Coordination Between USDA's Forest Inventory and Analysis (FIA) and Natural Resources Inventory (NRI) Programs
I. Introduction: Contextualizing National Resource Inventories
A. The Imperative for Comprehensive Resource Monitoring
The stewardship and sustainable management of the United States' extensive natural resources necessitate robust, scientifically sound information. National-level inventories of these resources play a critical role in informing public policy, guiding management decisions, and advancing scientific understanding of complex ecological systems. In an era marked by escalating environmental challenges—including climate change, rapid land-use conversion, and biodiversity decline—the demand for comprehensive, accurate, and timely data on the nation's lands, waters, and biological resources has never been more acute. Effective monitoring programs are foundational to addressing these challenges, enabling assessments of resource condition, tracking changes over time, and evaluating the efficacy of conservation and management interventions.
B. Overview of USDA's Key Inventory Programs: FIA and NRI
Within the U.S. Department of Agriculture (USDA), two principal programs are tasked with large-scale natural resource assessment: the Forest Inventory and Analysis (FIA) program and the Natural Resources Inventory (NRI) program.
The Forest Inventory and Analysis (FIA) program, administered by the USDA Forest Service (FS), serves as the United States' continuous forest census. Its core purpose is to measure and monitor key metrics related to the extent, condition, and trends of all forests across the nation.1 FIA provides the essential data for understanding forest health, productivity, and sustainability.
The Natural Resources Inventory (NRI) program, managed by the USDA Natural Resources Conservation Service (NRCS), is a primary source of information regarding land use and the condition of natural resources, specifically on the nation's non-Federal lands.3 The NRI collects and produces scientifically credible data on soil, water, and related resources, supporting efforts to protect, restore, and enhance these assets.
While FIA is primarily focused on forest ecosystems across all ownerships, and NRI concentrates on a broader suite of resources on non-Federal lands, their domains of interest inevitably intersect, particularly concerning rural landscapes, ecosystem health assessments, and the interface between forested and non-forested lands.
C. The Strategic Importance of FIA-NRI Coordination
Examining the coordination between these two major USDA inventory programs is of considerable strategic importance. Enhanced collaboration holds the potential for significant benefits, including improved data utility for a wider range of applications, reduction of redundancies in data collection and analysis, increased cost-effectiveness in program operations, and the development of more holistic and integrated national resource assessments. The value of such synergy has been recognized for some time; indeed, advisory Blue Ribbon Panels for both the FIA and NRI programs have historically recommended greater cooperation and collaboration between them.5
The effective coordination between FIA and NRI is not merely an issue of operational efficiency. Rather, it represents a strategic imperative for the USDA to fulfill its broad mission of managing and conserving the nation's natural resources in an era defined by complex, interconnected environmental challenges and ever-increasing demands for integrated data. Both FIA and NRI represent substantial federal investments in data collection, covering complementary and, in some instances, overlapping aspects of the U.S. landscape. National-scale environmental concerns, such as carbon accounting, wildfire risk assessment, water quality protection, and biodiversity conservation, do not neatly align with agency jurisdictions or specific land ownership types. Consequently, a lack of coordination between these key inventory programs could lead to a fragmented understanding of national resource conditions, inefficient allocation of federal resources, and potentially conflicting information, thereby hindering the formulation of effective national policies and management strategies. The historical recommendations from their respective advisory panels underscore this long-perceived need for greater synergy.5
II. The Forest Inventory and Analysis (FIA) Program: A National Forest Census
A. Mandate, Mission, and Historical Context
The Forest Inventory and Analysis (FIA) program of the USDA Forest Service operates under a comprehensive mandate "to make and keep current a comprehensive inventory and analysis of the present and prospective conditions of and requirements for the renewable resources of the forest and rangelands of the US".2 Its origins trace back to the McSweeney-McNary Forest Research Act of 1928, with its mission further solidified and guided by the Forest and Rangeland Renewable Resources Research Act of 1978.2 Having been in continuous operation since 1930 6, FIA stands as one of the world's oldest and most extensive national forest monitoring programs.
A pivotal development in FIA's history was the 1998 Farm Bill, which included an amendment (Section 3(e)) mandating a shift from periodic, state-by-state inventories to an annualized inventory system.6 This legislative change required FIA to implement a system involving a proportionate annual inventory of plots in each state, the production of analytical reports for each state every five years, and the adherence to national standards and definitions for data collection and reporting. This transition to an annualized approach marked a significant evolution, aiming to provide more current and consistent forest resource information.
B. Operational Scope and Methodology
The FIA program's scope is national, encompassing all public and private forest lands throughout the United States.2 It achieves this comprehensive coverage through the regular remeasurement of an extensive network of approximately 355,000 permanent inventory plots.1 These plots are distributed based on a systematic sampling grid, with an intensity of roughly one field sample location for every 6,000 acres (approximately 2,428 hectares).2
FIA employs a sophisticated multi-phase sampling design to optimize data collection efficiency and depth:
●	Phase 1 involves the use of remote sensing—historically aerial photography, but increasingly incorporating satellite imagery—for the initial stratification of land into forest and non-forest categories and for improving the precision of area estimates.5
●	Phase 2 consists of field visits by trained crews to a subset of these plots. During these visits, detailed measurements are collected on a wide array of variables related to tree characteristics, site conditions, forest structure, and composition.2
●	Phase 3 involves the collection of additional, more specialized data on a further subset of the Phase 2 plots. These data pertain to various indicators of forest health, such as soil properties, understory vegetation diversity, tree crown conditions, lichen community composition, and ozone bio-monitoring.
Under the annualized inventory system, FIA aims to remeasure between 10 and 20 percent of the plots in each state every year, ensuring a continuous flow of updated information.1 This systematic and statistically robust methodology underpins FIA's ability to deliver reliable assessments of the nation's forest resources.
