Hubbard Brook National Land Cover Dataset 1992

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Metadata:


Identification_Information:
Citation:
Citation_Information:
Originator: USDA Forest Service, Northeastern Research Station
Publication_Date: 20040624
Title: Hubbard Brook National Land Cover Dataset 1992
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Durham, New Hampshire
Publisher: USDA Forest Service, Northeastern Research Station
Online_Linkage: <http://www.hubbardbrook.org/research/gis/gisexe/nlcd_92.exe>
Description:
Abstract:
The National Land Cover Dataset was compiled from Landsat satellite TM imagery (circa 1992) with a spatial resolution of 30 meters and supplemented by various ancillary data (where available). The analysis and interpretation of the satellite imagery was conducted using very large, sometimes multi-state image mosaics (i.e. up to 18 Landsat scenes). Using a relatively small number of aerial photographs for 'ground truth', the thematic interpretations were necessarily conducted from a spatially-broad perspective. Furthermore, the accuracy assessments (see below) correspond to 'federal regions' which are groupings of contiguous states. Thus, the reliability of the data is greatest at the state or multi-State level. The statistical accuracy of the data is known only for the region.

Important Caution Advisory

With this in mind, users are cautioned to carefully scrutinize the data to see if they are of sufficient reliability before attempting to use the dataset for larger-scale or local analyses. This evaluation must be made remembering that the NLCD represents conditions in the early 1990s.

The New Hampshire portion of the NLCD was created as part of land cover mapping activities for Federal Region I that includes the States of Connecticut, Maine, Vermont, Rhode Island, New Hampshire, and Massachusetts. The NLCD classification contains 21 different land cover categories with a spatial resolution of 30 meters. The NLCD was produced as a cooperative effort between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA) to produce a consistent, land cover data layer for the conterminous U.S. using early 1990s Landsat thematic mapper (TM) data purchased by the Multi-resolution Land Characterization (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies that produce or use land cover data. Partners include the USGS (National Mapping, Biological Resources, and Water Resources Divisions), USEPA, the U.S. Forest Service, and the National Oceanic and Atmospheric Administration.

The original NLCD grid was projected into UTM Zone 19 and was clipped to a box surrounding the USDA Forest Service, Hubbard Brook Experimental Forest.

Purpose:
To provide the public with spatial data from the Hubbard Brook Experimental Forest for use in research and educational activities.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 19900101
Ending_Date: 19930101
Currentness_Reference: ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: As needed
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -71.829489
East_Bounding_Coordinate: -71.661710
North_Bounding_Coordinate: 43.977519
South_Bounding_Coordinate: 43.898077
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: land use
Theme_Keyword: land cover
Theme_Keyword: US Forest Service
Theme_Keyword: landsat
Theme_Keyword: imagery
Theme_Keyword: remote sensing
Theme_Keyword: forest
Theme_Keyword: vegetation
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Hubbard Brook
Place_Keyword: New Hampshire
Access_Constraints: None
Use_Constraints: Not for legal use
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service
Contact_Person: John Campbell
Contact_Position: Ecologist
Contact_Address:
Address_Type: physical address
Address: USDA Forest Service
Address: Northeastern Research Station
Address: 271 Mast Road
City: Durham
State_or_Province: NH
Postal_Code: 03824
Country: USA
Contact_Address:
Address_Type: mailing address
Address: USDA Forest Service
Address: Northeastern Research Station
Address: Attn: John Campbell
Address: P.O. Box 640
City: Durham
State_or_Province: NH
Postal_Code: 03801
Country: USA
Contact_Voice_Telephone: (603)868-7643
Contact_Facsimile_Telephone: (603)868-7604
Contact_Electronic_Mail_Address: jlcampbell@fs.fed.us
Browse_Graphic:
Browse_Graphic_File_Name: <http://www.hubbardbrook.org/research/gis/coverages/nlcd_92.gif>
Browse_Graphic_File_Description: Hubbard Brook National Land Cover Dataset 1992
Browse_Graphic_File_Type: GIF
Native_Data_Set_Environment:
Microsoft Windows 2000 Version 5.0 (Build 2195) Service Pack 3; ESRI ArcCatalog 8.1.0.642

Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
The National Land Cover Dataset was compiled from Landsat satellite TM imagery (circa 1992) with a spatial resolution of 30 meters and supplemented by various ancillary data (where available). The analysis and interpretation of the satellite imagery was conducted using very large, sometimes multi-state image mosaics (i.e. up to 18 Landsat scenes). Using a relatively small number of aerial photographs for 'ground truth', the thematic interpretations were necessarily conducted from a spatially-broad perspective. Furthermore, the accuracy assessments (see below) correspond to 'federal regions' which are groupings of contiguous states. Thus, the reliability of the data is greatest at the state or multi-State level. The statistical accuracy of the data is known only for the region.

Important Caution Advisory

With this in mind, users are cautioned to carefully scrutinize the data to see if they are of sufficient reliability before attempting to use the dataset for larger-scale or local analyses. This evaluation must be made remembering that the NLCD represents conditions in the early 1990s.

The New Hampshire portion of the NLCD was created as part of land cover mapping activities for Federal Region I that includes the States of Connecticut, Maine, Vermont, Rhode Island, New Hampshire, and Massachusetts. The NLCD classification contains 21 different land cover categories with a spatial resolution of 30 meters. The NLCD was produced as a cooperative effort between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA) to produce a consistent, land cover data layer for the conterminous U.S. using early 1990s Landsat thematic mapper (TM) data purchased by the Multi-resolution Land Characterization (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies that produce or use land cover data. Partners include the USGS (National Mapping, Biological Resources, and Water Resources Divisions), USEPA, the U.S. Forest Service, and the National Oceanic and Atmospheric Administration.

An accuracy assessment is done on all NLCD on a Federal Region basis following a revision cycle that incorporates feedback from MRLC Consortium partners and affiliated users. The accuracy assessments are conducted by private sector vendors under contract to the USEPA. A protocol has been established by the USGS and US EPA that incorporates a two-stage, geographically stratified cluster sampling plan (Zhu et al., 1999) utilizing National Aerial Photography Program (NAPP) photographs as the sampling frame and the basic sampling unit. In this design a NAPP photograph is defined as a 1st stage or primary sampling unit (PSU), and a sampled pixel within each PSU is treated as a 2nd stage or secondary sampling unit (SSU).

PSU's are selected from a sampling grid based on NAPP flight-lines and photo centers, each grid cell measures 15' X 15' (minutes of latitude/longitude) and consists of 32 NHAP photographs. A geographically stratified random sampling is performed with 1 NAPP photo being randomly selected from each cell (geographic strata), if a sampled photo falls outside of the regional boundary it is not used. Second stage sampling is accomplished by selecting SSU's (pixels) within each PSU (NAPP photo) to provide the actual locations for the reference land cover classification.

The SSU's are manually interpreted and misclassification errors are estimated and described using a traditional error matrix as well as a number of other important measures including the overall proportion of pixels correctly classified, user's and producer's accuracy's, and omission and commission error probabilities.

At the time of CD release (Summer 2000), the accuracy assessment was not complete. For the Region I accuracy assessment, please check the NLCD Website: <http://edcwww.usgs.gov/programs/lccp/nationallandcover.html>. The accuracy assessment numbers will be posted there around September, 2000.

While we believe that the approach taken has yielded a very good general land cover classification product for Region I, it is important to indicate to the user where there might be some potential problems. The biggest concerns for Region I are listed below: 1) Accurate definition of the transitional barren class was extremely difficult. The majority of pixels in this class correspond to clear-cut forests in various stages of regrowth. Spectrally, fresh clear-cuts are very similar to row-crops in the leaves-off data. Manual correction of coding errors was performed to improve differentiation between row-crops and clear-cuts, but some errors may still be found. As regrowth occurs in a clear-cut region, the definition of transitional barren versus a forested class becomes problematic. An attempt was made to classify only fresh clear-cuts or those in the earliest stages of regrowth, but there are likely forested regions classed as transitional barren and vice versa.

