We propose to implement a novel approach for mapping forest biomass and associated
errors using the fusion of airborne LiDAR, passive optical and thermal data and a
Bayesian hierarchical model that accounts for spatial variances between ground
observations and remotely sensed data. This method will be compared with the more
traditional approach of using a variety of plot-scale LiDAR metrics in a generalized,
multiple linear regression model for relatively large region of interest (e.g., county- or
state- scale). Also, we will use fine-resolution LiDAR and passive optical data (<1 m) to
delineate individual trees, identify species class, and derive additional tree-level attributes
(e.g., crown dimensions, crown area weighted heights, stem density) to improve upon
biomass estimates made with aggregated point cloud metrics and inventory data at the
plot-level (the traditional approach).
These three methods will be evaluated and compared at four study sites in the midAtlantic and New England regions of the eastern US: Howland Forest and Holt Research
Forest, ME; Harvard Forest, MA; and the Smithsonian Environmental Research Center
near Edgewater, MD. This study will leverage coincident and co-registered LiDAR,
passive optical, and thermal data that were collected at these sites for NASA s local-scale
biomass pilot project between 2011 and 2012. Remotely sensed data was collected with Goddard s LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager, which PI
Cook developed at NASA-GSFC for studying the complex relationship between
terrestrial ecosystem form and function. Large-area stem maps (3 to 35 ha per site, in
which all stems greater than 1 cm have been measured) exist at each of these study sites,
and these data will be used to verify crown delineations and enable the creation of a fineresolution spectral library. Subsets of the stem map areas will be used to simulate
inventory plots, which will then be used as inputs for the Bayesian spatial latent factor
model. Each of the stem map areas contain a variety of over/understory tree species,
variable topography and range of drainage conditions, which will allow us to validate
each of the methods over a wide range of forest types between and within each of the
four study sites.
Benefits of the proposed Bayesian spatial latent factor prediction model are 1) variables
are selected using an efficient, dimension reduction technique; 2) spatial dependencies
are incorporated into the model to and improve inference; 3) data compression is used to
reduce the computational burden; and 4) sources of uncertainty are acknowledged and
propagated through to prediction. Benefits of using data fusion for biomass mapping is
that LiDAR and passive optical data provide unique information on the 3- dimensional
structure and species composition of the forest, respectively. This synergy has been the
focus of recent research, and has spawned the development of multi-instrument airborne
systems such at the Carnegie Airborne Observatory (CAO), NASA s G-LiHT, and
National Ecological Observatory Network (NEON) system that will begin systematic
data collections in 2012. New algorithms and model variables for mapping forest
biomass, such as the Bayesian latent spatial factor model and individual tree attributes we
propose in this study, are needed to take full advantage of the synergy offered by these
new, complementary datasets.
Product Title: Forest biomass estimation using individual tree crown information
Time Period: 2008-2012
Description: - Quantify carbon stocks on local-scale at high spatial resolution for inventory and land management purposes.
Status: Preliminary
CMS Science Theme(s): Land Biomass
Keywords: Carbon Stocks (; terrestrial)
Spatial Extent: Smithsonian Environmental Research Center of Maryland and Sierra Nevada Mountains (Teakettle) of California
Spatial Resolution: Greater than or equal to 1 m (tree-scale)
Temporal Frequency: Single point time observation
Input Data Products: Small footprint airborne scanning Lidar (G-LiHT, commercial ALS): Area of Lidar data acquisition is 40 to 1,300 ha
Algorithm/Models Used: Single time delineation and physical attributes using CHM and Lidar point clouds
Evaluation: Comparison to stem-map data and field plots
Intercomparison Efforts/Gaps: Compared with traditional models using plot-scale Lidar metrics
Uncertainty Estimates: RMSE
Uncertainty Categories: deterministic
Application Areas: - Forest inventory; - Land management
Relevant Policies/Programs: USFS Forest Inventory and Analysis (FIA), SilvaCarbon, USDA Forest Service Experimental Forests & Ranges system
Potential Users: USFS, private timber firms that are interested in productivity and biomass estimates, forest ecologists, and carbon cycle scientists who are interested in using Lidar to quantify biomass and structure.
Stakeholders:
Current Application Readiness Level: 5
Start Application Readiness Level: 1
Target Application Readiness Level: 5
Future Developments: - Transfer the lessons learned with researchers on Nelson-03 and Dubayah-04 to improve methodology.
Limitations: - Small (but dense) sampling areas.; - Focus on evaluating methodology instead of producing maps.
Description: This data set includes estimates of aboveground biomass (AGB) in 2012 from the Penobscot Experimental Forest (PEF) in Bradley, Maine. The AGB was modeled using LiDAR data gathered with the LiDAR Hyperspectral and Thermal Imager (G-LiHT) operated by Goddard Space Flight Center and field inventory data from 604 permanent Forest Inventory and Analysis (FIA) plots within the PEF. The estimates were produced through a novel modeling approach that accommodates temporal misalignment between field measurements and remotely sensed data by including multiple time-indexed measurements at plot locations to estimate changes in AGB.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords: Carbon Stocks (; terrestrial); ; Uncertainties & Standard Errors
Spatial Extent: Penobscot Experimental Forest of Maine
Spatial Resolution: 10 – 20 m (plot-scale)
Temporal Frequency: Every 5 years for Maine and sampling snapshots for other sites
Input Data Products: Large and small footprint scanning Lidar (G-LiHT, LVIS): Area of Lidar data acquisition is 40 to 1,600 ha
Application Areas: - Forest inventory; - Land management
Relevant Policies/Programs: USFS Forest Inventory and Analysis (FIA), SilvaCarbon, USDA Forest Service Experimental Forests & Ranges system
Potential Users: USFS, private timber firms that are interested in productivity and biomass estimates, forest ecologists, and carbon cycle scientists who are interested in using Lidar to quantify biomass and structure.
