I’m from Connecticut where I got my bachelor’s in environmental science and master’s in natural resources at UConn. During my time at UConn I had the opportunity to work on number of research projects using remote sensing to study a variety of ecosystems, from algal blooms in Long Island Sound and seagrass beds in the Florida Keys to the boreal forests of Canada and oak woodlands of northern California. I spent my first summer as a graduate student at NASA’s Ames Research Center working in collaboration with the U.S Fish and Wildlife Service to develop bird habitat maps that would be used for management decision making. The following year I spent several months working at NASA’s Jet Propulsion Laboratory developing new methods for characterizing forest structure. The research, which became the subject of my master’s thesis, involved developing long term records of forest height and disturbance history using a combination of LiDAR, radar and optical remote sensing. I also had the invaluable opportunity of working in Dr. Heidi Dierssen’s COLORS lab on novel research using hyperspectral remote sensing to monitor and characterize coastal ecosystems. Our research has involved the detection and classification of algal and seagrass debris in the Florida Keys, characterization of seagrass beds in California and most recently the use of a hyperspectral imager aboard the International Space Station to identify algal blooms in Long Island Sound.
I am interested in how light interacts with vegetation and how relationships derived from those interactions can be used to monitor and measure ecosystems at multiple scales (leaf, suborbital and space). My research involves trying to understand the impacts of canopy structure on trait retrieval algorithms, the fusion of hyperspectral imagery and LiDAR to improve trait estimates and the development of multi-seasonal, cross-functional type leaf trait retrieval algorithms.
I also have an interest in electronics and optics, and as a hobby I’m working on developing open-source, low cost sensors for measuring light that can be used for ecological research and environmental monitoring. Right now I have couple of ongoing projects that I hope to finish by the time I graduate:
Other than that I enjoy swimming, watching UConn basketball and whenever I can I try to get out on the lake when the winds are howling to do some windsurfing.
I was born and raised in Beer-Sheva, Israel. Lived some time in Jerusalem where I earned my Israeli tour guiding license. Spent some time at the shores of Tel-Aviv and moved back to Beer-Sheva for BA (Geography) in Ben-Gurion University of the Negev where I meet Dr. Arnon Karnieli, joined his Remote Sensing Laboratory in Sede-Boker Campus for MA (desert studies) focusing on vegetation assessment by the short wave infrared spectral region from space and stayed also for PhD exploring hyperspectral applications of precision agriculture for field crops in drylands. For my PhD Dr. David J. Bonfil of the Agricultural Research Organization, Volcani Center has joined as an additional advisor. Later I Joined a company researching spectral analyses of chicken eggs and eventually went back to the lab of Dr. Arnon Karnieli as a postdoc researcher. As a result of me joining Dr. Phil Townsend lab my wife and our two kids are now enjoying Madison, WI.
My current research mainly explores time series of data obtained by multi and hyper spectral sensors for site specific agriculture of field crops. Here, together with Dr. Shawn Conley from the Department of Agronomy the focus is on Soy beans. Examining several soy trails by hyperspectral airborne imagery as well as sudden death syndrome spectral assessment on ground level. My main research interest is in spectral assessment of vegetation and specific topics are: nutrients, spider mites damage as well as yield prediction, weed detection and leaf area index. I see the importance of combining spectral data with other data sources as a key element in better understanding the agricultural ecosystems and the interactions with its surroundings ecosystems.
Born in D.C., raised in a lion’s den; recent history is not very interesting at all. So uninteresting, that the attached picture is perhaps 8 years old. I apologize.
My research has typically had an emphasis on providing information requisite for immediately pressing management or conservation applications. My interests fall under three general focus areas:
- Building spatially explicit predictions related to where species and individuals are distributed across large scales, and evaluating the reasons for these patterns, both with regard to environmental characteristics, inter-specific interactions, and specific demographic processes underlying the current state.
- Modifying and developing model frameworks that allow robust prediction. Most of the parameters of interest in ecology cannot be perfectly observed, and standard statistical techniques are not robust to incomplete observation. I typically prefer using models that treat parameters as latent variables, and when analytically tractable and computationally reasonable, I prefer to consider ecological and observation processes with explicit consideration for specific biological factors (e.g., individual space use, density dependence) that are rarely considered within predictive models.
- Determining cost-effective and easily implemented methods/analyses for applied usage. Most of my experience has involved carnivores that are inherently difficult and expensive to sample, and making incremental improvements in cost-efficiency or detectability can pay huge dividends in the success or failure of survey/monitoring efforts. Moreover, it can be computationally expensive to fit complex models with large, multi-year data sets, and finding tractable and accurate alternative frameworks can make robust analysis more accessible.
At U-W, I work on the Snapshot Wisconsin project (in conjunction with WDNR) focusing on providing broad-scale assessments of species distribution, abundance, and dynamics in relation to changes in land cover and phenology.
In June 2014, UW’s College of Agriculture and Life Sciences interviewed Phil about his experience training astronauts.
Read about it here.
Characterization of forest functional types and their role in mediating ecosystem response to environmental change
Terrestrial ecologists are now called upon to provide regional scale predictions of the delivery of key ecosystem services from complex forest environments that are increasingly subjected to multiple agents of global environmental change. This necessitates the identification of the spatial pattern and fundamental mechanistic linkages among (1) forest functional types (FFT), (2) the magnitude of ecosystem perturbations, and (3) the magnitude of ecosystem responses. We propose to use field and imaging spectroscopic measurements to test an overall hypothesis that for a given perturbation, spatial variability in ecosystem response can be characterized by using spectroscopy to measure a key suite of leaf-based functional traits that define FFT.
