Remote Sensing At the Dawn of a New Millennium:
A Washington, DC Perspective

Dr. Richard W. Guldin

Abstract.  A July 1999 study by RAND Corporation reviewed the types of forest monitoring conducted by federal agencies.  RAND recommended a number of changes in federal policies towards forest inventories and use of remotely sensed imagery.  The Forest Service’s Forest Inventory and Analysis (FIA) program was singled out by RAND as “the nation’s most ambitious forest monitoring program.”  The FIA program has taken a number of steps the past 18 months that are consistent with the RAND study recommendations, but more changes are needed to fully implement the RAND recommendations.  The first decade of the 21st Century is an era of unprecedented opportunity to develop new uses of remotely sensed digital data.  Strong leadership, innovative research, and aggressive development activities are needed to seize the opportunities.

Introduction

Over the past 12 years, the White House Office of Science and Technology Policy (OSTP) has conducted a number of studies on the nation’s science capacity and science policy issues, spanning the gamut from mathematics and medicine to natural resources.  During the 1990s, OSTP, through its Committee on the Environment and Natural Resources (CENR), evaluated issues related to natural resource inventories and monitoring activities.  In 1997, CENR issued a report titled, “Integrating the Nation’s Environmental Monitoring and Research Networks and Programs:  A Proposed Framework,” which called for the development of an integratedset of inventory and monitoring programs at various scales (CENR, 1997).  The Forest Service participated in the development of the Framework.  Two of the three tiers of the framework align well with aspects of both the Forest Inventory and Analysis (FIA) program and the Forest Health Monitoring (FHM) program.

In 1997, OSTP funded a study by RAND Corporation (Peterson, et. al. 1999) to examine the state-of-the-art in federal activities on monitoring the status and condition of America’s forests and the extent to which cutting edge technologies were being used.  Objectives of the RAND study included: 

•	To provide federal decision makers with a road map of forest monitoring programs and technologies currently in use in the U.S. and to assess the extent to which they provide the types of information about forest resources that decision makers need;

•	To examine to what degree remote sensing technologies are being exploited and assess whether they can enhance the timeliness, scope, and rigor of the forest measurement information available; and

•	To delineate opportunities for better integrating programs and measurement approaches, drawing on lessons learned from domestic and international efforts.

Information on the status of federal programs was collected in 1998 through detailed interviews with program managers involved in forest monitoring and through reviews of published reports.   

RAND Study Report Findings and Recommendations

The final report of the RAND study was issued in July 1999.  In addition to reviewing the situation in the U.S., the report also contrasts forest monitoring activities in the U.S. with activities in Russia, Finland, Canada, and Brazil—other countries with significant forest resources.  The final report contains the following observation:

Questions about the state of America’s forests have led to a widespread perception that existing monitoring efforts and capabilities—characterized by a decentralized set of programs that rely on ground and aerial observations—are failing to meet increasingly complex and large-scale forest management needs. (Page ix)

Several study findings were of special significance:

•	The U.S. lacks a national and timely forest data base
•	Non-vegetation monitoring remains limited
•	Despite these shortcomings, U.S. monitoring practices are, on average, as good or better than most other countries with significant forest resources

To respond to the findings in America, the RAND report made a number of recommendations, several of which were pointedly aimed at the Forest Service:

•	Set clear national priorities for forest management and use the priorities to guide development of forest monitoring programs
•	Implement mandatory forest monitoring standards across all Forest Service divisions
•	Augment federal funding dedicated to forest monitoring to provide continuous support and long-term commitment to a uniform, nationwide system of ground measurements
•	Explore utilization of Thematic Mapper imagery to speed up the forest inventory stratification process
•	Develop a strategic vision for remote sensing in forestry

In Chapter 3, “Current Monitoring Programs and Practices,” RAND focused on the Forest Service FIA program.  The report observes, “FIA is the nation’s most ambitious forest monitoring program. (Page 20)” The report went on to reiterate four key findings from the First and Second Blue Ribbon Panel reports on the FIA program (American Forest and Paper Assn. 1992 and 1998):

•	FIA collects too little non-tree vegetation information
•	Producing credible national level summary statistics is impossible because Forest Service inventory efforts are too decentralized and the bottom-up aggregation of data from the FIA program for private lands with data gathered by other means from national forests is too complex and inconsistent
•	The reliance on aerial photography for certain measures (e.g., forest area) and stratification for field sampling is too labor-intensive
•	FIA data is not timely due to limited funding 

