National and international programs have an increasing need for precise and accurate estimates of forest carbon and structure to support greenhouse gas reduction plans, climate initiatives, and other international climate treaty frameworks such as REDD++. Central to these activities is the development of MRV (measurement, reporting and verification) systems that provide an accounting of forest carbon emission and sequestration at high spatial resolution with appropriate temporal frequencies. Such systems can be used to support and sustain the development of an 'ecomarket' infrastructure centered on carbon, along with other ecosystem services, such as biodiversity, water resources, and the like. Central to ecomarkets is the creation of financial incentives that reward the preservation and enhancement of ecosystem services through time, as enabled from robust MRV systems.
NASA has recognized the urgent need for the development of MRV through its initiation of the Carbon Monitoring System (CMS) program. The University of Maryland, working with NASA centers, the USFS, and commercial entities has led research efforts in Phase I and Phase II that have laid the basic groundwork for MRV. Our Phase II project uses existing, wall-to-wall airborne lidar coverage and in-situ field data collection to produce high-resolution maps of carbon stocks for all of Maryland. These same data are also used to drive a prognostic ecosystem model to predict carbon fluxes and carbon sequestration potential. This work has demonstrated the feasibility of large-scale mapping using airborne lidar, an important first step, and suggests logical follow-on activities that should be undertaken towards the realization of operational MRV systems that are responsive to local, national and international interests in management and policy.
The overall goal of this project is the continuing development of a prototype MRV system based on commercial off-the-shelf (COTS) remote sensing and analysis capabilities to support ecomarket infrastructure in Sonoma County, California. Building on our East Coast county-level work as part of CMS I and CMS II, we seek to address the following questions:
- What accuracies are achievable using predominantly COTS-based approaches to high-resolution MRV for forest carbon?
- What is the 'price-of-precision' for MRV systems and how does this vary as a function of sample design, ground data, remote sensing data acquisition and analysis costs?
- How can stakeholder needs and requirements be integrated during the creation and implementation of MRV systems to provide effective decision support and compliance capabilities, and with better-informed policy decisions? Can a cloud-based architecture be used to facilitate the initiation and use of MRV systems to enable their implementation domestically and abroad?
We have identified five objectives to answer our research questions: (1) Integration of Sonoma County stakeholder needs and requirements into the MRV system design. (2) High-resolution wall-to-wall estimation of carbon stocks and their uncertainties for Sonoma County and mapping of sequestration potential under various development
scenarios using the Ecosystem Demography model. (3) Development of the key components of an end-to-end MRV system that includes data acquisition, warehousing, baseline quantification, data accessibility, accounting, reporting and stakeholder communication. (4) Analysis of the 'price-of-precision' through a cost-benefit analysis of data resolution relative to accuracy achievable at particular spatial scales e.g. United Nations Framework Conference on Climate Change (UNFCCC) Tier 1 vs. Tier 3. (5) Demonstration of a functional prototype MRV platform with visualization, and analytical capabilities for addressing Sonoma County initiatives. Our basic approach to high-resolution carbon stock mapping has been established in our CMS Phase 1 (two Maryland counties) and Phase 2 (23 Maryland counties) efforts.
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Project URL(s):
None provided.
Data Products:
Product Title: CMS: LiDAR-derived Biomass, Canopy Height and Cover, Sonoma County, California, 2013
Start Date: 09/2013End Date: 12/2013 (2013)
Description: This data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches. Estimates of AGB uncertainty are also provided. Maximum canopy height and tree cover were derived from LiDAR data and high-resolution National Agriculture Imagery Program (NAIP) images.
Input Data Products: The tree canopy cover map was created using an object-based, data-fusion approach (LiDAR and high-resolution National Agriculture Imagery Program (NAIP) images), and then aggregated to 30-m by averaging.
The canopy height map was generated using LiDAR-derived normalized digital surface model (ndsm) and tree cover map, and then aggregated to 30-m by maximum (Huang et al., 2017).
LiDAR data (~8 points/ sq.m.) were acquired over Sonoma County by Watershed Sciences Inc (WSI) in September – November of 2013 covering ~440,000 ha (44 flights). Airborne discrete return LiDAR instrument - Leica ALS70 sensor was mounted on a Cessna Grand Caravan. The LiDAR data were processed and classified to generate bare earth DEMs and Canopy Height Models for aboveground biomass estimation. Airborne discrete return Lidar: COTS instrument - Leica ALS70 sensor mounted on a Cessna Grand Caravan, during September - November 2013, covering ~440,000 ha (44 Lidar flight).
The field sample plots were located and selected through stratified sampling of land cover strata defined by the Classification Assessment with LANDSAT of Visible Ecological Groupings (CALVEG) land cover product (evergreen, deciduous, shrub, mixed and non-forest) and LiDAR canopy heights (low: 0 - 5 m, medium: 5 - 25 m and high: > 25 m). Tree measurements of diameter at breast height were recorded in each plot. Allometric estimates of AGB (Mg ha-1) were calculated for each tree using equations from FIA’s Component Ratio Method (Heath et al, 2008; Woodall et al., 2011) and appropriate blow up factors were applied to estimate biomass density for the variable radius plots. Model validation was performed through local comparisons with FIA data. Field sample data will be made available in forthcoming data sets (Huang et al., 2017).
