Evaluation of Local Land Cover Land Use Mapping Approachs for the Chesapeake Bay Watershed, Final...

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EVALUATION OF LOCAL LAND COVER / LAND USE MAPPING

APPROACHS FOR THE CHESAPEAKE BAY WATERSHED

FINAL REPORT

11-10-2005

Stewart Bruce

Rick L. Day

The Pennsylvania State University

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Table of Contents

Introduction..........................................................................................................................5Land Use..............................................................................................................................5OBJECTIVES......................................................................................................................6Objective #1: Evaluation of methods to produce local land use/land cover data...............6

Methodologies.................................................................................................................6County-Level Survey...................................................................................................6Selection of Pilot Counties...........................................................................................8Land use/Land Cover Classification System...............................................................8Land use/Land Cover Data Collection Methodologies................................................9

Results and Discussion..................................................................................................16County-Level Survey ................................................................................................16Locally Generated Land Use/Land Cover.................................................................18It is very important to note that these costs are for first time data collection. Once acounty has digital land use/land cover data, subsequent updating costs would besignificantly reduced resulting in lower long-term maintenance costs......................22

Parcel Based Land Use Codes...................................................................................23Other Options: Combining County Level Data with Satellite-Derived Data............23Objective #2: Comparison of locally produced land use/land cover data to satellitederived data........................................................................................................................28

Methodologies...............................................................................................................28Land Cover Comparisons..........................................................................................28Impervious Surface Comparisons..............................................................................31

Results and Discussion..................................................................................................35Land Cover Comparisons..........................................................................................35Impervious Surface Comparisons..............................................................................53

Appendix B: Anderson Land Use/Land Cover Classification Code............................61Appendix C: New York State Assessment codes translation to Anderson Code...... ...69Appendix D: QC Procedure for Land Use Digitizing Product Quality Control ..........78Appendix E: Procedure for creating confusion matrix to compare photo interpretationland use and satellite derived land use...........................................................................81

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Table of Figures

Figure 1: Breakdown of GIS Survey Access tables............................................................7Figure 2: Henrico County Zone Map................................................................................12Figure 3: Henrico County Residential and Transportation...............................................13Figure 4: Henrico County Null Space...............................................................................14

Figure 5: Henrico County Tile Index Map.......................................................................15Figure 6: Number of Dedicated GIS Workers by County................................................17Figure 7: Northern Henrico County parking lots (in grey)...............................................22Figure 8: Sample parcel from Mifflin County, PA...........................................................23Figure 9. RESAC land cover and residential parcel boundaries........................................24Figure 10. RESAC classification of residential parcels.....................................................25Figure 11: Harford County image showing planimetric impervious surface over coloraerial imagery.....................................................................................................................31Figure 12: Resample of Harford County planimetric to 5 meter cell raster................... ..32Figure 13: Aggregate of Harford County 5 meter to 30 meter impervious data layer......32Figure 14: Rural sample area............................................................................................33

Figure 15: Suburban sample area......................................................................................34Figure 16: Urban sample area...........................................................................................34Figure 17: Henrico County 10 meter LAL land cover data..............................................37Figure 18: Henrico County 30 meter RESAC land cover data.........................................37Figure 19: Henrico County 10 meter LAL land cover data..............................................38Figure 20: Henrico County 30 meter RESAC land cover data.........................................38Figure 21: RESAC land cover non-simplified..................................................................39Figure 22: RESAC simplified land cover showing major cloverleaf intersection ...........39Figure 23: LAL land cover data for Baltimore County....................................................42Figure 24: RESAC simplified land cover data for Baltimore County..............................42Figure 25: RESAC land cover data showing urban classifications .................................43Figure 26: LAL land cover for Treasure Lake area..........................................................46Figure 27: RESAC land cover for Treasure Lake area.....................................................46Figure 28: LAL land cover showing reclaimed strip mine area.......................................47Figure 29: RESAC land cover in reclaimed strip mine area.............................................47Figure 30: LAL land cover for York County, PA.............................................................50Figure 31: NLCD land cover for York County, PA..........................................................50Figure 32: Impervious Surface areas not mapped by RESAC..........................................54Figure 33: Impervious Surface overestimated urban areas...............................................55Figure 34: Planimetric overlaid onto RESAC data shown in Figure 31...........................55

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Table of Tables

Table 1 Comparison of Centre County, Pa local land cover data with 2000 NLCD landcover data..............................................................................................................................................5

