Andrew;Calders, Kim;Disney, Mathias
• 通讯作者: Disney, Mathias

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar.

• DOI: 10.1098/rsos.201458
• 发表时间: 2021-02-10
• 期刊: Royal Society open science
• 影响因子: 3.5
• 作者: Burt A;Boni Vicari M;da Costa ACL;Coughlin I;Meir P;Rowland L;Disney M
• 通讯作者: Disney M

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

通过直接收获和地面激光雷达对大型热带树木质量和结构的新见解

• DOI: 10.1101/2020.09.29.317198
• 发表时间: 2020
• 作者: Burt A

Extracting individual trees from lidar point clouds using treeseg

• DOI: 10.1111/2041-210x.13121
• 发表时间: 2019-03-01
• 期刊: METHODS IN ECOLOGY AND EVOLUTION
• 影响因子: 6.6
• 作者: Burt, Andrew;Disney, Mathias;Calders, Kim
• 通讯作者: Calders, Kim