Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD+

Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates

Dealing with locally-driven degradation: A quick start option under REDD+

The paper reviews a number of challenges associated with reducing degradation and its related emissions through national approaches to REDD+ under UNFCCC policy. It proposes that in many countries, it may in the short run be easier to deal with the kinds of degradation that result from locally driven community over-exploitation of forest for livelihoods, than from selective logging or fire control. Such degradation is low-level, but chronic, and is experienced over very large forest areas. Community forest management programmes tend to result not only in reduced degradation, but also in forest enhancement; moreover they are often popular, and do not require major political shifts. In principle these approaches therefore offer a quick start option for REDD+. Developing reference emissions levels for low-level locally driven degradation is difficult however given that stock losses and gains are too small to be identified and measured using remote sensing, and that in most countries there is little or no forest inventory data available. We therefore propose that forest management initiatives at the local level, such as those promoted by community forest management programmes, should monitor, and be credited for, only the net increase in carbon stock over the implementation period, as assessed by ground level surveys at the start and end of the period. This would also resolve the problem of nesting (ensuring that all credits are accounted for against the national reference emission level), since communities and others at the local level would be rewarded only for increased sequestration, while the national reference emission level would deal only with reductions in emissions from deforestation and degradation

Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo

Forest carbon stocks in rapidly developing tropical regions are highly heterogeneous, which challenges efforts to develop spatially-explicit conservation actions. In addition to field-based biodiversity information, mapping of carbon stocks can greatly accelerate the identification, protection and recovery of forests deemed to be of high conservation value (HCV). We combined airborne Light Detection and Ranging (LiDAR) with satellite imaging and other geospatial data to map forest aboveground carbon density at 30m (0.09ha) resolution throughout the Malaysian state of Sabah on the island of Borneo. We used the mapping results to assess how carbon stocks vary spatially based on forest use, deforestation, regrowth, and current forest protections. We found that unlogged, intact forests contain aboveground carbon densities averaging over 200MgCha−1, with peaks of 500MgCha−1. Critically, more than 40% of the highest carbon stock forests were discovered outside of areas designated for maximum protection. Previously logged forests have suppressed, but still high, carbon densities of 60–140MgCha−1. Our mapped distributions of forest carbon stock suggest that the state of Sabah could double its total aboveground carbon storage if previously logged forests are allowed to recover in the future. Our results guide ongoing efforts to identify HCV forests and to determine new areas for forest protection in Borneo

Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach

Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are easily neglected and can lead to spurious findings in downstream analyses. Here, we argue that technological approaches offer potential solutions, but there is no single silver bullet to resolve uncertainty in plant biodiversity quantification. Instead, we propose the use of artificial intelligence (AI) approaches to build a data-driven framework that integrates several data sources – including spectroscopy, DNA sequences, image recognition, and morphological data. Such a framework would provide a foundation for improving species identification in macroecological analyses while simultaneously improving the taxonomic process of species delimitation

Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data

Tropical forests are a key component of the global carbon cycle, and mapping their carbon density is essential for understanding human influences on climate and for ecosystem-service-based payments for forest protection. Discrete-return airborne laser scanning (ALS) is increasingly recognised as a high-quality technology for mapping tropical forest carbon, because it generates 3D point clouds of forest structure from which aboveground carbon density (ACD) can be estimated. Area-based models are state of the art when it comes to estimating ACD from ALS data, but discard tree-level information contained within the ALS point cloud. This paper compares area-based and tree-centric models for estimating ACD in lowland old-growth forests in Sabah, Malaysia. These forests are challenging to map because of their immense height. We compare the performance of (a) an area-based model developed by Asner and Mascaro (2014), and used primarily in the neotropics hitherto, with (b) a tree-centric approach that uses a new algorithm ($\textit{itcSegment}$) to locate trees within the ALS canopy height model, measures their heights and crown widths, and calculates biomass from these dimensions. We find that Asner and Mascaro's model needed regional calibration, reflecting the distinctive structure of Southeast Asian forests. We also discover that forest basal area is closely related to canopy gap fraction measured by ALS, and use this finding to refine Asner and Mascaro's model. Finally, we show that our tree-centric approach is less accurate at estimating ACD than the best-performing area-based model (RMSE 18% vs 13%). Tree-centric modelling is appealing because it is based on summing the biomass of individual trees, but until algorithms can detect understory trees reliably and estimate biomass from crown dimensions precisely, areas-based modelling will remain the method of choice.This project was supported by a grant through the Human Modified Tropical Forests programme of NERC (NE/K016377/1). We thank members of the NERC Airborne Remote Sensing Facility and NERC Data Analysis Node for collecting and processing the data (project code MA14-14). David Coomes was supported by an International Academic Fellowship from the Leverhulme Trust. Lindsay Banin contributed field allometry data which were collected during her PhD at University Leeds, supported by NERC and a RGS Henrietta Hutton Grant. Oliver Phillips, Simon Lewis and Lan Qie provided census data collected as part of an ERC Advanced Grant (T-Forces)