Table 2: Aerial Imagery Sources......................................................................................11Table 3: Time Study for Digitizing Land Use..................................................................19Table 4: Tioga County , Pennsylvania. Comparison of land use acreage total differencesbetween 1:2400 digitizing versus digitizing on one meter resolution 1:4800 DOQQ forAntrim Quad......................................................................................................................20Table 5: Tioga County , Pennsylvania. Comparison of land use acreage total differencesbetween 1:2400 digitizing (NAD83) versus digitizing on one meter resolutionDOQQ(UTM) for Knoxville Quad....................................................................................21Table 6: Cost Estimates to Digitize Land Use for Entire Bay..........................................22Table 7: List of Counties That Supplied GIS Datasets.....................................................27Table 8: UMD RESAC code translation to Anderson code.............................................30

Table 9: Henrico County versus RESAC Confusion Matrix............................................36Table 10: Baltimore County versus RESAC Confusion Matrix.......................................41Table 11: Clearfield County versus RESAC Confusion Matrix.......................................45Table 12: NLCD code translation to Anderson code........................................................48Table 13: York County, PA, LAL versus NLCD Confusion Matrix................................49Table 14: UMD RESAC and NLCD 2000 Pasture and Cropland acreage Totals............52Table 15: Pennsylvania Department of Agriculture 2002 Pasture and Cropland AcreageTotals..................................................................................................................................52Table 16: Impervious Surface Comparison between RESAC and Harford County.........54

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Introduction

The Penn State Cooperative Extension Program and the Cooperative Extension GISProgram (CEGIS) provide education and technical support to counties throughout the

Chesapeake Bay watershed and as such have developed close relationships withsponsoring county governments. Technical support to county governments includes theuse of geospatial technologies such as remote sensing, GIS, and GPS, for environmentalassessment, land use planning and agriculture. Through these efforts CEGIS has come torecognize that many local governments routinely collect and maintain high-quality GISinformation for their own internal purposes. Furthermore, we have realized the potentialfor these data to be integrated into regional-scale land cover assessment efforts to providea vastly improved land cover and land use database that will be valuable both locally andregionally.

Conventional land cover data bases produced for the Bay such as EMAP, EMAP2-

MRLC, and NLCD are often inadequate to address land use issues associated with urbansprawl and farmland protection and may lead to erroneous results for modeling effortsdealing with pollutant transport and sources. For example, a comparison of detailed landuse information collected for Centre County, PA showed significant differencescompared to NLCD land cover information (Table 2).

Table 1 Comparison of Centre County, Pa local land cover data with 2000 NLCD land cover data

The NLCD database significantly overestimated the amount of agricultural and forestland and conversely underestimated the amount of developed land. This is largely due to

the inability of Landsat satellite imagery to distinguish the utility of the land from thesurface cover. Many residential developments in forested areas were mistakenly mappedas forest cover and many low-density residential areas were mistakenly mapped asagriculture. These problems render such databases of limited utility for local and regionalplanning activities, especially those addressing urban sprawl. Additionally, anagricultural non-point source pollution model will be detrimentally impacted when suchlarge discrepancies exist in the land use database, especially when such models are so

Land UseLocal Land Use

(acres)

Aggregated NLCD

(acres)

Difference

(acres)

Difference

%

Agriculture 126071 155775 29704 +24Residential & OtherDeveloped

65511 10924 -54587 -83

Forest 499674 528692 29019 +6Quarries/MinedLands/Transitional

16402 13997 -2405 -15

Open Water 4608 4089 -519 -11

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closely dependent on land use practice and pollutant load relationships. Therefore moredetailed information is needed.

Most counties in the Chesapeake Bay watershed, and especially those that areexperiencing pressures from urban sprawl, are producing high-quality digital databases

that include data such as road networks, digital orthophotos, parcel boundaries, land use,and building locations etc. The local uses of these data within county governments arelargely for planning, tax assessment, and emergency communications (E911). Typicallythese data meet 1=400 National Map Accuracy Standards (NMAS) or better. Inaddition to their local usage, we feel these data provide a valuable resource that can beintegrated for use in land use/ land cover mapping for the Bay watershed. The majorlimitation to usage of locally produced databases is the lack of uniformity in quality,scales, timeliness, and legends.

OBJECTIVES

CEGIS has already worked with several counties to produce low-cost but detailed landuse data from their existing datasets. These local land use databases can enhance landcover data produced using currently available satellite systems. The goal of this effortwas to select methodologies that would be acceptable to local governments and providecost-effective methods to produce improved data for use in regional and Bay-wide effortsand also provide data useful for local-level purposes such as urban sprawl mapping,planning, and farmland protection.