C. Key Data Collected and Primary Outputs
The FIA program gathers a rich array of data covering numerous aspects of forest ecosystems. Core data elements include the extent and condition of forests, their geographic location, detailed information on tree species, size, growth, mortality, and removals (both from harvesting and natural causes), forest type, stand structure, and land ownership patterns.1 Beyond these fundamental forest attributes, FIA also conducts specialized surveys, such as the Timber Product Output (TPO) survey, which tracks the utilization of harvested wood, and the National Woodland Owner Survey (NWOS), which gathers information on the attitudes, objectives, and management practices of private forest landowners.1
The primary outputs of the FIA program include a wealth of vital statistics, in-depth analyses, and comprehensive reports on the status and trends of America's forests. A key deliverable is the production of an analytical report for each state every five years, summarizing the findings from recent inventory cycles.1 FIA is committed to making its data accessible to a wide range of partners and users, with an operational goal of releasing the data collected each year within six months of the conclusion of the field season.1 The extensive database maintained by FIA serves as a foundational resource for numerous critical national assessments, including those related to forest carbon dynamics, the inventory of mature and old-growth forests, and ensuring U.S. compliance with international environmental regulations and reporting requirements.9
The strategic shift to an annualized inventory system, mandated by the 1998 Farm Bill 6, combined with the program's commitment to the rapid dissemination of its findings 1, has profoundly increased FIA's relevance and utility. Prior to this annualization, FIA data for some states could be up to 15 years old, which limited its applicability for addressing contemporary policy questions or time-sensitive scientific research.8 The current high demand for up-to-date information for pressing issues such as carbon market verification, wildfire risk modeling, and sustainability assessments underscores the value of this increased timeliness. Annualized data provides a much more current snapshot of forest conditions, enabling more robust trend analysis and facilitating more responsive and adaptive management strategies. This enhanced currency makes FIA data a more viable and valuable partner for integration with other frequently updated datasets, such as those derived from the NRI program or advanced remote sensing platforms, for comprehensive national resource assessments.
III. The Natural Resources Inventory (NRI) Program: Assessing Non-Federal Lands
A. Mandate, Mission, and Historical Context
The Natural Resources Inventory (NRI) program, administered by the USDA Natural Resources Conservation Service (NRCS), operates under a legislative mandate originating from the Rural Development Act of 1972 (Public Law 92-419). This act directed the Secretary of Agriculture to conduct a "land inventory reflecting soil, water, and related resource conditions".3 The program's mandate was further reinforced and expanded by the Soil and Water Resources Conservation Act (RCA) of 1977.10
The core mission of the NRI is to collect, analyze, and disseminate scientifically credible information on the status, condition, and trends of land, soil, water, and related natural resources specifically on the nation's non-Federal lands.3 These non-Federal lands include privately owned lands, tribal and trust lands, and lands controlled by state and local governments. The historical antecedents of the NRI can be traced back to national soil erosion surveys conducted in the 1930s.4 More formalized, periodic NRI surveys were initiated in 1977, with subsequent inventories conducted in 1982, 1987, 1992, and 1997. A significant operational shift occurred in 2000, when the NRI began collecting data on an annual basis, although major comprehensive data releases continue to be reported at five-year intervals.10
B. Operational Scope and Methodology
The NRI program's operational scope is extensive, focusing on the non-Federal land base of the United States. This category of land ownership accounts for nearly 75 percent of the nation's total land area, underscoring the importance of the NRI in understanding a vast portion of the country's natural resource endowment.11 The survey is conducted across 49 states (Alaska is generally excluded from the main survey, although specific NRI summary reports for Alaska have been produced, e.g.3), as well as in Puerto Rico and the U.S. Virgin Islands.3
Methodologically, the NRI employs a scientifically rigorous stratified area sample design.3 The primary sampling units (PSUs) in this design are defined areas of land referred to as "segments." These segments are typically quarter-sections, encompassing 160 acres (approximately 64.75 hectares). Within each selected segment, detailed data are collected at a random sample of specific points.3
Data collection for the NRI relies on a combination of techniques. Historically and currently, there is a significant reliance on the interpretation of aerial photography and other remote sensing materials. This is complemented by information derived from county office records, existing soil survey maps, and wetland inventory maps. Field studies are also conducted to gather data on specific resource attributes that are not readily or accurately obtainable through remote sensing techniques.4 For instance, specialized protocols are employed for on-site data collection on rangelands to assess conditions such as vegetation composition and rangeland health.4
The NRI database is longitudinal, containing a rich historical record of land use and resource conditions. Data are available for the periodic inventories (1982, 1987, 1992, 1997) and annually from 2000 through 2017.3 The annual data collections post-2000 are based on subsamples of the 1997 inventory sites.3 A critical aspect of NRI operations is the maintenance of confidentiality regarding the precise geographic locations of its sample segments and points, as mandated by federal law (7 U.S.C. 2276).3 The NRI program is conducted by the NRCS in close cooperation with Iowa State University's Center for Survey Statistics and Methodology (CSSM), a partnership that ensures the statistical soundness and methodological rigor of the inventory.3
C. Key Data Collected and Primary Outputs
The NRI program collects a wide array of data pertaining to the status and condition of natural resources on non-Federal lands. Key variables include detailed information on land use and land cover, soil properties (such as erodibility and land capability class), rates of soil erosion by both wind and water, the extent and location of prime farmland, irrigation practices, the status and trends of wetlands, the adoption of various conservation practices, indicators of rangeland health, and measures of habitat diversity.3
The outputs generated by the NRI program are diverse and serve multiple purposes. They include comprehensive summary reports detailing national and state-level findings, as well as more specialized assessments focused on particular resource concerns, such as the condition of rangelands and pasturelands, or the prevalence of invasive plant species.3 A significant development in data dissemination is the Land Use and Cover Inventory Database (LUCID), launched in October 2023, which provides public access to NRI data, allowing users to create custom estimates.3 The data and analyses produced by the NRI are foundational for shaping agri-environmental policy at national and state levels, informing land use planning decisions, and guiding the implementation of conservation programs.3
The NRI's particular strength is its detailed and consistent assessment of land use, land cover, and resource conditions on the vast expanse of private and other non-federal lands across the United States. This focus makes the NRI uniquely valuable for understanding agricultural landscapes, tracking the adoption and effectiveness of conservation practices, and monitoring trends in soil erosion and water quality—areas that are typically less directly or comprehensively detailed by the FIA program. While both FIA and NRI deal with "land" as a broad category, their thematic emphases are quite distinct. FIA's mandate centers on forest resources across all ownerships 2, whereas NRI's mandate and its collected variables are heavily weighted towards agricultural systems, soil and water resources, and rangelands on non-federal properties.3 This distinction creates a powerful complementary relationship: FIA provides in-depth information about the nation's forests, while NRI offers critical data on the broader matrix in which many of these forests exist, particularly on private lands, as well as detailing the characteristics of the non-forest components of those private lands. This complementarity is essential for a holistic understanding of the nation's natural resource base.