But RAND also looked beyond current practices and identified several opportunities for the FIA program, which if adopted, could cure the major problems identified.  The result would be a program that would address the major gaps identified in the report and provide the information needed to meet increasingly complex and large-scale forest management needs.  According to RAND, the major opportunities for the FIA program were:

•	Integrate the detection monitoring phase of the FHM program into the FIA program
•	Build and enforce a consistent set of core inventory and monitoring data nationwide and for all parts of the agency.  That core should:
o	Include non-tree vegetation, such as grasses, shrubs, vines, lichens, and mosses
o	Add tree health parameters so that health and condition can be assessed in addition to productivity
o	Insist on consistency, using a top down approach and eliminate autonomy to change the core data.  If additional or different data are needed regionally or locally, those data should augment core data, not replace core data.
•	Go operational with remote sensing and reduce the reliance on aerial photography as soon as possible

Taken together, these observations, findings, and recommendations offer a clear pathway to the future for the FIA program.  

FIA Program Changes Since 1998

FIA leaders have moved aggressively over the past two years to implement many program changes (Gillespie, 1999; USDA Forest Service, 2000).  The primary impetus was provided by new language in the Agricultural Research, Extension, and Education Reform Act of 1998  (Section 253 of P.L. 101-624).  Many of these changes also address findings and recommendations in the RAND study.

Integrating FHM Detection Monitoring with the FIA Program.  

In 1999, program leaders in the FIA and FHM programs completed plans to fully integrate the detection-monitoring phase of FHM into the FIA program.  Previously, the FIA program had two primary phases—aerial photograph interpretation (Phase 1) and field data collection through plot visits (Phase 2).  Phase 1 activities developed information on the extent and distribution of forest cover and selected characteristics of the forest at the sampling point.  There are over 2 million Phase 1 sampling points across the United States, roughly one for every 250 acres of forest.  A sub-sample of the Phase 1 points is selected for Phase 2 data collection.  The field visits confirm data and interpretations made from the aerial photography and collect additional data not observable on the photography—some 300 different variables.  There are approximately 125,000 Phase 2 sampling points across the United States, roughly one for every 6000 acres of forest.  

The detection-monitoring phase of FHM was integrated into FIA as a new Phase 3.  On a subsample of Phase 2 sampling points, additional data are collected to chronicle the health and condition of the forest ecosystem.  There are approximately 8,500 sampling points in the United States where detailed health data are collected, roughly one for every 122,000 acres.   In FY 2000, detection monitoring will cover approximately 75 percent of the forest land in the U.S.  In addition, some of the variables previously collected only in the FHM program were added to the core data set for Phase 2 plots.  This will enhance the linkages between data collected at Phase 1 and Phase 2 sampling points and the data collected at Phase 3 points.  The acronyms for these three phases are P1, P2, and P3, respectively.

The integrated approach including all three phases is being implemented for the first time during the 2000 field season.  

Implementing Annualized FIA

Prior to the 1998 act, the Forest Service conducted the FIA program by rotating field crews from state to state.  This led to intervals between P2 data collection in a given state that varied from 8 or 9 years in the South to 15 to 17 years in the West.  The national average was 10 to 12 years.  In areas where change was rapid, special studies and mid-cycle updates were used in an attempt to keep inventory information relatively current.  

The clear preference of FIA customers has always been to maintain strict standards on data quality and accuracy.  The accuracy standards require a certain sampling intensity and a minimum number of plots in P2. 

Given customer’s accuracy expectations and the fact that two-thirds of program costs arise from P2 field plot visits, the most viable program response to a decline in funding is to lengthen the cycle by sending out fewer field crews and slowing the P2 activity.  For any single year, the marginal increase in the cycle length is tolerable.  But by 1996-1997, after several consecutive years of declining budgets, this approach to dealing with eroding budgets had lengthened the cycle in some areas to the point where it was too long for customers.  

Starting the in the early 1990s, the North Central Research Station FIA unit had begun experimenting with an annualized approach, whereby some P2 data are collected in a state every year.  This new approach was based on statistical research and inventory methods developed jointly by a research unit at Rocky Mountain Research Station and the State of Minnesota. The Second Blue Ribbon Panel Report (AF&PA, 1998) recommended, and the 1998 act directed, the Forest Service to shift from the traditional approach, to this new annualized approach.   

