Breakout Session C
Quantifying Carbon Storage with Remote Sensing Techniques
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The need for more accessible and feasible ecosystem service assessments is critical to the future of land protection efforts. Uncoordinated development around urban areas is associated with the loss and fragmentation of rural open space, wildlife habitat, and wetlands, and the consequential decline in biodiversity. The City of Ann Arbor Greenbelt Program was developed in 2003 to confront this issue through the preservation and protection of open space, farmland, natural habitats, and source waters inside and outside the city limits. Now in its 18th year, the Greenbelt Program consists of over 6,100 acres of protected land across more than 65 parcels. While successful, the program lacks adequate ways to report impact and motivate support beyond acres preserved and funds leveraged. The goal of our project was to develop two carbon storage geoprocessing tools: one for soil organic carbon and another for aboveground carbon storage. To measure biomass and in turn carbon storage, we used airborne LiDAR data combined with in-situ biomass measurements to create predictive models that estimate biomass in the Greenbelt District. We collected field data from three densely forested sites within Washtenaw County and then compared the effectiveness of various model types to predict biomass. We plan to apply these models to all properties currently protected by the Ann Arbor Greenbelt Program to provide the ability to assess and report on the ecosystem services of individual parcels, the total acreage protected, and prospective future acquisitions. We hope that our methodology can serve as a blueprint for regional land conservancies to effectively advocate for the importance of conservation easements.
Jackie Edinger, Master’s student at the University of Michigan School for Environment and Sustainability
Jessica Einck, Master’s student at the University of Michigan School for Environment and Sustainability
Lavran Pagano, Master’s student at the University of Michigan School for Environment and Sustainability
Sebastian Kasparian, Master’s student at the University of Michigan School for Environment and Sustainability
Giving Credit Where Credit is Due - Techniques for Identifying the Source of Soil Organic Matter
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Land use conversions can strongly impact soil organic matter (SOM) storage and are a primary opportunity for sequestering atmospheric carbon into the soil. It is known that land uses such as annual cropping and afforestation can decrease and increase SOM, respectively; however, the rates of these changes over time and the stability of the SOM remain elusive. Several techniques are available that can determine the source (plant type and/or species) and the stability of the different pools of SOM. Many trees are cool season (C3) species while several major crops (e.g. corn) are warm season (C4) species. Differences between the plant tissues of C3 and C4 species are transferred to the SOM as those residues decompose. This study focused on extracting the kinetics (k) of turnover rates in SOM that describe long-term changes in soil C storage and also quantifying the sources of soil C. We used topsoil organic carbon density and stable C isotopic composition data from multiple locations and paired sites in Russia and United States. Reconstruction of soil C storage trajectory over 250 years following conversion from native grassland to continual annual cropland revealed a C depletion rate of 0.010 years-1 (first-order k rate constant), which translates into a mean residence time (MRT) of 100 years (R2≥ 0.90). Conversely, soil C accumulation was observed over 70 years following afforestation of annual croplands at a much faster k rate of 0.055 years-1. The corresponding MRT was only 18 years (R2= 0.997) after a lag phase of 5 years. Over these 23 years of afforestation, trees contributed with 14 Mg C Ha-1 to soil C accrual in the 0 to 15 cm depth increment. This tree-C contribution reached 22 Mg C Ha-1 at 70 years after tree planting. Over these 70 years of afforestation, the proportion of tree-C to whole soil C increased to reach a sizeable 79%. Assuming steady state conditions of SOM in the adjacent croplands, we also estimated that 45% of the prairie-C existent at the time of tree planting was still present in the afforested soils 70 years later. The derived turnover rates that represent soil C changes over time are nonlinear. Soil C changes were much more dynamic during the first decades following a land use conversion than afterwards when the new land use system became mature. These results demonstrate that C sequestration in afforested lands is a suitable means to proactively mitigate escalating climate change within a typical person’s lifetime.
Tom Sauer,National Laboratory for Agriculture and the Environment, Ames, Iowa
Guillermo Hernandez Ramirez, University of Alberta, Edmonton, AB Canada
Yuri G. Chendev, Belgorod State University, Belgorod, Russia
Alexander N. Gennadiev, Lomonosov University, Moscow, Russia
High resolution environmental suitability maps for targeting tree adoption on agricultural land
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Planting trees is a major climate change mitigation strategy, and the benefits of tree planting can be maximized with careful consideration of where particular species are suited to grow. Among the factors to consider are the species-specific environmental variables limiting tree growth and survival. Planning efforts at a variety of scales will benefit from high-resolution maps of environmental suitability for individual tree species. However, such maps exist for only a few species, mapping approaches vary among species making comparisons difficult, and geographic extent and resolution varies. In addition, many tree suitability mapping efforts focus on existing forest land, despite the potential for growing trees in agricultural settings (i.e. agroforestry). To address these challenges, we developed an approach for mapping the suitability of multiple tree and shrub species on agricultural land at a high resolution and regional scale. For each species, we established unique suitability criteria for acceptable and ideal ranges of climate, soil, topographic, and hydrological variables based on data from peer-reviewed literature, books, extension bulletins, academic and governmental web pages, and expert consultation. We created a workflow that uses the unique suitability criteria in combination with high resolution spatial environmental data to produce suitability maps for each species using a rules-based approach. Here we present the suitability maps that we produced for several promising tree species in the US Midwest. These maps will be made available online, where users can interact with them by selecting different crops and zooming in on specific areas. The process for producing the maps will be freely available as an R package and may be applied in any region within the USA. In addition to aiding tree planting efforts for carbon sequestration, such maps can be combined with socioeconomic and ecological data for conservation planning in general. For example, such maps can be used to identify target adoption areas for agroforestry. Doing so could aid landscape planning and outreach efforts and could help increase broader adoption of agroforestry, thereby enhancing carbon sequestration along with additional ecological and socioeconomic benefits.
Monika Shea, Savanna Institute
Kevin Wolz, Savanna Institute
Using ground penetrating radar to characterize soil physical properties in a silvopasture system
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The use of ground penetrating radar (GPR) has grown rapidly in recent years due to significant increases in computer processing power and advances in GPR methodologies and analysis. Despite this growth, applications of GPR for analysis of subsurface features in agricultural settings have been sparse. In this study, we explore some qualitative applications of GPR for subsurface characterization and analysis in a silvopasture system. We use amplitude, instantaneous attributes, and texture features generated from a Grey-Level Co-occurrence Matrix (GLCM) to analyze GPR data. We then train a random forest regression model to predict soil physical properties such as moisture, percent sand, silt, and clay. In this study, GPR attributes were found to be good predictors of soil physical properties, with R2 values from random forest regression ranging from 0.6 to 0.75. Our results demonstrate that GPR attributes can provide valuable information on subsurface features in our study area without the need for destructive sampling. These results also demonstrate the usefulness of methods such as texture analysis and random forest for applications in GPR data analysis that can inform management decisions. The data generated using these methods could be integrated with or used to validate existing digital soil mapping methods and contribute to a better understanding of subsurface characteristics in agricultural soils.
Harrison Smith, University of Arkansas
Phillip Owens, USDA – Agricultural Research Service