The Surface Management Agency (SMA) Geographic Information System (GIS) dataset depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. The SMA feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details. The SMA Withdrawals feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA Withdrawal is defined by formal actions that set aside, withhold, or reserve Federal land by statute or administrative order for public purposes. A withdrawal creates a title encumbrance on the land. Withdrawals must accomplish one or more of the following: A. Transfer total or partial jurisdiction of Federal land between Federal agencies. B. Close (segregate) Federal land to operation of all or some of the public land laws and/or mineral laws. C. Dedicate Federal land to a specific public purpose. There are four major categories of formal withdrawals: (1) Administrative, (2) Presidential Proclamations, (3) Congressional, and (4) Federal Power Act (FPA) or Federal Energy Regulatory Commission (FERC) Withdrawals. These SMA Withdrawals will include the present total extent of withdrawn areas rather than all of the individual withdrawal actions that created them over time. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details.

Land cover has been interpreted from Satellite images and field checked, other information has been digitized from topographic maps

 Members informations:
 Attached Vector(s):
  MemberID: 1
 Vector Name: Land use
 Source Map Name: SPOT Pan
 Source Map Scale: 50000
 Source Map Date: 1989/90
 Projection: Polyconic on Modified Everest Ellipsoid
 Feature_type: polygon
 Vector 
 Land use maps, interpreted from SPOT panchromatic imagery and field
 checked (18 classes)

 Members informations:
 Attached Vector(s):
  MemberID: 2
 Vector Name: Administrative boundaries
 Source Map Name: topo sheets
 Source Map Scale: 50000
 Source Map Date: ?
 Feature_type: polygon
 Vector 
 Dzongkhags (Districts) and Gewogs

 Members informations:
 Attached Vector(s):
  MemberID: 3
 Vector Name: Roads
 Source Map Name: topo sheets
 Source Map Scale: 50000
 Source Map Date: ?
 Feature_type: lines
 Vector 
 Road network

 Attached Report(s)
 Member ID: 4
 Report Name: Atlas of Bhutan
 Report Authors: Land use planning section
 Report Publisher: Ministry of Agriculture, Thimpu
 Report Date: 1997-06-01
 Report 
 Land cover (1:250000) and area statistics of 20 Dzongkhags

Land Management Software Market size was valued at USD 1.69 Billion in 2024 and is projected to reach USD 2.62 Billion by 2031, growing at a CAGR of 5.65% from 2024 to 2031.

The growth of land management software is primarily driven by the increasing demand for efficient land use, advancements in geospatial technology, regulatory compliance, and the need for data-driven decision-making. As global populations grow and urbanization accelerates, there is a growing need for efficient land resource management. Land management software offers tools to optimize land use, enhance productivity in agriculture, forestry, and urban planning, and ensure sustainable development practices.

Advancements in geospatial technology, such as Geographic Information Systems (GIS), remote sensing, and satellite imagery, have significantly enhanced the capabilities of land management software, enabling more accurate mapping, monitoring, and analysis of land resources. Regulatory compliance and environmental concerns also drive the adoption of land management software among government agencies, landowners, and businesses.

Data-driven decision-making is another driving factor, as land management software provides powerful analytical tools for processing large volumes of spatial data, generating insights, and supporting data-driven decision-making processes. The growing awareness of climate change risks and the need for resilient land management practices drives the adoption of software solutions that enable climate-smart land management.

Precision agriculture practices are increasingly emphasized in the agricultural sector, with land management software playing a critical role in supporting these practices. The emergence of integrated land management platforms that combine GIS, asset management, and workflow automation capabilities is also driving the adoption of comprehensive software solutions.

In conclusion, the growth of land management software is driven by the need for efficient land use, advancements in technology, regulatory requirements, and the recognition of the importance of sustainable land management practices in addressing global challenges such as food security, environmental degradation, and climate change.

The PLSS First Division feature class is the sections and other types of divisions that divide the PLSS Townships. This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance.The service referred in this item is published by BLM. Please refer to the metadata for contact information.Contact: GIS.Librarian@FloridaDEP.gov

The data included are geographic information layers relevant to sportfish ecology, land development, community planning, zoning, coastal zone management, storm water management, future land use, land ownership, and planning tools and techniques aggregated from other sources during the project titled "Knowledge Co-Production for Place-Based Recreational Fishery Conservation in Charlotte Harbor, Florida". Data contributors assume no liability for any errors, omissions, or inaccuracies in the information provided regardless of how caused. The layers are each provided as separate KMZ files.

