Project Funding: 2014 - 2019

NRA: 2014 NASA: Carbon Monitoring System   

Funded by NASA

Abstract:
Current Monitoring Reporting and Verification (MRV) needs cannot be met by using only available NASA satellite data products, but must be integrated with commercial off-the-shelf technologies. The exceptional sensitivity of commercial, airborne scanning lidar data to forest canopy structure has made it the best remote sensing technology for predicting vegetation attributes, including biomass. We propose to use multiple, landscape-level lidar datasets, previously acquired in conjunction with project-level field plot datasets for model calibration/validation, to predict aboveground biomass stores across representative vegetation types in the northwestern USA. The predicted biomass maps will serve as training area for upscaling biomass carbon predictions to the regional level, as predicted from Landsat time series imagery processed through LandTrendr. Regional maps will be validated with FIA data summarized at the county level, along with error statistics. Bias between biomass predictions and FIA observations summarized for the representative vegetation types will be quantified, and bias corrections applied, with the goal of maintaining a transparent record of bias corrections at the county level. We envision a lidar and field plot database that can continue to be updated as new project-level forest inventory data are collected. This strategy will actively engage forest managers by utilizing existing data collected by and maintained by land managers of the US Forest Service (USFS) and other public and private stakeholders. Our chosen study region is the northwestern USA, where multiple commercial lidar and field plot datasets exist, LandTrendr data products are farthest along in the production line, and steep environmental gradient provide an exceptional diversity in vegetation types. The cumulative area of LiDAR collections across multiple ownerships in the northwestern USA has reached the point that land managers of the USFS and other stakeholders need to develop a strategy for how to utilize LiDAR for improved regional inventory, and because these inventories are the initial conditions for simulation modeling of future conditions, the strategy will result in more accurate estimates of projected conditions. We have assembled and consistently processed field plot and lidar datasets at >21 landscape-level project areas distributed along a broad climate gradient across the northwestern USA from temperate rainforest to cold desert. We propose to employ imputation as our predictive modeling strategy because it assigns actual ground observations at representative sample locations, to unsampled locations. Further, imputation modeling is firmly ensconced within the forest management community, and has been used for decades to assign stand attributes from reference stands to target stands. Therefore, forest and rangeland managers of the USFS and other major public and private land management stakeholders will have little difficulty buying in to our proposed methodology, and would benefit enormously by making more effective use of available LiDAR and ground inventory data. Fortunately, the USFS has also developed a carbon management capability with greater utility to local forest managers: the carbon accounting tool of the Forest Vegetation Simulator (FVS) (http://www.fs.fed.us/fmsc/fvs/). FVS remains freely available, is now open source (Open-FVS), is approved by the American Carbon Registry to estimate carbon stock changes, and provides the option of climate change projections using Climate-FVS. Our chosen modeling methods and tools lend themselves to transparency and verifiability. Our goal is to develop a prototype CMS that works with acceptable accuracy, objectivity, transparency, and reproducibility in the northwestern USA, it will be ready for replication and application elsewhere in the USA, and globally with ties to SilvaCarbon and REDD+.

Publications:

Fekety, P. A., Crookston, N. L., Hudak, A. T., Filippelli, S. K., Vogeler, J. C., Falkowski, M. J. 2020. Hundred year projected carbon loads and species compositions for four National Forests in the northwestern USA. Carbon Balance and Management. 15(1). DOI: 10.1186/s13021-020-00140-9

Fekety, P. A., Falkowski, M. J., Hudak, A. T. 2015. Temporal transferability of LiDAR-based imputation of forest inventory attributes. Canadian Journal of Forest Research. 45(4), 422-435. DOI: 10.1139/cjfr-2014-0405

Fekety, P. A., Falkowski, M. J., Hudak, A. T., Jain, T. B., Evans, J. S. 2018. Transferability of Lidar-derived Basal Area and Stem Density Models within a Northern Idaho Ecoregion. Canadian Journal of Remote Sensing. 44(2), 131-143. DOI: 10.1080/07038992.2018.1461557

Fekety, P. A., Sadak, R. B., Sauder, J. D., Hudak, A. T., Falkowski, M. J. 2019. Predicting forest understory habitat for Canada lynx using LIDAR data. Wildlife Society Bulletin. 43(4), 619-629. DOI: 10.1002/wsb.1018

Filippelli, S. K., Falkowski, M. J., Hudak, A. T., Fekety, P. A., Vogeler, J. C., Khalyani, A. H., Rau, B. M., Strand, E. K. 2020. Monitoring pinyon-juniper cover and aboveground biomass across the Great Basin. Environmental Research Letters. 15(2), 025004. DOI: 10.1088/1748-9326/ab6785

Fusco, E. J., Rau, B. M., Falkowski, M., Filippelli, S., Bradley, B. A. 2019. Accounting for aboveground carbon storage in shrubland and woodland ecosystems in the Great Basin. Ecosphere. 10(8). DOI: 10.1002/ecs2.2821

Hudak, A. T., Fekety, P. A., Kane, V. R., Kennedy, R. E., Filippelli, S. K., Falkowski, M. J., Tinkham, W. T., Smith, A. M. S., Crookston, N. L., Domke, G. M., Corrao, M. V., Bright, B. C., Churchill, D. J., Gould, P. J., McGaughey, R. J., Kane, J. T., Dong, J. 2020. A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA. Environmental Research Letters. 15(9), 095003. DOI: 10.1088/1748-9326/ab93f9

Sanchez-Lopez, N., Boschetti, L., Hudak, A. 2018. Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy. Remote Sensing. 10(10), 1622. DOI: 10.3390/rs10101622

Sanchez-Lopez, N., Boschetti, L., Hudak, A. T. 2019. Reconstruction of the disturbance history of a temperate coniferous forest through stand-level analysis of airborne LiDAR data. Forestry: An International Journal of Forest Research. DOI: 10.1093/forestry/cpz048

Stitt, J. M., Hudak, A. T., Silva, C. A., Vierling, L. A., Vierling, K. T. 2021. Characterizing individual tree-level snags using airborne lidar-derived forest canopy gaps within closed-canopy conifer forests. Methods in Ecology and Evolution. 13(2), 473-484. DOI: 10.1111/2041-210X.13752

Stitt, J. M., Hudak, A. T., Silva, C. A., Vierling, L. A., Vierling, K. T. 2022. Evaluating the Use of Lidar to Discern Snag Characteristics Important for Wildlife. Remote Sensing. 14(3), 720. DOI: 10.3390/rs14030720

Tinkham, W. T., Mahoney, P. R., Hudak, A. T., Domke, G. M., Falkowski, M. J., Woodall, C. W., Smith, A. M. 2018. Applications of the United States Forest Inventory and Analysis dataset: a review and future directions. Canadian Journal of Forest Research. 48(11), 1251-1268. DOI: 10.1139/cjfr-2018-0196

Deo, R. K., Froese, R. E., Falkowski, M. J., Hudak, A. T. 2016. Optimizing Variable Radius Plot Size and LiDAR Resolution to Model Standing Volume in Conifer Forests. Canadian Journal of Remote Sensing. 42(5), 428-442. DOI: 10.1080/07038992.2016.1220826

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

• Developing an Ecoregion-level Imputation Model From LiDAR-derived Biomass Maps   --   (Andrew Thomas Hudak, Patrick A Fekety, Michael J Falkowski, Robert E Kennedy, Alistair Matthew Stuart Smith)   [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