Major Research Projects

(1)   The EMBYR wildfire model

From 1991-1993 I developed a spatially-explicit grid-based forest fire model, EMBYR (Ecological Model for Burning the Yellowstone Region), for simulating wildfire in Yellowstone National Park, USA.  EMBYR is a probabilistic model that predicts potential burn patterns of large fires relative to variations in fuel types and weather patterns in an area.  Ignitions can occur at random points or specific locations, and ignitions from firebrands can be simulated relative to fuel type.  EMBYR requires a GIS layer of fuel types based upon age classes and species composition.  Fire spread probabilities are specified for three possible fuel moisture conditions; wet, intermediate, or dry. Probabilities are then adjusted using one of three wind speed categories and one of eight wind directions.  The output from EMBYR indicates the final burn pattern of one or more potential landscape-scale fires allowing impacts from future fires to be estimated.  The spatial resolution is 50m.  EMBYR is easily parameterized, fast and efficient, and shows interactions between landscape pattern and process.  EMBYR development was sponsored by the National Science Foundation (NSF) under "Causes and Consequences of Large-Scale Fires" (web page describing early results here, source code available here, see Publication #36 describing the EMBYR model and its behavior, with 181 citations).

In 1994, Bob Gardner (ORNL staff) and I used EMBYR to simulate the wildfire regime over the next millennium for the Greater Yellowstone Ecosystem under three alternative synthetic fractally-generated climate scenarios: dry, moderate, and wet.  Fuel growth and tree succession under each scenario were simulated by a Markov transition probability model.  We performed 10 replications of 1000 years into the future for each of the three climate scenarios (see Publication #17, with 327 citations).  Total area burned each year was nearly constant, regardless of the climate; only the average size and intensity of the wildfires changed

In a series of three papers (Agricultural and Forest Meteorology 1998, Ecological Modeling 2004, and Landscape Ecology 2006), Bob Keane of USDA Forest Service, Missoula Fire Lab, and G.J. Cary of the Canadian Forest Service independently compared and evaluated the sensitivity of the EMBYR model with four other wildfire simulation models (FIRESCAPE, LANDSUM, SEM-LAND, and LAMOS(DS)).  EMBYR was found to be one of the most sensitive models to landscape fuel patterns, in terms of changes in area burned.

In 2004, Town Peterson and I used a modified version of EMBYR to predict the spread of Asiatic longhorned beetle, Anoplophora glabripennis, across its suitable range within the United States, as modeled by the GARP (Genetic Algorithm for Ruleset Prediction) niche model.  EMBYR provided an excellent parallel to species dispersal; fires spread via ignition of adjacent areas, and also through longer-distance dispersal by means of ‘firebrands’ — similar to the ways that an invasive species spreads across a landscape.  Our parameterization of the EMBYR was only intended to assess the spatial pattern of invading populations, not the actual rates of spread.  We initiated the EMBYR spread model at 32 known points of warehouse or tree infestation in North America (see Publication # 51, with 227 citations).  This is a Modeling and Knowledge Development Accomplishment.

(2)   The Fractal Realizer and MapCurves

In 1997, I devised and programmed the Fractal Landscape Realizer, which generates synthetic landscape maps to user specifications.  The alternative landscape realizations are not identical to the actual maps after which they are patterned, but are similar statistically (i.e., the areas and fractal character of each category are replicated).  A fractal or self-similar pattern generator is used to provide a spatial probability surface for each category in the synthetic map.  The Fractal Realizer preserves the fractal patterns of all the categories in the resulting synthetic landscape.  Each synthetic landscape is one realization from among an infinite ensemble of possible fractal landscape map combinations.  One can use the Fractal Landscape Realizer to simulate an actual example landscape on-the-fly at http://www.geobabble.org/cgi-bin/realizer/turing-map?3.  Click reload to generate another custom synthetic landscape.  Every realization is new and different.