Land Management Software Market Outlook

The global land management software market size is projected to grow significantly from USD 1.5 billion in 2023 to USD 3.8 billion by 2032, reflecting an impressive compound annual growth rate (CAGR) of 10.5% during this period. This robust growth is driven by multiple factors including advancements in geospatial technologies, the increasing need for efficient land utilization, and heightened regulatory requirements for land management practices.

One of the primary growth factors of the land management software market is the rapid technological advancements in geospatial and remote sensing technologies. These innovations are making it easier to manage land resources more efficiently and accurately. The integration of Geographic Information System (GIS) technologies and remote sensing allows for real-time data collection and analysis, which significantly enhances decision-making processes. Furthermore, the advent of Artificial Intelligence (AI) and Machine Learning (ML) in land management software is expected to optimize land use and improve predictive capabilities, driving the market’s growth.

Another significant growth factor is the increasing global emphasis on sustainable land management practices. As governments and private enterprises become more aware of the environmental impact of land use, there is a growing demand for software solutions that can help monitor, manage, and mitigate these impacts. Policies and regulations aimed at promoting sustainable land use are being enacted globally, compelling landowners and managers to adopt advanced land management software. These regulatory pressures are expected to drive significant adoption of advanced land management solutions over the forecast period.

The rising need for efficient land utilization, particularly in urban areas, is also a crucial growth driver. With global urbanization rates climbing, the need to manage land resources in urban settings has never been more critical. Land management software helps in the optimal allocation and use of land resources, facilitating better urban planning and development. This is particularly vital in densely populated regions where space is at a premium and efficient land use can significantly impact economic and social outcomes.

Regionally, North America is anticipated to dominate the land management software market, attributed to the region's advanced technological infrastructure and high adoption rates of innovative land management solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing investments in smart city projects, and the rising need for efficient land management practices in agriculture and forestry sectors.

Component Analysis

The land management software market is segmented by component into software and services. The software segment is expected to account for the largest market share during the forecast period, driven by continuous advancements in software capabilities and increasing demand for integrated land management solutions. These software solutions offer comprehensive functionalities, including land use planning, property management, and environmental monitoring, which are crucial for efficient land resource management.

Software solutions in land management are increasingly incorporating advanced technologies such as GIS, AI, and ML to provide enhanced functionalities and greater accuracy. These technologies enable real-time data analysis and predictive modeling, which are essential for making informed decisions about land use. The growing adoption of cloud-based land management software is also contributing to the segment’s growth, as it offers greater flexibility, scalability, and cost-effectiveness compared to traditional on-premises solutions.

On the services front, there is a rising demand for consulting, implementation, and maintenance services. As organizations and governments adopt more sophisticated land management software, they require expert guidance to ensure successful deployment and integration with existing systems. Professional services help in customizing the software solutions to meet specific needs, training users, and providing ongoing support, thereby enhancing the overall efficiency and effectiveness of land management practices.

Furthermore, the increasing complexity of land management projects, particularly in urban and environmentally sensitive areas, is driving the demand for comprehensiv

The Land Management Software market is experiencing robust growth, driven by increasing demand for efficient resource allocation, improved regulatory compliance, and the need for data-driven decision-making across various sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and accessibility, appealing to businesses of all sizes. Furthermore, the increasing integration of GIS (Geographic Information Systems) technology enhances spatial data analysis and visualization, improving operational efficiency. The Oil and Gas sector, a significant user of land management software, is a major contributor to market growth, along with expanding applications in urban planning and lease management. While data security concerns and the need for specialized expertise present some restraints, the overall market outlook remains positive, driven by technological advancements and increasing awareness of the benefits of efficient land management practices. The competitive landscape is marked by a mix of established players and emerging technology providers. Companies like Trimble, Tyler Technologies, and others are leveraging their expertise in GIS and software development to offer comprehensive solutions. Smaller companies are focusing on niche applications or specialized functionalities to gain market share. The market exhibits geographical diversity, with North America and Europe currently holding the largest market shares due to established infrastructure and early adoption. However, rapid growth is expected in the Asia-Pacific region, driven by increasing urbanization and infrastructure development projects. Continued innovation in areas like AI-powered analytics, automated workflows, and improved data integration will further shape the market's trajectory in the coming years, particularly in supporting complex land management challenges across diverse geographies and regulatory environments.