Specific objectives for this project were to:

o Evaluate methods to produce local land use/land cover data.

o Compare locally produced land use/land cover data to satellite derived data.

Each objective will be discussed in detail in the following sections.

Objective #1: Evaluation of methods to produce localland use/land cover data.

Methodologies

County-Level Survey

In order to understand what local GIS resources might be available to help with theproject, and to be a guide to future projects, a comprehensive survey of county levelgovernments throughout the Chesapeake Bay Watershed was conducted. The

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comprehensive survey was conducted between 2002 and 2004. A total of 178 countieswere surveyed in the Bay. Some counties where less then 5 % of the land area of thecounty was in the Bay watershed were excluded from the survey. For example, in NewYork State, Ontario and Yates counties were not surveyed. The Pennsylvania surveyincluded every county in the state regardless of whether or not it was in the Bay

watershed.

Other organizational partners assisted with the survey. In Maryland, the GeographicInformation Services Division, Maryland Department of Natural Resources conducted theentire survey for Maryland Counties. In Pennsylvania, the Pennsylvania Department ofConservation and Natural Resources, the U.S. Census Bureau, and PAMAGIC assistedwith the survey. The remaining counties in New York, Virginia, West Virginia, andDelaware were surveyed entirely by staff at Penn State.

With the assistance of the partnering organizations, a survey design was completed toinsure that sufficient information was collected about the use of geographic information

systems to satisfy the data requirements of each organization. A complete data dictionaryof the questions asked is contained in Appendix A.

The survey was conducted by a mail survey to each individual county followed by phonecalls, and in some cases, site visits to the respective counties. Survey responses wereentered into an Access database.

Due to the complexity of the survey it was divided into separate tables within an Accessdatabase. As shown in Appendix A, very detailed questions were asked of each countysurveyed. Each state has two Access database tables that contain the responses as shownin Figure 1.

Figure 1: Breakdown of GIS Survey Access tables.

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Selection of Pilot Counties

Several study areas were selected to test methodologies for this project. These areas were

selected based on several criteria. One of the important criteria was that study sites hadto be geographically distributed across the entire Chesapeake Bay Watershed andrepresentative of the range of GIS capabilities found in county governments. Availabilityof locally-produced GIS data such as cadastral mapping was also an importantconsideration. The completed GIS survey was especially useful in determining whichcounties were selected. Finally, access to aerial imagery for the study area was also afactor in choosing the pilot county areas. The study areas that were selected are asfollows:

New York

o Broome County

o Steuben Countyo Tioga County

Pennsylvania

o Tioga County

o Sullivan County

o Clearfield County

o Centre County

o Mifflin County

o Lancaster County

o

York County

Maryland

o Baltimore County

Virginia

o Henrico County

Land use/Land Cover Classification System

There are a large number of existing land use and land cover classification systems thatwere considered for use in this project. The American Planning Association (APA) hasperhaps the most definitive list of land use/land cover classification schema on their website.

http://www.planning.org/lbcs/OtherStandards/

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http://www.planning.org/lbcs/OtherStandards/RLUDBClassSysTOCToWebCodingLevels_2.htmlhttp://www.planning.org/lbcs/OtherStandards/RLUDBClassSysTOCToWebCodingLevels_2.html


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Several of these classification schemes were reviewed but it became apparent that onlythe LBCS and the Anderson classification scheme were being widely used by local, state,and federal government organizations. The other systems were outdated and not beingused by many organizations, or were historical schemas no longer in use at all.

The APA is promoting the Land Based Classification System (LBCS). To quote fromtheir Executive Summary:

LBCS provides a consistent model for classifying land uses based on theircharacteristics. The model extends the notion of classifying land uses by refining

traditional categories into multiple dimensions, such as activities, functions,

building types, site development character, and ownership constraints. Each

dimension has its own set of categories and subcategories. These multipledimensions allow users to have precise control over land-use classifications.

The analysis of classification schemes conducted indicated this system to be far too

complicated and unwieldy for the project purposes. The reasoning for this decision is asfollows:

1. Maryland RESAC, NLCD, MRLC, and other remote sensed land cover systemsall use some variation of the Anderson land cover classification system.

2. There was a need to be able to directly compare the projects developed landuse/land cover datasets with these products.