Algorithm/Models Used:
Evaluation:
Intercomparison Efforts/Gaps:
Uncertainty Estimates:
Uncertainty Categories:
Application Areas: MRV; Land management; Forest inventory
Relevant Policies/Programs: REDD+, Sonoma County initiatives, California Assembly Bill 32: Global Warming Solutions Act (CA-AB32), CAP
Potential Users: Habitat preservation groups (i.e. Sonoma County Agriculture & Open Space Preservation District, The Conservation Fund, The Nature Conservancy), nutrient trading & hydrology groups (i.e. city wastewater treatment facilities, California Department of Environment), commercial agriculture groups (precision agriculture and yield productivity consultants, fertilizer companies providing variable rate application services), wildfire fuels modeling groups (California Department of Forestry and Fire Protection, U.S. Forest Service in California), forest management companies (Mendocino Redwood company), national and global entities that want to validate top down products.
Stakeholders: Sonoma County Agricultural Preservation and Open Space District (Point of Contact: Karen Gaffney, karen.gaffney@sonoma-county.org)
Current Application Readiness Level: 6
Start Application Readiness Level: 6
Target Application Readiness Level: 8,9
Future Developments: - Hold a workshop at the beginning of the project (mid-2014) to identify individual practices, goals, and requirements.; - Hold a workshop at the end of the project to showcase the progress and identify long-term action items.; - Hold at least one focus s
Archived Data Citation: Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, H. Tang, K. Johnson, J.O. Dunne, and G.C. Hurtt. 2017. CMS: LiDAR-derived Biomass, Canopy Height and Cover, Sonoma County, California, 2013. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1523
Bounding Coordinates:
West Longitude:
-123.54000
East Longitude:
-122.34000
North Latitude:
38.86000
South Latitude:
38.10000
Publications:
Duncanson, L., Rourke, O., Dubayah, R. 2015. Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests. Scientific Reports. 5(1). DOI: 10.1038/srep17153
Huang, W., Dolan, K., Swatantran, A., Johnson, K., Tang, H., O'Neil-Dunne, J., Dubayah, R., Hurtt, G. 2019. High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environmental Research Letters. 14(9), 095002. DOI: 10.1088/1748-9326/ab2917
Huang, W., Swatantran, A., Duncanson, L., Johnson, K., Watkinson, D., Dolan, K., O'Neil-Dunne, J., Hurtt, G., Dubayah, R. 2017. County-scale biomass map comparison: a case study for Sonoma, California. Carbon Management. 8(5-6), 417-434. DOI: 10.1080/17583004.2017.1396840
Finley, A. O., Banerjee, S., Zhou, Y., Cook, B. D., Babcock, C. 2017. Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables. Remote Sensing of Environment. 190, 149-161. DOI: 10.1016/j.rse.2016.12.004
Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. On nearest-neighbor Gaussian process models for massive spatial data. WIREs Computational Statistics. 8(5), 162-171. DOI: 10.1002/wics.1383
Salazar, E., Hammerling, D., Wang, X., Sanso, B., Finley, A. O., Mearns, L. O. 2016. Observation-based blended projections from ensembles of regional climate models. Climatic Change. 138(1-2), 55-69. DOI: 10.1007/s10584-016-1722-1
Swatantran, A., Tang, H., Barrett, T., DeCola, P., Dubayah, R. 2016. Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar. Scientific Reports. 6(1). DOI: 10.1038/srep28277
Babcock, C., Finley, A. O., Cook, B. D., Weiskittel, A., Woodall, C. W. 2016. Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data. Remote Sensing of Environment. 182, 1-12. DOI: 10.1016/j.rse.2016.04.014
Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets. Journal of the American Statistical Association. 111(514), 800-812. DOI: 10.1080/01621459.2015.1044091
Archived Data Citations:
Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, H. Tang, K. Johnson, J.O. Dunne, and G.C. Hurtt. 2017. CMS: LiDAR-derived Biomass, Canopy Height and Cover, Sonoma County, California, 2013. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1523
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
County-level Aboveground Biomass Estimation Implications of Allometric Equation Selection
-- (Laura Duncanson, Kristofer Johnson, Wenli Huang, Ralph Dubayah)
[abstract]
Integrating Lidar Canopy Height and Landsat-based Forest Disturbance History with Ecosystem Demography Model for Carbon Change Estimation, A Case in Charles County, Maryland
-- (Maosheng Zhao, Chengquan Huang, George Hurtt, Ralph Dubayah, Justin Fisk, Anu Swatantran, Wenli Huang, Hao Tang)
[abstract]