Curriculum Vitae

Eli’s CV


Friedl, M.A., Gray, J.M., Melaas, E.K., Richardson, A.D., Hufkens, K., Keenan, T., Bailey, A., & O’Keefe, J. (2014) A tale of two springs: using climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environmental Research Letters, 9, 5

By the end of this century, mean annual temperatures in the Northeastern United States are expected to warm by 3–5 °C, which will have significant impacts on the structure and function of temperate forests in this region. To improve understanding of these impacts, we exploited two recent climate anomalies to explore how the springtime phenology of Northeastern temperate deciduous forests will respond to future climate warming. Specifically, springtime temperatures in 2010 and 2012 were the warmest on record in the Northeastern United States, with temperatures that were roughly equivalent to the lower end of warming scenarios that are projected for this region decades from now. Climate conditions in these two years therefore provide a unique empirical basis, that complements model-based studies, for improving understanding of how northeastern temperate forest phenology will change in the future. To perform our investigation, we analyzed near surface air temperatures from the United States Historical Climatology Network, time series of satellite-derived vegetation indices from NASA’s Moderate Resolution Imaging Spectroradiometer, and in situ phenological observations. Our study region encompassed the northern third of the eastern temperate forest ecoregion, extending from Pennsylvania to Canada. Springtime temperatures in 2010 and 2012 were nearly 3 °C warmer than long-term average temperatures from 1971–2000 over the region, leading to median anomalies of more than 100 growing degree days. In response, satellite and ground observations show that leaf emergence occurred up to two weeks earlier than normal, but with significant sensitivity to the specific timing of thermal forcing. These results are important for two reasons. First, they provide an empirical demonstration of the sensitivity of springtime phenology in northeastern temperate forests to future climate change that supports and complements model-based predictions. Second, our results show that subtle differences in the character of thermal forcing can substantially alter the timing of leaf emergence and canopy development. By explicitly comparing and contrasting the timing of thermal forcing and leaf phenology in 2010 and 2012, we show that even though temperatures were warmer in 2012 than in 2010, the nature and timing of thermal forcing in 2010 lead to leaf emergence that was almost a week earlier than 2012.

Melaas, E.M., Friedl, M.A., & Zhu, Z. 2013. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM + data. Remote Sensing of Environment, 132, 176-185

Observations of vegetation phenology provide valuable information regarding ecosystem responses to climate variability and change. Phenology is also a first-order control on terrestrial carbon and energy budgets, and remotely sensed observations of phenology are often used to parameterize seasonal vegetation dynamics in ecosystem models. Current land surface phenology products are only available at moderate spatial resolution and possess considerable uncertainty. Higher resolution products that resolve finer spatial detail are therefore needed. A need also exists for data sets and methods that link ground-based observations of phenology to moderate resolution land surface phenology products. Data from the Landsat TM and ETM + sensors have the potential to meet these needs, but have been largely unexplored by the phenology research community. In this paper we present a method for characterizing both long-term average and interannual dynamics in the phenology of temperate deciduous broadleaf forests using multi-decadal time series of Landsat TM/ETM + images. Results show that spring and autumn phenological transition dates estimated from Landsat data agree closely with in-situ measurements of phenology collected at the Harvard Forest in central Massachusetts, and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. Application of this method over larger scales has the potential to provide valuable information related to landscape-scale patterns and long term dynamics in phenology, and for bridging the gap between in-situ phenological measurements collected at local scales and land surface phenology metrics derived from moderate spatial resolution of instruments such as MODIS and AVHRR.

Melaas, E.M., Richardson, A.D., Frield, M.A., Dragoni, D., Gough, C.M., Herbst, M., Montagnani, L., & Moors, E. 2013. Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems. Agricultural and Forest Meteorology, 171-172, 46-56

Vegetation phenology is sensitive to climate change and variability, and is a first order control on the carbon budget of forest ecosystems. Robust representation of phenology is therefore needed to support model-based projections of how climate change will affect ecosystem function. A variety of models have been developed to predict species or site-specific phenology of trees. However, extension of these models to other sites or species has proven difficult. Using meteorological and eddy covariance data for 29 forest sites (encompassing 173 site-years), we evaluated the accuracy with which 11 different models were able to simulate, as a function of air temperature and photoperiod, spatial and temporal variability in the onset of spring photosynthetic activity. In parallel, we also evaluated the accuracy with which dynamics in remotely sensed vegetation indices from MODIS captured the timing of spring onset. To do this, we used a subset of sites in the FLUXNET La Thuile database located in evergreen needleleaf and deciduous broadleaf forests with distinct active and dormant seasons and where temperature is the primary driver of seasonality. As part of this analysis we evaluated predictions from refined versions of the 11 original models that include parameterizations for geographic variation in both thermal and photoperiod constraints on phenology. Results from cross-validation analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than the original models. Estimates for the timing of spring onset from MODIS were highly correlated with the onset of photosynthesis derived from flux measurements, but were biased late for needleleaf sites. Our results demonstrate that simple phenology models can be used to predict the timing of spring photosynthetic onset both across sites and across years at individual sites. By extension, these models provide an improved basis for predicting how the phenology and carbon budgets of temperature-limited forest ecosystems may change in the coming decades.