IV. Comparative Analysis: FIA and NRI Programmatic Elements
A systematic comparison of the Forest Inventory and Analysis (FIA) and Natural Resources Inventory (NRI) programs reveals both distinct characteristics and areas of potential overlap or divergence. These differences are crucial for understanding the challenges and opportunities associated with their coordination and data integration.5
A. Definitions and Land Classifications
One of the most significant areas of divergence lies in the definitions and protocols used for land classification, which can lead to notable discrepancies in resource estimates.
●	Forest Land: Both FIA and NRI nominally define forest land as areas with at least 10 percent tree stocking. However, the protocols for applying this definition differ.5 For instance, FIA tends to classify extensive areas of oak, pinyon, and juniper woodlands in the interior western U.S. as forest, whereas NRI might classify these same areas as shrubland or rangeland.5 This single definitional variance is a primary contributor to differing national estimates of forest area.
●	Urban and Built-up Lands: The criteria used by FIA and NRI to delineate urban and built-up lands also differ, which can affect overall land use accounting and comparisons between the two programs' datasets.5
●	Conservation Reserve Program (CRP) Lands: Programmatic objectives influence classification. NRI may classify certain lands with existing tree cover as CRP land, reflecting its agricultural conservation focus. In contrast, FIA is more likely to classify these same lands as forest if they meet the stocking criteria.5
●	Shrubland: Regional variations in interpretation also exist. For example, in Minnesota, NRI has been noted to classify some tall shrubland areas within transition zones between forest and inland marshes as forest, while FIA classifies these same areas as nonforest.5
These definitional and classification differences are not merely academic; they have tangible consequences, directly impacting the comparability of data produced by the two programs. As noted, discrepancies in forest area estimates between FIA and NRI can exceed 30 percent in some cases 5, posing challenges for users seeking a single, unified national figure.
B. Land Ownership and Geographic Coverage
A fundamental distinction between the two programs lies in their focus on land ownership and overall geographic scope:
●	FIA: The FIA program's mandate extends to all land ownership categories, including federal, state, private industrial, and non-industrial private forest lands, across the entirety of the United States.2
●	NRI: The NRI program, by contrast, concentrates exclusively on non-Federal lands. This includes privately owned lands, tribal and trust lands, and lands administered by state and local governments.3 Critically, NRI does not currently measure sample plots located on Federal lands.5
This difference is paramount. FIA aims to provide a complete inventory of forest resources regardless of who owns the land, offering a national perspective on all forests. NRI provides highly detailed information on the non-federal portion of the landscape (nearly 75% of the U.S. land base 11) for a broader suite of natural resources, including but not limited to forests.
C. Sampling Designs, Plot Characteristics, and Data Collection Methodologies
The methodologies employed by FIA and NRI for sampling and data collection are tailored to their respective missions and also contribute to differences in their data products:
●	FIA:
○	Employs a systematic national sampling grid, with an intensity of approximately one plot per 6,000 acres, and uses equal selection probabilities for each plot location.2
○	The standard FIA field plot consists of a cluster of four circular subplots, which collectively cover about 0.17 acres (approximately 0.07 hectares).5
○	Data collection relies primarily on intensive, on-the-ground remeasurement of these field plots by trained crews to gather detailed data on tree attributes, site characteristics, and forest health indicators. Remote sensing (historically aerial photography, now increasingly satellite imagery) is used to improve the precision of statistical estimates of forest area and to stratify the landscape.5
●	NRI:
○	Utilizes a stratified area sample design. The sampling intensity is approximately one Primary Sampling Unit (PSU) per 8,000 acres of non-Federal land, but it employs unequal selection probabilities. This means sampling is more intensive in areas where land use and resource patterns are more heterogeneous, a strategy designed to increase statistical efficiency for specific analyses.5
○	The NRI PSU is typically a 160-acre (approximately 64.75 hectares) segment of land. Within most PSUs, detailed measurements are made at three secondary sampling points. These measurements have historically been made through the photo-interpretation of custom-flown 1:8,000-scale aerial photographs.5
○	Data collection for NRI primarily relies on the interpretation of high-resolution aerial photography and other remote sensing data to assess and monitor changes in land cover, land use, and land management practices. Fieldwork is conducted on a limited basis, typically to measure features that are difficult to assess accurately with aerial photography or that are believed to change infrequently over time.4
The contrasting plot configurations (FIA's small, intensively measured field plots versus NRI's large, primarily photo-interpreted PSUs) and differing sampling intensities directly reflect their core programmatic objectives. FIA requires detailed, direct measurements of individual trees and stand conditions, necessitating field visits. NRI, on the other hand, needs to assess broader land cover and land use patterns across extensive non-federal landscapes, for which remote sensing is often a more efficient primary tool. Figure 2 in a 2002 comparison illustrates this scale difference, noting that due to NRI's aerial photo coverage (approximately 5% of the landscape), only about 5% of FIA field plots would typically fall within an NRI sample photograph.5
D. Primary Variables Measured and Data Dissemination
The types of data collected and the primary focus of each inventory also differ significantly:
●	FIA: Concentrates on variables related to forest structure (e.g., tree diameter, height, volume), species composition, forest health indicators (e.g., damage, mortality), tree growth and removals, forest ownership patterns, and timber product output.1 Data are disseminated through published reports, online databases, and analytical tools.
●	NRI: Focuses on a broader suite of variables including land cover and land use categories, soil characteristics (e.g., erosion potential, capability class), measured rates of soil erosion, extent of prime farmland, wetland acreage and changes, adoption of conservation practices on agricultural lands, and indicators of rangeland health.3 Data are made available through summary reports, specialized topical reports, and, more recently, the publicly accessible Land Use and Cover Inventory Database (LUCID).3
While both programs measure "land cover" in some capacity, the specific attributes recorded and the thematic depth of these measurements vary substantially, reflecting the distinct missions and expertise of their parent agencies—the Forest Service for FIA and the Natural Resources Conservation Service for NRI.
