Project Funding: 2016 - 2019

NRA: 2015 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
Monitoring U.S. forest carbon stocks is critical for natural resource management and national greenhouse gas reporting activities. The USFS Forest Inventory and Analysis (FIA) program—the largest network of permanent forest inventory plots in the world covers most U.S. forestlands. However, more than 450,000 km2 of forests in Interior Alaska (15% of US forestland) are not included in the FIA program, as these remote regions are difficult and expensive to monitor with standard field methods. Recent warming and projected future impacts from climate change on forest carbon stocks, composition, and extent have elevated the need to develop new approaches for forest monitoring in Alaska. The broader policy focus on land carbon sinks also encourages monitoring and accounting of the complete US land carbon sink, including Interior Alaska. Article 4 of the Paris Agreement recognizes the importance of “removals by sinks of greenhouse gases,” and specifically requests that national inventories include information on removals. Here, we propose to expand the joint NASA-USFS Pilot Project in the Tanana Inventory Unit, funded in part by ROSES-2013 CMS, to inventory a second USFS region in Interior Alaska, the Susitna-Copper River (SCR) Inventory Unit. Based on the success of the pilot project, the USFS has initiated a 10-year, $25M inventory plan for Interior Alaska using remote sensing data from Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager (http://gliht.gsfc.nasa.gov). The proposed research leverages USFS funding for G-LiHT data collection. However, the USFS inventory activity does not support research collaboration between NASA and USFS scientists, data analysis, or methods development. The proposed CMS project supports the transition of lidar- assisted forest inventory activities from research to operations, targeting specific objectives for NASA’s CMS program to use “remote sensing data products to produce and evaluate prototype MRV system approaches” and “studies that address research needs to advance remote sensing-based approaches to MRV” identified in Section 2.1 of the ROSES-2015 CMS solicitation (A.7). The proposed project has five components. The first three activities represent a continuation of research themes and data products outlined in the NASA-USFS Pilot Project, including specific requests for core inventory products by the USFS Forest Inventory & Analysis (FIA) Program, a key stakeholder for this effort. Core project components include 1) collaboration between USFS and NASA scientists on experimental design for optimal integration of field and lidar data for forest carbon monitoring, 2) estimation of forest carbon stocks for the SCR Inventory Unit using established methods to combine plot and lidar data, and 3) development of new, spatially explicit estimates of carbon stocks and uncertainties using hierarchical Bayesian statistical methods. In addition to these core inventory activities, we will use the combination of field inventory plots and G-LiHT data to 4) develop estimates of woody shrub biomass (e.g., alder and willow), a dominant feature of boreal forest landscapes that are not included in FIA inventory estimates, and 5) collaborate with USFS Forest Health experts to identify mortality and carbon losses from insects and disease (e.g., spruce bark beetle, aspen and birch leaf miners, birch leaf roller, alder dieback and canker disease). These additional project components target two specific needs identified by USFS scientists and stakeholders. The main outcomes from this work will be estimates of total (live + dead) forest carbon stocks, including woody shrubs, and associated uncertainties for the SCR Inventory Unit of Interior Alaska. These estimates provide critical and timely information for carbon monitoring and resource management, and baseline conditions for the spatial distribution of vegetation carbon stocks in a region undergoing rapid climate change.

Publications:

Alonzo, M., Andersen, H., Morton, D., Cook, B. 2018. Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion. Forests. 9(3), 119. DOI: 10.3390/f9030119

Alonzo, M., Dial, R. J., Schulz, B. K., Andersen, H., Lewis-Clark, E., Cook, B. D., Morton, D. C. 2020. Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar. Remote Sensing of Environment. 245, 111841. DOI: 10.1016/j.rse.2020.111841

Alonzo, M., Morton, D. C., Cook, B. D., Andersen, H., Babcock, C., Pattison, R. 2017. Patterns of canopy and surface layer consumption in a boreal forest fire from repeat airborne lidar. Environmental Research Letters. 12(6), 065004. DOI: 10.1088/1748-9326/aa6ade

Andersen, H. -E., C. Babcock, B. Cook, D. Morton, A. Finley and M. Alonzo. Using remote sensing to support forest inventory in interior Alaska – demonstration of a generalized regression estimator in a two-phase, model-assisted sampling design using two-sources of auxiliary data. Forests (submitted).

Babcock, C., Finley, A. O., Andersen, H., Pattison, R., Cook, B. D., Morton, D. C., Alonzo, M., Nelson, R., Gregoire, T., Ene, L., Gobakken, T., Naesset, E. 2018. Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations. Remote Sensing of Environment. 212, 212-230. DOI: 10.1016/j.rse.2018.04.044

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

Ene, L. T., Gobakken, T., Andersen, H., Naesset, E., Cook, B. D., Morton, D. C., Babcock, C., Nelson, R. 2018. Large-area hybrid estimation of aboveground biomass in interior Alaska using airborne laser scanning data. Remote Sensing of Environment. 204, 741-755. DOI: 10.1016/j.rse.2017.09.027

Finley, A. O., Datta, A., Cook, B. D., Morton, D. C., Andersen, H. E., Banerjee, S. 2019. Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes. Journal of Computational and Graphical Statistics. 28(2), 401-414. DOI: 10.1080/10618600.2018.1537924

Finley, A. O., S. Banerjee, Y. Zhou and B. D. Cook. 2016. Process-based hierarchical models for coupling high-dimensional LiDAR and forest variables over large geographic domains. Journal of the American Statistical Association, arXiv: 1603.07409

Montesano, P. M., Neigh, C. S., Wagner, W., Wooten, M., Cook, B. D. 2019. Boreal canopy surfaces from spaceborne stereogrammetry. Remote Sensing of Environment. 225, 148-159. DOI: 10.1016/j.rse.2019.02.012

Pattison, R., Andersen, H., Gray, A., Schulz, B., Smith, R. J., Jovan, S. 2018. Forests of the Tanana Valley State Forest and Tetlin National Wildlife Refuge, Alaska: results of the 2014 pilot inventory DOI: 10.2737/pnw-gtr-967

Shirota, S., A. O. Finley, B. D. Cook and S. Banerjee. Conjugate nearest neighbor Gaussian process models for efficient statistical interpolation of large spatial data. IEEE Transactions on Geoscience and Remote Sensing (submitted).

Shoot, C., H. -E. Andersen, Monika Moskal, C. Babcock, B. Cook and D. Morton. Classifying Forest Type in the National Forest Inventory Context from a Fusion of Hyperspectral and Lidar Data. Remote Sensing of Environment (submitted).

Taylor-Rodriguez, D., Finley, A. O., Datta, A., Babcock, C., Andersen, H., Cook, B. D., Morton, D. C., Banerjee, S. 2019. Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping. Statistica Sinica. DOI: 10.5705/ss.202018.0005

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

More details may be found in the following project profile(s):

• NASA Terrestrial Ecology (TE) Project Profile
• NASA Carbon Monitoring System (CMS) Project Profile
• NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Project Profile