3. The Anderson classification system is relatively easy to use in a productionenvironment and is widely utilized by counties within the Chesapeake BayWatershed.

4. The use of the LBCS would have introduced problems with translation andcomplicated the production of data.

These reasons provided a compelling argument to use the Anderson system. Therefore,the Anderson classification scheme was adopted. The exact specifications of theAnderson classification that was used for this project is found in Appendix B. One areawhere the Anderson code was modified for use in this project was the introduction of thecode 201 for residential grass and the code 401 for residential forest. These were forareas that were of a residential land use but were mostly either grass or forest land cover.

Land use/Land Cover Data Collection Methodologies

This project tested three primary methods for deriving land use/land cover data fromlocally produced GIS datasets.

Aerial imagery interpretation

Aerial imagery interpretation mixed with attribute information stored within

planimetric GIS datasets

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Derived land use/land cover from attribute information stored within

planimetric GIS datasets

Method 1: Aerial Imagery Interpretation

The primary mapping methodology used for developing the land use/land cover data wasaerial imagery interpretation and on-screen digitizing of land use/land cover boundaries..Utilizing ArcView 3.3 software in a production digitizing mode various aerial imageswere used as a base map. Using interpretive skills, operators determined what the landuse/land cover was from observing the base aerial images, and either digitized polygonsaround specific land use/land covers or split polygons from an index base polygon file.Upon completion, each polygon was appropriately assigned an Anderson classificationcode. The imagery that was utilized for this project is indicated in Table 2.

To aid in interpretation of what was observed on the aerial imagery, county cadastral datawas utilized when available. By examining information such as property ownership,

assessment land use code, and building information it was possible to increase theaccuracy of the interpretive process.

Several operators were involved in the digitizing process and were trained in the properprocedures to create the GIS datasets. During the initial training phases 100 percent oftheir work was checked for accuracy and any problems were brought to the operatorsattention in a Continuous Quality Improvement process. Operators were taken into thefield so they could see first-hand what was actually on the ground so they could improvetheir interpretive skills. While time did not permit for 100 percent field verification ofevery dataset produced, spot checks were conducted. Clearfield County was 75 % fieldverified with the cooperation of the Clearfield County GIS Department

The following quality control general procedures were utilized:

The land use polygon dataset was viewed without using .avl (ArcView legend file whichshows land use classification). This allowed the verifier to see the aerial imagery beneaththe land use tile. Then the verifier scrolled through the entire tile zoomed to 1=4800foot scale starting in NW corner, moving east until the NE corner, then moving down onelevel and moving back to the west. This scan was continued, evaluating every polygonfor land use. During this scan, the verifier changed land use codes if a misinterpretationwas observed and created new polygons, if required. Once the entire tile was finished (abulk of the QC time), the .avl was applied so that the land use classifications could be

observed on the screen. The verifier scrolled through the entire tile again and checked foroddities and neighboring polygons that have the same classification that should becombined, or further checked for accuracy. Once the second scan was completed, theverifier then checked the tile by using the attribute table. First all LU Codes were

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Table 2: Aerial Imagery Sources

checked for mistypes. Then all 300 (rangeland) level codes were checked for accuracyand to ensure that all strip mined areas were marked as such. Then 111 (single-familyresidential) were checked for codes for 0 or large R densities. Then all polygons werechecked with small acreages. All polygons with less than 0.5 acres and odd polygonswith less than 1.0 acres (forests, agricultural land, etc.) were then reviewed. Any

significant errors were noted and the information was passed back to the operator as partof our continuous quality control improvement process.

The full written procedure for quality control checking can be found in Appendix D.

Time records were kept for various operators so an estimate of the total time necessary todigitize land use/land cover via this method would be recorded. The time impact of usingdifferent resolution aerial imagery was also obtained by keeping time records.

Method 2: Aerial imagery interpretation mixed with attribute information stored

within planimetric GIS datasets

In some cases, such as Henrico County, Virginia, and Baltimore County, Maryland,cadastral data were used as a seed file to start the interpretive process. A seed filecontains polygons derived from the cadastral GIS dataset that are coded based upon acounty-assigned assessment land use code classification. Henrico County and BaltimoreCounty had an advantage in that these counties have very good, spatially accurate parceldata that aligned well with the aerial imagery. It was expected that by using accurate