The Fractal Realizer is useful as a generator of “neutral models” against which to test for the presence of natural spatial patterns.  The Fractal Realizer generates null models in the absence of any structuring process, or using well-defined structuring processes which are under the users' control.  Replicate landscape maps generated using the Fractal Realizer all possess statistical properties that are similar to a particular empirical landscape, and these synthetic maps can provide a baseline upon which to simulate natural processes in order to predict or test for expected pattern.  The sensitivity of stochastic spatial simulations to prescribed input landscapes can be evaluated by supplying them with a series of synthetic maps that obey particular statistical characteristics and by monitoring changes in selected output responses.  Statistically similar input landscapes with different spatial arrangements can be generated and supplied to spatial models as a hedge against pseudoreplication.

The quality of synthetic landscapes produced by the Fractal Realizer was tested using an online variant of the Turing Test.  More than 1000 ecologists and mapping specialists were presented over the web with a series of 20 selections of paired maps, and asked to distinguish the real map from the synthetic realization from the Fractal Realizer.  The resulting population of scores was not significantly different from a random binomial, proving that the experts were unable to discern the synthetic maps from the actual ones.  Anyone can take the test at any time over the web at http://www.geobabble.org/realizer/turing.html

Since its publication in 2002, many thousands of people have taken the Turing Test of the Fractal Realizer.  Several landscape ecology and GIS classes across the country have made the Turing Test of the Fractal Realizer a regular part of their scheduled laboratory exercises each year, and source code for the Fractal Realizer is available for download over the web.  In April 2004, I was awarded the Outstanding Landscape Ecology Paper by the International Association of Landscape Ecology (IALE) for the publication in Conservation Ecology describing the Fractal Realizer  (see Publication #46, http://www.ecologyandsociety.org/vol6/iss1/art2/)

In 2005, I developed MapCurves, a generalized algorithm for the quantitative, comparison of multiple categorical maps.  MapCurves is a quantitative goodness-of-fit (GOF) method that unambiguously shows the degree of spatial concordance between two or more categorical maps.  MapCurves graphically and quantitatively evaluates the degree of fit among any number of categorical maps and quantifies a GOF for each polygon, as well as for the entire map.  The MapCurves method will even indicate a perfect fit between ecoregion maps drawn by a “lumper” and a “splitter,” e.g., if all ecoregions in one map are comprised of unique sets of smaller ecoregions in the other map, so long as the spatial coincidence among the lumped and split ecorgegion categories is perfect.  It is not necessary to interpret (or even know) legend descriptors for the categories in the maps to be compared, since the degree of fit in the spatial overlay alone forms the basis for the comparison.  MapCurves produces the best translation table between categories in each map as an output product rather than starting with a translation table as an input.  Prior to MapCurves, meaningful quantitative comparison of two categorical maps was nearly impossible.  One can compare two or more ecoregion maps using MapCurves, even if the maps contain radically different numbers of ecoregions.  Two dozen well-known ecoregion and landcover maps were compared quantitatively using MapCurves (see Publication #59).  One can also use MapCurves to “borrow” and apply the best, most appropriate labels (of ecoregions or forest types, for example) from another map.

(3)   Clustering Quantitative Ecoregions and LANDFIRE National Wildfire Biophysical Settings Map

About 1997 I started experimenting with multivariate clustering as a way to statistically delineate homogeneous ecoregions, using a set of digital maps within a GIS as ecoregion characteristics.  While recognizing the utility and popularity of ecoregions among ecologists, I was dissatisfied with the reliance on human experts and expertise to produce them.  I quickly realized that multivariate clustering represented a quantitative alternative that was transparent, objective, and repeatable.  The algorithm was computationally demanding, however, mostly because of the large data volumes involved.  This computational need drove my research interest in the Stone Soupercomputer (see Factor 3C and Publication #40).

