Craig Wayson

Approaches to monitoring changes in carbon stocks for REDD

Reducing emissions from deforestation and forest degradation plus improving forest-management (REDD+) is a mechanism to facilitate tropical countries’ participation in climate change mitigation. In this review we focus on the current state of monitoring systems to support implementing REDD+. The main elements of current monitoring systems – Landsat satellites and traditional forest inventories – will continue to be the backbone of many forest-monitoring systems around the world, but new remote-sensing and analytical approaches are addressing monitoring problems specific to the tropics and implementing REDD+. There is increasing recognition of the utility of combining remote sensing with field data using models that integrate information from many sources, which will continue to evolve as new sensors are deployed and as the availability of field data increases.

Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system

BackgroundForest Inventory and Analysis (FIA) data may be a valuable component of a LIDAR-based carbon monitoring system, but integration of the two observation systems is not without challenges. To explore integration methods, two wall-to-wall LIDAR-derived biomass maps were compared to FIA data at both the plot and county levels in Anne Arundel and Howard Counties in Maryland. Allometric model-related errors were also considered.ResultsIn areas of medium to dense biomass, the FIA data were valuable for evaluating map accuracy by comparing plot biomass to pixel values. However, at plots that were defined as “nonforest”, FIA plots had limited value because tree data was not collected even though trees may be present. When the FIA data were combined with a previous inventory that included sampling of nonforest plots, 21 to 27% of the total biomass of all trees was accounted for in nonforest conditions, resulting in a more accurate benchmark for comparing to total biomass derived from the LIDAR maps. Allometric model error was relatively small, but there was as much as 31% difference in mean biomass based on local diameter-based equations compared to regional volume-based equations, suggesting that the choice of allometric model is important.ConclusionsTo be successfully integrated with LIDAR, FIA sampling would need to be enhanced to include measurements of all trees in a landscape, not just those on land defined as “forest”. Improved GPS accuracy of plot locations, intensifying data collection in small areas with few FIA plots, and other enhancements are also recommended.

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).

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).

Estimating uncertainty of allometric biomass equations with incomplete fit error information using a pseudo-data approach: methods

Key messageKnowing the uncertainty for biomass equations is critical for their use and error propagation of biomass estimates. Presented here is a method to estimate uncertainty for equations where only n and R2 values from the original equations are available.ContextTree allometric equations form the basis of research and assessments of forest biomass. Frequently, uncertainty estimations do not propagate errors from these equations since the necessary information about sampling and tree measurements is not included in the original publication. Many biomass studies were conducted decades ago and the original, raw data is unavailable.AimsBecause of this information deficiency, and to improve error estimates in applications, a system to estimate the error structures of such equations is presented.MethodsA pseudo-data approach involving the creation of possible (pseudo) data using only R2 and n with a simple Monte-Carlo process generates probable error structures that can be used to propagate errors.ResultsIn a test of five different species with varying n input data and population variability, the original error structures were successfully recreated.ConclusionThis method of creating pseudo-data is simple and extensible and requires commonly published information about the original dataset. The method can be employed to create new ecosystem-level equations from species-specific equations, implemented in systems to select allometric equations to reduce uncertainty, and aid in the design of large-scale campaigns to generate new allometric equations for improving local to national scale estimates of forest biomass. The R code will be made freely available to anyone upon request to the authors.

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.

Database for landscape-scale carbon monitoring sites

This report describes the database used to compile, store, and manage intensive ground-based biometric data collected at research sites in Colorado, Minnesota, New Hampshire, New Jersey, North Carolina, and Wyoming, supporting research activities of the U.S. North American Carbon Program (NACP). This report also provides details of each site, the sampling design and collection standards for biometric measurements, the database design, data summary examples, and the uses of intensive ground-based biometric data. Additional information on location descriptions, data, databases, and documentation may be accessed at http://www.nrs.fs.fed.us/data/lcms.

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

Is Indonesian peatland loss a cautionary tale for Peru? A two-country comparison of the magnitude and causes of tropical peatland degradation

Indonesia and Peru harbor some of the largest lowland tropical peatland areas. Indonesian peatlands are subject to much greater anthropogenic activity than Peru’s, including drainage, logging, agricultural conversion, and burning, resulting in high greenhouse gas and particulate emissions. To derive insights from the Indonesian experience, we explored patterns of impact in the two countries, and compared their predisposing factors. Impacts differ greatly among Indonesian regions and the Peruvian Amazon in the following order: Sumatra > Kalimantan > Papua > Peru. All impacts, except fire, are positively related to population density. Factors enhancing Indonesian peatlands’ susceptibility to disturbance include peat doming that facilitates drainage, coastal location, high local population, road access, government policies permitting peatland use, lack of enforcement of protections, and dry seasons that favor extensive burning. The main factors that could reduce peatland degradation in Peru compared with Indonesia are geographic isolation from coastal population centers, more compact peatland geomorphology, lower population and road density, more peatlands in protected areas, different land tenure policies, and different climatic drivers of fire; whereas factors that could enhance peatland degradation include oil and gas development, road expansion in peatland areas, and an absence of government policies explicitly protecting peatlands. We conclude that current peatland integrity in Peru arises from a confluence of factors that has slowed development, with no absolute barriers protecting Peruvian peatlands from a similar fate to Indonesia’s. If the goal is to maintain the integrity of Peruvian peatlands, government policies recognizing unique peatland functions and sensitivities will be necessary.

Past and prospective carbon stocks in forests of northern Wisconsin: a report from the Chequamegon-Nicolet National Forest Climate Change Response Framework

This report assesses past and prospective carbon stocks for 4.5 million ha of forest land in northern Wisconsin, including a baseline assessment and analysis of the impacts of disturbance and management on carbon stocks. Carbon density (amount of carbon stock per unit area) averages 237 megagrams (Mg) per ha, with the National Forest lands having slightly higher carbon density than other ownership classes. Over the last decade, carbon stocks of northern Wisconsin forests have been increasing by about one teragram (Tg) per year or 0.22 megagrams per ha per year, with most of the increase in live biomass. Harvest, wind, and fire have been principal drivers of forest carbon dynamics over the last century. For all forest types in northern Wisconsin, there is potential to increase stocking on the land by allowing more of the forested area to reach older age classes or by increasing productivity. Opportunities to increase afforestation and reduce deforestation are limited, but the potential exists for utilizing biomass energy as a substitute for fossil fuels. There are several options for private landowners to participate in carbon markets or greenhouse gas registries and receive some credit for additional actions to reduce emissions or increase sequestration of carbon. The methods used here can be adapted for use by other regions or forests to assess carbon stocks and effects of management on future carbon stocks.