Dataset Information

Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon.

ABSTRACT: BACKGROUND:Brazilian Amazon forests contain a large stock of carbon that could be released into the atmosphere as a result of land use and cover change. To quantify the carbon stocks, Brazil has forest inventory plots from different sources, but they are unstandardized and not always available to the scientific community. Considering the Brazilian Amazon extension, the use of remote sensing, combined with forest inventory plots, is one of the best options to estimate forest aboveground biomass (AGB). Nevertheless, the combination of limited forest inventory data and different remote sensing products has resulted in significant differences in the spatial distribution of AGB estimates. This study evaluates the spatial coverage of AGB data (forest inventory plots, AGB maps and remote sensing products) in undisturbed forests in the Brazilian Amazon. Additionally, we analyze the interconnection between these data and AGB stakeholders producing the information. Specifically, we provide the first benchmark of the existing field plots in terms of their size, frequency, and spatial distribution. RESULTS:We synthesized the coverage of forest inventory plots, AGB maps and airborne light detection and ranging (LiDAR) transects of the Brazilian Amazon. Although several extensive forest inventories have been implemented, these AGB data cover a small fraction of this region (e.g., central Amazon remains largely uncovered). Although the use of new technology such as airborne LiDAR cover a significant extension of AGB surveys, these data and forest plots represent only 1% of the entire forest area of the Brazilian Amazon. CONCLUSIONS:Considering that several institutions involved in forest inventories of the Brazilian Amazon have different goals, protocols, and time frames for forest surveys, forest inventory data of the Brazilian Amazon remain unstandardized. Research funding agencies have a very important role in establishing a clear sharing policy to make data free and open as well as in harmonizing the collection procedure. Nevertheless, the use of old and new forest inventory plots combined with airborne LiDAR data and satellite images will likely reduce the uncertainty of the AGB distribution of the Brazilian Amazon.

SUBMITTER: Tejada G

PROVIDER: S-EPMC7226941 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon.

Tejada Graciela G Görgens Eric Bastos EB Espírito-Santo Fernando Del Bon FDB Cantinho Roberta Zecchini RZ Ometto Jean Pierre JP

Carbon balance and management 20190903 1

<h4>Background</h4>Brazilian Amazon forests contain a large stock of carbon that could be released into the atmosphere as a result of land use and cover change. To quantify the carbon stocks, Brazil has forest inventory plots from different sources, but they are unstandardized and not always available to the scientific community. Considering the Brazilian Amazon extension, the use of remote sensing, combined with forest inventory plots, is one of the best options to estimate forest aboveground b ...[more]

PMID: 31482475

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Project description:Tropical forests are biologically diverse and structurally complex ecosystems that can store a large quantity of carbon and support a great variety of plant and animal species. However, tropical forest structure can vary dramatically within seemingly homogeneous landscapes due to subtle changes in topography, soil fertility, species composition and past disturbances. Although numerous studies have reported the effects of field-based stand structure attributes on aboveground biomass (AGB) in tropical forests, the relative effects and contributions of UAV LiDAR-based canopy structure and ground-based stand structural attributes in shaping AGB remain unclear. Here, we hypothesize that mean top-of-canopy height (TCH) enhances AGB directly and indirectly via species richness and horizontal stand structural attributes, but these positive relationships are stronger at a larger spatial scale. We used a combined approach of field inventory and LiDAR-based remote sensing to explore how stand structural attributes (stem abundance, size variation and TCH) and tree species richness affect AGB along an elevational gradient in tropical forests at two spatial scales, i.e., 20 m × 20 m (small scale), and 50 m × 50 m (large scale) in southwest China. Specifically, we used structural equation models to test the proposed hypothesis. We found that TCH, stem size variation and abundance were strongly positively associated with AGB at both spatial scales, in addition to which increasing TCH led to greater AGB indirectly through increased stem size variation. Species richness had negative to negligible influences on AGB, but species richness increased with increasing stem abundance at both spatial scales. Our results suggest that light capture and use, modulated by stand structure, are key to promoting high AGB stocks in tropical forests. Thus, we argue that both horizontal and vertical stand structures are important for shaping AGB, but the relative contributions vary across spatial scales in tropical forests. Importantly, our results highlight the importance of including vertical forest stand attributes for predicting AGB and carbon sequestration that underpins human wellbeing.

Project description:BackgroundReliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates.ResultsCorrecting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R2?=?0.44 vs R2?=?0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R2?=?0.26).ConclusionsThis study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

Project description:Understanding the effects of plant species diversity and trait composition on aboveground biomass is a central focus of ecology and has important implications for biodiversity conservation. However, the simultaneous direct and indirect effects of soil nutrients, species asynchrony, functional trait diversity, and trait composition for explaining the community temporal stability of aboveground biomass remain underrepresented in natural forests. Here, we hypothesized that species asynchrony relative to soil nutrients, functional trait diversity, and trait composition plays a central role in stabilizing the community temporal stability of natural forests. We tested this hypothesis using a structural equation model based on 10-year continuous monitoring data (i.e., three-time repeated forest inventories) in both second-growth and old-growth temperate forests in northeast China. Our results showed that the community temporal stability of aboveground biomass was driven by a strong direct positive effect of species asynchrony in both second-growth and old-growth temperate forests, whereas functional trait diversity and composition (i.e. community-weighted mean of leaf nitrogen content) were of additional importance in an old-growth forest only. Functional trait diversity decreased community-weighted mean of leaf nitrogen content in an old-growth forest, whereas this relationship was non-significant in a second-growth forest. Soil nutrients had non-significant effects on the community temporal stability of both second-growth and old-growth forests. Species asynchrony was the direct determinant of the community temporal stability of aboveground biomass in temperate forests. The direct effect of species asynchrony increased with forest succession, implying that temporal niche differentiation and facilitation increase over time. This study suggests that managing forests with mixtures of both early and late successional species or shade intolerant and tolerant species, not only species diversity, is important for maintaining forest stability in a changing environment. We argue that the species asynchrony effect is crucial to understand the underlying ecological mechanisms for a diversity-biomass relationship in natural forests.

Project description:As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (?AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ?AGB rates, which are values per ecological zone, per continent. Similarly, research into forest biomass change at a large scale also makes use of these rates. IPCC 2006 default rates come from a handful of studies, provide no uncertainty indications and do not distinguish between older secondary forests and old-growth forests. As part of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, we incorporate ?AGB data available from 2006 onwards, comprising 176 chronosequences in secondary forests and 536 permanent plots in old-growth and managed/logged forests located in 42 countries in Africa, North and South America and Asia. We generated ?AGB rate estimates for younger secondary forests (?20 years), older secondary forests (>20 years and up to 100 years) and old-growth forests, and accounted for uncertainties in our estimates. In tropical rainforests, for which data availability was the highest, our ?AGB rate estimates ranged from 3.4 (Asia) to 7.6 (Africa) Mg ha-1 year-1 in younger secondary forests, from 2.3 (North and South America) to 3.5 (Africa) Mg ha-1 year-1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha-1 year-1 in old-growth forests. We provide a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identify which areas require more research on ?AGB. In this respect, this study should be considered as an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy; our new rates can be used for large-scale GHG accounting by governmental bodies, nongovernmental organizations and in scientific research.

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Dataset Information

Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon.

Publications

Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon.

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