The fundamental differences in sampling design, plot configuration, and primary data collection methods (field-intensive for FIA versus remote sensing-heavy for NRI) are not merely technical distinctions. They are manifestations of deeply ingrained programmatic philosophies and historical development trajectories. FIA evolved from a tradition of timber cruising and forest mensuration, which emphasizes direct field measurement of trees and stand attributes.2 NRI, conversely, has its roots in soil erosion surveys and broad-scale land assessment, where remote observation and interpretation have long been key tools for evaluating landscape conditions.4 These distinct historical paths have led to highly optimized but very different methodological approaches. FIA's methodology is designed to "touch the trees" and gather detailed ecological data from the ground up. NRI's methodology is designed to "see the landscape" and assess broader patterns of use and condition, often from an aerial perspective. Consequently, achieving true data harmonization or simple merging of their datasets is often not straightforward. It requires more than just aligning definitions; it necessitates bridging fundamentally different ways of observing, measuring, and interpreting the environment. This inherent complexity explains why direct "stacking" of FIA and NRI data is generally not feasible for many applications and why more sophisticated integration strategies, such as those employed in initiatives like the LANDFIRE program (which uses both FIA and NRI data as inputs to geospatial models and map layers), are essential.
To provide a consolidated view of these programmatic elements, Table 1 offers a side-by-side comparison of key features of the FIA and NRI programs.
Table 1: Comparative Overview of FIA and NRI Programs

Feature	Forest Inventory and Analysis (FIA) Program	Natural Resources Inventory (NRI) Program	
Lead Agency	USDA Forest Service (FS) 1	USDA Natural Resources Conservation Service (NRCS) 3	
Primary Mandate	"Make and keep current a comprehensive inventory and analysis of the present and prospective conditions of and requirements for the renewable resources of the forest and rangelands of the US." 2	Conduct a "land inventory reflecting soil, water, and related resource conditions" on non-Federal lands. 3	
Legislative Authority	McSweeney-McNary Forest Research Act of 1928; Forest and Rangeland Renewable Resources Research Act of 1978; 1998 Farm Bill amendment 2	Rural Development Act of 1972; Soil and Water Resources Conservation Act (RCA) of 1977 3	
Land Coverage Focus	All forest lands, regardless of ownership (federal, state, private, etc.) 2	Non-Federal lands only (private, tribal, state, local government) 3	
Geographic Scope	Entire United States 2	49 States (excludes Alaska for main survey), Puerto Rico, U.S. Virgin Islands 3	
Sampling Unit Size/Type	~0.17-acre field plot (cluster of 4 subplots) 5	Typically 160-acre Primary Sampling Unit (PSU) (segment) with 3 photo-interpreted points 5	
Sampling Design	Systematic sampling grid; one plot per ~6,000 acres; equal selection probabilities 2	Stratified area sample; one PSU per ~8,000 non-Federal acres; unequal selection probabilities 5	
Primary Data Collection Method	Primarily field measurements by crews; supplemented by remote sensing for area estimates and stratification 5	Primarily interpretation of high-resolution aerial photography and other remote sensing; limited fieldwork 4	
Key Variables Measured	Forest extent, condition, tree growth, mortality, removals, composition, structure, ownership, timber products, forest health indicators 1	Land use/cover, soil properties, erosion, prime farmland, irrigation, wetlands, conservation practices, rangeland health, habitat diversity 3	
Data Collection Cycle	Annual remeasurement of 10-20% of plots per state; 5-year state reports 1	Periodic (1982-1997), then annual data collection (2000-2017) with 5-year major releases 3	
Primary Outputs	Statistical reports, state analytical reports, public databases, online tools 1	Summary reports, specialized assessments, Land Use and Cover Inventory Database (LUCID) 3	
Definition of 'Forest Land'	At least 10% stocked by trees; includes pinyon-juniper in West; may classify some CRP with trees as forest 5	At least 10% stocked by trees; may classify pinyon-juniper as shrub/rangeland; may classify some tree-covered land as CRP 5	
Definition of 'Non-Federal Land'	N/A (covers all ownerships)	Privately owned lands, tribal and trust lands, and lands controlled by State and local governments 3	
V. Inter-Agency Coordination: Mechanisms and Key Initiatives
Despite the programmatic differences outlined above, there are notable instances of inter-agency coordination between the FIA and NRI programs, primarily driven by the need for more comprehensive national datasets.