State County Source Date Type Resolution

New York

Broome County New York State 2002 Color/Panchromatic 1/2 foot

Steuben County New York State 2002 Color/Panchromatic 1/2 foot

Tioga County New York State 2002 Color/Panchromatic 1/2 foot

Pennsylvania

Tioga County USGS/County 1998 Panchromatic2 foot/1meter

Sullivan County USGS 1996 Panchromatic 1 Meter

Clearfield County County 1997 Panchromatic 2 foot

Centre County County 2002 Panchromatic 2 foot

Mifflin County County 1996 Panchromatic 2 foot

York County PA DCNR 2003 Color 2 foot

MarylandBaltimore County County 2001 Color 2 foot

Virginia

Henrico County County 1998/2002 Color 1 foot

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planimetric parcel based land use data it would be possible to reduce the time it took todigitize the land use from aerial imagery interpretation.

It was determined that residential land use that occurs within a subdivision could beeasily extracted. Residential coded parcels of less then 2 acres could also be easily

extracted from the cadastral data. Although the parcel map did not include any of theroad right-of-way information as a polygon feature it was still possible to use this nulldata to determine transportation land use. The procedure used in Henrico County will bediscussed to explain the overall methodology.

In order to expedite geoprocessing the county was split into four arbitrary zones (Figure2). Each of these zones was derived by selecting tiles from the county tile indexshapefile and then performing a combine operation to form a single polygon shapefile.The software used to create a master land use layer was ArcView 3.3.

Figure 2: Henrico County Zone Map

The next operation one of the zone files (the southern most zone was tried first) to clipthe parcel shapefile. Once this operation was completed the clipped parcel shapefile wasunioned with the corresponding zone shapefile. The primary reason for doing this was tocreate a polygon for the road right-of-way since this was null space within the parcelshapefile.

Next the query builder was used to select all use description where the use was residentialsingle family subdivision (Res Subd(1 family) and the acreage was less than or equal to2 acres. The road right-of-way polygon was then added to the selection. At this point anew shapefile was created using the Convert Theme to Shapefile command. This filewas named Residential_and_Roads.shp and consisted of 2,138 polygons (Figure 3).Excel was then used to fill out the Lu_code field to 111 for single family residential and a

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1 was entered for the density figure. The road space, one single polygon, was coded 141for transportation.

Figure 3: Henrico County Residential and Transportation

The query builder was then used again to select all use description where the use wasNOT residential single family subdivision (Res Subd(1 family). Then the NOT roadright-of-way polygon was added to the selection. At this point a new shapefile wascreated using the Convert Theme to Shapefile command (Figure 4).

In order to simplify the Not_Residential_and_Not_Roads shapefile, this file was used toclip the zone file so that all the mixed use descriptions would be merged into singlepolygons. The resultant clipped file contained 244 polygons.

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Figure 4: Henrico County Null Space

The next step was to Merge the two shapefiles together using the merge command in theGeoprocessing Wizard. The resultant merged file had 2,382 which equals the sum of the

two input shapefiles indicating that no slivers polygons were created. From this mergedshapefile it was then possible to edit the shapefile for those areas not already assigned aland use code.

This exact process was used for the other southern zones. In the northern section ofHenrico County the density of parcels did not lend itself to this procedure as the cadastrallayer was too complex for ArcView 3.3 to handle efficiently. A different process wasused for the northern section and will be discussed later.

Once the merged files were created a tile index was derived from Henrico Countyoriginal tile index. Each original Henrico tile covered 144 million square feet. To create

the land use tiles 16 of the original tiles were put together and then combined to form onenew tile that covered 2,304 million square feet. Then the new tile was used to clip themerged files created in the previous step. In this way it was possible to digitize one tile ata time. Each tile was numbered starting in the upper right corner. The diagram shown inFigure 5 indicates numbering scheme (note that the northern portion of the county is notshown)

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Figure 5: Henrico County Tile Index Map

Upon completion of the individual tiles they were appended together and then importedinto a personal GeoDatabase feature class. A topology validation process was then run tomake sure the data was topologically correct.

In the northern section of Henrico County the utilization of parcel data was expandedbeyond just residential and included commercial, offices, and industrial sections 5 acresor less in size. The parking lot layer was intersected with the resulting cadastral file. Theremaining non-classified polygons were identified through the use of aerial imageryinterpretation. In areas where the imagery did not show parking lots (planimetric datawere from 2004 and the imagery was from 1998), these areas were classified these areasas future development.

In Baltimore County a similar methodology was used but since a tile grid from the countydid not exist it was decided to just do one bigger polygon.