In 1999, I produced a national map of 1000 ecoregions created quantitatively by statistically clustering nine environmental variables, including physiographic, edaphic, and climatic variables at 1-km resolution.  I also created a total soil Kjeldahl nitrogen map for the continental United States at 1-km resolution by combining non-agricultural data from the National Soils Characterization Database (NSCD) and STATSGO.  I realized that the sharpness or fuzziness of ecological borders, or ecotones, between ecoregions could be quantitatively characterized, even if it changes along the length of the border (see Publication #27, with 57 citations).  I collaborated with others to link disparate tree physiology models to simulate tree growth across spatial scales (from leaf to stand to regions of stands), using forcing functions to drive models at larger scales (i.e., across the southeastern US, see Publication #44).  Ironically, this Integrated Modeling Project was initially sponsored by Steve McNulty of the Southern Global Change Program, USDA Forest Service, which is now a part of EFETAC, my own current Forest Service unit.  (Exhibit #1, Publication #53, with 100 citations)

In 2003, I developed the first quantitative global ecoregion maps, sponsored by and in coordination with The Nature Conservancy (TNC).  Quantitative ecoregions were statistically generated for both current global environmental conditions and future environments, as predicted for 2050 and 2100 by two global climate models under two possible future scenarios (http://www.geobabble.org/~hnw/global/ORNL-TNC/index.simcolors.html).  In 2005, also with TNC, we developed the first quantitative ecoregion maps for Papua, New Guinea and China (see Publication #83, with 58 citations).  These defensible and repeatable quantitative global ecoregions can be used to prioritize ecological preservation and restoration worldwide.

Using the same statistical quantitative ecoregion method, I was funded by the USDA Forest Service LANDFIRE project to produce a set of national wildfire biophysical regions to measure departure of ecosystems from their potential vegetation state for wildfire management (http://www.geobabble.org/~hnw/landfire/).  Forrest Hoffman and I also designed and constructed a 136-node, 272-processor parallel supercomputer for the LANDFIRE, project, and most official LANDFIRE products were produced using this parallel machine.

Parallel Multivariate Geographic Clustering has become one focus of my research, and I have developed this quantitative approach into a rich and powerful quantitative statistical foundation that underlies many of my subsequent future achievements, including Scientific Accomplishments 4, 5, and 7 listed here.  Spatial Clustering has proven to be a fertile recurrent theme throughout my research, and continues to be a thread of continuity throughout my scientific career.  This is a Knowledge Discovery and Knowledge Development Accomplishment.

(4)   Network Analysis, Including AmeriFlux and NEON Designs

When ecoregions are delineated using quantitative methods rather than expert judgment (see Scientific Accomplishment 3, above), the quantitative treatment provides a number of ecologically useful related concepts.  Two of the most interesting of these are representativeness, which allows maps to be drawn which show the geographic location of all regions which are similar to a selected ecoregion, and network site analysis, which shows how well a particular network of sites represents a larger area containing the network.

These concepts are of great practical use in the design and analysis of networks of installations or sample locations.  Once input variables of appropriate relevance, scale and quality are chosen, the coverage and sampling intensity of any network of sites can be analyzed statistically with respect to those selected variables.  Because the ecoregions are statistically derived, one can select a single ecoregion of particular interest, and then produce a sorted vector of the similarity of all other ecoregions to the selected one.  Coding these pairwise similarity values as gray levels, a map can be drawn which cartographically shows the degree of similarity of all ecoregions in the map to the selected ecoregion of interest.  Such maps show the degree of innate multivariate similarity between a particular selected ecoregion and the rest of the map.

This similarity concept can also be used to quantify how well a particular established network represents all of the conditions occurring within a map that contains it.  A network in this sense consists of a geographic constellation of installations or facilities, or can simply represent locations where samples have been (or will be) taken.  The quantitative similarity is now based on comparisons with multiple site locations within the established (or planned) network.

The best location for adding an additional new site or installation will be shown as the place that is the least well-represented by the current network of existing sites.  Importance values for each site can be calculated, based on the marginal representation it adds to the network.  Such importance values can be used to minimize the impact on representation if a site must be removed from the network.  Finally, a network with a given number of sites can be designed which is theoretically optimum, having the highest possible representation on the map.

Until now, sites in even large-budget networks have been established in opportunistic, political, or logistically-driven ways, resulting in undirected, organic growth.  Network analysis is simple, quantitative and defensible, and provides the first objective guidance for network design and evaluation (http://www.geobabble.org/~hnw/networks/).