A. Formal Agreements: Memoranda of Understanding (MOUs)
The most significant and well-documented formal coordination mechanism is a Memorandum of Understanding (MOU) pertaining to the LANDFIRE (Landscape Fire and Resource Management Planning Tools) program. This MOU facilitates the sharing of NRI data from NRCS with the USDA Forest Service for incorporation into LANDFIRE products.2
The purpose of this MOU is to establish a collaborative framework wherein NRCS provides NRI data to enhance the quality and comprehensiveness of LANDFIRE's national geospatial data products. This is particularly valuable for improving the representation of vegetation types that are currently underrepresented in FIA's predominantly forest-focused plot network, such as rangelands, grasslands, and agricultural lands.13 In return for this data contribution, the LANDFIRE program commits to providing its extensive suite of geospatial data products (which include over 20 layers covering vegetation, disturbance, fuel conditions, biophysical settings, and ecological models) and associated training to NRCS-NRI personnel.13
Under the terms of the MOU, NRCS-NRI's key responsibilities include providing LANDFIRE with NRI data in agreed-upon formats, accompanied by sufficient metadata to facilitate its integration. This data transfer is specifically directed to FIA staff who are assigned to work on the LANDFIRE program.13
Conversely, the key responsibilities of the USDA FS and the LANDFIRE program (acting through these assigned FIA staff) involve ensuring the rigorous protection of NRI point-level data. This includes preventing any disclosure of confidential information, such as precise sample plot locations or any data that could link NRI information to individual private landowners.13 A crucial aspect of this process is that FIA staff use the NRI data to derive intermediate products that mask the original plot locations, a procedure similar to how FIA's own confidential plot data is handled within LANDFIRE.13
The data shared by NRI encompasses field-collected data and associated estimates. LANDFIRE, in turn, provides access to its comprehensive library of geospatial data layers covering the entire United States, its territories, and insular areas.13
Confidentiality is a paramount concern and a central tenet of the agreement. Both programs operate under legal mandates to protect the privacy of landowners and the integrity of their sample locations (for NRI, this is specified under 7 U.S.C. 2276).3 The MOU explicitly details that FIA staff are responsible for handling the sensitive NRI data in a manner that maintains this confidentiality throughout its use in the LANDFIRE production process.13
The stated benefits of this collaboration are manifold. For LANDFIRE, the inclusion of NRI data significantly improves the quality and accuracy of its products, especially for non-forest vegetation types like shrublands and grasslands, leading to more robust national maps of vegetation and fuel conditions.13 For NRCS-NRI, the collaboration offers access to advanced geospatial products derived from LANDFIRE, enhances its internal technical capabilities through exposure to new mapping efforts, and allows for the leveraging of LANDFIRE data to support NRCS program work and conservation planning.13
Regarding the status of the MOU, the specific 2015 agreement had an expiration date of June 30, 2019.13 However, documentation from the LANDFIRE program itself explicitly states that the data sharing relationship with NRCS-NRI was "renewed again in 2020".15 Furthermore, LANDFIRE continues to actively solicit NRI data as part of its ongoing data acquisition efforts for program updates 15 and routinely releases updated products that incorporate various data sources, including partner contributions.16 This indicates a continued, operational commitment to the collaboration, even if the precise legal instrument has evolved. This MOU and its continuation represent a cornerstone of practical FIA-NRI data integration, demonstrating a tangible and successful pathway for leveraging NRI's rich non-forest data to complement FIA's primarily forest-focused dataset within a nationally critical resource management program. The renewal of this relationship underscores an ongoing recognition of its mutual benefits.
Table 2 summarizes the key elements of this important inter-agency agreement.
Table 2: Key Aspects of the NRCS-NRI and USDA FS (FIA) MOU for LANDFIRE

Aspect	Details	
Purpose of MOU	Designate framework for NRCS-NRI to share data with USDA FS for the LANDFIRE program to improve LANDFIRE data products, especially in underrepresented vegetation types. 13	
Key Responsibilities of NRCS-NRI	Provide NRI data (field data, estimates) in agreed formats with metadata to FIA staff assigned to LANDFIRE. 13	
Key Responsibilities of USDA FS/FIA/LANDFIRE	Protect confidential NRI point-level data; use NRI data to derive intermediate products that do not disclose sample plot locations or link data to individual landowners. Provide geospatial data and training to NRCS-NRI. 13	
Data Shared by NRI	Field data collected by NRI programs and associated estimates. 13	
Products/Data Shared by LANDFIRE	Over 20 geospatial data layers (e.g., vegetation, disturbance, fuel/fire, biophysical models), public databases covering the U.S., territories, and insular areas; training on its use. 13	
Confidentiality Provisions	Strict protection of NRI sample plot locations and landowner privacy, as mandated by law (7 USC 2276). FIA staff handle NRI data to maintain confidentiality. Non-confidential NRI data may be freely used by LANDFIRE partners. 3	
Stated Benefits for LANDFIRE	Improved quality of LANDFIRE data products, especially for underrepresented vegetation types (e.g., shrubland, grassland); better forest/non-forest separation. 13	
Stated Benefits for NRI/NRCS	Access to LANDFIRE data products for NRCS program work; enhanced technical capabilities through mapping effort; multi-agency professional cooperation; increased applicability of NRI data to national fire and land management. 13	
Original Expiration Date (2015 MOU)	June 30, 2019, subject to review and renewal. 13	
Renewal Status/Date	LANDFIRE program documentation indicates the data sharing relationship with NRCS-NRI was "renewed again in 2020." LANDFIRE continues to solicit and utilize NRI data for ongoing product updates. 15	
B. Role in Multi-Agency Initiatives (LANDFIRE)
The LANDFIRE program itself is a significant multi-agency initiative. It operates as an interdepartmental endeavor involving key land management and science agencies, primarily the USDA Forest Service and various bureaus within the Department of the Interior (DOI), with The Nature Conservancy also playing a crucial partnership role.13
Within this collaborative structure, the FIA program is recognized as the single largest contributor of geo-referenced vegetation plot data to LANDFIRE.14 This extensive FIA dataset forms a critical foundation for developing and calibrating many of LANDFIRE's core vegetation and fuel map products. However, because FIA's plot network is most dense in forested areas, there are inherent data gaps in other vegetation types, particularly extensive rangelands and agricultural landscapes.
This is where the contribution of NRI data becomes exceptionally valuable. NRI data, with its specific focus on rangelands and other non-federal lands (which often include significant non-forest components), helps to fill these critical data gaps within the LANDFIRE program.13 The collaboration facilitates improved mapping of forest/non-forest boundaries and enhances the characterization and mapping of important non-forest vegetation communities, such as various shrubland and grassland types.13 Thus, LANDFIRE serves as the primary and most successful operational platform where FIA's forest-centric data and NRI's broader non-federal land data are actively integrated to produce nationally consistent, comprehensive geospatial products essential for fire management, ecological assessment, and broader resource management planning.
C. Data Harmonization Efforts and Joint Working Groups (Implicit)
While the LANDFIRE collaboration represents a significant and tangible example of data integration, the provided materials do not explicitly detail the existence of broader, systematic, and ongoing joint working groups between FIA and NRI that are specifically focused on harmonizing core definitions, sampling strategies, or data collection protocols outside of the direct operational needs of LANDFIRE.
However, the USDA has recognized the overarching need for improved data management and collaboration across its various agencies and programs. The Department's Data Strategy for Fiscal Years 2024-2026 emphasizes enhancing data governance, expanding inter-agency collaboration, and supporting the development of mission-area specific data and analytics modernization plans.19 This departmental strategy provides a high-level framework under which more targeted FIA-NRI harmonization initiatives could potentially be fostered or strengthened.