Stakeholders:
Current Application Readiness Level: 5
Start Application Readiness Level: 1
Target Application Readiness Level: 5
Future Developments: - Transfer the lessons learned with researchers on Nelson-03 to improve methodology.
Limitations: - Small (but dense) sampling areas.; - Focus on evaluating methodology instead of producing maps.
Archived Data Citation: Babcock, C., A.O. Finley, B.D. Cook, A. Weiskittel, and C.W. Woodall. 2016. CMS: Aboveground Biomass from Penobscot Experimental Forest, Maine, 2012. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1318
Bounding Coordinates:
West Longitude:
-68.64000
East Longitude:
-68.59000
North Latitude:
44.87000
South Latitude:
44.83000
Publications:
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
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
Duncanson, L. I., Dubayah, R. O., Cook, B. D., Rosette, J., Parker, G. 2015. The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sensing of Environment. 168, 102-112. DOI: 10.1016/j.rse.2015.06.021
Rosette, J., Cook, B., Nelson, R., Huang, C., Masek, J., Tucker, C., Sun, G., Huang, W., Montesano, P., Rubio-Gil, J., Ranson, J. 2015. Sensor Compatibility for Biomass Change Estimation Using Remote Sensing Data Sets: Part of NASA's Carbon Monitoring System Initiative. IEEE Geoscience and Remote Sensing Letters. 12(7), 1511-1515. DOI: 10.1109/LGRS.2015.2411262
Goetz, S. J., Hansen, M., Houghton, R. A., Walker, W., Laporte, N., Busch, J. 2015. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environmental Research Letters. 10(12), 123001. DOI: 10.1088/1748-9326/10/12/123001
Finley, A. O., Banerjee, S., Cook, B. D. 2014. Bayesian hierarchical models for spatially misaligned data in R. Methods in Ecology and Evolution. 5(6), 514-523. DOI: 10.1111/2041-210X.12189
White, J. C., Wulder, M. A., Varhola, A., Vastaranta, M., Coops, N. C., Cook, B. D., Pitt, D., Woods, M. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. The Forestry Chronicle. 89(06), 722-723. DOI: 10.5558/tfc2013-132
White, J.C., M. A. Wulder, A. Varhola, M. Vastaranta, N. C. Coops, B. D. Cook, D. Pitt, and M. Woods. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using the area-based approach. Information Report FI-X-10. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre, Victoria, BC. 50 p. http://cfs.nrcan.gc.ca/pubwarehouse/pdfs/34887.pdf
Duncanson, L. I., Cook, B. D., Hurtt, G. C., Dubayah, R. O. 2014. An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sensing of Environment. 154, 378-386. DOI: 10.1016/j.rse.2013.07.044
Cook, B., Corp, L., Nelson, R., Middleton, E., Morton, D., McCorkel, J., Masek, J., Ranson, K., Ly, V., Montesano, P. 2013. NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager. Remote Sensing. 5(8), 4045-4066. DOI: 10.3390/rs5084045
Huang, W., Sun, G., Dubayah, R., Cook, B., Montesano, P., Ni, W., Zhang, Z. 2013. Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment. 134, 319-332. DOI: 10.1016/j.rse.2013.03.017
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
Montesano, P. M., Cook, B. D., Sun, G., Simard, M., Nelson, R. F., Ranson, K. J., Zhang, Z., Luthcke, S. 2013. Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error. Remote Sensing of Environment. 130, 153-170. DOI: 10.1016/j.rse.2012.11.016
Archived Data Citations:
Babcock, C., A.O. Finley, B.D. Cook, A. Weiskittel, and C.W. Woodall. 2016. CMS: Aboveground Biomass from Penobscot Experimental Forest, Maine, 2012. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1318
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]
Application of Airborne Remote Sensing to Define Terrestrial Ecosystem Form & Function -- (Lawrence A Corp, Bruce Cook, Elizabeth M. Middleton, Petya Krasteva Entcheva Campbell, Karl Fred Huemmrich) [abstract]
Examining the Carbon Sequestration Potential of Recently Disturbed Trees in a Managed Northern Wisconsin Forest -- (Jamon Van Den Hoek, Bruce Cook, Jeffrey Masek, Robert E Kennedy, Compton Tucker) [abstract]
[poster]
2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
Lidar derived canopy height models of Harvard Forest
-- (Ian Paynter, Edward Saenz, Xiaoyuan Yang, Yan Liu, Zhuosen Wang, Crystal Schaaf, Zhan Li, Alan Strahler, Bruce Cook, Keith Krause, Nathan Leisso, Courtney Meier, Darius Culvenor, Glenn Newnham, David Jupp, Jenny Lovell, Ewan Douglas, Jason Martel, Supriya Chakrabarti, Timothy Cook, Glenn Howe, Kuravi Hewawasam, Jeffrey Thomas, Jihyun Kim, Shabnam Rouhani, Yun Yang, Nima Pahlevan, Qingsong Sun, Francesco Peri, Angela Erb)
[abstract]
G-LiHT: Goddard’s LiDAR, Hyperspectral and Thermal Airborne Imager
-- (Bruce Cook, Lawrence Corp, Ross Nelson, Douglas Morton, Kenneth J Ranson, Jeffery Masek, Elizabeth Middleton)
[abstract]