Logical_Consistency_Report:
An unsupervised classification algorithm was used to classify the mosaicked multiple leaf-off TM scenes. Aerial photographs were used to interpret and label classes into land cover categories and ancillary data sources resolved the class confusion. Further land cover information from leaf-on TM data, NWI data, and other sources were incorporated to refine and augment the "basic" classification.
Completeness_Report: All photo-interpretable data are mapped.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
Each Landsat Thematic Mapper image used to create the NLCD was precision terrain-corrected using 3-arc-second digital terrain elevation data (DTED), and georegistered using ground control points. This resulted in a root mean square registration error of less than 1 pixel (30 meters).
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: US Geological Survey, EROS Data Center
Publication_Date: 19990525
Title: New Hampshire Land Cover Data Set
Edition: 1
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: US Geological Survey
Online_Linkage: <http://landcover.usgs.gov/index.asp>
Type_of_Source_Media: online
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Time_Period_Information:
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Beginning_Date: 19900101
Ending_Date: 19930101
Source_Currentness_Reference: ground condition
Source_Information:
Source_Citation:
Citation_Information:
Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: EROS DAta Ceneter
Other_Citation_Details: Path/Row 011/031
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Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: image1
Source_Contribution:
The image provides the base from which the landcover classification is determined.
Source_Information:
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
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Publication_Place: Sioux Falls, SD
Publisher: EROS DAta Ceneter
Other_Citation_Details: Path/Row 012/030
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The image provides the base from which the landcover classification is determined.
Source_Information:
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
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The image provides the base from which the landcover classification is determined.
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
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Publisher: EROS DAta Ceneter
Other_Citation_Details: Path/Row 013/030
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Source_Citation_Abbreviation: image5
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The image provides the base from which the landcover classification is determined.
Source_Information:
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: EROS DAta Ceneter
Other_Citation_Details: Path/Row 013/031
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The image provides the base from which the landcover classification is determined.
Source_Information:
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: EROS DAta Ceneter
Other_Citation_Details: Path/Row 011/031
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The image provides the base from which the landcover classification is determined.
Source_Information:
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: EROS DAta Ceneter
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The image provides the base from which the landcover classification is determined.
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
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The image provides the base from which the landcover classification is determined.
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Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
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Publisher: EROS DAta Ceneter
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The image provides the base from which the landcover classification is determined.
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Citation_Information:
Originator: US Geological Survey, EROS Data Center
Publication_Date: Unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
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Source_Citation_Abbreviation: image11
Source_Contribution:
The image provides the base from which the landcover classification is determined.
Process_Step:
Process_Description:
Land Cover Characterization: The project is being carried out on the basis of 10 Federal Regions that make up the conterminous United States; each region is comprised of multiple states; each region is processed in subregional units that are limited to the area covered by no more than 18 Landsat TM scenes. The general NLCD procedure is to: (1) mosaic subregional TM scenes and classify them using an unsupervised clustering algorithm, (2) interpret and label the clusters/classes using aerial photographs as reference data, (3) resolve the labeling of confused clusters/classes using the appropriate ancillary data source(s), and (4) incorporate land cover information from other data sets and perform manual edits to augment and refine the "basic" classification developed above.

Two seasonally distinct TM mosaics are produced, a leaves-on version (summer) and a leaves-off (spring/fall) version. TM bands 3, 4, 5, and 7 are mosaicked for both the leaves-on and leaves-off versions. For mosaick purposes, a base scene is selected for each mosaic and the other scenes are adjusted to mimic spectral properties of the base scene using histogram matching in regions of spatial overlap. Following mosaicking, either the leaves-off version or leaves-on version is selected to be the "base" for the land cover mapping process. The 4 TM bands of the "base" mosaic are clustered to produce a single 100- class image using an unsupervised clustering algorithm. Each of the spectrally distinct clusters/classes is then assigned to one or more Anderson level 1 and 2 land cover classes using National High Altitude Photography program (NHAP) and National Aerial Photography program (NAPP) aerial photographs as a reference. Almost invariably, individual spectral clusters/classes are confused between two or more land cover classes.