The annualized approach also built upon the conceptual framework developed by the FHM program.  FHM was based on a rotating panel design where a subset of points was sampled in two consecutive years to determine health changes.  Over a period of four of five years, all sample plots in a state were measured.  So there was also some experience with analyzing annualized data in the FHM program.  

In the 1998 act, Congress directed the Forest Service to develop a strategic plan (USDA Forest Service, 1998) that described how to make a transition from the traditional approach to the annualized approach over a five-year period (1998-2003).  Southern Research Station and North Central Research Station led the way with implementing annualized FIA in selected states in 1998.  Initial implementation depended heavily on shared funding and personnel from participating state forestry agencies.  Funding increases in FY 1999 and FY 2000 enabled the FIA program to make good progress in implementing the transition as described in the strategic plan.  In FY 2000, there are 17 states, 47 percent of America’s forests, being inventoried using the annualized approach.  

Defining a National Set of Core Data and Insisting on Consistent Application of the Program Across All Public and Private Lands

FIA program leaders devoted 1997 and 1998 to definition of a national set of core data—ecological variables to be collected throughout the U.S. on all land ownerships according to the same set of measurement protocols.  Approximately 75 variables (roughly 25 percent of the possible variables) were identified as part of the national core.  The principal difficulty in defining this core set was preparing the measurement protocols to deal with unusual or exceptional circumstances.  For example, at the beginning of the process, 14 different ways were found to measure tree diameter.  Most of the variation arose from differences in handling unusual situations, such as what to do if the main stem of a tree forked less than 4.5 feet above the ground—the height where diameter is typically measured.  

Some protocol changes were also necessitated by the integration of FHM as P3.  For example, tree diameter measurement protocols from the 1930s indicated that if vines were growing on the tree trunk, the vine should be cut above and below the diameter measurement point so an accurate tree diameter measurement could be obtained with a steel tape.  When the vines were poison ivy (Toxicodendron radicans), cutting the vines was also considered to be a matter of personal safety.  But with the new emphasis on measuring all the vegetation on the site and on computing total biomass productivity, cutting the vines could no longer be tolerated.  So new protocols had to be defined that minimized adverse impacts on vine growth because they too were now an important component of ecosystem productivity.  In some cases, new techniques had to be developed and new training modules developed to assure consistency and safety nationwide.

The new national field manual, including the core data measurement protocols was released for field use in the autumn of 1999.  The new procedures are being implemented in the 2000 field season.  They include measurements of non-tree vegetation, such as grasses, shrubs, vines, mosses, and lichens.    

Obtaining consistency in the program on all public and private lands has been among the more difficult challenges confronted.  On some public lands, long-term inventory and monitoring programs are partially inconsistent with the FIA program.  Field personnel strongly support their existing programs, and in some cases, believe that the long-term utility of their data will be compromised by changes in sampling approaches and data collection protocols.  

The FIA program has evolved considerably since it originated in the 1930s.  It has used a succession of improvements in sampling approaches and protocols over the past 70 years.  In all cases, the value added by the changes and improvements has outweighed any losses in utility or credibility.  Indeed, care in making the transitions has helped assure backward comparability of data so time trends can be analyzed.  In some cases, the sampling framework has been intensified so that adequate statistical accuracy exists at finer geographic scales.  In other cases, additional variables have been added for a given state or region to meet critical information needs of key customers, such as individual states, to assure that past data retain their value.  These steps are a normal part of business for the FIA program and reflect the strong customer service ethic indigenous to the FIA program culture.

Negotiations are ongoing on promoting consistency across all public and private lands.  I remain optimistic that we can reach a mutually agreeable resolution of the few outstanding issues that exist and that during the 2001 field season, full national consistency will be achieved, as called for the by the 1998 act, the Blue Ribbon Panel Reports, and the RAND study.

Going Operational with Remote Sensing As Soon As Possible

Of all the recommendations made by the RAND study, this is the one where much additional work remains to be done.  Progress has been limited primarily because other matters—defining core data, insisting on national consistency, and shortening the cycle by shifting rapidly to the annualized approach—were regarded as higher priorities.  Now that those other issues have been largely resolved, it is time to turn our attention to this remaining issue.  

