The timing of vegetation development, or phenology, is one of the most clearly observed terrestrial responses to changing climate, making it a focus of current research efforts. Much of these involve remote sensing of the timing of foliage emergence. However, these efforts have been limited to coarse spatial or temporal resolution, and often lacked a direct link to field measurements for interpretation and accuracy assessment. Addressing this gap, we compared multiple remote sensing methodologies to extensive field measurements in order to establish a robust method of quantifying phenology and a better understanding of what physiological characteristics the sensors are best able to detect. Using this methodology, we are currently working to quantify historical phenology across the region based on a 27 year archive of Landsat TM 5 and 7 data. This work will inform the investigation of both inter-annual decadal changes and regional microclimate patterns in phenology for northern Vermont and New York.
Results/Conclusions
Comparisons of 5 vegetation indices derived from Landsat 5 and 7 TM data, 5 different mathematical fits to model a continuous temporal response, a suite of index thresholds for “date of spring” assessments (DOS), and field measurements of budburst stage, canopy transparency, and leaf area index found that a four parameter nonlinear model (Zhang 2004) based on at least 6 spring coverages of the Enhanced Vegetation Index (EVI) and a DOS threshold of 0.3 was most closely related to field metrics across all 32 plots. This approach was able to match the field measured date when plots first reached full leaf out to within 11 days (CV= 11.21). This indicates that landscape scale remote sensing efforts may be useful to compare the change in canopy phenology over a season and relative differences in timing across the landscape. But estimates of spring to single day accuracy are not likely accurate across forest types. Of the various field metrics, the visual ranks of budburst stage were more closely related to vegetation indices (r= 0.9554) than photo metrics of canopy characteristics.