I initially used this approach to determine the degree to which the existing network of carbon eddy flux towers within the AmeriFlux network are representative of flux environments across the conterminous United States (http://www.geobabble.org/~hnw/networks2/).  This network representativeness information was used to determine how many additional AmeriFlux towers will be required, and where additional towers should be placed.  In addition, the importance and uniqueness of each existing tower to the Ameriflux network were calculated.  This quantitative ecoregion-based approach to stratifying carbon flux may be the fastest way to fulfill the North American Carbon Program (NACP) and AmeriFlux goals of seasonally mapping sources and sinks of carbon within the North American continent.  Sponsored initially by the Office of Biological and Ecological Research (OBER), DOE, I soon received additional funding from AmeriFlux to continue this AmeriFlux network analysis, resulting in Publications #48 and #50.

After 8 years of additional tower site additions and losses within AmeriFlux, Beverly (Bev) Law, the director of AmeriFlux, called and asked me early in 2011 if I would repeat this analysis for the current configuration of the AmeriFlux network.  Although I now worked for the Forest Service, I repeated the AmeriFlux network analysis, and the results were presented at the 2011 annual AmeriFlux meeting (See Exhibit #3, letter 2).  A manuscript is being prepared to publish these updated AmeriFlux network representativeness results.

Because of the development of these research capabilities, I became involved with the early design of National Ecological Observatory Network (NEON).  NEON will be the first ecological measurement system designed to answer regional- to national-scale scientific questions.  A system of identical nodes was envisioned, each representing the ecological environments within a mother geographic "domain."  All nodes are focused in unison on a few transformational ecological questions of national relevance.  To better sample a phenomenon as diverse as the ecological environments of the United States, those environments were first divided into a set of more homogeneous "strata."  NEON nodes could then be located within each stratum, helping to ensure that their measurements can be scaled up to represent the entire range of environments within the United States.  Multivariate clustering based on national maps of 9 ecologically relevant climatic "state" variables was used to repeatably define 25 national climatic zones.  These 25 climate zones were combined with dynamic air mass seasonality data to create 20 NEON domains, each having relatively homogeneous climate.

I was invited to become a member of the 15-person NEON National Network Design Committee (NNDC), which met two dozen times over a period of 4 years.  The NNDC was responsible for drafting the Integrated Science and Education Plan, the Networking and Informatics Baseline Design, and the Project Execution Plan (PEP) for NEON.  I was also involved in the NEON Conceptual Design Review (CDR).  These reports and review results were given to the National Science Foundation and the U.S. Congress, which has now funded NEON for the initial build-out phase.

Using the quantitative ecoregions and optimal network design, I suggested the national regionalization on which the 20 official NEON domains are now based (http://www.geobabble.org/~hnw/neon/neonindex/).  I was also an invited member of the 10-person Land Use Subcommittee of the NEON Science and Human Dimensions Committee.  These efforts resulted in Publications #65 and #69, and a more detailed description of the history of development of the official NEON domains map is being currently prepared for publication.  NEON broke ground in June 2012, starting construction of its first two sites (See Exhibit #3, letter 1).

Now we have been funded by the Office of Biological and Environmental Research (OBER) within the Department of Energy’s Office of Science to use this network analysis to analyze DOE’s two new Next Generation Ecosystem Experiments (NGEE)-Arctic Alaskan Climate Change ecosystem warming sites, to study the placement of the sites within Alaska, and the representativeness of their measurements.  NGEE-Arctic is a major new 10-year, $8M DOE research effort.  DOE may also establish an NGEE-Tropics Program, with multiple sites in equatorial locations.

(5)   Invasive Species Predictions for the Great Lakes and Sudden Oak Death

Hired as a consultant by the Environmental Protection Agency (EPA), I predicted the exotic aquatic organisms most likely to invade the Great Lakes from the Ponto-Caspian Sea region.  I identified the most likely aquatic invaders across all taxa, and predicted the geographic extent of the potentially susceptible areas for each species within the Great Lakes (See Exhibit #3, letter 3).