A Vision for the Future of Remote Sensing

Any discussion of the future of remotely sensed imagery must begin by recognizing that it is critical to specify the geographic scale of interest.  At the global scale, distinguishing among forest cover in four broad categories (e.g., needle-leaved trees, broad-leaved trees, bamboos, and palms) may be sufficient.  At the hemispheric to national scale and considering all land covers (e.g., including croplands, grasslands, and shrublands), distinguishing among perhaps eight to ten forest cover types may be adequate.  At the national to regional scale and focusing on forest cover types, distinguishing among 15 to 20 forest cover types is important.  Below the regional scale, further refinements may be important.  The important point is to select imagery whose resolution is appropriate to the geographic scale of information needed.  For example, AVHRR with one-kilometer resolution produces information eminently suitable for reports at the global scale, perhaps even the national scale.  But MODUS (250 meter) or Thematic Mapper (30 meter) imagery may be most appropriate for regional scale inventory and monitoring activities.   When very high resolution information is required at the sub-regional scale, SPOT (10 meter) or IKONOS (1-5 meter) imagery may be needed to adequately evaluate the desired attributes.  Of vital importance is creating a framework for use of remotely sensed imagery in inventory and monitoring programs that enables information to be seamlessly aggregated or disaggregated in a consistent fashion as one moves to a higher or lower scale.  Consistency and scalability are critical needs for the FIA program.       

It’s a well-known organizational management tenet that no one can generate results without knowing how the “bottom line” is defined; what the performance target is.  So today, I want to explicitly state a performance target for the use of remotely sensed imagery in the FIA program:

We will complete the transition from reliance on aerial photography to use of remotely sensed satellite imagery by the end of FY 2003

In particular, this performance target means four things:

1.	The FIA program will rely on satellite imagery to produce all area estimates
2.	The FIA program will produce satellite-derived maps of forest attributes at a high resolution, sufficient for land navigation and for providing meaningful estimates for small geographic areas (small scales)
3.	The FIA program will provide maps that are both accurate and pretty.  
4.	The FIA program will link satellite imagery to all the spatial data in the FIA database, enabling a wide variety of spatial analyses

Each of these points is discussed in detail, below.  

Use Satellite Imagery for All Area Estimates

Using satellite imagery to make area estimates means that we will need to develop image-processing algorithms (models) to make all of the area estimates that the FIA program has traditionally provided to our customers.  These models need to go beyond simple 2x2 comparisons (e.g., forest versus non-forest; open versus closed stands) to finer levels of detail.  There is a distinct tradeoff here between the resolution of the imagery and the level of detail that can be interpreted from the imagery.  Imagery with a resolution between 30 and 250 meters is probably the best place to start.  In mixed-use landscapes, one-kilometer resolution is simply too coarse for the models and estimates needed.  The 5- to 10-meter products available may create files too large to acquire, process, and store, given the goal of having wall-to-wall coverage of the United States for P1.  So beginning with Landsat (Thematic Mapper) or EOS-AM1 (MODUS) imagery seems most logical.  

Goals for producing models and area estimates from remotely sensed imagery include:

•	Distinguishing among forest cover types.  Simple delineations—forest versus non-forest, or softwood versus hardwood—are insufficient.  What is needed is the ability to identify the 10 to 15 most important forest cover types in the East and the 10 to 15 most important types in the West.  This is the same level of detail as in the 1992 forest cover type map produced by FIA from AVHRR data for all P1 points.  We need to remodel forest cover using sensors with higher resolution to create accurate maps at the regional and sub-regional scales that aggregate upward consistently to the national scale and beyond.  
•	Global positioning accuracy sufficient to rectify and reference imagery with the precise locations of FIA sampling points.  It is critical that we develop the capability to reliably select the pixels that correspond to known points on the ground for which P2 and P3 data exist so that ground data can be used to validate interpretation algorithms applied to imagery.  
•	Developing accurate, consistent models and estimates.  The ability is needed to take accurate, geo-referenced information, build models that depict true resource conditions, and validate the models’ accuracy and precision using ground plot data.  It may be that some areas contain sufficient heterogeneity in cover types and land uses that the standard errors of the estimates derived from 30 to 250 meter imagery are too large.  In those cases, new statistical approaches may need to be invented.  For example, nested sub-sampling using higher resolution imagery may help to reduce the standard errors of area estimates for heterogeneous areas.  
•	Refining change detection models.  The ability is needed to compare pixels from imagery collected at two different times and to reliably discern if observed differences are really changes in the land cover type or land use or artifacts of the collection process (e.g., different time of day or year).  This will include the ability to compare imagery from different sensors and platforms.     
•	Reducing the need for human intervention in the image processing algorithms.  To develop efficient image processing procedures, initial approaches may require significant “hands on” activity by the person performing the image analysis.  Alternatives may need to be evaluated and judgments may need to be rendered.  But in doing so, the twin objectives should be: (1) to obtain the best possible image-derived product; and (2) to learn the cheapest and fastest way to produce that product.  As more experience is gained, the algorithm development task should explicitly shift from the first to the second of the goals.  Some human intervention may be needed in the ultimate algorithm developed, but the focus should be on reducing the amount of human assistance required.