Separation of the confused spectral clusters/classes into appropriate NLCD class is accomplished using ancillary data layers. Standard ancillary data layers include: the "non-base" mosaic TM bands and 100- class cluster image; derived TM normalized vegetation index (NDVI), various TM band ratios, TM date bands; 3-arc second Digital Terrain Elevation Data (DTED) and derived slope, aspect and shaded relief; population and housing density data; USGS land use and land cover (LUDA); and National Wetlands Inventory(NWI) data if available. Other ancillary data sources may include soils data, unique state or regional land cover data sets, or data from other federal programs such as the National Gap Analysis Program (GAP) of the USGS Biological Resources Division (BRD). For a given confused spectral cluster/class, digital values of the various ancillary data layers are compared to determine: (1) which data layers are the most effective for splitting the confused cluster/class into the appropriate NLCD class, and (2) the appropriate layer thresholds for making the split(s). Models are then developed using one to several ancillary data layers to split the confused cluster/class into the NLCD class. For example, a population density threshold is used to separate high-intensity residential areas from commercial/industrial/transportation. Or a cluster/class might be confused between row crop and grasslands. To split this particular cluster/class, a TM NDVI threshold might be identified and used with an elevation threshold in a class-splitting model to make the appropriate NLCD class assignments. A purely spectral example is using the temporally opposite TM layers to discriminate confused cluster/classes such as hay pasture vs. row crops and deciduous forests vs. evergreen forests; simple thresholds that contrast the seasonal differences in vegetation between leaves-on vs. leaves-off.

Not all cluster/class confusion can be successfully modeled out. Certain classes such as urban/recreational grasses or quarries/strip mines/gravel pits that are not spectrally unique require manual editing. These class features are typically visually identified and then reclassified using on-screen digitizing and recoding. Other classes such as wetlands require the use of specific data sets such as NWI to provide the most accurate classification. Areas lacking NWI data are typically subset out and modeling is used to estimate wetlands in these localized areas. The final NLCD product results from the classification (interpretation and labeling) of the 100-class "base" cluster mosaic using both automated and manual processes, incorporating both spectral and conditional data layers. For a more detailed explanation please see Vogelmann et al. 1998 and Vogelmann et al. 1998.

Discussion:

While we believe that the approach taken has yielded a very good general land cover classification product for the nation, it is important to indicate to the user where there might be some potential problems. The biggest concerns are listed below:

1) Some of the TM data sets are not temporally ideal. Leaves-off data Sets are heavily relied upon for discriminating between hay/pasture and row crop, and also for discriminating between forest classes. The success of discriminating between these classes using leaves-off data sets hinges on the time of data acquisition. When hay/pasture areas are non-green, they are not easily distinguishable from other agricultural areas using remotely sensed data. However, there is a temporal window during which hay and pasture areas green up before most other vegetation (excluding evergreens, which have different spectral properties); during this window these areas are easily distinguishable from other crop areas. The discrimination between hay/pasture and deciduous forest is likewise optimized by selecting data in a temporal window where deciduous vegetation has yet to leaf out. It is difficult to acquire a single-date of imagery (leaves-on or leaves-off) that adequately differentiates between both deciduous/hay and pasture and hay pasture /row crop.

2) The data sets used cover a range of years (see data sources), and Changes that have taken place across the landscape over the time period may not have been captured. While this is not viewed as a major problem for most classes, it is possible that some land cover features change more rapidly than might be expected (e.g. hay one year, row crop the next).

3) Wetlands classes are extremely difficult to extract from Landsat TM spectral information alone. The use of ancillary information such as National Wetlands Inventory (NWI) data is highly desirable. We relied On GAP, LUDA, or proximity to streams and rivers as well as spectral data to delineate wetlands in areas without NWI data.

4) Separation of natural grass and shrub is problematic. Areas observed on the ground to be shrub or grass are not always distinguishable spectrally. Likewise, there was often disagreement between LUDA and GAP on these classes.

Acknowledgments

This work was performed under contract the U.S. Geological Survey(Contract 1434-CR-97-CN-40274).

References

More detailed information on the methodologies and techniques employed In this work can be found in the following:

Kelly, P.M., and White, J.M., 1993. Preprocessing remotely sensed data for efficient analysis and classification, Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, Proceeding of SPIE, 1993, 24-30.

Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe, 1979. Classification of Wetlands and Deepwater Habitats of the United States, Fish and Wildlife Service, U.S. Department of the Interior, Washington, D.C.