We will characterize FFT by canopy-based measurement of three key functional traits: cell structure, shade tolerance, and recalcitrance. A synthesis of the literature and our recent research results indicate that these three traits describe fundamental axes of variability in plant physiology. Moreover, they synthetically define a spectrum of FFT ranging from “open” to “closed” patterns of forest nutrient cycling. We will use field spectroscopy, laboratory measurements, and assessments of species composition to directly measure canopy-based values of the biophysical and biochemical properties (i.e. Wm [ratio of leaf water mass to leaf dry mass], chlorophyll, lignin, foliar delta N-15, and leaf mass per area) that are hypothesized to define variation in cell structure, shade tolerance, and recalcitrance. By relating these field measurements to spectra obtained from AVIRIS or Hyperion, we propose to demonstrate the spectroscopic basis and quantify the error and uncertainty in our characterization of FFTs. Moreover, by collecting this field and hyperspectral information across a range of forest ecosystems (15 study sites ranging from boreal to tropical forests), we will identify the degree to which our characterization of FFT may be generalized to all forest ecosystems. Finally, we will test our overall hypothesis by relating characterizations of FFTs to field and remote sensing data describing ecosystem perturbations and ecosystem responses.
Our research meets several key NASA programmatic and scientific objectives. First, we will test and validate methods for using imaging spectroscopy to characterize plant physiology and assess ecosystem-relevant FFTs (program sub-element 1). This provides a basis for the development of algorithms suitable for future hyperspectral sensors. Second, we will integrate these assessments of FFTs with our ongoing work utilizing remote sensing information for characterizing disturbance and ecosystem response program sub-element 2). Finally, our proposed work will generate the data, geographic scope, and synthetic focus that is urgently needed in the ongoing development of a general theory linking plant ecophysiology, ecosystem ecology, and global change science.
Contact: Shawn Serbin, Aditya Singh
Infrared photo of trees in landscape
Yellowstone Ecoregion Mountain Pine Beetle project
A range of wood-boring beetles (including the mountain pine beetle) have affected broad
areas of the Rocky Mountains over the past two decades. These infestations are unprecedented in historical times, and correspond to a period of warming climate that has decreased winter mortality of beetles and led to latitudinal and longitudinal range expansion. Our objective was to develop an automated and repeatable method to map the timing, extent and severity of beetle-caused mortality in the Greater Yellowstone Ecosystem. We calibrated a long Landsat image time series against field measurements of mortality made between 2006 and 2012. We have been able to map the year-to-year progression of beetle-caused forest percent mortality through the ecoregion. This method provides a tool for estimating forest mortality in other locations using similar methods.
Bark beetle exiting tree
- Ken Raffa and Erinn Powell, Department of Entomology, UW-Madison
- Monica Turner, Department of Zoology, UW-Madison
Nutrients are exchanged between ecosystems in many ways.
The direction and magnitude of fluxes across the landscape can dramatically influence ecosystem processes. In Lake Myvatn in northern Iceland, an abundance of midges (in Icelandic, ‘my’ is midge, and ‘vatn’ is lake) emerge from the lake on a boom-and-bust cycle approximately every 7 years and create a visible ‘ring’ of enhanced vegetation growth around the the lake due to a nutrient subsidy from midges deposited on land.
From 2008 to 2011 we measured midge emergence from the lake and their subsequent deposition on land. We developed a model to map midge deposition as a function of distance-from-lake and wind direction. As well, we employed ASTER, Landsat, and Hyperion imagery to map foliar nitrogen, species composition, and aboveground biomass around the lake. The results show the strong influence of midge movement from lake to land on terrestrial ecosystem dynamics, the effects of which are clearly visible in spaceborne satellite imagery.
Contacts: Clayton Kingdon, Phil Townsend
EcoSIS Spectral Library
Spectroscopy allows indirect measurement of vegetation physical and chemical properties, assessment of plant functional types and biodiversity, and quantification of biosphere-atmosphere gas and energy exchange. While extensive spectral libraries exist, diverse collection procedures, storage formats, and limited metadata have prevented or complicated their use in comparative studies, meta-analyses, and global-scale ecosystem models. We are developing the Ecological Spectral Information System (EcoSIS) as a framework to bring together pre-existing spectral libraries, build new datasets and enable more effective and wider use of spectral data.
- Augment and expand current datasets through community-provided spectral data and allow identification of critical gaps in current spectroscopic holdings and applications;
- Establish spectral data collection standards and best practices in concert with the international community and NEON;
- Establish metadata standards and best practices consistent with ISO 19115 and other standards accepted by the remote sensing, ecology, modeling and climate change communities;
- Develop queryable spectral databases to hold collected spectral datasets, available via the web, and openly accessible to the community at-large;
- Develop linked databases of associated vegetation properties, used to interpret spectra and to scale biological, chemical and physical measurements;
- Create accessible, open-source tools from existing source-code for users to visualize and analyze spectral data and ancillary measurements including implementation of application programming interfaces (APIs), with rigorous QAQC and error reporting; and
- Evaluate approaches for data inter-comparison.
The ultimate objectives of EcoSIS are to facilitate question-based ecological and remote sensing research, and to provide the foundation for long-term, community-driven collaboration. EcoSIS is designed to meet key NASA objectives by archiving, curating, and enabling effective access to data obtained through NASA funded activities (e.g. in support of AVIRIS and HyspIRI activities) and contribute to broader efforts such as NEON. We see EcoSIS as having a critical linkage to other efforts such as SpecNet, the TRY Plant Trait Database, and Specchio.