Project Funding: 2013 - 2017

NRA: 2013 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 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. In particular, airborne remote sensing offers unique advantages for monitoring remote forested regions. In many respects, the methods, logistics, and timeliness of carbon monitoring in Alaska are analogous to ongoing efforts to develop carbon monitoring systems for remote tropical forest regions to Reduce Emissions from Deforestation and forest Degradation and enhancing forest carbon stocks (REDD+). Here, we propose to develop the first regional estimates of forest carbon stocks for the Tanana Inventory Unit of interior Alaska (146,000 km2). The proposed research leverages a sizable investment ($800k) by the USFS FIA Program in 2014 for new forest inventory plots and airborne data collection with Goddard's LiDAR, Hyperspectral, and Thermal Airborne Imager (G-LiHT; http://gliht.gsfc.nasa.gov). G LiHT is a well-calibrated airborne remote sensing package that is assembled from commercial off-the-shelf (COTS) instruments and a proven track record of timely, free, and open access to both low-and high-level products. The USFS project, a pilot study for LiDAR-assisted forest inventory in interior Alaska, does not provide support for research collaboration between NASA and USFS scientists, data analysis, or methods development. In this project, we will expand the Tanana research activity to 1) collaborate on the experimental design for optimal integration of field and LiDAR data for forest carbon monitoring; 2) compare established model-based and model-assisted methods for estimating forest carbon stocks using both plot and LiDAR information; 3) enhance the inventory activity using individual tree, species composition, and vegetation cover information from the combination of G-LiHT hyperspectral, thermal, and LiDAR sensors; and 4) characterize the impacts of recent fires and risk of future fire-driven carbon losses using the systematic sample of G-LiHT flight lines over ~2.5% of the Tanana region (3800 km2); and 5) develop new, spatially explicit estimates of carbon stocks and uncertainties using Bayesian statistical methods. The main outcomes from this work will be estimates of forest carbon stocks and associated uncertainties for the Tanana Inventory Unit. These estimates provide critical and timely information for resource management, and baseline conditions for the spatial distribution of forest cover and carbon stocks in a region that is rapidly changing from climate warming.

Publications:

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

Cahoon, S. M. P., Sullivan, P. F., Brownlee, A. H., Pattison, R. R., Andersen, H., Legner, K., Hollingsworth, T. N. 2018. Contrasting drivers and trends of coniferous and deciduous tree growth in interior Alaska. Ecology. 99(6), 1284-1295. DOI: 10.1002/ecy.2223

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

Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D. W., Sun, F., Zammit-Mangion, A. 2018. A Case Study Competition Among Methods for Analyzing Large Spatial Data. Journal of Agricultural, Biological and Environmental Statistics. 24(3), 398-425. DOI: 10.1007/s13253-018-00348-w

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

Junttila, V., Finley, A. O., Bradford, J. B., Kauranne, T. 2013. Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory. Forest Ecology and Management. 292, 75-85. DOI: 10.1016/j.foreco.2012.12.019

Babcock, C., Matney, J., Finley, A. O., Weiskittel, A., Cook, B. D. 2013. Multivariate Spatial Regression Models for Predicting Individual Tree Structure Variables Using LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6(1), 6-14. DOI: 10.1109/JSTARS.2012.2215582

Finley, A. O., Banerjee, S., Cook, B. D., Bradford, J. B. 2013. Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets. International Journal of Applied Earth Observation and Geoinformation. 22, 147-160. DOI: 10.1016/j.jag.2012.04.007

Guhaniyogi, R., Finley, A. O., Banerjee, S., Kobe, R. K. 2013. Modeling Complex Spatial Dependencies: Low-Rank Spatially Varying Cross-Covariances With Application to Soil Nutrient Data. Journal of Agricultural, Biological, and Environmental Statistics. 18(3), 274-298. DOI: 10.1007/s13253-013-0140-3

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

2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)

• G-LiHT: Multi-Sensor Airborne Image Data from Denali to the Yucatan   --   (Bruce Cook, Lawrence A Corp, Douglas Morton, Joel McCorkel)   [abstract]   [poster]
• Large-area inventory of boreal forest carbon stocks in interior Alaska using G-LiHT data and forest inventory plots   --   (Douglas Morton, Bruce Cook, Hans Erik Andersen, Robert Pattison, Ross Nelson, Andrew Finley, Chad Babcock, Lawrence A Corp, Matthew E Fagan, Laura Duncanson)   [abstract]

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

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