Administered Lands is a BLM Alaska GIS dataset that combines publicly available borough, municipality, state, federal, and other entity management and ownership GIS data. This is the basis for BLM’s national Surface Management Agency GIS dataset that was developed to fulfill the public and Government’s need to know what agency is managing Federal land in a given area. This data set is comprised of various sources of geospatial information that have been acquired from local, state and federal agencies in order to assemble a comprehensive representation of current land surface manager. There are many land managing agencies and branches of government and this dataset attempts to classify these entities into general categories. This data does not demonstrate or infer land ownership. The business need for this data includes, but is not limited to, land use planning, permitting, recreation, and emergency response. Due to the nature of assembling geospatial information from multiple sources, integration of features into a single layer may introduce inaccurate artifacts. Acquired datasets have been cross-walked to a standardized schema to aid in the depiction of land surface manager across the state of Alaska. This dataset will contain errors. For the most up to date and accurate information, please contact the surface manager agency for the area in which you are interested.

This dataset (2017-2023) is a compilation of the Land Use/Land Cover datasets created by the 5 Water Management Districts in Florida based on imagery -- Northwest Florida Water Management District (NWFWMD) 2022.Bay (1/4/2022 – 3/24/2022), Calhoun (1/7/2022 – 1/18/2022),Escambia (11/13/2021 – 1/15/2021), Franklin (1/7/2022 – 1/18/2022), Gadsden (1/7/2022 – 1/16/2022), Gulf (1/7/2022 – 1/14/2022), Holmes (1/8/2022 – 1/18/2022), Jackson (1/7/2022 – 1/14/2022), Jefferson (1/7/2022 – 2/16/2022), Leon (February 2022), Liberty (1/7/2022 – 1/16/2022), Okaloosa (10/31/2021 – 2/13/2022), Santa Rosa (10/26/2021-1/17/2022), Wakulla (1/7/2022 – 1/14/2022), Walton (1/7/2022-1/14/2022), Washington (1/13/2022 – 1/19/2022).Suwannee River Water Management District (SRWMD) 2019-2023.(Alachua 20200102-20200106), (Baker 20200108-20200126), (Bradford 20181020-20190128), (Columbia 20181213-20190106), (Gilchrist 20181020-20190128), (Levy 20181020-20190128), (Suwannee 20181217-20190116), (Union 20181020-20190128).(Dixie 12/17/2021-01/29/2022), (Hamilton 12/17/2021-01/29/2022), (Jefferson 01/07/2022-02/16/2022), (Lafayette 12/17/2021-01/29/2022), (Madison 12/17/2021-01/29/2022), (Taylor 12/17/2021-01/29/2022.Southwest Florida Water Management District (SWFWMD) 2020. South Florida Water Management District (SFWMD) 2021-2023.St. John's River Water Management District (SJRWMD) 2020.Year Flight Season Counties:2020 (Dec. 2019 - Mar 2020) Alachua, Baker, Clay, Flagler, Lake, Marion, Osceola, Polk, Putnam.2021 (Dec. 2020 - Mar 2021) Brevard, Indian River, Nassau, Okeechobee, Orange, St. Johns, Seminole, Volusia. 2022 (Dec. 2021 - Mar 2022) Bradford, Union. Codes are derived from the Florida Land Use, Cover, and Forms Classification System (FLUCCS-DOT 1999) but may have been altered to accommodate region differences by each of the Water Management Districts.

The SMA implementation is comprised of one feature dataset, with several polygon feature classes, rather than a single feature class. SurfaceManagementAgency: The Surface Management Agency (SMA) Geographic Information System (GIS) dataset depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency's surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details. This layer is also updated whenever BLM is notified that Lands have been acquired by other Federal Agencies. For additional information regarding an acquisition search the Bureau's LR2000 system: The LND_SurfaceEstate data is edited and maintained in a single polygon feature class. Whenever possible, BLM lands are constructed from the Public Land Survey System (PLSS), also available to the public (PublicLandSurvey.gdb). Alignment of BLM data with the PLSS is a continual process, as the accuracy and density of PLSS data continues to improve and develop. Issues of misalignment with the PLSS are more common with non-BLM management areas. These discrepancies are being addressed at the BLM California State office based on U.S. Department of Interior priorities throughout the State of California

This layer is sourced from gis.wim.usgs.gov.

1.27.2014 --ESM-- created for DOI building location map.

This layer represents the boundaries for existing and in-progress BLM Land Use Planning Area (LUPA) polygons. Land Use Planning Areas are geographic areas within which the BLM will make decisions during a land use planning effort. Land Use Planning Area Boundaries shift from an "in-progress" status and become Existing Land Use Planning Areas when the Land Use Plan has been approved and a Record of Decision Date has been established.

Managing 245 million acres of land and 700 million acres of mineral estate is a big task. The BLM recognizes that geospatial information is a critical tool for managing public lands. We’ve already made great strides in creating national datasets, supporting almost every program in the Bureau. The BLM has adopted a ground-up approach to managing public lands, and the geospatial program is providing the structure and tools to accomplish this strategy. We manage spatial data to support multiple activities at varying scales.