Produce Satellite-derived Maps of Forest Attributes at High Resolution, Sufficient for Land Navigation and for Meaningful Estimates for Small Geographic Areas

One of the primary virtues of aerial photographs is the ability to use them for land navigation to the P2/P3 sampling point.  Doing so requires considerable administrative burden to sort the photos into the appropriate plot folders, transport them to the field, return them to the office, and refile them with other imagery.  This workload should be eliminated.  Methods must be developed to process images on a routine, operational basis, as they are obtained.  Map files or land cover data layers should be produced and added to the map library on a continuous basis.  Field crews should be able to download the latest version of the map file to their field computers, as needed, and for land navigation purposes.  This will give field crews the option to print out a digital ortho-quad on their portable printer or simply call up the file on their field computer screen.  Either way, the workload involved in shuffling and tracking the photos would be dramatically reduced.  

Because so many of our field crews are staffed by employees of non-federal partners, it goes without saying that digital map files used for land navigation must be image derived products that can be shared with non-federal partners.    

Choosing the forest attributes to map at high resolution is also challenging.  The types of monitoring questions being asked of the FIA program in the 21st Century require a substantially different suite of information to answer than the questions asked in the mid-20th Century.  For example, in the 1950s and 1960s, estimating the growth and mortality on commercial timber species was of vital interest.  Today, questions focus on things like whether management is sustainable, whether net primary productivity or photosynthesis rates are rising or falling, whether observed declines in productivity or photosynthesis  rates are due to specific stresses (e.g., drought, acidic deposition, or pest attack), or whether the rates of change in forestland use are contributing to forest fragmentation.    

Given these 21st Century questions, the challenge is to determine what set of spectral bands from various sensors can best be used to model forest attributes that are linked to the key questions.  Several decades ago, models were acceptable that simply showed correlations among observed phenomena.  But today, simple correlation models are insufficient.  What is needed are models of specific processes that are integral drivers of dynamic changes in forest ecosystems.  In some cases, the research into processes and functions remains to be done, complicating the task even further.  

In addition to gathering data on forest attributes at high resolution, there is substantial interest in being able to model attributes accurately for small geographic areas---fine scale.  Simple summations of data from ground sampling points on the P2 and P3 sampling grids are not well-suited to provide meaningful estimates for small geographic areas (e.g., less than 100,000 acres).  So other techniques are needed to model conditions for small geographic areas.  Answering questions for small geographic areas will require models that meet different standards than the models used to make estimates for large areas.  Small area models may use techniques that interpolate pixel values between known ground sampling points.  Statistical methods, such as the “k-nearest neighbor” approach, have shown utility in this regard.  Small area models may also use high-resolution data from other sources (e.g., map layers created by others) to improve the modeling and validation processes for small areas.  Considerable creativity will be needed to develop and test new approaches that meet user needs.  

Two key points are worth noting.  First, this sort of work requires imagery where fine scale attributes like buildings, roads, and stand boundaries are visible.  Second, this sort of work is very labor-intensive because it depends on a seasoned analyst to identify specific objects or changes and to do so consistently from photograph to photograph.  This suggests two key criteria for operational algorithms to process remotely sensed imagery:

•	The imagery used to answer certain questions must be of high enough resolution that the fine scale attributes needed to answer key questions can be identified. This adds to the importance of identifying early on the key questions that need to be answered. 
•	Algorithms need to be developed that reduce the amount of human intervention needed to make interpretations and take measurements.  Initially, developing maps of forest attributes at high resolution may require considerable human intervention.  Doing this sort of work using aerial photography is very labor-intensive and costly because it requires a seasoned analyst to identify specific objects or changes and to do so consistently from photo to photo.  In pursuing the goal of reducing human intervention and labor costs, it is critical to identify an efficient interpretation process before writing the computer code to automate it.  

