Matieu Henry

Improved allometric models to estimate the aboveground biomass of tropical trees

Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter-height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter-height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development.

The carbon balance of Africa: synthesis of recent research studies

The African continent contributes one of the largest uncertainties to the global CO2 budget, because very few long-term measurements are carried out in this region. The contribution of Africa to the global carbon cycle is characterized by its low fossil fuel emissions, a rapidly increasing population causing cropland expansion, and degradation and deforestation risk to extensive dryland and savannah ecosystems and to tropical forests in Central Africa. A synthesis of the carbon balance of African ecosystems is provided at different scales, including observations of land–atmosphere CO2 flux and soil carbon and biomass carbon stocks. A review of the most recent estimates of the net long-term carbon balance of African ecosystems is provided, including losses from fire disturbance, based upon observations, giving a sink of the order of 0.2 Pg C yr−1 with a large uncertainty around this number. By comparison, fossil fuel emissions are only of the order of 0.2 Pg C yr−1 and land-use emissions are of the order of 0.24 Pg C yr−1. The sources of year-to-year variations in the ecosystem carbon-balance are also discussed. Recommendations for the deployment of a coordinated carbon-monitoring system for African ecosystems are given.

Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda

BackgroundAssessing biomass is gaining increasing interest mainly for bioenergy, climate change research and mitigation activities, such as reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). In response to these needs, a number of biomass/carbon maps have been recently produced using different approaches but the lack of comparable reference data limits their proper validation. The objectives of this study are to compare the available maps for Uganda and to understand the sources of variability in the estimation. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset.ResultsThe comparison of the biomass/carbon maps show strong disagreement between the products, with estimates of total aboveground biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Compared to the reference map based on country-specific field data and a national Land Cover (LC) dataset (estimating 468 Tg), maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change (IPCC) default values, and global LC datasets tend to strongly overestimate biomass availability of Uganda (ranging from 578 to 2201 Tg), while maps based on satellite data and regression models provide conservative estimates (ranging from 343 to 443 Tg). The comparison of the maps predictions with field data, upscaled to map resolution using LC data, is in accordance with the above findings. This study also demonstrates that the biomass estimates are primarily driven by the biomass reference data while the type of spatial maps used for their stratification has a smaller, but not negligible, impact. The differences in format, resolution and biomass definition used by the maps, as well as the fact that some datasets are not independent from the reference data to which they are compared, are considered in the interpretation of the results.ConclusionsThe strong disagreement between existing products and the large impact of biomass reference data on the estimates indicate that the first, critical step to improve the accuracy of the biomass maps consists of the collection of accurate biomass field data for all relevant vegetation types. However, detailed and accurate spatial datasets are crucial to obtain accurate estimates at specific locations.

Evaluating the Potential of Commercial Forest Inventory Data to Report on Forest Carbon Stock and Forest Carbon Stock Changes for REDD+ under the UNFCCC

In the context of the adoption at the 16th Conference of the Parties in 2010 on the REDD+ mitigation mechanism, it is important to obtain reliable data on the spatiotemporal variation of forest carbon stocks and changes (called Emission Factor, EF). A re-occurring debate in estimating EF for REDD+ is the use of existing field measurement data. We provide an assessment of the use of commercial logging inventory data and ecological data to estimate a conservative EF (REDD+ phase 2) or to report on EF following IPCC Guidance and Guidelines (REDD+ phase 3). The data presented originate from five logging companies dispersed over Gabon, totalling 2,240 plots of 0.3 hectares.We distinguish three Forest Types (FTs) in the dataset based on floristic conditions. Estimated mean aboveground biomass (AGB) in the FTs ranges from 312 to 333 Mg ha−1. A 5% accuracy is reached with the number of plots put in place for the FTs and a low sampling uncertainty obtained (± 10 to 13 Mg ha−1). The data could be used to estimate a conservative EF in REDD+ phase 2 and only partially to report on EF following tier 2 requirements for a phase 3.