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Derived land use/land cover from attribute information stored within planimetric

GIS datasets

Many county governments have excellent cadastral data and often the county assessmentdepartment will assign a land use code to each individual parcel. Typically these codes

are assigned by a field operator when the property in question is being assessed forcounty tax purposes. Using GIS it is then possible to classify the parcels by the land useattribute. For many counties this is an easy way for them to determine their land usealthough there are some concerns with this approach which will be discussed later.

In order to translate the county assigned codes to an Anderson based classification systemis necessary to derive a translation table to convert the county assigned codes to theAnderson Code. For this project three counties in the state of New York were chosen dueto the fact that New York has a uniform assessment code so the same translation could beapplied to all three counties. The translation code table that was utilized can be found inAppendix E.

Results and Discussion

County-Level Survey

Out of the 178 counties surveyed, less then 2% failed to respond to the survey for a 98%survey completion rate. For some counties, with no GIS capability at all, the survey wasvery easy to complete. Contact information for key personnel in these counties wasobtained for future reference.

Figure 6 shows an example of a map produced with the data from the survey showing the

numbers of dedicated GIS workers with the unit of government. The survey indicatedthat there is a direct relationship to the amount of GIS data available and the number ofGIS workers so the map below is indicative of where GIS data can be found.

When examining this map it is clear that some of the major metropolitan areas were notsurveyed. For example, Washington D.C was not surveyed. Other major metropolitanareas such as Baltimore and Richmond were actually surveyed but are difficult to map.The problem lies in the use of the county FIPS code to join the survey database to thespatial feature of county boundaries. Cities do not use a county FIPS code so when thejoin was accomplished these data did not transfer over. Due to this reason cities wereexcluded from any further discussion of some of the results.

Nearly 68% of the counties who responded indicated they would share their data withstate and federal government agencies. This is deceiving to some degree as manycounties do not have much data to share even if they wanted to share it. During theprocess of acquiring GIS data from these counties over 70 percent of those contactedreleased the data at no charge. Several counties did have policies that required them tocollect reasonable costs for duplication of data. And a few counties with good data had

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unreasonable costs so this data was not acquired. Only one county refused to sell orshare their data when asked.

Figure 6: Number of Dedicated GIS Workers by County

Out of the 175 counties who responded, 54% indicated they had GIS cadastral datalayers. 65 % of respondents indicated they used GIS for planning purposes while only17% indicated they used GIS for environmental purposes. In regards to land use and land

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cover GIS datasets, only 6 % of the respondents indicated they had developed their ownland use/land cover that was not generated from the cadastral GIS attribute data. Duringthe phone interview process, the use of high quality land use/land cover was discussed,and without exception, all of the counties who were using GIS for planning purposesindicated they would like to have access to good land use/land cover data.

It became apparent during the survey that many organizations that had GIS staff and GISdata within the Chesapeake Bay Watershed were not being surveyed. This project surveywas designed to focus strictly on county level government. Other local levelgovernmental groups such as regional planning agencies, municipal authorities, cities,towns, townships, boroughs, and economic development agencies in many cases havesignificant staff and GIS data holdings. If a new survey is conducted for the ChesapeakeBay Watershed these groups need to be included. The only major stumbling block isthere are a lot of these other organizations. In Pennsylvania alone there are over 2,600municipal governments.

Locally Generated Land Use/Land Cover

One of the sub-objectives was to test various land use/land cover creation methodologiesto gauge accuracy of the final product and production time. Time records were kept forthe operators who did the production work for this project. Table 3 indicates the actualtime it took to digitize three of our pilot areas. All three of these areas were digitized bythe same highly qualified operator who had several years of digitizing experience. Allthree study areas used the digitizing method of starting with a polygon seed file and thencutting out polygons from the seed file. In York County the aerial imagery interpretationmethod was utilized. This method took on average 1.63 hours to digitize the land use forone square mile. In Baltimore County the mixed aerial/planimetric interpretation method

was used. This method took on average 2.55 hours to digitize the land use for one squaremile. This comparison shows that it did not benefit the production rate to try to usecadastral GIS data. In fact it took almost an hour longer per square mile.

Upon further review, and discussion with the operator, it became apparent that while theaccuracy of the cadastral data was excellent, the additional polygons added to the masterpolygon seed file caused additional digitizing work rather then reduce it. In the case ofBaltimore County the additional polygons had a dramatic impact on the time it tookArcView to redraw the map after panning or zooming to a new location. These extraseconds add up to a significant amount of time. One of the key problems was the attemptto utilize the road right of way data. The created polygons in some cases covered the

entire county as a single polygon. When the operator tried to do an edit and one of thesepolygons was involved it would require the software to verify the entire boundary of theroad right of way polygon. In York County the actual digitizing went quicker eventhough more polygons were created by the operator.

