Author Correction: Climatic controls of decomposition drive the global biogeography of forest-tree symbioses
Steidinger, B.S. ; Crowther, T.W. ; Liang, J. ; Nuland, M.E. Van; Werner, G.D.A. ; Reich, P.B. ; Nabuurs, G.J. ; de-Miguel, S. ; Zhou, M. ; Picard, N. ; Herault, B. ; Zhao, X. ; Zhang, C. ; Routh, D. ; Peay, K.G. ; Abegg, Meinrad ; Adou Yao, C.Y. ; Alberti, Giorgio ; Almeyda Zambrano, Angelica ; Alvarez-Davila, Esteban ; Alvarez-Loayza, Patricia ; Alves, Luciana F. ; Ammer, Christian ; Antón-Fernández, Clara ; Araujo-Murakami, Alejandro ; Arroyo, Luzmila ; Avitabile, Valerio ; Aymard, Gerardo ; Baker, Timothy ; Bałazy, Radomir ; Banki, Olaf ; Barroso, Jorcely ; Bastian, Meredith ; Bastin, Jean Francois ; Birigazzi, Luca ; Birnbaum, Philippe ; Bitariho, Robert ; Boeckx, Pascal ; Bongers, Frans ; Bouriaud, Olivier ; Brancalion, Pedro H.H.S. ; Decuyper, Mathieu ; Hengeveld, Geerten ; Herold, Martin ; Lu, Huicui ; Parren, Marc ; Poorter, Lourens ; Schelhaas, Mart Jan ; Sheil, Douglas ; Zagt, Roderick - \ 2019
Nature 571 (2019)7765. - ISSN 0028-0836
In this Letter, the middle initial of author G. J. Nabuurs was omitted, and he should have been associated with an additional affiliation: ‘Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, The Netherlands’ (now added as affiliation 182). In addition, the following two statements have been added to the Supplementary Acknowledgements. (1): ‘We would particularly like to thank The French NFI for the work of the many field teams and engineers, who have made extraordinary efforts to make forest inventory data publicly available.’ (1): ‘Sergio de Miguel benefited from a Serra- Húnter Fellowship provided by the Generalitat of Catalonia.’ Finally, the second sentence of the Methods section should have cited the French NFI, which provided a national forestry database used in our analysis, to read as follows: ‘The GFBi database consists of individual-based data that we compiled from all the regional and national GFBi forest-inventory datasets, including the French NFI (IGN—French National Forest Inventory, raw data, annual campaigns 2005 and following, https://inventaire-forestier.ign.fr/spip.php?rubrique159, site accessed on 01 January 2015)’. All of these errors have been corrected online.
The Importance of Consistent Global Forest Aboveground Biomass Product Validation
Duncanson, L. ; Armston, J. ; Disney, M. ; Avitabile, V. ; Barbier, N. ; Calders, K. ; Carter, S. ; Chave, J. ; Herold, M. ; Crowther, T.W. ; Falkowski, M. ; Kellner, J.R. ; Labrière, N. ; Lucas, R. ; Macbean, N. ; Mcroberts, R.E. ; Meyer, V. ; Næsset, E. ; Nickeson, J.E. ; Paul, K.I. ; Phillips, O.L. ; Réjou-méchain, M. ; Román, M. ; Roxburgh, S. ; Saatchi, S. ; Schepaschenko, D. ; Scipal, K. ; Siqueira, P.R. ; Whitehurst, A. ; Williams, M. - \ 2019
Surveys in Geophysics 40 (2019)4. - ISSN 0169-3298 - p. 979 - 999.
Several upcoming satellite missions have core science requirements to produce data for accurate forest aboveground biomass mapping. Largely because of these mission datasets, the number of available biomass products is expected to greatly increase over the coming decade. Despite the recognized importance of biomass mapping for a wide range of science, policy and management applications, there remains no community accepted standard for satellite-based biomass map validation. The Committee on Earth Observing Satellites (CEOS) is developing a protocol to fill this need in advance of the next generation of biomass-relevant satellites, and this paper presents a review of biomass validation practices from a CEOS perspective. We outline the wide range of anticipated user requirements for product accuracy assessment and provide recommendations for the validation of biomass products. These recommendations include the collection of new, high-quality in situ data and the use of airborne lidar biomass maps as tools toward transparent multi-resolution validation. Adoption of community-vetted validation standards and practices will facilitate the uptake of the next generation of biomass products.
