A new grant sponsored by members of the BU community has been awarded funding by the National Science Foundation.
The goal of the project, “A Smart-city Cloud-based Open Platform and Ecosystem (SCOPE),” is to “research, prototype, and evaluate novel ‘smart-city’ services for the city of Boston and for the Commonwealth of Massachusetts” through the creation of the cloud-based open platform, SCOPE (“NSF“). In developing and utilizing SCOPE, the investigators hope to learn more about the functionality of smart cities and implement “specific SCOPE-enabled smart-city services” some of which include technology to improve traffic congestion and technology to monitor carbon emissions.
The grant is led by Principal Investigator Azer Bestavros, a Computer Science Professor at BU. Earth & Environment Assistant Professor Lucy Hutyra is a Co-PI on the project. Working with Hutyra on the project is Ph.D. candidate Conor Gately.
To learn more about the SCOPE project, visit the NSF award information page here.
To learn more about the work of Lucy Hutyra, visit her profile page on our website.
The highly competitive appointment, which comes with $1,000,000, is given to “accomplished research scientists who also are deeply committed to making science more engaging for undergraduates. … The 40 scientists who have been named HHMI professors since the program began in 2002 have introduced innovative approaches for teaching science in the classroom, expanded and enhanced student research opportunities, developed new educational resources, and implemented novel mentoring programs for student support” (HHMI Professors).
To view the announcement directly from HHMI, click here.
Marchant’s plans for his grant are described in the BU Today feature article that can be read here.
Assistant Professor Lucy Hutyra‘s new grant has been awarded funding from the National Oceanic and Atmospheric Administration (NOAA)‘s Atmospheric Chemistry, Carbon Cycle, and Climate Program (AC4).
Hurtyra is Principle Investigator (PI) on the grant, titled “Quantifying Carbon Signatures Across Urban-to-Rural Gradients: Advancing the Capacity for Monitoring, Reporting, and Verification Through Observations, Models, and Remote Sensing,” which has been awarded for the period of August 2014 to July 2017.
You can review all of Hutyra’s active grants by visiting the grant section of our website.
Earth & Environment Professor and Chair Curtis Woodcock has just been awarded funding for a new NASA grant.
The new grant, “Near real-time monitoring of land cover disturbance by fusion of MODIS and Landsat data,” began on May 28, 2014 and will run until May 2017.
Professor Woodcock is listed as Principle Investigator on the new grant.
You can view this and other grants by Prof. Woodcock in the grants section of our website.
Earth & Environment graduate student Jon Wang was recently awarded a National Science Foundation (NSF) Graduate Research Fellowship Award.
Wang’s research will focus on the remote sensing of urban heat islands and the climate-mediated effects of urbanization on phenology in New England.
Wang is advised by Professor Mark Friedl. Wang’s graduate studies focus on the topics of remote sensing and urban ecology.
Earth & Environment Associate Professor Sergio Fagherazzi recently was awarded funding for a new grant.
The grant, “Impact of Hurricane Sandy on the Salt Marshes of Chincoteague Bay, Virginia, and Barnegat Bay, New Jersey,” will focus on studying the effects of 2012 Hurricane Sandy.
Department of Earth & Environment Professor Sergio Fagherazzi was recently awarded a new NSF grant to study the effect of fertilizers on salt-marsh erosion.
The title of Fagerhazzi’s new grant is “Ecosystem evolution and sustainability of nutrient enriched coastal saltmarshes.”
Department of Earth and Environment Professor Mark Friedl was recently awarded funding from NASA for his new project, “Using Three Decades of Landsat Data to Characterize Changes and Vulnerability of Temperature and Boreal Forest Phenology to Climate Change.”
Friedl and his team will use Landsat data collected over the past 30 years to analyze the growing season of temperate and boreal forests in North America. Their goal is to use the data to better understand how these forests have responded to climate change. Working with Friedl as Co-investigators on the project will be Department of Earth and Environment Professor and Chair Curtis Woodcock and current Ph.D. student Eli Melaas.
The project will begin this spring and take place over the next three years.