Internally, the FIA program itself has established teams dedicated to data distribution and the maintenance and updating of its complex data management systems, such as the National Information Management System (NIMS).6 These internal efforts aim to ensure consistency and accessibility of FIA data.
The aspiration for deeper harmonization is not new. A 1999 paper by Czaplewski, for instance, argued for the necessity of "unprecedented partnerships" and the integration of remote sensing operations and sampling strategies among FIA, NRI, and the U.S. Geological Survey (USGS) to create a more efficient and comprehensive national monitoring system.8 This historical perspective suggests a long-standing awareness within the scientific and programmatic communities of the potential benefits of more profound integration.
Although the LANDFIRE collaboration stands out as a significant achievement in data sharing and joint product development, the available information does not point to widespread, systematic joint working groups or initiatives aimed at resolving fundamental differences in core definitions or sampling methodologies between FIA and NRI beyond what is necessary for LANDFIRE's specific product suite. The persistence of these foundational discrepancies 5 suggests that comprehensive harmonization remains a complex challenge. The USDA's overarching Data Strategy 19 may create new opportunities or impetus for such efforts, but the current evidence does not detail their widespread operationalization for general FIA-NRI alignment. This could indicate an area where future collaborative efforts might be directed to further enhance the interoperability and combined analytical power of these two vital national inventories.
VI. Documented Benefits and Outcomes of FIA-NRI Collaboration
The coordination between the FIA and NRI programs, particularly through their contributions to the LANDFIRE initiative, has yielded several documented benefits and positive outcomes for national resource assessment and management.
A. Enhancement of National Geospatial Data Products (LANDFIRE)
The most prominent and tangible benefit stemming from FIA-NRI collaboration is the significant enhancement of the LANDFIRE program's national geospatial data products. NRI data, with its strong focus on rangelands, agricultural lands, and other non-federal areas, provides crucial information that improves the accuracy, completeness, and thematic detail of LANDFIRE maps. This is especially true for vegetation types that are typically underrepresented or less intensively sampled by FIA's forest-focused plot network.13
The integration of NRI data allows LANDFIRE to produce more reliable characterizations of these non-forest and mixed landscapes, leading to better overall data quality for applications such as fire behavior modeling, wildfire risk assessment, habitat mapping, and strategic land management planning across the entire United States.13 For example, specific LANDFIRE products, such as those mapping shrubland and grassland vegetation type groups or estimating percent canopy cover at a 30-meter spatial resolution nationwide, directly benefit from the inclusion of NRI data, enabling their completion with higher quality and greater confidence.13 This demonstrates a clear return on the investment in inter-agency coordination, resulting in improved decision-support tools for a diverse community of users, from federal land managers to state agencies and non-governmental organizations.
B. Contributions to Comprehensive Ecosystem and Land Cover Assessments
By bringing together FIA's detailed information on forests across all ownerships with NRI's comprehensive data on non-federal lands (which often encompass significant non-forest or mixed agricultural and rangeland ecosystems), their collaboration facilitates a more holistic and comprehensive understanding of national land cover patterns and ecosystem conditions than either program could achieve in isolation.5
More than two decades ago, Czaplewski (1999) envisioned that such inter-programmatic coordination, particularly in the realm of remote sensing and sample design, could culminate in an efficient, integrated system capable of evaluating the sustainability of the nation's diverse ecosystems and assessing overall ecosystem health across all land types.8 While the full extent of that vision for deep operational integration may not yet be realized, the collaborative efforts within the LANDFIRE framework represent a significant step in this direction. Furthermore, historical analyses have shown that plot data from both FIA and NRI have proven useful as critical training and labeling datasets for broader efforts to classify land cover and detect land cover changes using satellite imagery, thereby contributing to wider remote sensing-based assessments.5 This highlights the scientific and analytical value derived from combining these two rich, ground-referenced national datasets.
C. Potential for Increased Efficiency and Cost-Effectiveness
The principle of leveraging existing data and expertise across programs to avoid duplication and enhance efficiency is a strong driver for collaboration. The 1999 Czaplewski paper posited that extensive coordination in areas like remote sensing data acquisition and the potential co-location of sampling units could lead to substantial cost reductions for both the FIA and NRI programs.8
While the currently available documentation does not provide recent, specific quantitative evidence of direct cost savings stemming from current FIA-NRI coordination activities outside of LANDFIRE, the underlying logic remains compelling. By avoiding duplicative data collection efforts for similar parameters or by strategically leveraging each other's core strengths—for instance, NRI's extensive experience in remote sensing of non-forest landscapes and FIA's deep expertise in field-based forest measurements—efficiencies should theoretically be achievable.
The LANDFIRE program itself exemplifies a degree of this cost-effectiveness. By utilizing existing, high-quality FIA and NRI plot data as foundational inputs for its mapping and modeling processes, rather than initiating entirely new, large-scale national plot inventories for its specific purposes, LANDFIRE inherently operates in a more resource-efficient manner for achieving its nationwide geospatial product goals.
The collaborative framework established for LANDFIRE, particularly the development of protocols for secure data sharing and the innovative use of FIA staff to process sensitive NRI data while maintaining confidentiality 13, has created a valuable precedent. This demonstrates that institutional and technical barriers to sharing sensitive, high-value data can be successfully navigated with appropriate agreements, trusted intermediary roles, and a shared commitment to data security. If this model of inter-agency data handling and collaborative product generation has proven effective for LANDFIRE, it offers a potential blueprint that could be adapted and extended to other joint analytical products or shared services between FIA, NRI, and potentially other federal resource monitoring programs. Such extensions could further reduce the need for each agency to independently develop certain specialized capacities or datasets, ultimately leading to greater cost savings and a more efficient use of public funds, aligning with the foundational arguments for integration put forth by early proponents like Czaplewski.8
VII. Challenges and Limitations in Programmatic Integration and Coordination
Despite the successes achieved, particularly within the LANDFIRE framework, significant challenges and limitations persist in achieving broader and deeper programmatic integration and coordination between the FIA and NRI programs.
A. Data Harmonization and Compatibility Issues
Fundamental differences in how the two programs define, collect, and classify data pose substantial hurdles to seamless data fusion and direct comparison.