Climatic controls of decomposition drive the global biogeography of forest-tree symbioses
Steidinger, B.S. ; Crowther, T.W. ; Liang, J. ; Nuland, M.E. Van; Werner, G.D.A. ; Reich, P.B. ; Nabuurs, G. ; de-Miguel, S. ; Zhou, M. ; Picard, N. ; Herault, B. ; Zhao, X. ; Zhang, C. ; Routh, D. ; Peay, K.G. ; Herold, M. ; Decuyper, M. ; Avitabile, V. ; DeVries, B.R. ; Hengeveld, G.M. ; Poorter, L. ; Schelhaas, M. ; Bongers, F. - \ 2019
Nature 569 (2019)7756. - ISSN 0028-0836 - p. 404 - 408.
The identity of the dominant root-associated microbial symbionts in a forest determines the ability of trees to access limiting nutrients from atmospheric or soil pools1,2, sequester carbon3,4 and withstand the effects of climate change5,6. Characterizing the global distribution of these symbioses and identifying the factors that control this distribution are thus integral to understanding the present and future functioning of forest ecosystems. Here we generate a spatially explicit global map of the symbiotic status of forests, using a database of over 1.1 million forest inventory plots that collectively contain over 28,000 tree species. Our analyses indicate that climate variables—in particular, climatically controlled variation in the rate of decomposition—are the primary drivers of the global distribution of major symbioses. We estimate that ectomycorrhizal trees, which represent only 2% of all plant species7, constitute approximately 60% of tree stems on Earth. Ectomycorrhizal symbiosis dominates forests in which seasonally cold and dry climates inhibit decomposition, and is the predominant form of symbiosis at high latitudes and elevation. By contrast, arbuscular mycorrhizal trees dominate in aseasonal, warm tropical forests, and occur with ectomycorrhizal trees in temperate biomes in which seasonally warm-and-wet climates enhance decomposition. Continental transitions between forests dominated by ectomycorrhizal or arbuscular mycorrhizal trees occur relatively abruptly along climate-driven decomposition gradients; these transitions are probably caused by positive feedback effects between plants and microorganisms. Symbiotic nitrogen fixers—which are insensitive to climatic controls on decomposition (compared with mycorrhizal fungi)—are most abundant in arid biomes with alkaline soils and high maximum temperatures. The climatically driven global symbiosis gradient that we document provides a spatially explicit quantitative understanding of microbial symbioses at the global scale, and demonstrates the critical role of microbial mutualisms in shaping the distribution of plant species.
The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy
Herold, Martin ; Carter, Sarah ; Avitabile, Valerio ; Espejo, Andrés B. ; Jonckheere, Inge ; Lucas, Richard ; McRoberts, Ronald E. ; Næsset, Erik ; Nightingale, Joanne ; Petersen, Rachael ; Reiche, Johannes ; Romijn, Erika ; Rosenqvist, Ake ; Rozendaal, Danaë M.A. ; Seifert, Frank Martin ; Sanz, María J. ; Sy, V. de - \ 2019
Surveys in Geophysics 40 (2019)4. - ISSN 0169-3298 - p. 757 - 778.
The achievement of international goals and national commitments related to forest conservation and management, climate change, and sustainable development requires credible, accurate, and reliable monitoring of stocks and changes in forest biomass and carbon. Most prominently, the Paris Agreement on Climate Change and the United Nations’ Sustainable Development Goals in particular require data on biomass to monitor progress. Unprecedented opportunities to provide forest biomass data are created by a series of upcoming space-based missions, many of which provide open data targeted at large areas and better spatial resolution biomass monitoring than has previously been achieved. We assess various policy needs for biomass data and recommend a long-term collaborative effort among forest biomass data producers and users to meet these needs. A gap remains, however, between what can be achieved in the research domain and what is required to support policy making and meet reporting requirements. There is no single biomass dataset that serves all users in terms of definition and type of biomass measurement, geographic area, and uncertainty requirements, and whether there is need for the most recent up-to-date biomass estimate or a long-term biomass trend. The research and user communities should embrace the potential strength of the multitude of upcoming missions in combination to provide for these varying needs and to ensure continuity for long-term data provision which one-off research missions cannot provide. International coordination bodies such as Global Forest Observations Initiative (GFOI), Committee on Earth Observation Satellites (CEOS), and Global Observation of Forest Cover and Land Dynamics (GOFC‐GOLD) will be integral in addressing these issues in a way that fulfils these needs in a timely fashion. Further coordination work should particularly look into how space-based data can be better linked with field reference data sources such as forest plot networks, and there is also a need to ensure that reference data cover a range of forest types, management regimes, and disturbance regimes worldwide.