It is clear that the digitizing would have gone much faster if the road right-of-ways werenot used. This would have created many island polygons within the seed file that couldhave been easily split off without the software having to verify boundary topology over

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huge adjacent rod polygons. At the completion of the digitizing the road right-of-wayscould have then been created by intersecting the land use layer with a county boundarypolygon and then simply attribute the one large polygon that would be created throughthis process. Another time saver would have been to dissolve the small single-familyresidential lots (code 111) to reduce the total count of polygons.

The difference in time needed to digitize if different resolution aerial images were usedwas also analyzed. This comparison revealed that when lower resolution aerial imageswere used the time was reduced to digitize the land use (Table 3). This method only took0.73 hours to digitize one square mile of land use. A comparison of the differences in theresultant land use determination does not show any significant difference; especiallywhen looking at Level 1 Anderson coding (see Table 3). The biggest overall change wasin determining residential areas. This is because isolated residential areas were moredifficult to determine at the lower resolution imagery. Table 5 shows more detailedbreakdowns at the Anderson Level 2/3 for another quad study area in Tioga County,Pennsylvania.

Table 3: Time Study for Digitizing Land Use

______________________________________________________________________Baltimore County 1:2400 digitizing scale, 2 foot pixel color aerials, cadastral seed oftwo acre residential and roads used.

o 73 square miles

o It took 186 hours

o 2.55 hours per square mile

o

York County 1:2400 digitizing scale, 2 foot pixel color aerials, no cadastral seed used.o 11 tiles @ 43 square miles per tile.

o Average time of 70 hours per tileo 1.63 hours per square mile

Various USGS Quadrangles 1:4800 digitizing scale, one meter resolution black andwhite, no cadastral seed

o 10 quads @ 55 square miles per quad

o Average time of 39 hours per quad

o 0.71 hours per square mile

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Table 4: Tioga County , Pennsylvania. Comparison of land use acreage total differences between

1:2400 digitizing versus digitizing on one meter resolution 1:4800 DOQQ for Antrim Quad.

Comparison of 1" = 200' scale base map versus DOQQ basemap - Antrim Quad

Lu_code 1:2400 1:4800 Difference % Change

100 2637 2275 362 13.7%

200 9605 9737 -132 -1.4%

300 5413 5265 148 2.7%

400 17790 17610 180 1.0%

500 231 250 -19 -8.2%

600 31 24 7 22.6%

700 172 365 -193 -112.2%

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Table 5: Tioga County , Pennsylvania. Comparison of land use acreage total differences between

1:2400 digitizing (NAD83) versus digitizing on one meter resolution DOQQ(UTM) for Knoxville

Quad.

1:2400 1:4800

LUCODE ACRES LUCODE_UTM ACRES

Difference in

Acres100 16.3 100 13 3.2

110 148.6 110 143 5.2

111 527.0 111 537 -9.6

114 13.8 Code not used 0 13.8

120 0.9 Code not used 0 0.9

123 4.3 Code not used 0 4.3

130 36.0 130 17 19.2

141 461.7 141 210 252.0

150 36.0 150 63 -27.1

186 16.4 Code not used 0 16.4

193 113.4 193 109 4.4

210 141.1 Code not used 0 141.1211 8790.5 211 9911 -1120.1

211a 552.6 211a 126 426.2

212 19.0 212 68 -49.0

213 205.8 213 274 -67.8

240 115.8 240 131 -14.8

310 2820.5 310 1931 889.4

320 629.5 320 817 -187.1

330 1553.8 330 2587 -1033.6

400 18810.0 400 18271 538.5

411 9.0 Code not used 93 -84.3

421 1.4 412 66 -65.1

423 29.1 Code not used 110 -81.3500 74.1 500 31 43.6

511 133.6 511 59 75.0

600 65.3 600 10 55.6

720 149.6 720 0 149.6

999 91.8 999 0 91.8

35566.6 35576 -9.7

In northern Henrico County the parking lots were burned into the seed file. Inhindsight, the use of the parking lot layer caused more problems then it was worth

especially as the parking lot layer excluded all the small grassy areas one finds in atypical parking lot. This incredibly complicated the planimetric land use layer when theparking lot data was burned into the seed file as shown in the figure 7. This couldpossibly be resolved by running a topology validation with a cluster tolerance slightlylarger then the distance across the largest island within the parking lot such that the voidsclosed and could then be classified as parking lots.

