14-9. From Local to Landscape: Harmonics and Synthesis of Phenology and Climate Data Across Spatial and Temporal Scales
The need to understand local patterns of recurring seasonal biological events (phenology) in the context of environmental variation at regional to continental scales has emerged as a priority for natural resource management, especially in response to global climate change (Janetos et al., 2012). The USA National Phenology Network (USA-NPN, www.usanpn.org) was established in 2007 to create a framework and system by which phenological observations of organisms are obtained across the nation in a comprehensive and standardized way (Schwartz et al., 2012). Other observing systems, such as the National Ecological Observatory Network (NEON), the Long-Term Ecological Research Network (LTER), and National Park Service (NPS) and National Wildlife Refuge System (NWRS) Inventory and Monitoring Programs, are part of this national framework for production and application of standardized organismal phenology information. Whereas observations of organismal (ground-based, or in situ) phenophases occur at discrete points, natural resource management decisions are made within the context of landscapes. Although landscape-level remotely-sensed land surface phenology (LSP)—in the form of time-series vegetation indices (e.g., NDVI, EVI) and derivatives known as phenometrics—is a powerful tool to understand the response of a landscape to the sum of its environmental conditions (Reed et al., 2003), the linkages between LSP, ground-based observations of phenology, and environmental forcings, such as climate, remain poorly understood.
Spatially extensive, ground-based, standardized datasets being collected by USA-NPN, NEON, NPS, NWRS, coupled with continental biophysical observation systems managed by NASA, NOAA and others, represent a rapidly emerging resource for the development of techniques to cross-walk and identify linkages between LSP captured by satellites and phenological activity observed on the ground (Melaas et al., 2013). The continent-wide coverage and frequent repeat times of LSP (e.g., from MODIS), the national network of historic meteorological data, and the more recent ground-based phenophase observations have the potential to be integrated together to understand both pattern and process that can be translated broadly across the landscape. To be utilized effectively, the environmental information contained in these spatially and temporally complex time series must be integrated into meaningful variables (e.g., phenometrics or other intermediate-level products) that can be understood in terms of the local climate, global change forcings, and/or organismal response at spatial scales relevant to resource managers.
Methods for deriving phenometrics from LSP (e.g., start of season, peak season, end of season), include curve derivatives, use of global or local thresholds, and other model fitting methods (White et al., 2009; de Beurs and Henebry, 2010). For example, the USGS calculates and distributes nine LSP phenometrics (online at http://phenology.cr.usgs.gov/.). The start of season phenometric is calculated using a “delayed moving average” technique to estimate the start of spring. USGS scientists are also exploring the use of additional methods to characterize the shape of complex curves that might better enable inter-comparison of response curves derived for any number of disparate variables collected as a time series, e.g., vegetation indices from satellites or cameras, climate data, and/or ground-based observations.
Fourier harmonic analysis is one example of a method currently being explored by USGS scientists to provide a framework for integration, analysis and synthesis of disparate types of times-series data (Wallace, 2002; Sankey et al., 2013). Fourier harmonic analysis expresses a complex curve as the sum of an “additive term” and simple cosine waves of different frequencies (Jakubauskas, 2001). Each frequency component estimated by Fourier analysis is defined by its amplitude and phase, and these values can be understood ecologically for each variable (Wallace et. al accepted for publication, Wallace, 2002; Jakubauskas, 2001). For natural resource management, harmonics of LSP are a demonstrated and powerful tool for characterizing spatially and temporally complex land cover change (Jakubauskas, 2001; Moody and Johnson, 2001; Wallace, 2002; Sankey et al., 2013). Characterizing the resonance between the harmonics of LSP, climate and in situ observations will produce ecosystem-specific signatures that can be monitored through time and provide an assessment of ecosystem condition and change.