Guidelines for documenting and reporting tree allometric equations

1 IntroductionGiven the pressing need to quantify carbon fluxes associatedwith terrestrial vegetation dynamics, an increasing number ofresearchers have sought to improve estimates of tree volume,biomass, and carbon stocks. Tree allometric equations arecritical tools for such purpose and have the potential toimprove our understanding about carbon sequestration inwoody vegetation, to support the implementation of policiesand mechanisms designed to mitigate climate change (e.g.CDM and REDD+; Agrawal et al. 2011), to calculate costsand benefits associated with forest carbon projects, and toimprove bioenergy systems and sustainable forest manage-ment (Henry et al. 2013).

Allometric Models for Estimating Tree Volume and Aboveground Biomass in Lowland Forests of Tanzania

Models to assist management of lowland forests in Tanzania are in most cases lacking. Using a sample of 60 trees which were destructively harvested from both dry and wet lowland forests of Dindili in Morogoro Region (30 trees) and Rondo in Lindi Region (30 trees), respectively, this study developed site specific and general models for estimating total tree volume and aboveground biomass. Specifically the study developed (i) height-diameter (ht-dbh) models for trees found in the two sites, (ii) total, merchantable, and branches volume models, and (iii) total and sectional aboveground biomass models of trees found in the two study sites. The findings show that site specific ht-dbh model appears to be suitable in estimating tree height since the tree allometry was found to differ significantly between studied forests. The developed general volume models yielded unbiased mean prediction error and hence can adequately be applied to estimate tree volume in dry and wet lowland forests in Tanzania. General aboveground biomass model appears to yield biased estimates; hence, it is not suitable when accurate results are required. In this case, site specific biomass allometric models are recommended. Biomass allometric models which include basic wood density are highly recommended for improved estimates accuracy when such information is available.

An overview of existing and promising technologies for national forest monitoring

The main goal of national forest programs is to lead and steer forest policy development and implementation processes in an inter-sectoral way (FAO 2006). National forest monitoring systems contribute to forest programs through monitoring forest changes and forest services over time (FAO 2013). To do so, they generally collect and analyze forest-related data and provide knowledge and recommendations at regular intervals. The collection of forest-related data and their analyses have continually evolved with technological and computational advances (Kleinn 2002).

Recommendations for the use of tree models to estimate national forest biomass and assess their uncertainty

Key messageThree options are proposed to improve the accuracy of national forest biomass estimates and decrease the uncertainty related to tree model selection depending on available data and national contexts.IntroductionDifferent tree volume and biomass equations result in different estimates. At national scale, differences of estimates can be important while they constitute the basis to guide policies and measures, particularly in the context of climate change mitigation.MethodFew countries have developed national tree volume and biomass equation databases and have explored its potential to decrease uncertainty of volume and biomasttags estimates. With the launch of the GlobAllomeTree webplatform, most countries in the world could have access to country-specific databases. The aim of this article is to recommend approaches for assessing tree and forest volume and biomass at national level with the lowest uncertainty. The article highlights the crucial need to link allometric equation development with national forest inventory planning efforts.ResultsModels must represent the tree population considered. Data availability; technical, financial, and human capacities; and biophysical context, among other factors, will influence the calculation process.ConclusionThree options are proposed to improve accuracy of national forest assessment depending on identified contexts. Further improvements could be obtained through improved forest stratification and additional non-destructive field campaigns.

Overcoming obstacles to sharing data on tree allometric equations

Miguel Cifuentes Jara & Matieu Henry & Maxime Rejou Mechain & Omar R. Lopez & Craig Wayson & Jose Maria Michel Fuentes & Edwin Castellanos & Mauricio Zapata-Cuartas & Daniel Piotto & Federico Alice Guier & Hector Castaneda Lombis & Ruby Cuenca Lara & Kelvin Cueva Rojas & Jhon del Aguila Pasquel & Alvaro Duque Montoya & Javier Fernandez Vega & Abner Jimenez Galo & Lars Gunnar Marklund & Fabian Milla & Jose de Jesus Navar Chaidez & Edgar Ortiz Malavassi & Johnny Perez & Carla Ramirez Zea & Luis Rangel Garcia & Rafael Rubilar Pons & Laurent Saint-Andre & Carlos Sanquetta & Charles Scott & James Westfall