Aquatic invasive species are transported with normal ship traffic, often carried in ballast water. This study predicted susceptibility by quantifying the degree of multivariate similarity of aquatic environments worldwide to selected locations within the Great Lakes, USA.  The approach assumes that, sooner or later, transport of invasive aquatic organisms will occur to and from all points on the globe.  Following such human-mediated accidental transplantations, it is the degree of similarity of the new aquatic environment to the original environment that determines whether the invader will successfully establish a population in the new location.

We produced multiple sets of aquatic ecoregions, based on six characteristics of the surface aquatic environment.  Using Multivariate Geographic Clustering (MGC) on a parallel supercomputer, we grouped over 50M 4 km map cells into groups or clusters having similar combinations of the six environmental conditions.  When placed back into geographic map space, these groups form geographic regions across all global aquatic habitats which share similar environmental conditions (http://www.geobabble.org/~hnw/global/aquaticinvaders/, aquaticinvaders2).

Because the ecoregionalization process is quantitative, a world map can be drawn in which the degree of multivariate similarity between the aquatic environment in the selected location and the aquatic environment in every other location is shown as a shade of gray.  By quantifying the similarity between aquatic environments, such maps show both the locations from which aquatic invasive organisms that are likely to survive here might come, and locations to which invasive aquatic forms from this location might go and establish a viable population (aquaticinvaders3 and aquaticinvaders4).  Using this similarity-based approach, it is not necessary to select particular donor and recipient locations, nor to do the analysis on a tedious species-by-species basis (see Publication #76).

The same quantitative ecoregionalization-based process has proven useful for mapping the risk that Sudden Oak Death (SOD), Phytophthora ramorum, will spread to other parts of the U.S.  The susceptibility of forests beyond the west coast of the United States to SOD is unknown, but is the subject of speculation, since the spread of the SOD epidemic could represent a serious threat to eastern forests.  I createdcustom statistical SOD-relevant ecoregions using national maps of conditions likely to be limiting for P. ramorum, including humidity, leaf-wetness, and cool temperatures.  My analysis of the quantitative multivariate similarity of each of these 1500 homogeneous SOD-regions with conditions in known SOD outbreak areas produced a continuous national estimate of risk or susceptibility to SOD (http://www.geobabble.org/~hnw/sod/report).

(6)   A Practical Map-Analysis Tool for Corridor Detection

I led the development of a landscape map analyzer tool which will identify and map corridors and barriers to plant and animal movement across any map.  Corridors are important, since they show the "roadways" most commonly used by plants and animals as they move or disperse across a mapped landscape.  The tool is based on the idea of island biogeography, and considers the map as isolated patches of high-quality habitat embedded in a matrix "sea" of all other patches of lower quality.

Corridor connectance, whether we wish to preserve it for a threatened species or impede it for an invasive exotic, is a critical concept in biodiversity management.  Despite this importance, the idea of corridors remains largely conceptual.  Few analytical management tools exist which can examine a real-world map, quantify connectance, and identify potential corridors.

The tool we developed, called “Pathway Analysis Through Habitat,” or PATH, uses simulated virtual plant and animal "walkers" which are imbued with movement characteristics and preferences of particular animal or plant species, and allows large numbers of these imaginary digital walkers to travel over the map.  Walkers representing individuals "try" to successfully disperse from one "island" patch of favorable habitat to another "island" in the archipelago, and, in so doing, define and map the best potential movement dispersal corridors.  The spatial arrangement and amount of patches of habitat, roads, urban areas and other real-world landscape features will affect the movements and successful dispersal of walkers, and therefore the routes of potential corridors across the map.

PATH provides realistic guidance for conservation and management decisions.  PATH patch importance values can be used to direct and prioritize planning for conservation and remediation.  A land manager can easily see which habitat patches are the most important targets for conservation or strenuous remediation (for a threatened species) or for elimination (in the case of an invasive species).  In the case of an invasive species, patches important as connecting corridors, once identified, would be the first places that a manager would want make inhospitable for the invader.  Construction of unsuitable or barrier patches, or elimination of particular favorable "bridge" patches may be suggested which will discourage movement of invasive species along existing corridors.  For threatened species, patches with high importance should be protected strenuously or preferentially remediated; while patches with low importance are more available for alternative use or development.