A detailed abstract of the project is as follows:
“Climate change is creating well-documented impacts on terrestrial ecosystems. Among the best known of these impacts are changes to the growing season of temperate and boreal forests. Changes in phenology provide useful diagnostics of climate change impacts in these biomes, influence coupled biosphere-atmosphere interactions, and also affect regional-to-global carbon budgets. Extreme events and climate variability complicate the response of ecosystems and increase vulnerability by inducing large phenological responses that affect ecosystem function at seasonal (and longer) time scales. Studies using in-situ measurements have suggested that the growing season of temperate and boreal ecosystems is changing, and remote sensing-based research using time series imagery from coarse resolution sensors appear to confirm this trend. Specifically, studies using AVHRR NDVI data have documented changes in growing season NDVI that indicate widespread perturbations to boreal and temperate forests in response to climate change. However, the coarse spatial resolution and other limitations of AVHRR data constrain the types of inferences that can be drawn from these data. Sub-pixel contamination of AVHRR time series by snow and disturbance events introduce sources of variation unrelated to phenology, and challenges associated with instrument calibration, atmospheric correction, and geo-location uncertainty further reduce the utility of these data for long-term phenology studies. In this proposal we describe research to address these challenges using Landsat data. Specifically, we propose to use a new methodology that exploits dense time series of Landsat images to quantify spatio-temporal patterns in North American temperate and boreal forest growing season dynamics. Our proposed methodology uses a sampling strategy designed to capture geographic variation in temperate and boreal forest properties, and focuses on regions of overlap between adjacent Landsat scenes, thereby significantly increasing the temporal sampling of Landsat images. Because temperate and boreal ecosystems are characterized by frequent disturbance and have snow on the ground at times of the year that are especially important for detecting changes in phenology, our methodology will exploit datasets related to fire disturbance such as the Canadian and Alaskan Large Fire databases, and will include strategies to screen and remove snow-contaminated pixels. Results from this research will yield methods and datasets for retrospective characterization of changes to temperate and boreal forest growing seasons spanning 30+ years at 30-meter spatial resolution. In doing so, this research will (1) dramatically improve information about how temperate and boreal forests have changed in response to climate change, and (2) improve understanding regarding the sensitivity and vulnerability of these forests to climate change.”
Department of Earth and Environment Assistant Professor Mike Dietze‘s project, “Assimilation of imaging spectroscopy data to improve the representation of vegetation dynamics in ecosystem models,” was recently awarded funding by NASA.
The project will span three years and will be the product of the collaboration of Dietze and two other Principle Investigators, Shawn Serbin and Phil Townsend. Serbin (a former Dietze Lab post-doc), Townsend, and projector collaborator Ankur Desai are based at the University of Wisconsin. Tristan Quaife at the University of Reading in the United Kingdom will also collaborate on the project.
For more information about currently funded research by Asst. Prof. Dietze and other members of the Department of Earth and Environment see the grants section of our website.
A summary of the project is as follows:
The ability to seamlessly integrate information on forest function across a continuum of scales, from field to satellite observations, greatly enhances our ability to understand how terrestrial vegetation-atmosphere interactions change over time and in response to anthropogenic and natural disturbances. This project focuses on the use of field and high-spectral resolution remote sensing observations (i.e. imaging spectroscopy, IS), within an efficient model-data assimilation framework, to improve the characterization of vegetation dynamics in terrestrial ecosystem models. This effort comes at a crucial time because the experimental, remote sensing, and modeling communities have entered into an increasingly data-rich era; however the tools needed to make use of the numerous but disparate data for model improvements are currently lacking. For example, remote sensing can provide detailed spatial and temporal information on a number of important biophysical and biochemical properties of ecosystems. State-of-the-art dynamic vegetation ecosystem models, such as Ecosystem Demography (ED2.2) model (Medvigy et al., 2009), a physiologically-based forest community model, can potentially use this information to improve model representation of vegetation dynamics. ED2 is especially relevant to these efforts because it contains a sophisticated structure for scaling ecological processes across a range of spatial scales: from tree- level physiology to stand demography to landscape heterogeneity to regional carbon, water, and energy fluxes, which allows for the direct use of remotely sensed data at the appropriate spatial scale. The project leverages extensive field and imaging spectroscopy (IS) data that have been collected by Co-PI’s Shawn Serbin and Phil Townsend within the upper Midwest, US, directly within an ecosystem modeling framework. We are working to utilize a radiative transfer modeling (RTM) module being developed by Serbin and Dietze for use with the ED2 model and Predictive Ecosystem Analyzer (PEcAn, LeBauer et al., 2013) workflow system (www.pecanproject.org) to enable efficient assimilation of spectral reflectance observations from imaging spectroscopy data (and eventually any optical remote sensing observations, such as Landsat and MODIS/VIIRS). Through this open-source workflow system we will facilitate direct assimilation of spectral observations rather than derived products. This will improve the models parameterization of canopy optical properties and the surface energy balance. Through state-variable data assimilation we will fuse AVIRIS, flux towers, forest inventories, and model projections to reconcile estimates of vegetation composition and carbon pools and fluxes. The resulting data product will be analyzed to better understand the drivers of spatial and temporal variability in the carbon cycle and the sources of uncertainty in these estimates. This project would be an important step toward the operational capacity to assimilate reflectance observations, uniformly, within sophisticated ecosystem models with the goal to accurately constraining model projections of carbon pools and fluxes of terrestrial ecosystems.