Develop Maps that are Both Accurate and Pretty 

In the mid-20th Century, the state-of-the-art for analytical skills in inventory and monitoring programs was modeling relationships in two-dimensional space.  In the 21st Century,  statistical skill requirements have moved beyond two-dimensional to three-dimensional spaces and a whole different set of modeling and statistical analysis tools also are required.  Rather than the tables, graphs, analyses of variance and regression equations that were key analytical outputs 30 and 50 years ago, the key analytical outputs today are maps, map layers, or other spatial representations of information and complex models.  

Too often, maps are produced that are pretty to look at, but whose data layers are not validated.  For the FIA program, it is important to produce accurate maps—maps that are validated using ground plot data to establish the accuracy of the models used to process the imagery.  Although remotely sensed imagery will become an increasingly important component of the inventory process, ground plot data will always remain an important part of the FIA program because it is the critical component for validating the accuracy of interpretations made from the imagery.  FIA maps should put accuracy first and prettiness second.       

To achieve the desired accuracy, it may be necessary to integrate imagery collected by different types of sensors.  For example, it may be most efficient to combine data from synthetic aperture radar (SAR) systems with data from electro-optical systems to answer questions about forest structure.  It may also be helpful to augment satellite imagery with imagery collected from sensors carried by high-altitude or low-altitude aircraft.  When collecting, processing, and interpreting imagery, adhering to Federal Geographic Data Committee (FGDC) standards is essential.  The FGDC standards are the minimum quality standards for geo-referenced data.  Their standards also cover metadata.  Assuring that our activities conform to FGDC standards will make it easier to integrate imagery collected by different types of sensors and to share map layers among agencies with confidence in the accuracy and precision of the information.  

The central point is that maps must be accurate.  Achieving the desired accuracy is not only a function of the image processing conducted by the agency.  It is also a function of: (1) the validation activities based on ground plot data, (2) the linkages created to other spatial data, based on FGDC standards, and (3) the quality of the geo-rectification that makes it possible to blend imagery from multiple sensors.  All of these are essential components of achieving the vision.  

Link Satellite Imagery to Other Spatial Data 

There is a substantial amount of historical information in the FIA database.  Although areas, volumes, growth, and mortality have been important categories of data in years past, other variables are assuming increasing importance in the 21st Century.  Do you remember that old cliché from the real estate sales business—What are the three most important determinants of property value?  The answers, of course, are,  (1) location, (2) location, and (3) location.  Indeed, I believe the most important datum for forest information management in the 21st Century is geographic location.  

Data from previous FIA inventories retains high potential for use in modern spatial analyses because the geographic location of the plots has been carefully preserved.  The data collected last century can be reanalyzed and reinterpreted using modern spatial analysis tools.  This will provide useful benchmarks and anchors for analyses of time trends and detection of changes based on today’s remotely sensed imagery.  The new tools must also be used to develop map layers based on the old data that are comparable to the map layers to be developed based on the new imagery.  That will make possible a whole new array of spatial analyses of change in forest ecosystems.  

A second point deserving emphasis is that any image interpretation process must provide for accurate linkages to other spatial data sets, such as census demographics or digital elevation models.  Other forms of spatial data can add great value to information obtained remotely sensed imagery.  By adhering to the standards FGDC standards, data from various agencies and programs can be combined more effectively to answer a much broader range of questions than any agency could tackle alone.  The ability to link to other value-added data sets is another critical need for the FIA program.

Going Operational With the Vision

Those who are involved with cutting edge remote sensing applications in the academic realm will recognize that most, if not all, these statements of vision have already been achieved in the laboratory.  What, they may ask, is the big deal?

The big deal is, there is a huge difference between doing these tasks one time as a semester-long project for a 25,000-acre university forest and doing the tasks on a daily, routine basis so that the entire U.S. is covered every 3 to 5 years.  In short, the challenge facing us is not so much a research task as it is a development task—to develop the means to process the imagery on a routine basis in P1 to support P2 and P3 field operations and to use the processed imagery to make estimates, build models, and conduct geospatial analyses of the P2 and P3 field data.  

To achieve success will take more than merely adding more resources to the task, because the issue is not a linear one of simply scaling up to do more work in a shorter time period.  The increase in complexity is geometric.  What is needed is a new approach to doing image processing and interpretation that results in consistent information at multiple geographic scales and degrees of resolution in all parts of the United States.  