The PATH tool was developed using funding from the Southern Appalachian Information Node (SAIN) of the National Biological Information Infrastructure (NBII) of the US Geological Survey, the Army Corps of Engineers ERDC-CERL, and the National Petroleum Technology Office, DOE.  The prototype was initially run on small artificial test maps to evaluate its behavior for simple artificial landscapes designed to produce expected intuitive results , and then simple actual landscapes (Exhibit #2, Publication #60, with 43 citations).  As a parallel application running on a supercomputer, the PATH tool is computationally powerful enough to analyze even extensive, highly-fragmented real-world landscapes.  A CERL Technical Report, for example, describes how the PATH tool was used to analyze Red-cockaded woodpecker movement across the Southeastern United States (Publication #62).  A manuscript showing potential gopher tortoise movement corridors within and around Fort Benning, GA using the PATH tool is being prepared for publication (http://www.geobabble.org/~hnw/walkers/gophertortoise).  Because of the level of interest shown by the US Armed Forces in analyzing connectance within and near military bases, I worked with ERDC-CERL to translate the PATH tool into the NetLogo language, so that PATH no longer requires the use of a parallel supercomputer, thus making it more accessible to resource managers (Publication #89).

(7)   Forest Tree Species Range Shifts Under Two Alternative Climate Change Forecasts (ForeCASTS)

Climate change poses a severe threat to the viability of several forest tree species, which may be forced either to adapt to new conditions or to shift their ranges to more favorable environments.  Species already having limited geographic ranges may be at highest risk.  Along with Kevin Potter, I used spatial models of future environmental conditions to predict the location and quality of habitat for several hundred tree species under different climate change models and emissions scenarios.  We also are determining where each species, within its current range, is most susceptible to local extirpation as a result of climate change.

In our Forecasts of Climate-Associated Shifts in Tree Species (ForeCASTS) project, range shifts for 215 tree species were predicted globally following future climate changes forecast by the Parallel Climate Model (PCM) and the Hadley climate model under IPCC scenarios A2FI and B2 for the years 2050 and 2100 (See Exhibit #4).  Most species' predicted suitable (or fundamental) ranges closely followed or were slightly more extensive than actual (or realized) ranges under present conditions (as compared to Elbert Little’s tree range maps).  We have also developed a new climate change impact analysis method, Minimum Required Movement (MRM) Distances.  MRM distance determines how far a species would have to move in order to arrive at the nearest location with the same combination of conditions they had prior to a climatic change.  Global maps showing MRM distance to return to the closest geographic locations offering suitable conditions in the future directly show the likelihood of local extirpation following climate change.  Locations that are the nearest "lifeboats" for large surrounding areas may represent management and conservation targets.

Resource managers, land-use planners and conservation organizations can view ForeCASTS future host range maps for any species at http://www.geobabble.org/~hnw/global/treeranges3/climate_change/atlas.html.  Unlike existing tree range shift prediction atlases, which are limited to the eastern or western United States, ForeCASTS maps are global in extent.  With maps for 215 tree species, ForeCASTS already covers many more tree types than earlier tree-shift climate change efforts.  Although the Version 3 future range maps in the atlas are still considered provisional, results for several tree species have already been used in planning for several NFs (Francis Marion and Sumter NFs) and several states (NC, Linda Pearsall, NCDENR, and WA and OR, Carol Aubry, USDA FS, Olympic NF).  ForeCASTS species range shift results are also being cited in the National Climate Assessment (NCA) Forestry Technical Report that is being currently prepared for Congress.

The ForeCASTS project is still underway, and our poster showing preliminary results received the “Most Exciting Science” Award at the Annual Forest Service Forest Health Monitoring (FHM) Work Group meeting held in Albuquerque, New Mexico in April 2010.  We anticipate that Version 4 of the ForeCASTS species atlas will contain predicted future host range maps for more than 325 tree species, covering essentially every woody species whose home range extends into the conterminous United States.  Version 4 is expected to be completed and available to managers by the end of FY2012.  This is a Knowledge Discovery and Knowledge Synthesis and Assessment Accomplishment.