Ecological research in the Large-scale Biosphere-Atmosphere Experiment in Amazonia: Early results

Copyright by the Ecological Society of America ©2004 Michael Keller, Ane Alencar, Gregory P. Asner, Bobby Braswell, Mercedes Bustamante, Eric Davidson, Ted Feldpausch, Erick Fernandes, Michael Goulden, Pavel Kabat, Bart Kruijt, Flavio Luizão, Scott Miller, Daniel Markewitz, Antonio D. Nobre, Carlos A. Nobre, Nicolau Priante Filho, Humberto da Rocha, Pedro Silva Dias, Celso von Randow, and George L. Vourlitis 2004. ECOLOGICAL RESEARCH IN THE LARGE-SCALE BIOSPHERE– ATMOSPHERE EXPERIMENT IN AMAZONIA: EARLY RESULTS. Ecological Applications 14:3–16. http://dx.doi.org/10.1890/03-6003The Large-scale Biosphere–Atmosphere Experiment in Amazonia (LBA) is a multinational, interdisciplinary research program led by Brazil. Ecological studies in LBA focus on how tropical forest conversion, regrowth, and selective logging influence carbon storage, nutrient dynamics, trace gas fluxes, and the prospect for sustainable land use in the Amazon region. Early results from ecological studies within LBA emphasize the variability within the vast Amazon region and the profound effects that land-use and land-cover changes are having on that landscape. The predominant land cover of the Amazon region is evergreen forest; nonetheless, LBA studies have observed strong seasonal patterns in gross primary production, ecosystem respiration, and net ecosystem exchange, as well as phenology and tree growth. The seasonal patterns vary spatially and interannually and evidence suggests that these patterns are driven not only by variations in weather but also by innate biological rhythms of the forest species. Rapid rates of deforestation have marked the forests of the Amazon region over the past three decades. Evidence from ground-based surveys and remote sensing show that substantial areas of forest are being degraded by logging activities and through the collapse of forest edges. Because forest edges and logged forests are susceptible to fire, positive feedback cycles of forest degradation may be initiated by land-use-change events. LBA studies indicate that cleared lands in the Amazon, once released from cultivation or pasture usage, regenerate biomass rapidly. However, the pace of biomass accumulation is dependent upon past land use and the depletion of nutrients by unsustainable land-management practices. The challenge for ongoing research within LBA is to integrate the recognition of diverse patterns and processes into general models for prediction of regional ecosystem function