●	Definitional Discrepancies: As detailed in Section IV.A, the FIA and NRI programs employ different definitions and application protocols for key land classifications such as 'forest land,' 'urban land,' and 'Conservation Reserve Program (CRP) land'.5 These varying definitions directly result in different area estimates for what might nominally appear to be the same land category. For example, FIA's inclusion of certain pinyon-juniper woodlands as "forest" contrasts with NRI's potential classification of the same areas as "shrubland" or "rangeland." This lack of common definitional standards is a primary barrier to straightforward data integration and can lead to confusion when comparing outputs.
●	Methodological Differences: The contrasting sampling designs (systematic grid for FIA vs. stratified area sample for NRI), plot sizes and configurations (small field plots for FIA vs. large photo-interpreted PSUs for NRI), and primary data collection methodologies (field-intensive for FIA vs. remote sensing-heavy for NRI) make direct statistical aggregation or simple merging of the datasets problematic.5 For instance, comparing FIA's detailed, ground-measured tree attributes with NRI's photo-interpreted land cover classifications requires careful consideration of what each dataset truly represents and the inherent scale and precision differences.
●	Data Heterogeneity: The general challenge of integrating data from diverse sources, each with its own unique formats, schemas, data structures, and terminologies, is also applicable here.20 While this observation is from a general context of data integration, the principle directly applies to the complexities of combining FIA and NRI datasets, which have evolved independently over decades.
These issues are not superficial technicalities but represent deep-rooted structural differences that have proven challenging to fully reconcile, despite long-standing calls for improved collaboration and harmonization.5
B. Addressing Discrepancies in Resource Estimates
A direct consequence of the aforementioned definitional and methodological differences is that the FIA and NRI programs can, and often do, produce significantly different statistical estimates for ostensibly the same natural resource. The notable example is forest area, where estimates from the two programs can differ by more than 30 percent for a given region or state.5
Such discrepancies can cause considerable confusion for data users, including policymakers, resource managers, and the scientific community, who may be seeking a single, authoritative figure for national or regional resource assessments. Understanding the precise reasons for these differences—which stem from the varying scopes, definitions, and methodologies—and clearly communicating these nuances to data users is crucial, yet it remains a complex and ongoing challenge. The existence of differing resource estimates from within the same Department (USDA) can impact the perceived reliability and overall utility of national resource statistics if the underlying reasons for such variations are not well understood or articulated.
C. Institutional and Logistical Hurdles
Beyond the technical data-related challenges, institutional and logistical factors also impede deeper integration.
●	Institutional Complexity: As Czaplewski noted in 1999, achieving true, deep integration between large federal monitoring programs requires "unprecedented partnerships," and he observed that, at the time, most leaders were "not aware of the magnitude of the logistical and institutional challenges" involved.8 While the LANDFIRE collaboration demonstrates significant progress in project-specific integration, many of these underlying institutional challenges likely persist when considering broader, more systematic harmonization efforts.
●	Data Confidentiality: The paramount importance of maintaining data confidentiality, particularly concerning NRI sample plot locations and personally identifiable information (PII) associated with landowners in both programs, dictates the need for stringent and carefully managed data sharing protocols.6 While essential for legal compliance and maintaining landowner trust, these necessary safeguards add a layer of complexity and administrative overhead to any data integration initiative.
●	General Data Integration Obstacles: Common challenges inherent in any large-scale data integration effort also apply. These include ensuring consistent data quality across disparate sources, managing data accessibility and security, and fostering effective communication, collaboration, and teamwork among different agency units and program staff who may have different operational cultures and priorities.20
The very organizational structure of federal agencies, with programs like FIA situated within the Forest Service's Research & Development arm and NRI operating under NRCS's conservation operations, contributes to a natural inertia against deep, programmatic integration. These programs have distinct primary missions, are driven by different legislative mandates, often operate under separate budgetary allocations, and have evolved unique operational cultures and priorities.1 Each program has also developed and maintained complex, well-established data collection, management, and analysis systems that are highly optimized for its own specific needs and historical trajectory.6 Any attempt at profound integration or a wholesale merging of operations would necessitate significant modifications to these deeply embedded systems, potentially disrupting ongoing core functions and requiring substantial investment in new joint infrastructure, common standards, or shared processes. Consequently, inter-agency collaboration tends to be most successful when focused on specific projects, like LANDFIRE, where the mutual benefits are clearly defined, the scope is manageable, and the potential disruption to the core programmatic activities of each agency is minimized. This contrasts with the more ambitious visions of complete operational fusion that were articulated by some early proponents of integration.8 The USDA's broader departmental data strategy 19 aims to create a more conducive environment for collaboration, but navigating the practicalities and constraints within specific, long-standing program structures remains a significant undertaking.
VIII. Future Outlook and Potential Enhancements in Coordination
Looking ahead, several avenues hold promise for enhancing coordination between the FIA and NRI programs, driven by technological advancements, evolving information demands, and strategic departmental initiatives.
A. Leveraging New Technologies for Greater Synergy
Technological innovation offers powerful tools to facilitate greater synergy and overcome some of the traditional barriers to data integration.
●	Remote Sensing: Both FIA and NRI extensively utilize remote sensing technologies, but there remains significant potential for enhanced coordination in the acquisition, processing, interpretation, and application of these data, as astutely pointed out by Czaplewski over two decades ago.8 Modern satellite platforms (such as the Landsat and Sentinel series) provide increasingly rich, frequent, and high-resolution Earth observation data. Coupled with advanced analytical techniques, including Artificial Intelligence (AI) and Machine Learning (ML), these capabilities open new avenues for harmonizing observations, developing consistent land cover products, and monitoring changes more effectively across the diverse landscapes covered by both programs. The Multi-Resolution Land Characteristics (MRLC) Consortium, a collaborative effort among multiple federal agencies to pool resources for acquiring national Landsat datasets and cooperatively developing land cover map products 22, serves as an established and successful model. This consortium approach could be further leveraged or adapted to meet specific joint FIA-NRI objectives, potentially leading to shared remote sensing products or analytical workflows.
