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哥伦比亚乔科地区的森林退化和生物量损失。

Forest degradation and biomass loss along the Chocó region of Colombia.

作者信息

Meyer Victoria, Saatchi Sassan, Ferraz António, Xu Liang, Duque Alvaro, García Mariano, Chave Jérôme

机构信息

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.

Institute of the Environment and Sustainability, University of California, Los Angeles, CA, 90095, USA.

出版信息

Carbon Balance Manag. 2019 Mar 23;14(1):2. doi: 10.1186/s13021-019-0117-9.


DOI:10.1186/s13021-019-0117-9
PMID:30904964
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6446973/
Abstract

BACKGROUND: Wet tropical forests of Chocó, along the Pacific Coast of Colombia, are known for their high plant diversity and endemic species. With increasing pressure of degradation and deforestation, these forests have been prioritized for conservation and carbon offset through Reducing Emissions from Deforestation and forest Degradation (REDD+) mechanisms. We provide the first regional assessment of forest structure and aboveground biomass using measurements from a combination of ground tree inventories and airborne Light Detection and Ranging (Lidar). More than 80,000 ha of lidar samples were collected based on a stratified random sampling to provide a regionally unbiased quantification of forest structure of Chocó across gradients of vegetation structure, disturbance and elevation. We developed a model to convert measurements of vertical structure of forests into aboveground biomass (AGB) for terra firme, wetlands, and mangrove forests. We used the Random Forest machine learning model and a formal uncertainty analysis to map forest height and AGB at 1-ha spatial resolution for the entire pacific coastal region using spaceborne data, extending from the coast to higher elevation of Andean forests. RESULTS: Upland Chocó forests have a mean canopy height of 21.8 m and AGB of 233.0 Mg/ha, while wetland forests are characterized by a lower height and AGB (13.5 m and 117.5 Mg/a). Mangroves have a lower mean height than upland forests (16.5 m), but have a similar AGB as upland forests (229.9 Mg/ha) due to their high wood density. Within the terra firme forest class, intact forests have the highest AGB (244.3 ± 34.8 Mg/ha) followed by degraded and secondary forests with 212.57 ± 62.40 Mg/ha of biomass. Forest degradation varies in biomass loss from small-scale selective logging and firewood harvesting to large-scale tree removals for gold mining, settlements, and illegal logging. Our findings suggest that the forest degradation has already caused the loss of more than 115 million tons of dry biomass, or 58 million tons of carbon. CONCLUSIONS: Our assessment of carbon stocks and forest degradation can be used as a reference for reporting on the state of the Chocó forests to REDD+ projects and to encourage restoration efforts through conservation and climate mitigation policies.

摘要

背景:位于哥伦比亚太平洋沿岸的乔科省湿润热带森林,以其丰富的植物多样性和特有物种而闻名。随着森林退化和砍伐压力的不断增加,这些森林已被列为通过减少毁林和森林退化所致排放量(REDD+)机制进行保护和碳抵消的优先区域。我们结合地面树木清查和机载激光雷达探测(Lidar)测量数据,首次对森林结构和地上生物量进行了区域评估。基于分层随机抽样收集了超过80000公顷的激光雷达样本,以便在植被结构、干扰和海拔梯度上对乔科省的森林结构进行区域无偏量化。我们开发了一个模型,将森林垂直结构的测量值转换为高地森林、湿地森林和红树林的地上生物量(AGB)。我们使用随机森林机器学习模型和正式的不确定性分析,利用星载数据绘制了整个太平洋沿海地区1公顷空间分辨率下的森林高度和AGB图,范围从海岸延伸至安第斯森林的较高海拔地区。 结果:乔科省高地森林的平均树冠高度为21.8米,地上生物量为233.0吨/公顷,而湿地森林的特点是高度和地上生物量较低(分别为13.5米和117.5吨/公顷)。红树林的平均高度低于高地森林(16.5米),但由于其木材密度高,地上生物量与高地森林相似(229.9吨/公顷)。在高地森林类别中,完整森林的地上生物量最高(244.3±34.8吨/公顷),其次是退化森林和次生森林,生物量为212.57±62.40吨/公顷。森林退化导致的生物量损失程度各不相同,从小规模选择性采伐和薪材采集到为金矿开采、定居点建设和非法采伐而进行的大规模树木砍伐。我们的研究结果表明,森林退化已经导致超过1.15亿吨干生物量的损失,即5800万吨碳的损失。 结论:我们对碳储量和森林退化的评估可作为向REDD+项目报告乔科省森林状况的参考,并通过保护和气候缓解政策鼓励开展恢复工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/c4026c286cc1/13021_2019_117_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/ecae61ddb796/13021_2019_117_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/bcd47cf66e46/13021_2019_117_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/f3e2772d5d17/13021_2019_117_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/3ef402a46ed3/13021_2019_117_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/c4026c286cc1/13021_2019_117_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/ecae61ddb796/13021_2019_117_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/bcd47cf66e46/13021_2019_117_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/f3e2772d5d17/13021_2019_117_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/3ef402a46ed3/13021_2019_117_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b0/6446973/c4026c286cc1/13021_2019_117_Fig5_HTML.jpg

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引用本文的文献

[1]
Estimating mangrove aboveground biomass in the Colombian Pacific coast: A multisensor and machine learning approach.

Heliyon. 2023-10-19

[2]
Changes in global terrestrial live biomass over the 21st century.

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