Integrating Multiple Measures of Spring and Autumn Phenology: Phase Three

Authors: Mark D Schwartz*, University Of Wisconsin-Milwaukee
Topics: Climatology and Meteorology, Environmental Science, Remote Sensing
Keywords: Phenology, Spring, Autumn
Session Type: Paper
Day: 4/12/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Astor Ballroom I, Astor, 2nd Floor
Presentation File: No File Uploaded

Improved understanding and utilization of phenological measures offer important opportunities to further understanding of ecosystem processes. Combining detailed conventional (visual) ground observations, which provide necessary information on species timing differences, and satellite-derived remote sensing data, which facilitate needed spatial integration and large area coverage, is an important avenue for progress in this area. A relatively new resource to address this scaling issue is near-surface remote sensing data collected from fixed position cameras. This paper presents on-going findings from a multi-year comparison of the spring and autumn seasonal transitions in Downer Woods (a small urban woodlot on the University of Wisconsin-Milwaukee campus dominated by white ash and basswood trees) and several research sites in the northern mixed forests of WI. The study areas are under observation from visible/near-infrared cameras (part of the Phenocam network), have detailed ground-based species-specific visual phenological observations collected in both spring and autumn, air/soil temperatures and light sensor data measured under the canopy, and flux tower measurements (in northern WI). The results show that Phenocam visible information can be successfully compared to all these other phenology-related data series. Further, these changes can be in turn simulated by process models based on seasonal temperatures. Thus, the concurrent collection of these data suggest a coherent process whereby more robust ground-based species-aggregated "pixel" data can be produced which will be scalable to large areas, and potentially be applicable to more complex environments and ecosystems. Such an approach could potentially improve phenology-based spatial estimates of carbon and energy flux.

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