Kristofer Johnson

Permafrost and organic layer interactions over a climate gradient in a discontinuous permafrost zone

Permafrost is tightly coupled to the organic soil layer, an interaction that mediates permafrost degradation in response to regional warming. We analyzed changes in permafrost occurrence and organic layer thickness (OLT) using more than 3000 soil pedons across a mean annual temperature (MAT) gradient. Cause and effect relationships between permafrost probability (PF), OLT, and other topographic factors were investigated using structural equation modeling in a multi-group analysis. Groups were defined by slope, soil texture type, and shallow (<28 cm) versus deep organic ( 28 cm) layers. The probability of observing permafrost sharply increased by 0.32 for every 10-cm OLT increase in shallow OLT soils (OLTs) due to an insulation effect, but PF decreased in deep OLT soils (OLTd) by 0.06 for every 10-cm increase. Across the MAT gradient, PF in sandy soils varied little, but PF in loamy and silty soils decreased substantially from cooler to warmer temperatures. The change in OLT was more heterogeneous across soil texture types—in some there was no change while in others OLTs soils thinned and/or OLTd soils thickened at warmer locations. Furthermore, when soil organic carbon was estimated using a relationship with thickness, the average increase in carbon in OLTd soils was almost four times greater compared to the average decrease in carbon in OLTs soils across all soil types. If soils follow a trajectory of warming that mimics the spatial gradients found today, then heterogeneities of permafrost degradation and organic layer thinning and thickening should be considered in the regional carbon balance.

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.

Assessment of boreal forest historical C dynamics in the Yukon River Basin: relative roles of warming and fire regime change

Carbon (C) dynamics of boreal forest ecosystems have substantial implications for efforts to mitigate the rise of atmospheric CO2 and may be substantially influenced by warming and changing wildfire regimes. In this study we applied a large-scale ecosystem model that included dynamics of organic soil horizons and soil organic matter characteristics of multiple pools to assess forest C stock changes of the Yukon River Basin (YRB) in Alaska, USA, and Canada from 1960 through 2006, a period characterized by substantial climate warming and increases in wildfire. The model was calibrated for major forests with data from long-term research sites and evaluated using a forest inventory database. The regional assessment indicates that forest vegetation C storage increased by 46 Tg C, but that total soil C storage did not change appreciably during this period. However, further analysis suggests that C has been continuously lost from the mineral soil horizon since warming began in the 1970s, but has increased in the amorphous organic soil horizon. Based on a factorial experiment, soil C stocks would have increased by 158 Tg C if the YRB had not undergone warming and changes in fire regime. The analysis also identified that warming and changes in fire regime were approximately equivalent in their effects on soil C storage, and interactions between these two suggests that the loss of organic horizon thickness associated with increases in wildfire made deeper soil C stocks more vulnerable to loss via decomposition. Subbasin analyses indicate that C stock changes were primarily sensitive to the fraction of burned forest area within each subbasin and that boreal forest ecosystems in the YRB are currently transitioning from being sinks to sources at -0.7% annual area burned. We conclude that it is important for international mitigation efforts focused on controlling atmospheric CO2 to consider how climate warming and changes in fire regime may concurrently affect the CO2 sink strength of boreal forests. It is also important for large-scale biogeochemical and earth system models to include organic soil dynamics in applications to assess regional C dynamics of boreal forests responding to warming and changes in fire regime.

Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR

The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

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.

Enhancing interoperability to facilitate implementation of REDD+: case study of Mexico

ABSTRACT There is an increasing need for approaches to determine reference emission levels and implement policies to address the objectives of Reducing Emissions from Deforestation and Forest Degradation, plus improving forest management, carbon stock enhancement and conservation (REDD+). Important aspects of approaching emissions reductions include coordination and sharing of technology, data, protocols and experiences within and among countries to maximize resources and apply knowledge to build robust monitoring, reporting and verification (MRV) systems. We propose that enhancing the multiple facets of interoperability could facilitate implementation of REDD+ programs and actions. For this case, interoperability is a collective effort with the ultimate goal of sharing and using information to produce knowledge and apply knowledge gained, by removing conceptual, technological, organizational and cultural barriers. These efforts must come from various actors and institutions, including government ministries/agencies, scientific community, landowners, civil society groups and businesses. Here, we review the case of Mexico as an example of evolving interoperability in developing countries, and highlight challenges and opportunities for implementation of REDD+. Country-specific actions toward a higher degree of interoperability can be complex, expensive and even risky. These efforts provide leadership opportunities and will facilitate science–policy integration for implementation of REDD+, particularly in developing counties.

An Alaska Soil Carbon Database: Database Collaborator's Meeting; Fairbanks, Alaska, 4 March 2009

Soil carbon pools in northern high-latitude regions and their response to climate changes are highly uncertain, and collaboration is required from field scientists and modelers to establish baseline data for carbon cycle studies. The Global Change Program at the U.S. Geological Survey has funded a 2-year effort to establish a soil carbon network and database for Alaska based on collaborations from numerous institutions. To initiate a community effort, a workshop for the development of an Alaska soil carbon database was held at the University of Alaska Fairbanks. The database will be a resource for spatial and biogeochemical models of Alaska ecosystems and will serve as a prototype for a nationwide community project: the National Soil Carbon Network (http://www.soilcarb.net). Studies will benefit from the combination of multiple academic and government data sets. This collaborative effort is expected to identify data gaps and uncertainties more comprehensively. Future applications of information contained in the database will identify specific vulnerabilities of soil carbon in Alaska to climate change, disturbance, and vegetation change.

County-scale biomass map comparison: a case study for Sonoma, California

ABSTRACT The amount of carbon stored in forests affects a wide range of regional- to global-scale climate change processes. However, current maps often show large differences in carbon accounting. In this study, we present a framework to evaluate and compare multiple recent biomass maps at the county scale (4119 km2). We first compare the differences in the forest and non-forest areas at the pixel and county levels from multiple maps. Map-based estimates of county-level mean and total biomass are compared to the United States Forest Service (USFS) sample-based estimates. Comparison of raster-based biomass products shows differences in mean and total biomass at both pixel- and county-levels. Despite all the maps using USFSs plot data for model training, only the three active sensor derived products compare well to USFSs estimates of total biomass (within 10%), while the three passive sensor derived map products underestimated total biomass by as much as 47%. Our evaluation demonstrates that the biomass map generated using combined Light Detection and Ranging (LiDAR) and auxiliary data achieve accurate estimates at plot-level (R2 = 0.67; RMSE = 97.9 Mg.ha-1). This comparison study confirmed that missing direct height information either from active sensors tends to underestimate total biomass and mean biomass density at county-level.