Stehman, Stephen (Steve): State University of New York (Project Lead)
Project Funding:
2013 - 2016
NRA: 2013 NASA: Carbon Monitoring System
Funded by NASA
Abstract:
The research described in this proposal will develop statistically rigorous sampling design and analysis protocols that will reduce uncertainty of key estimates of target parameters of a carbon monitoring system (CMS) and lead to better quantification of uncertainty. The IPCC Good Practice Guidance emphasizes the importance of land area to estimates of carbon stocks and emissions and removals of greenhouse gases associated with land use, land-use change and forestry activities. Effective regional, national and global carbon monitoring systems can exploit satellite remote sensing in a variety of ways to substantially reduce the uncertainty of area estimates and to reduce costs associated with field sampling. A central theme of the proposed research is to develop and evaluate methods for advantageously combining remote sensing and ground data obtained from multiple sources to obtain more accurate (i.e., unbiased) and more precise estimates of land area and other key parameters of a CMS. Sampling is a key component of a CMS because much of the information needed for monitoring can only be collected in a cost-effective way via a sample. The proposed research is heavily focused on sampling methods. The outcome of the research will be recommendations for choosing a sampling design and estimation protocol that effectively combines information from multiple data sources emphasizing airborne and satellite remote sensing and field plot data. The specific objectives addressed include: 1) identify effective sampling designs and estimators that take advantage of remote sensing information to reduce costs and uncertainty associated with sample-based estimates; 2) compare different sample-based estimators proposed for a commonly used design in monitoring (two-stage cluster sampling) and provide a recommendation for which estimator(s) most effectively use remote sensing information to reduce uncertainty; 3) develop methods for quantifying measurement error (in particular, reference data error associated with assessing accuracy of land cover and land change maps) and for estimating land cover or land change area taking into account this measurement error; 4) develop rigorous sampling design and estimation protocols for incorporating community based monitoring and volunteered geographic information into land change monitoring protocols; and 5) investigate approaches for combining information from two probability samples to improve precision of estimates. Two obvious desirable goals for designing a CMS are to reduce uncertainties and lower costs. This research will achieve both of these benefits because the results of the research will guide selection of a cost effective sampling design and use of statistical estimators that take advantage of combining airborne and satellite remote sensing to reduce variability of key sample-based estimates required of the CMS. The proposed work not only contributes to a more efficient and effective CMS but also contributes to the wider NASA mission of validating land cover and land change products.
Publications:
Stehman, S. V., Fonte, C. C., Foody, G. M., See, L. 2018. Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover. Remote Sensing of Environment. 212, 47-59. DOI: 10.1016/j.rse.2018.04.014
Stehman, S. V., Foody, G. M. 2019. Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment. 231, 111199. DOI: 10.1016/j.rse.2019.05.018
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., Wulder, M. A. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment. 148, 42-57. DOI: 10.1016/j.rse.2014.02.015
Potapov, P. V., Dempewolf, J., Talero, Y., Hansen, M. C., Stehman, S. V., Vargas, C., Rojas, E. J., Castillo, D., Mendoza, E., Calderon, A., Giudice, R., Malaga, N., Zutta, B. R. 2014. National satellite-based humid tropical forest change assessment in Peru in support of REDD+ implementation. Environmental Research Letters. 9(12), 124012. DOI: 10.1088/1748-9326/9/12/124012
Boschetti, L., Stehman, S. V., Roy, D. P. 2016. A stratified random sampling design in space and time for regional to global scale burned area product validation. Remote Sensing of Environment. 186, 465-478. DOI: 10.1016/j.rse.2016.09.016
Cohen, W. B., Yang, Z., Stehman, S. V., Schroeder, T. A., Bell, D. M., Masek, J. G., Huang, C., Meigs, G. W. 2016. Forest disturbance across the conterminous United States from 1985-2012: The emerging dominance of forest decline. Forest Ecology and Management. 360, 242-252. DOI: 10.1016/j.foreco.2015.10.042
Wagner, J. E., Stehman, S. V. 2015. Optimizing sample size allocation to strata for estimating area and map accuracy. Remote Sensing of Environment. 168, 126-133. DOI: 10.1016/j.rse.2015.06.027
Tyukavina, A., Baccini, A., Hansen, M. C., Potapov, P. V., Stehman, S. V., Houghton, R. A., Krylov, A. M., Turubanova, S., Goetz, S. J. 2015. Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environmental Research Letters. 10(7), 074002. DOI: 10.1088/1748-9326/10/7/074002
More details may be found in the following project profile(s):