The BLM's geospatial strategy focuses on collection, organization, and use of baseline resource management data, like fenceline and transportation data and enhancing predictions based on geospatial data. Examples of activities that require geospatial data include planning and resource management, special status species monitoring, regional mitigation, and renewable energy projects, just to name a few.

An important factor in implementing our strategy is using a geographic information system (GIS) that is consistent and integrated within the Bureau and the Department of the Interior. This internal cohesion enhances the BLM's ability to partner with other Federal agencies, collaborate with State and Tribal governments, and communicate with the public.

Geospatial Services Land management within the US Forest Service and on the 900,000+ acre Monongahela National Forest (NF) is driven by a wide mix of resource and societal demands that prove a challenge in fulfilling the Forest Service’s mission of “Caring for the Land and Serving the People.” Programmatically, the 2006 Land and Resource Management Plan guide natural resource management activities on lands administered by the Monongahela National Forest. The Forest Plan describes management direction and practices, resource protection methods and monitoring, desired resource conditions, and the availability and suitability of lands for resource management. Technology enables staff to address these land management issues and Forest Plan direction by using a science-based approach to facilitate effective decisions. Monongahela NF geospatial services, using enabling-technologies, incorporate key tools such as Environmental Systems Research Institute’s ArcGIS desktop suite and Trimble’s global positioning system (GPS) units to meet program and Forest needs. Geospatial Datasets The Forest has a broad set of geospatial datasets that capture geographic features across the eastern West Virginia landscape. Many of these datasets are available to the public through our download site. Selected geospatial data that encompass the Monongahela National Forest are available for download from this page. A link to the FGDC-compliant metadata is provided for each dataset. All data are in zipped format (or available from the specified source), in one of two spatial data formats, and in the following coordinate system: Coordinate System: Universal Transverse Mercator Zone: 17 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Map files – All map files are in pdf format. These maps illustrate the correlated geospatial data. All maps are under 1 MB unless otherwise noted. Metadata file – This FGDC-compliant metadata file contains information pertaining to the specific geospatial dataset. Shapefile – This downloadable zipped file is in ESRI’s shapefile format. KML file – This downloadable zipped file is in Google Earth’s KML format. Resources in this dataset:Resource Title: Monongahela National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/mnf/landmanagement/gis/?cid=stelprdb5108081 Selected geospatial data that encompass the Monongahela National Forest are available for download from this page.

[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976

This hosted feature layer has been published in RI State Plane Feet NAD 83.A statewide, seamless, vector-formatted geospatial dataset depicting 2020 land use and land cover ground conditions. The product was developed by comparing high resolution 2020 and 2011 leaf-off aerial orthoimagery and employing both automated and manual processes to detect, delineate and photointerpret changes since 2011. The project area encompasses the State of Rhode Island and also extends 1/2 mile into the neighboring states of Connecticut and Massachusetts, or to the limits of the source orthoimagery. The minimum mapping unit for this dataset is 0.5 acre.The classification scheme is based on the same RI-modified Anderson Level III scheme used in previous classifications (1988, 1995, 2003/2004, and 2011) with the addition of two new classes (148) Ground-mounted Solar Energy Systems and (149) Wind Energy Systems. If data are used for change detection using the 2003/2004 edition be aware that marinas were coded from other transportation and developed recreation to commercial in the 2020 data to more accurately fit the classification system. The RI classification is based upon Anderson Level III coding described in the United States Geological Survey Publication: "A Land Use And Land Cover Classification System for Use With Remote Sensor Data, Geological Survey Professional Paper 964" Available Online at: https://landcover.usgs.gov/pdf/anderson.pdfPlease consider the source, spatial accuracy, attribute accuracy, and scale of these data before incorporating them into your project. These data were derived from both automated and manual photointerpretation processes and should be used for planning purposes only. The wetland areas contained in this dataset do not include all wetlands previously identified in other RIGIS land use and land cover datasets or in other separate GIS wetland datasets and interpretation of wetland areas should lean toward the side of caution. Wetland areas previously classified as forested wetlands are shown as forested areas in this dataset. Statistical comparisons with RIGIS land use and land cover data prior to 2003 should be treated with caution since some differences in the methodologies used to delineate features were employed

The 2020 Generalized Land Use Inventory dataset encompasses the seven county Twin Cities (Minneapolis and St. Paul) Metropolitan Area in Minnesota. The dataset was developed by the Metropolitan Council, a regional governmental organization that deals, in part, with regional issues and long range planning for the Twin Cities area. The data were interpreted from April 2020 air photos, with additional assistance from county parcel data and assessor's information, Internet information, field checks , and community review.