●	Data Integration Platforms & AI: The USDA is making strategic investments in AI, ML, and improved data governance frameworks as part of its departmental data strategy.19 These emerging technologies could play a crucial role in addressing issues of data heterogeneity between FIA and NRI. Sophisticated algorithms could help in aligning disparate datasets, identifying complex patterns across combined data, and deriving new insights that are not easily achievable through manual comparison or traditional statistical methods. However, it is also important to acknowledge and address the challenges associated with applying AI to complex data preparation and integration tasks, such as ensuring robustness when dealing with noisy or ambiguous data and validating domain-specific accuracy, particularly when integrating data from distinct scientific disciplines.21
The effective deployment of these technologies for inter-agency coordination is not solely a technical matter; it also requires sustained institutional will, strategic planning, dedicated resources, and a shared vision for integrated information products.
B. Addressing Evolving Demands for Integrated Natural Resource Information
The demand for comprehensive, integrated natural resource information is steadily increasing, driven by pressing environmental and policy concerns.
●	Issues such as assessing climate change impacts on ecosystems, quantifying forest and soil carbon stocks and fluxes for national and international reporting 9, modeling wildfire risk and behavior, conserving biodiversity, and promoting sustainable development all necessitate data that often transcends traditional programmatic or jurisdictional boundaries.
●	High-level mandates, such as Executive Orders (e.g., the directive to inventory mature and old-growth forests 9) and commitments to international reporting frameworks (e.g., the United Nations Framework Convention on Climate Change Global Stocktake 9), frequently require the synthesis of data from multiple sources, including both FIA and NRI.
●	The USDA's own Data Strategy for FY24-26 explicitly aims to equip its Mission Areas to meet new expectations for oversight, transparency, and evidence-based decision-making through a more data-driven approach, clearer governance structures, and the application of more advanced analytical techniques.19
These external and internal drivers create a strong and persistent impetus for the FIA and NRI programs to collaborate more effectively and to develop innovative ways to deliver integrated information products that can address these complex, multifaceted demands.
C. Strengthening Data Sharing and Harmonization Frameworks
Building upon existing successes and addressing known challenges will be key to future enhancements.
●	The LANDFIRE collaboration provides a valuable model for data sharing and joint product development. Exploring other thematic areas where similar joint data products or integrated analyses could yield significant benefits would be a logical next step.
●	Continued and transparent efforts to document, explain, and reconcile (where possible) the differences in definitions, methodologies, and resulting estimates between FIA and NRI are crucial for reducing user confusion and enhancing the credibility of both programs.
●	Revisiting some of the more ambitious proposals for deeper integration, such as the potential for strategically collocating a subset of FIA and NRI sample plots or designing more complementary sampling strategies in areas of high mutual interest (as suggested by Czaplewski 8), could be worthwhile. Such efforts would need to be carefully designed to respect the core mission requirements and statistical integrity of each individual program while maximizing the potential for synergistic data collection.
The trajectory of future FIA-NRI coordination will likely be shaped less by attempts to achieve a full operational merger of these large and complex, historically distinct systems. Instead, progress is more probable through the development of flexible, "networked" approaches to data sharing, collaborative analytics, and joint product development. This philosophy aligns with the "network of networks" concept, which has been discussed in the context of FIA's collaborations with academic institutions for advancing carbon science 9, and could be metaphorically extended to inter-agency interactions between FIA and NRI. Such an approach would focus on leveraging the unique strengths and datasets of each program without mandating identical systems or methodologies across the board. This can be facilitated by the development and adoption of common data standards where feasible, the creation of interoperable data platforms, and the use of advanced technological tools for data integration and analysis, all under the supportive umbrella of the broader USDA data strategy.19 This pragmatic path emphasizes functional integration for specific, high-value outcomes rather than complete structural amalgamation.
IX. Concluding Remarks
A. Summary of the Current State of FIA-NRI Coordination
The Forest Inventory and Analysis (FIA) program of the USDA Forest Service and the Natural Resources Inventory (NRI) program of the USDA Natural Resources Conservation Service stand as two of the nation's most critical assets for monitoring the status and trends of its natural resources. FIA provides a comprehensive census of America's forests across all ownerships, while NRI delivers detailed information on land use, soil, water, and related resources, primarily on non-Federal lands. These roles are distinct yet inherently complementary.
Formal coordination between FIA and NRI is most prominently and successfully manifested through their collaborative contributions to the LANDFIRE program. This inter-agency effort, facilitated by Memoranda of Understanding and specific data sharing agreements, allows for the integration of NRI's rangeland and agricultural data with FIA's forest plot data to produce enhanced national geospatial products vital for fire and resource management.13 This collaboration demonstrates a practical mechanism for leveraging the strengths of both programs.
However, significant challenges to broader and deeper integration persist. These primarily stem from long-standing differences in fundamental definitions (e.g., of "forest land"), sampling methodologies, plot configurations, and primary data collection techniques.5 These programmatic distinctions, rooted in the unique historical development and specific mandates of each inventory, result in differing resource estimates and complexities in direct data fusion.
B. The Enduring Significance and Future Imperative
Despite the inherent challenges, the existing collaboration between FIA and NRI, particularly through the LANDFIRE initiative, yields considerable benefits for national resource assessment, scientific understanding, and informed management. The ability to combine FIA's detailed forest data with NRI's extensive coverage of non-federal, often non-forested, landscapes provides a more complete picture of the nation's ecological and land use patterns than either program could offer independently.
Looking forward, the imperative for enhanced coordination and data integration between these vital USDA programs is likely to intensify. The increasing complexity of environmental issues—from climate change adaptation and mitigation to biodiversity conservation and sustainable land management—coupled with escalating policy demands for comprehensive and timely information, will continue to drive the need for more synergistic approaches. The USDA's evolving departmental Data Strategy 19, along with ongoing advancements in remote sensing, artificial intelligence, and data analytics, offers promising pathways to strengthen this crucial inter-agency relationship. Future progress will likely hinge on building upon existing successes like LANDFIRE, fostering flexible and networked models of collaboration, and strategically investing in technologies and frameworks that promote data interoperability and joint analytical capabilities, all while respecting the core missions and operational integrity of each program. The continued effective stewardship of the nation's natural resources depends, in no small part, on the ability of its premier inventory programs to work together cohesively and innovatively.
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