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An Historically Consistent and Broadly Applicable MRV System Based on Lidar Sampling and Landsat Time-series (Tested in the US, and applied to the US NGHGI reporting system)
Project Funding: 2013 - 2016
NRA: 2013 NASA: Carbon Monitoring System
Funded by NASA
Abstract:We focus our attention on the development of a Monitoring, Reporting, and Verification (MRV) accounting system that could be used by developing countries within the context of the United Nations (UN) REDD Programme. Because one system will not fit all needs, we consider different biomass estimation frameworks and different components for inclusion in the system. Design-based inference is commonly applied to a sample field plot network, as it is for the US National Greenhouse Gas Inventory (NGHGI) baseline reporting to the UN Framework Convention on Climate Change (UNFCCC). But field plot networks are expensive to install and maintain. Sampling with lidar strips, supported by a smaller set of plots may be an attractive alternative that is highly relevant to many REDD countries, as is the use of Landsat for disturbance monitoring. Biomass estimation uncertainties associated with use of these different datasets in a design-based inference framework will be examined. We will also develop and test estimators that rely primarily on Landsat data within a model-based inference framework. The contributions from Landsat are the current (e.g., 2013) spectral response and metrics that describe disturbance history derived from a time series leading up to the current date. In this context, either plot data or lidar data can be used to parameterize the model and we will contrast the uncertainty effects of these datasets. A key advantage of the model-based framework is that it can be extended back in time (e.g., to 1990) using a consistent approach. The main feature of the model-based approach is that it relies directly on disturbance history as an indicator of biomass density. Using Landsat spectral data from a given date (e.g., 2000) and disturbance history metrics derived from a time series leading up to that date (e.g., 1984-2000), the statistical model developed for the current period (e.g., 2013) can be applied historically. This is critical because REDD requires a way to estimate biomass historically, back to a baseline year of 1990. For the approach to take maximum advantage of disturbance history metrics to predict biomass density, a sufficient time series length is critical. This requires that we reach back into the MSS archive to develop the disturbance history metrics for the approach to be fully effective in estimating biomass for the 1990 baseline. The US, while not a REDD country, is a party to the UNFCCC and has a need for similar NGHGI baseline information. The various components of our MRV system will be tested in the US, where the best data are available for parsing the uncertainty contributions of the several system components we will test. In doing so, we will develop and test an historical biomass mapping approach that, if implemented, would provide REDD countries a practical set of workflows for integrated monitoring of current and historic baseline carbon stocks and trends, with an understanding of the uncertainties associated with different components of the alternative workflows. Additionally, with the improvements expected from including Landsat-derived disturbance history into the methods used for the US NGHGI, this research would provide NASA and CMS with a collaborative roll in the process of reporting US forest carbon estimates to the UNFCCC.
Publications:
Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., Gorelick, N. 2018. A LandTrendr multispectral ensemble for forest disturbance detection. Remote Sensing of Environment. 205, 131-140. DOI: 10.1016/j.rse.2017.11.015
Cohen, W., Healey, S., Yang, Z., Stehman, S., Brewer, C., Brooks, E., Gorelick, N., Huang, C., Hughes, M., Kennedy, R., Loveland, T., Moisen, G., Schroeder, T., Vogelmann, J., Woodcock, C., Yang, L., Zhu, Z. 2017. How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms? Forests. 8(4), 98. DOI: 10.3390/f8040098
Deo, R. K., Russell, M. B., Domke, G. M., Woodall, C. W., Falkowski, M. J., Cohen, W. B. 2016. Using Landsat Time-Series and LiDAR to Inform Aboveground Forest Biomass Baselines in Northern Minnesota, USA. Canadian Journal of Remote Sensing. 43(1), 28-47. DOI: 10.1080/07038992.2017.1259556
Deo, R., Russell, M., Domke, G., Andersen, H., Cohen, W., Woodall, C. 2017. Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA. Remote Sensing. 9(6), 598. DOI: 10.3390/rs9060598
Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., Zhu, Z. 2018. Mapping forest change using stacked generalization: An ensemble approach. Remote Sensing of Environment. 204, 717-728. DOI: 10.1016/j.rse.2017.09.029
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
More details may be found in the following project profile(s):