We seek proposals to explicitly use one or more methods to characterize the shape of complex curves and identify resonance and relationships among landscape and local phenology and environmental time series. The successful candidate will identify a suite of datasets with appropriate spatial and temporal scales, including in situ phenophase data (e.g., from the USA-NPN database or other known datasets), LSP data and/or associated phenometrics, and climate data. The optimal proposal would also identify a potential application of the integrated and synthesized analysis to maximize the potential that the research will be relevant to science-informed decision-making on Department of Interior lands. Comparison of the selected method(s) with other LSP products or tools would strengthen the phenology community of practice and provide further validation of the technique (cf White et al., 2009). Opportunities for applications include using the metrics to quantify the impact of restoration activities; predict optimal timing for treatment of invasive vegetation; predict optimal time to inventory birds, flowers, mammals; and quantify the spatially explicit predictability of interannual ecosystem dynamics to, thereby, assess the reliability of ecosystem services provisioning.
The candidate will produce not only scholarly products (e.g., peer-reviewed journal articles), but ideally will also contribute to products or applications that provide science information in support of decision-making, conservation or natural resource management, especially in support of lands or species managed by Department of Interior.
de Beurs, KM and Henebry, GM, 2010. Chapter 9: Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology, in: I.L. Hudson, M.R. Keatley (eds.), Phenological Research, 177 DOI 10.1007/978-90-481-3335-2_9, Springer Science+Business Media B.V. 2010.
Jakubauskas, M.E., D.R. Legates, and J. Kastens, 2001. Harmonic Analysis of Time-Series AVHRR NDVI data. Photogrammetric Engineering and Remote Sensing 67(4):461-470.
Janetos, A.C., R.S. Chen, D. Arndt, M.Kenney (Coordinating Lead Authors with 25 Contributing Authors), 2012. National Climate Assessment Indicators: Background, Development, & Examples. Technical Input Report for the US Global Change Research Program 2013 National Climate Assessment 2013. Accessed 25 September 2012.
Melaas, E.K., Friedl, M.A. and Zhu, Z. 2013. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data. Remote Sensing of Environment 132:176-185.
Moody, A. and Johnson, D.M. 2001. Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform, Remote Sensing of Environment, 75(3):305-323.
Reed, B.C., White, M.A., and Brown, J.F., 2003. Remote Sensing Phenology, In: Phenology: an integrative science, Editor: Schwartz, M.D., Dordrecht, The Netherlands, Kluwer Academic Publishing. 365-381.
Schwartz, M.D., Betancourt, J.L. and Weltzin, J.F. 2012. From Caprio’s Lilacs to the USA National Phenology Network. Frontiers in Ecology and the Environment 10:324-327.
Sankey, JB, Wallace, CSA, Ravi, S (2013), Phenology-based, remote sensing of post-burn disturbance windows in rangelands. Ecological Indicators. doi: 10.1016/j.ecolind.2013.02.004
Wallace, C.S.A., Villarreal, M.L., van Riper III, C. (accepted for publication), Influence of Monsoon-related Riparian Phenology to Yellow-billed Cuckoo Habitat Selection in Arizona. Journal of Biogeography
Wallace, C.S.A., 2002. Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat. ( Tucson, Arizona: University of Arizona) Ph.D. Dissertation.
White, M.A., de Beurs, K.M., Didan, K., Inouye, D.W., Richardson, A.D., Jensen, O.P., O’Keefe, J., Zhang, G., Nemani, R.R., van Leeuwen, W.J.D., Brown, J.F., de Wit, A., Schaepman, M., Lin, X., Dettinger, M., Bailey, A.S., Kimball, J., Schwartz, M.D., Baldocchi, D.D., Lee, J.T., and Lauenroth, W.K., 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982 to 2006, Global Change Biology, doi: 10.1111/j.1356-2486.2009.01910.x.
Proposed Duty Station: Tucson, AZ; Sioux Falls, SD; Flagstaff, AZ
Areas of Ph.D.: Geography, biology, environmental science, climatology, ecology, GIS/remote sensing science, spatial modeling, or related field.
Qualifications: Applicants must meet one of the following qualifications - Research Geographer, Research Biologist, Research Physical Scientist, Research Ecologist.
(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of theposition will be made by the Human Resources specialist).
Research Advisors: Cynthia Wallace, (520) 670-5589, firstname.lastname@example.org.; JakeWeltzin, (520) 626-3821, email@example.com.; Joel Sankey, (928) 556-7289, firstname.lastname@example.org.; Jesslyn Brown, (605) 594-6003, email@example.com
Human Resources Office contact: Lisa James, (916) 278-9405, firstname.lastname@example.org.
|Summary of Opportunities|