Forest biomass retrieval approaches from earth observation in different biomes
Rodríguez-Veiga, Pedro ; Quegan, Shaun ; Carreiras, Joao ; Persson, Henrik J. ; Fransson, Johan E.S. ; Hoscilo, Agata ; Ziółkowski, Dariusz ; Stereńczak, Krzysztof ; Lohberger, Sandra ; Stängel, Matthias ; Berninger, Anna ; Siegert, Florian ; Avitabile, Valerio ; Herold, Martin ; Mermoz, Stéphane ; Bouvet, Alexandre ; Toan, Thuy Le; Carvalhais, Nuno ; Santoro, Maurizio ; Cartus, Oliver ; Rauste, Yrjö ; Mathieu, Renaud ; Asner, Gregory P. ; Thiel, Christian ; Pathe, Carsten ; Schmullius, Chris ; Seifert, Frank Martin ; Tansey, Kevin ; Balzter, Heiko - \ 2019
International Journal of applied Earth Observation and Geoinformation 77 (2019). - ISSN 0303-2434 - p. 53 - 68.
The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of numerous forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha−1 to 55 t ha−1 (37% to 67% relative RMSE), and an overall bias ranging from −1 t ha−1 to +5 t ha−1 at pixel level. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha−1) in the lower AGB classes, and underestimation (up to 85 t ha−1) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level.
Independent data for transparent monitoring of greenhouse gas emissions from the land use sector – What do stakeholders think and need?
Romijn, Erika ; Sy, Veronique De; Herold, Martin ; Böttcher, Hannes ; Roman-Cuesta, Rosa Maria ; Fritz, Steffen ; Schepaschenko, Dmitry ; Avitabile, Valerio ; Gaveau, David ; Verchot, Louis ; Martius, Christopher - \ 2018
Environmental Science & Policy 85 (2018). - ISSN 1462-9011 - p. 101 - 112.
The agriculture, forestry and other land use (AFOLU) sectors contribute substantially to the net global anthropogenic greenhouse gas (GHG) emissions. To reduce these emissions under the Paris Agreement, effective mitigation actions are needed that require engagement of multiple stakeholders. Emission reduction also requires that accurate, consistent and comparable datasets are available for transparent reference and progress monitoring. Availability of free and open datasets and portals (referred to as independent data) increases, offering opportunities for improving and reconciling estimates of GHG emissions and mitigation options. Through an online survey, we investigated stakeholders’ data needs for estimating forest area and change, forest biomass and emission factors, and AFOLU GHG emissions. The survey was completed by 359 respondents from governmental, intergovernmental and non-governmental organizations, research institutes and universities, and public and private companies. These can be grouped into data users and data providers. Our results show that current open and freely available datasets and portals are only able to fulfil stakeholder needs to a certain degree. Users require a) detailed documentation regarding the scope and usability of the data, b) comparability between alternative data sources, c) uncertainty estimates for evaluating mitigation options, d) more region-specific and detailed data with higher accuracy for sub-national application, e) regular updates and continuity for establishing consistent time series. These requirements are found to be key elements for increasing overall transparency of data sources, definitions, methodologies and assumptions, which is required under the Paris
Agreement. Raising awareness and improving data availability through centralized platforms are important for increasing engagement of data users. In countries with low capacities, independent data can support countries’
mitigation planning and implementation, and related GHG reporting. However, there is a strong need for further guidance and capacity development (i.e.‘
readiness support’) on how to make proper use of independent datasets. Continued investments will be needed to sustain programmes and keep improving datasets to serve the objectives of the many stakeholders involved in climate change mitigation and should focus on increased accessibility and transparency of data to encourage stakeholder involvement.
Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR
Gonzalez De Tanago, Jose ; Lau, Alvaro ; Bartholomeus, Harm ; Herold, Martin ; Avitabile, Valerio ; Raumonen, Pasi ; Martius, Christopher ; Goodman, Rosa C. ; Disney, Mathias ; Manuri, Solichin ; Burt, Andrew ; Calders, Kim - \ 2018
Methods in Ecology and Evolution 9 (2018)2. - ISSN 2041-210X - p. 223 - 234.
1. Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above-ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three-dimensional (3D) tree modelling of TLS point clouds. 2. Twenty-nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5 cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species-specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements. 3. AGB estimates by the TLS–QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV-RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%–54.95% CV-RMSE and CCC of 0.89–0.73). TLS–QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%–35.7%) increasing linearly with tree size. The TLS–QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics. 4. Our TLS–QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non-destructive method can be further used for testing and calibrating new allometric models, reducing the current under-representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests.
Agriculture-driven deforestation in the tropics from 1990 to-2015: emissions, trends and uncertainties
Carter, Sarah ; Herold, Martin ; Avitabile, Valerio ; Bruin, Sytze de; Sy, Veronique de; Kooistra, Lammert ; Rufino, Mariana C. - \ 2018
Environmental Research Letters 13 (2018)1. - ISSN 1748-9326
Limited data exists on emissions from agriculture-driven deforestation, and available data are typically uncertain. In this paper, we provide comparable estimates of emissions from both all deforestation and agriculture-driven deforestation, with uncertainties for 91 countries across the tropics between 1990 and 2015. Uncertainties associated with input datasets (activity data and emissions factors) were used to combine the datasets, where most certain datasets contribute the most. This method utilizes all the input data, while minimizing the uncertainty of the emissions estimate. The uncertainty of input datasets was influenced by the quality of the data, the sample size (for sample-based datasets), and the extent to which the timeframe of the data matches the period of interest. Area of deforestation, and the agriculture-driver factor (extent to which agriculture drives deforestation), were the most uncertain components of the emissions estimates, thus improvement in the uncertainties related to these estimates will provide the greatest reductions in uncertainties of emissions estimates. Over the period of the study, Latin America had the highest proportion of deforestation driven by agriculture (78%), and Africa had the lowest (62%). Latin America had the highest emissions from agriculture-driven deforestation, and these peaked at 974 ± 148 Mt CO2 yr−1 in 2000–2005. Africa saw a continuous increase in emissions between 1990 and 2015 (from 154 ± 21–412 ± 75 Mt CO2 yr−1), so mitigation initiatives could be prioritized there. Uncertainties for emissions from agriculture-driven deforestation are ± 62.4% (average over 1990–2015), and uncertainties were highest in Asia and lowest in Latin America. Uncertainty information is crucial for transparency when reporting, and gives credibility to related mitigation initiatives. We demonstrate that uncertainty data can also be useful when combining multiple open datasets, so we recommend new data providers to include this information.
Above-ground biomass assessment of tropical trees with Terrestrial LiDAR and 3D architecture models
Lau Sarmiento, A.I. ; Gonzalez de Tanago Meñaca, J. ; Bartholomeus, H.M. ; Herold, M. ; Avitabile, V. ; Raumonen, Pasi ; Martius, Christopher ; Goodman, R.C. ; Disney, Mathias ; Manuri, Solichin ; Burt, Andrew ; Calders, Kim - \ 2017
In: SilviLaser 2017 Program. - Blacksburg : Virginia Tech - p. 123 - 124.
Comparación de métodos de evaluación de efectividad de iniciativas subnacionales REDD+
Bos, A.B. ; Duchelle, Amy E. ; Angelsen, Arild ; Avitabile, V. ; Sy, V. de; Herold, M. ; Joseph, Shijo ; Sassi, Claudio de; Sills, Erin O. ; Sunderlin, William D. ; Wunder, Sven - \ 2017
Bogor Indonesia : CIFOR (Documentos Ocasionales 172) - ISBN 9786023870578 - 21 p.
Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations
Li, Wei ; Ciais, Philippe ; Peng, Shushi ; Yue, Chao ; Wang, Yilong ; Thurner, Martin ; Saatchi, Sassan S. ; Arneth, Almut ; Avitabile, Valerio ; Carvalhais, Nuno ; Harper, Anna B. ; Kato, Etsushi ; Koven, Charles ; Liu, Yi Y. ; Nabel, Julia E.M.S. ; Pan, Yude ; Pongratz, Julia ; Poulter, Benjamin ; Pugh, Thomas A.M. ; Santoro, Maurizio ; Sitch, Stephen ; Stocker, Benjamin D. ; Viovy, Nicolas ; Wiltshire, Andy ; Yousefpour, Rasoul ; Zaehle, Sönke - \ 2017
Biogeosciences 14 (2017)22. - ISSN 1726-4170 - p. 5053 - 5067.
The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.
|Unravelling uncertainty - Combining forest cover change products and biomass datasets in the context of REDD+
Bos, A.B. ; Avitabile, V. ; Sy, V. de; Duchelle, Amy E. ; Herold, M. ; Martius, Christopher - \ 2017
In: Book of abstracts. - - p. 119 - 119.
To measure the effectiveness of efforts to reduce emissions from deforestation and forest degradation (REDD+), one requires data on
both forest cover (change) and biomass stocks. Regional to global datasets with increasing levels of coverage, spatial and temporal detail, and
accuracy stem from innovations in remote sensing and forest monitoring. Still, these datasets do not necessarily align with each other, and it
remains unclear how their uncertainties influence carbon emission estimates.
Our study area covers six REDD+ subnational initiatives in five countries across the tropics. We compared approaches to quantifying impacts on
carbon emissions. We performed an accuracy assessment on the activity data using several tree cover change datasets such as locally
calibrated products based on dense time series data and a global dataset on annual tree cover change. For the error estimation, we used
validation tools for human interpretation based on Landsat Time Series data and high resolution optical imagery. Next, we calculated carbon
emission estimates based on pantropical biomass maps and field inventory data. We differentiated emissions from before and after the start of
the REDD+ initiatives and also considered control areas to estimate REDD+ impact.
We found that forest change products based on locally calibrated algorithms had a higher accuracy than the global product assessed. Biomass
datasets built on both remote sensing and local field inventory data led to better carbon emission estimates. REDD+ impact was limited but
varied considerably between initiatives. Still, the choice of datasets and assessment methods had an influence on the measured local and
regional REDD+ performance. This study contributes to carbon measurement, reporting and verification by providing insight into what extent both
activity data and biomass data influence the uncertainty of carbon emission estimates.
Forest Change and REDD+ Strategies
Avitabile, V. ; Schultz, M. ; Salvini, G. ; Pratihast, A.K. ; Bos, A.B. ; Herold, Nadine ; Manh, Cuong Pham ; Quang, Hien Vu ; Herold, M. - \ 2017
In: Land Use and Climate Change Interactions in Central Vietnam / Nauditt, A., Ribbe, L., Springer Science (Water Resources Development and Management ) - ISBN 9789811026232 - p. 33 - 68.
In recent years the United Nations initiative on Reducing Emissions from Deforestation and forest Degradation (REDD+) program gained increasing attention in the policy arena, representing a valuable incentive for developing countries to take actions to reduce greenhouse gas emissions and at the same time promote sustainable forest management and improve local livelihoods. To design an effective REDD+ implementation plan at the local level it is crucial to make an in-depth analysis of the international and national requirements, analyse the forest change processes and related drivers at sub-national scale, and assess the local management options and constrains to ultimately select the appropriate policy mix and land management interventions. The present chapter first describes the state and historical changes of forests in Vietnam, identifies the direct and underlying drivers of deforestation and forest degradation at national scale, discusses the role of forests for climate change mitigation and indicates the key activities for reducing carbon emissions in Vietnam. Second, the main biophysical parameters and processes are assessed at sub-national scale for the Vu Gia Thu Bon river basin. The land cover and carbon stocks are mapped and quantified for the year 2010 and the land cover change and related carbon emissions are estimated for the period 2001–2010, allowing to model the land cover change and predict deforestation risks until the year 2020. Among the areas at higher risk of deforestation, the Tra Bui commune located in Quang Nam province is selected to design a sub-national REDD+ implementation plan in the third part of the chapter. The plan is based upon an in-depth analysis of the local context and land cover change dynamics, the local drivers of deforestation and a conducted stakeholder involvement process in the commune. Based upon this analysis, the last section provides recommendations about the local land management strategies that could be introduced in the commune and discusses the policy interventions are likely to enable their implementation.
Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations
Zscheischler, Jakob ; Mahecha, Miguel D. ; Avitabile, Valerio ; Calle, Leonardo ; Carvalhais, Nuno ; Ciais, Philippe ; Gans, Fabian ; Gruber, Nicolas ; Hartmann, Jens ; Herold, Martin ; Ichii, Kazuhito ; Jung, Martin ; Landschützer, Peter ; Laruelle, Goulven G. ; Lauerwald, Ronny ; Papale, Dario ; Peylin, Philippe ; Poulter, Benjamin ; Ray, Deepak K. ; Regnier, Pierre ; Rödenbeck, Christian ; Roman-Cuesta, Rosa M. ; Schwalm, Christopher ; Tramontana, Gianluca ; Tyukavina, Alexandra ; Valentini, Riccardo ; Werf, Guido R. van der; West, Tristram O. ; Wolf, Julie E. ; Reichstein, Markus - \ 2017
Biogeosciences 14 (2017)15. - ISSN 1726-4170 - p. 3685 - 3703.
Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface–atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface–atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean ± 1 SD: 0.8 ± 0.1 PgC yr−1, positive numbers are sources to the atmosphere), Russia (0.1 ± 0.4 PgC yr−1), East Asia (1.6 ± 0.3 PgC yr−1), South Asia (0.3 ± 0.1 PgC yr−1), Australia (0.2 ± 0.3 PgC yr−1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of −5.4 ± 2.0 PgC yr−1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001–2010 indicates that the true value of NCE is a net CO2 source of 4.3 ± 0.1 PgC yr−1. This mismatch of nearly 10 PgC yr−1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water–atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.
Comparing methods for assessing the effectiveness of subnational REDD+ initiatives
Bos, Astrid B. ; Duchelle, Amy E. ; Angelsen, Arild ; Avitabile, Valerio ; Sy, V. de; Herold, Martin ; Joseph, Shijo ; Sassi, Claudio De ; Sills, Erin O. ; Sunderlin, William D. ; Wunder, Sven - \ 2017
Environmental Research Letters 12 (2017)7. - ISSN 1748-9326 - 12 p.
The central role of forests in climate change mitigation, as recognized in the Paris agreement, makes it increasingly important to develop and test methods for monitoring and evaluating the carbon effectiveness of REDD+. Over the last decade, hundreds of subnational REDD+ initiatives have emerged, presenting an opportunity to pilot and compare different approaches to quantifying impacts on carbon emissions. This study (1) develops a Before-After-Control-Intervention (BACI) method to assess the effectiveness of these REDD+ initiatives; (2) compares the results at the meso (initiative) and micro (village) scales; and (3) compares BACI with the simpler Before-After (BA) results. Our study covers 23 subnational REDD+ initiatives in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam. As a proxy for deforestation, we use annual tree cover loss. We aggregate data into two periods (before and after the start of each initiative). Analysis using control areas ('control-intervention') suggests better REDD+ performance, although the effect is more pronounced at the micro than at the meso level. Yet, BACI requires more data than BA, and is subject to possible bias in the before period. Selection of proper control areas is vital, but at either scale is not straightforward. Low absolute deforestation numbers and peak years influence both our BA and BACI results. In principle, BACI is superior, with its potential to effectively control for confounding factors. We conclude that the more local the scale of performance assessment, the more relevant is the use of the BACI approach. For various reasons, we find overall minimal impact of REDD+ in reducing deforestation on the ground thus far. Incorporating results from micro and meso level monitoring into national reporting systems is important, since overall REDD+ impact depends on land use decisions on the ground.
|Assessing the effectiveness of subnational REDD+ initiatives by tree cover change analysis
Bos, A.B. ; Avitabile, V. ; Herold, M. ; Duchelle, A.E. ; Joseph, Shijo ; Sassi, C. de; Sunderlin, W.D. ; Sills, E.O. ; Angelsen, A. ; Wunder, Sven - \ 2016
|Terrestrial LiDAR and 3D Reconstruction Models for Estimation of Large Tree Biomass in the Tropics
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