The following generalized land use classes are used (some of which have subclasses):

Single Family Residential
Multifamily Residential
Office
Retail and Other Commercial
Mixed Use
Industrial and Utility
Extractive
Institutional
Park, Recreational, or Preserve
Golf Course
Major Highway
Railway
Airport
Agriculture
Undeveloped
Water

See Section 5 of the metadata for a detailed description of each of these land use categories and available subcategories.

Note: Although this dataset does contain an 'Undeveloped' land category, this dataset does not attempt to delineate what lands might be considered developable. The definition of that category can be found in Section 5 of this metadata.

More information about the Metropolitan Council's generalized land use data can be found here Landuse Notes

The Surface Management Agency (SMA) Geographic Information System (GIS) dataset, found in the A-16 National Geospatial Data Asset Portfolio, depicts federal land in the U.S. This tile layer portrays a subset of the SMA GIS dataset. It depicts the SMA land in the U.S. not including private or unknown lands. It is available to the ArcGIS.com tiling scheme level 14 (town level or approximately 1:36,000 scale). It currently covers all of the Bureau of Land Management (BLM) Western State Offices including Alaska. The SMA data are extracted from federal land status records. The official federal land status records of the appropriate surface land managing agency should be consulted concerning ownership details including interest in the federal subsurface mineral estate.The GIS data contained in this dataset depict the surface extent of each federal agency’s surface administrative jurisdiction. SMA data depict current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details. For more information about this BLM National SMA dataset, click here.Thumbnail source image courtesy of: Bureau of Land Management

The classification of land according to what activities take place on it or how it is being used; for example, agricultural, industrial, residential, rural, or commercial. Land use information and analysis is a fundamental tool in the planning process.

DVRPC’s 2020 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2020. This dataset supports many of DVRPC's planning analysis goals.

Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.

lu20cat: Land use main category two-digit code.

lu20catn: Land use main category name.

lu20cat

lu20catn

1 - Residential

3 - Industrial

4 - Transportation

5 - Utility

6 - Commercial

7 - Institutional

8 - Military

9 - Recreation

10 - Agriculture

11 - Mining

12 - Wooded

13 - Water

14 - Undeveloped

lu20sub: Land use subcategory five-digit code. (refer to this data dictionary for code description)

lu20subn: Land use subcategory name.

lu20dev: Development status.

mixeduse: Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use).

acres: Area of feature, in US acres.

geoid: 10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA).

state_name, co_name, mun_name: State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.

description: The primary objective of the project is to develop an integrated ecological and socioeconomic land use evaluation model (the Ecosystem Portfolio Model, EPM) for Department of the Interior (DOI) resource managers to use to reconcile the need to maintain the ecological health of South Florida parks and refuges with increasing pressures for higher density development in the agricultural lands outside of the Urban Development Boundary in Miami-Dade County. The EPM has three major components: (1) an ecological value model based on ecological criteria relevant to National Park Service and US Fish & Wildlife Service resource management and species protection mandates; (2) a real estate market-based land value model sensitive to relevant land use/cover attributes indicative of conservation and development decisions; and (3) a set of socioeconomic indicators sensitive to land use/cover changes relevant to regional environmental and ecological planning. The current version is implemented for Miami-Dade County, with the protection of ecological values in the lands between the Everglades and Biscayne National Parks as the focus. The first two components have been implemented in the GIS web-enabled prototype interface and the third component is being developed in draft form in FY08 in consultation with the Florida Atlantic University Dept of Urban and Regional Planning.; abstract: The primary objective of the project is to develop an integrated ecological and socioeconomic land use evaluation model (the Ecosystem Portfolio Model, EPM) for Department of the Interior (DOI) resource managers to use to reconcile the need to maintain the ecological health of South Florida parks and refuges with increasing pressures for higher density development in the agricultural lands outside of the Urban Development Boundary in Miami-Dade County. The EPM has three major components: (1) an ecological value model based on ecological criteria relevant to National Park Service and US Fish & Wildlife Service resource management and species protection mandates; (2) a real estate market-based land value model sensitive to relevant land use/cover attributes indicative of conservation and development decisions; and (3) a set of socioeconomic indicators sensitive to land use/cover changes relevant to regional environmental and ecological planning. The current version is implemented for Miami-Dade County, with the protection of ecological values in the lands between the Everglades and Biscayne National Parks as the focus. The first two components have been implemented in the GIS web-enabled prototype interface and the third component is being developed in draft form in FY08 in consultation with the Florida Atlantic University Dept of Urban and Regional Planning.