(8)   Forest Service “Eye in the Sky” Early Warning System Monitors Forest Disturbances Nationally

Along with our cooperators/collaborators, I conceived and established the ForWarn National Early Warning System (see Exhibit #5, Publication #53, describing the prototype), which produces maps showing potential forest disturbance across the conterminous United States at 231m resolution every 8 days, based on images obtained over the preceding 24-day analysis window (view introductory video [large download!]).  The EFETAC/WWETAC ForWarn system provides a strategic national overview of potential forest disturbances, identifying and directing attention and resources to locations whose forest behavior seems unusual or abnormal.  The purpose of ForWarn is to alert, focus and direct ground and aircraft observation efforts, resulting in maximum utility and effectiveness.  ForWarn has been operating since January 2010, and generates national disturbance maps covering the entire lower 48 United States every 8 days, even throughout the winter (to permit, for example, tracking ice storm damage, and the accumulation and melting of snowpack).  ForWarn detects most types of forest disturbances, including insects, disease, wildfires, frost and ice damage, tornadoes, hurricanes, blowdowns, harvest, urbanization, and landslides.  It also detects drought, flood, and temperature effects, and shows early and delayed seasonal vegetation development.  Cells in the map are about 5 ha, or 13 acres, or 9 football fields each.  No such national-scale system based on remote sensing has been developed specifically for forest disturbances before.  ForWarn is the result of an ongoing, substantive cooperation among four different government agencies:  USDA, NASA, USGS, and DOE.

ForWarn works by comparing current greenness with the “normal” greenness that would be expected for healthy, undisturbed vegetation growing at this location during this time.  Locations that are currently less green than expected are marked as potentially disturbed.  A set of three disturbance products use differing lengths of historical baseline periods to calculate the expected normal greenness, highlighting how recent the potential forest disturbance may be.  The long-term baseline products show all disturbances since 2003, while the intermediate baseline products show disturbances occurring within the prior three years, and the short-term baseline products show only forest disturbances that occurred within the last year.  Disturbance maps are available to anyone via a web site, the Forest Change Assessment Viewer (see Section 4D4), which showcases recent and historical ForWarn national disturbance maps in context at http://forwarn.forestthreats.org/fcav  Using this Assessment Viewer, resource managers can see the newest national ForWarn disturbance maps at the same time the Threat Centers do.

ForWarn proved especially useful for mapping disturbances during the 2011 growing season, including tornadoes, wildfires, extreme drought, and insect defoliations (see Exhibit #6, also the article in Space News, April 2012, and the Capital Ideas - Live! interview).  The number of ForWarn-based alerts we issue varies widely, but we average about 2-3 alerts from every new 8-day product.  Regional FHM Coordinators are always copied on alerts for potential disturbances within their region.  ForWarn was used to map tornado scars from the historic April 27, 2011 tornado outbreak, and detected and mapped timber damage within more than a dozen tornado tracks across northern Mississippi, Alabama and Georgia.

In at least three cases so far (Atchafalaya, LA in 2010 and 2012, forest tent caterpillars and bald-cypress leafrollers, and Allegheny NF, PA in 2011, fall webworms), ForWarn has alerted local resource managers to otherwise unknown insect defoliation activity.  In the Atchafalaya 2010 and 2012 cases, an extra, unplanned ADM flight was made which verified the defoliation.  In the Allegheny NF case, defoliation was verified by ground observations.

ForWarn tracks disturbance in all vegetation, not just forests, and includes potential disturbances in rangeland vegetation and agricultural crops.  This all-vegetation feature of ForWarn is expected ultimately to widen the potential user audience to include farmers and range livestock managers as well as forest owners and managers.  We also plan to inform USDA National Agricultural Statistics Service (NASS) and USDA Risk Management Agency (RMA) to see if ForWarn results can be leveraged elsewhere within our own agency.