To my way of thinking, our situation today is quite similar to the situation at the beginning of the 20th Century regarding the automobile.  In the 1890s, there were a large number of wagon smiths and wheelwrights producing all manner of horse-drawn carriages.  Gasoline engines were just being introduced to run threshers and other stationary equipment via long leather belts.  Enterprising mechanics recognized the potential of mating gasoline engines with carriages to produce self-propelled “horseless-carriages.”  From the early experimentation arose a large number of firms dedicated to producing cars and trucks.  Do you remember the Duryea?  Indeed, some innovators tried other technologies—the Stanley Steamer had a boiler mounted on a vehicle and used steam for propulsion.   Most of these vehicles were custom-made or built one at a time, and they needed lots of human intervention—maintenance—to keep them running.  Automobiles and trucks didn’t transform American transportation, commerce, and lifestyles until a particular innovation occurred—the development of the assembly line to mass-produce vehicles of consistent quality at an affordable cost.  Henry Ford launched the automobile industry and automobiles and trucks became widely used.  The result was a radical transformation of American transportation, commerce, and lifestyle.

My sense of the remote sensing situation in the 1990s is that a large number of people were experimenting with image processing and interpretation methods and technologies.  Customized applications for individual university forests or specific landowners demonstrated the possibilities.  Each application was custom built to a significant extent and required lots of human intervention to operate properly.  But no “Henry Ford” emerged to develop an assembly line for mass-production of satellite-derived products covering the entire United States for routine and consistent use in forest inventory and monitoring programs 

A new approach is needed to image processing and interpretation to serve the daily needs of the annualized FIA program.  I am searching for a few “Henry Fords,” people with the vision and energy to help build an operational-scale processing system for remotely sensed imagery to fully support the annualized FIA program and realize the vision outlined above.  This is not to demean the research and modeling activities that must also be continued.  But we can no longer afford not to devote substantial efforts to building the assembly line needed to process remotely sensed imagery for daily use by the annualized forest inventory program.  

This will require new investments; investments in people and in image processing hardware and software.  People will need to be retrained as we transition from use of aerial photographs to use of digital remotely sensed imagery.  People newly hired will need a significantly different mix of skills and abilities than those hired a decade ago.  Hardware and software investments will be expensive.  It may be most effective to pool funds among Stations to create national or regional work centers to support the image processing and interpretation needs of multiple Stations.  The economies of scale and the need for national consistency may make this a more sensible approach for launching this investment strategy than having each Station build their own capacity initially.  

Now is the time to define the national core products and regional enhancements and their specifications needed from satellite imagery.  Now is the time to develop detailed plans for an operational image processing system.  Now is the time to figure out the best ways of making the initial investments needed to capture the opportunities provided by remotely sensed data.  If we don’t get on with these tasks today, then we will not meet the goal of completing the transition to the first-generation operational system by the end of FY 2003.  

The vision is a stretch goal.  The challenge facing us is large.  The opportunities are immense.  Now is the time to forge ahead aggressively.  To try and fail, that can be forgiven.  To fail to try, that is unforgivable.  

Bibliography

American Forest and Paper Association.  1992.  The report of the first blue ribbon panel on forest inventory and analysis.  Washington, DC.

__________.  1998.  The report of the second blue ribbon panel on forest inventory and analysis.  Washington, DC.

CENR, 1997, Integrating the nation’s environmental monitoring and research networks and programs:  A proposed framework.  National Science and Technology Council, Committee on Environment and Natural Resources, Environmental Monitoring Team, Washington, DC.

Gillespie, A.J.R. 1999. Rationale.  Journal of Forestry 97(12): 16-20.

Peterson, D.J.; Resetar, Susan; Brower, Jennifer; and Diver, Ronald. 1999.  Forest monitoring and remote sensing—A survey of accomplishments and opportunities for the future.  Report No. MR-1111.0-OSTP.  Science and Technology Policy Institute,  RAND Corporation.  92 p.

USDA Forest Service, 1998, A strategic plan for forest inventory and monitoring—A report prepared for Congress in response to the Agricultural Research, Extension, and Education Reform Act of 1998.  Science Policy, Planning, Inventory, and Information Staff, Research & Development.  12/16/1998.  17 p.

USDA Forest Service, 2000, Service wide Memorandum of Understanding between USDA Forest Service and the National Association of State Foresters.  2/15/2000. 4 p.