• 3:30pm – Megan Coffer – Eyes in the Sky Lend Support for Seagrass Monitoring in Coastal Environments
• 3:40pm – Xiaojie Gao – Quantifying Long-term 30-m Land Surface Phenology with Uncertainty by a Bayesian Hierarchical Model
• 3:50pm – Nikki Inglis – Future Declines in Quaking Aspen Disproportionately Affect Rocky Mountain Viewscapes along Scenic Byways
• 4:00pm – Laura Tomkins – A New Method for Visualizing Precipitation Types within Winter Storms using Weather Radar Data

Eyes in the Sky Lend Support for Seagrass Monitoring in Coastal Environments

Satellite remote sensing can complement field observations by offering improved spatial and temporal resolutions, collecting imagery more frequently and over larger areas. Here, imagery from two commercial satellite platforms, Maxar’s WorldView-2 satellite sensor and Planet’s RapidEye satellite constellation, was used to quantify seagrass extent in coastal systems. First, an automated workflow was created to process imagery from a basic product, as delivered from each company, into an analysis ready product. Next, an image classification technique was used to classify each satellite pixel into one of five classes, including seagrass. This approach was demonstrated at St. Joseph Bay, Florida.

Quantifying Long-term 30-m Land Surface Phenology with Uncertainty by a Bayesian Hierarchical Model

Land surface phenology (LSP) is a consistent and sensitive indicator of climate change effects on Earth’s vegetation. Existing methods of estimating LSP require time series densities that, until recently, have only been available from coarse spatial resolution imagery such as MODIS (500 m) and AVHRR (1 km). LSP products from these datasets have improved our understanding of phenological change at the global scale, especially over the MODIS era (2001–present). Nevertheless, these products may obscure important finer-scale spatial patterns and longer-term changes. Therefore, we have developed a Bayesian hierarchical model to estimate annual LSP from Landsat imagery (1984–present), which has medium spatial resolution (30 m) but relatively sparse temporal frequency. Our approach uses Markov Chain Monte Carlo (MCMC) sampling to quantify individual phenometric uncertainty, which is especially important when considering long time series with variable data quality and observation density, but has rarely been demonstrated. The estimated spring LSP had strong agreements with ground phenology records at Harvard Forest and Hubbard Brook Experimental Forest. The Bayesian phenometrics were consistent with the recently released 30-m HLS-based LSP product, MSLSP30NA, in its time period of 2016 to 2018. Our Bayesian hierarchical model is an important step forward in extending medium-resolution LSP records back in time as it accomplishes both critical goals of retrieving LSP from sparse time series and accurately estimating uncertainty.

Future Declines in Quaking Aspen Disproportionately Affect Rocky Mountain Viewscapes along Scenic Byways

Expansive vistas in mountain systems make scenic viewscapes––the visible portions of a landscape with which people form a connection––essential providers of cultural ecosystem services (CES). Like the dynamic systems they encapsulate, mountain viewscapes are subject to change, but the cultural ecosystem services they provide are rarely considered from future or dynamic perspectives. Here we forecasted change in a CES, using climatic shifts of a culturally valuable tree species, quaking aspen (Populus tremuloides), along scenic byways as an example of how viewscapes change through time. Across three climate scenarios (warmer and drier, warmer and wetter, and no change) the total area of aspen and its visibility from byways declined, but visible declines were 1.5–3.1 fold greater than declines in the study area overall. Differences between visible and total aspen peaked in mid-elevations (2,000–3,000 m) where aspen is most abundant. Mismatch between total and visible declines in aspen highlights opportunities for tighter connections between landscape planning and ecological research, and for gaining more comprehensive understanding of future changes in CES.

A New Method for Visualizing Precipitation Types within Winter Storms using Weather Radar Data

The network of National Weather Service (NWS) radars provides real-time information on storm conditions. In winter, both heavier snow rates and melting precipitation can both yield higher radar reflectivity, making it difficult to distinguish areas of heavy snowfall from wintery mix (combinations of snow, sleet, freezing rain and rain). To remedy this problem, we combine reflectivity and correlation coefficient data from NWS radars to discern areas within the storm that are likely to have surface snow, wintery mix, and rain. The additional information about current conditions and storm evolution that can be used for warnings of dangerous winter weather conditions.