Long-term carbon loss in fragmented Neotropical forests

Tropical forests play an important role in the global carbon cycle, as they store a large amount of carbon (C). Tropical forest deforestation has been identified as a major source of CO2 emissions, though biomass loss due to fragmentation—the creation of additional forest edges—has been largely overlooked as an additional CO2 source. Here, through the combination of remote sensing and knowledge on ecological processes, we present long-term carbon loss estimates due to fragmentation of Neotropical forests: within 10 years the Brazilian Atlantic Forest has lost 69 (±14) Tg C, and the Amazon 599 (±120) Tg C due to fragmentation alone. For all tropical forests, we estimate emissions up to 0.2 Pg C y−1 or 9 to 24% of the annual global C loss due to deforestation. In conclusion, tropical forest fragmentation increases carbon loss and should be accounted for when attempting to understand the role of vegetation in the global carbon balance.This study was part of the project ‘Biodiversity conservation in a fragmented landscape at the Atlantic Plateau of São Paulo’ (BIOTA/Caucaia and BioCAPSP) funded by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, project no. 99/05123-4, 01/13309-2, 02/02125-0, 02/02126-7), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, project no. 690144/01-6), Fundação O Boticário de Proteção à Natureza, and by BMBF (German Federal Ministry of Education and Research, project n. 01LB0202). J.P.M. and M.C.R. thank the Brazilian Science Council (Conselho Nacional de Desenvolvimento Científico) for his research fellowship (process no. 307934/2011-0 and 312045/2013-1, respectively). A.H. and S.P. were supported by the ERC advanced grant 233066. M.M. has been supported by BMBF (project n. 01LB0202), and the Department of Ecological Modelling of the Helmholtz Centre for Environmental Research (UFZ). We thank Birgit Felinks for the support during the Mata Atlântica project. Florian Hartig provided valuable comments on an earlier version of this manuscript. S.P. has been funded by the Helmholtz Association of German Research Centres within the project ‘Biomass and Bioenergy systems’. A.H. was also supported by the Helmholtz-Alliance Remote Sensing and Earth System Dynamics. A.H. thanks C. Wissel and H. Bossel for supporting the FORMIND project over the years

Conservation performance of different conservation governance regimes in the Peruvian Amazon

State-controlled protected areas (PAs) have dominated conservation strategies globally, yet their performance relative to other governance regimes is rarely assessed comprehensively. Furthermore, performance indicators of forest PAs are typically restricted to deforestation, although the extent of forest degradation is greater. We address these shortfalls through an empirical impact evaluation of state PAs, Indigenous Territories (ITs), and civil society and private Conservation Concessions (CCs) on deforestation and degradation throughout the Peruvian Amazon. We integrated remote-sensing data with environmental and socio-economic datasets, and used propensity-score matching to assess: (i) how deforestation and degradation varied across governance regimes between 2006–2011; (ii) their proximate drivers; and (iii) whether state PAs, CCs and ITs avoided deforestation and degradation compared with logging and mining concessions, and the unprotected landscape. CCs, state PAs, and ITs all avoided deforestation and degradation compared to analogous areas in the unprotected landscape. CCs and ITs were on average more effective in this respect than state PAs, showing that local governance can be equally or more effective than centralized state regimes. However, there were no consistent differences between conservation governance regimes when matched to logging and mining concessions. Future impact assessments would therefore benefit from further disentangling governance regimes across unprotected land.This work was supported by the Economic and Social Research Council (grant number ES/I019650/1); Cambridge Political Economy Society; Cambridge Philosophical Society; St John’s College; and the Geography Department at the University of Cambridge

Targeting deforestation rates in climate change policy: a "Preservation Pathway" approach

We present a new methodological approach to incorporating deforestation within the international climate change negotiating regime. The approach, called "Preservation Pathway" combines the desire for forest preservation with the need to reduce emissions associated with forest loss by focusing on the relative rate of change of forest cover as the criteria by which countries gain access to trading preserved forest carbon stocks. This approach avoids the technically challenging task of quantifying historical or future deforestation emission baselines. Rather, it places emphasis on improving quantification of contemporary stocks and the relative decline in deforestation rates necessary to preserve those stocks. This approach places emphasis on the complete emissions trajectory necessary to attain an agreed-upon preserved forest and as such, meets both forest conservation and climate goals simultaneously

Spatial Pattern of Standing Timber Value across the Brazilian Amazon

The Amazon is a globally important system, providing a host of ecosystem services from climate regulation to food sources. It is also home to a quarter of all global diversity. Large swathes of forest are removed each year, and many models have attempted to predict the spatial patterns of this forest loss. The spatial patterns of deforestation are determined largely by the patterns of roads that open access to frontier areas and expansion of the road network in the Amazon is largely determined by profit seeking logging activities. Here we present predictions for the spatial distribution of standing value of timber across the Amazon. We show that the patterns of timber value reflect large-scale ecological gradients, determining the spatial distribution of functional traits of trees which are, in turn, correlated with timber values. We expect that understanding the spatial patterns of timber value across the Amazon will aid predictions of logging movements and thus predictions of potential future road developments. These predictions in turn will be of great use in estimating the spatial patterns of deforestation in this globally important biome