Vogelmann, J.E., Sohl, T., and Howard, S.M., 1998. "Regional Characterization of Land Cover Using Multiple Sources of Data." Photogrammetric Engineering & Remote Sensing, Vol. 64, No. 1, pp. 45-47.

Vogelmann, J.E., Sohl, T., Campbell, P.V., and Shaw, D.M., 1998. "Regional Land Cover Characterization Using Landsat Thematic Mapper Data and Ancillary Data Sources." Environmental Monitoring and Assessment, Vol. 51, pp. 415-428.

Zhu, Z., Yang, L., Stehman, S., and Czaplewski, R., 1999. "Designing an Accuracy Assessment for USGS Regional Land Cover Mapping Program." (In review) Photogrametric Engineering & Remote Sensing.

Source_Used_Citation_Abbreviation: Landsat thematic mapper (TM)
Process_Date: 19950525
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Organization: US Geological Survey, EROS Data Center
Contact_Address:
Address_Type: mailing and physical address
Address: US Geological Survey, EROS Data Center
City: Sioux Falls
State_or_Province: SD
Postal_Code: 57198
Country: USA
Contact_Voice_Telephone: 605-594-6551
Contact_TDD/TTY_Telephone: 605-594-6933
Contact_Facsimile_Telephone: 605-594-6589
Contact_Electronic_Mail_Address: CUSTSERV@EDCMAIL.CR.USGS.GOV

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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service
Contact_Person: John Campbell
Contact_Position: Ecologist
Contact_Address:
Address_Type: mailing address
Address: USDA Forest Service
Address: Northeastern Research Station
Address: Attn: John Campbell
Address: P.O. Box 640
City: Durham
State_or_Province: NH
Postal_Code: 03824
Country: USA
Contact_Address:
Address_Type: physical address
Address: USDA Forest Service
Address: Northeastern Research Station
Address: 271 Mast Road
City: Durham
State_or_Province: NH
Postal_Code: 03824
Country: USA
Contact_Voice_Telephone: (603)868-7643
Contact_Facsimile_Telephone: (603)868-7604
Contact_Electronic_Mail_Address: jlcampbell@fs.fed.us
Distribution_Liability:
The USDA Forest Service manages resource information and derived data as a service to USDA Forest Service users of digital geographic data. The USDA Forest Service is in no way condoning or endorsing the application of these data for any given purpose. It is the sole responsibility of the user to determine whether or not the data are suitable for the intended purpose. It is also the obligation of the user to apply the data in an appropriate and conscientious manner. The USDA Forest Service provides no warranty, nor accepts any liability occurring from any incorrect, incomplete, or misleading data, or from any incorrect, incomplete, or misleading use of these data.

Much of the USDA Forest Service data are compiled, processed, and maintained with ARC/INFO software developed by the Environmental Systems Research Institute (ESRI). Much of the information presented uses conventions and terms popularized by ARC/INFO and its user community. The mention of commercial firms or products is for clarity and identification of procedures and methods only, and no endorsements are implied by the USDA Forest Service.

Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: ARCE
Format_Version_Number: ARC 8.1
Transfer_Size: 0.136
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: <http://www.hubbardbrook.org/research/gis/gis.htm>
Fees: None

Metadata_Reference_Information:
Metadata_Date: 20040624
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service
Contact_Person: John Campbell
Contact_Position: Ecologist
Contact_Address:
Address_Type: mailing address
Address: USDA Forest Service
Address: Northeastern Research Station
Address: Attn: John Campbell
Address: P.O. Box 640
City: Durham
State_or_Province: NH
Postal_Code: 03801
Country: USA
Contact_Address:
Address_Type: physical address
Address: USDA Forest Service
Address: Northeastern Research Station
Address: 271 Mast Road
City: Durham
State_or_Province: NH
Postal_Code: 03801
Country: USA
Contact_Voice_Telephone: (603)868-7643
Contact_Facsimile_Telephone: (603)868-7604
Contact_Electronic_Mail_Address: jlcampbell@fs.fed.us
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time
Metadata_Extensions:
Online_Linkage: <http://www.esri.com/metadata/esriprof80.html>
Profile_Name: ESRI Metadata Profile

Generated by mp version 2.7.3 on Thu Jun 24 15:34:04 2004