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Figure 7: Northern Henrico County parking lots (in grey).

Table 5 indicates the total estimated time needed to digitize the entire Chesapeake Baywatershed by the methods tested. These estimates assume that for high-density urbanareas a simple 100 land use code is assigned.

Table 6: Cost Estimates to Digitize Land Use for Entire Bay

___________________________________________________________________

Chesapeake Bay Watershed Total Area = 64,000 square miles

Aerial/Planemetric Method: 163,200 hours@ $15 per hour labor rate $2,448,000

Aerial Image Only Method: 104,320 hours@ $15 per hour labor rate $1,564,800

Reduced Resolution Method: 45,440 hours@ $15 per hour labor rate $681,600___________________________________________________________________

It is very important to note that these costs are for first time data collection. Once acounty has digital land use/land cover data, subsequent updating costs would besignificantly reduced resulting in lower long-term maintenance costs.

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Parcel Based Land Use Codes

Many counties have high quality land use codes assigned to their cadastral GIS data. Theproblem is that these codes only describe the predominant land use for the entire parcel.

Figure 8 shows a 187 acre parcel that is coded as agriculture land use. 1/3 of the parcel isclearly forest land cover while at least 1-2 acres are residential or farmstead. Thereforethe parcel based land use code, alone, describes the use and not the actual land coverinformation needed by the Bay Program. If parcels are less then 2 acres in size, the landuse code can provide an accurate assessment of the actual land use.

Although not tested, it might be possible to use other available data layers such as adigital elevation model or soils data to make decisions about the true land use/land cover.In the example provided the forested areas exist on steep slope and poor soils.

Figure 8: Sample parcel from Mifflin County, PA.

Other Options: Combining County Level Data with Satellite-Derived Data

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The use of satellite derived land cover can provide valuable information. In the case ofthe RESAC land cover dataset, RESAC utilized planimetric data in the decision tree usedto classify land cover. They utilized road centerline data to help make a determination ofurban and impervious land covers. It would be possible to also use local county levelGIS data in the decision tree. In cases where the GIS parcel acreage is less then 2 acres,

the land use code carried in the attribute table for the cadastral dataset could be used inthe decision tree.

Another possibility is to combine information found in County-level planimetric datawith satellite-derived data to improve the classification. Figure 9 illustrates theimprovement in satellite data classification if local parcel information were incorporatedinto the process. Residential parcels are highlighted in Figure 9 (left image) for atownship in Centre County, Pa based on the land use code assigned by the county.RESAC land cover data for the same area appears on the right image. Note that most ofthe residential parcels are classified incorrectly as agriculture. This is because the averagelot size is approximately 1-2 acres in size and the satellite imagery interprets the non-

impervious area as agriculture rather than residential, thereby overestimating agriculturallands. This type of residential density is very typical throughout developing areas ofPennsylvania, and incidentally, is contributing to our urban sprawl problems.

Figure 9. RESAC land cover and residential parcel boundaries.

Figure 10 illustrates RESAC classification of just the residential parcels for the area withthe legend adjusted so that low, medium and high density residential classes are all blue.The figures clearly show that almost none of the parcels were classified correctly asresidential and that over half were incorrectly classified as agricultural lands. The use ofparcel data with land use codes combined with RESAC data could be used to adjust the

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RESAC classification or better yet, it could be used in the classification process at earlystages of the project.

Figure 10. RESAC classification of residential parcels

Data Acquisition

During the initial phases of the project the Land Analysis Lab supplied Maryland-

RESAC with planimetric datasets from two counties in Pennsylvania, Lancaster Countyand Centre County. These planimetric datasets included such features as parcelboundaries, building outlines, and edge of pavement. In addition detailed land use datalayers were provided for both of these counties. Land use and parcel data from MifflinCounty was also provided but Mifflin County did not have building outline or edge ofpavement data layers. RESAC needed these datasets for ground-truth purposes.

Subsequent to the initial data deliveries to be used as ground truth data the Land AnalysisLab has acquired many different additional data sets from counties throughout theChesapeake Bay Watershed. A total of 42 counties supplied data to the project as shownin Table 7. This represents about 1/5 of the counties within the bay watershed. These

counties were selected based upon the quality of data that they might have. This dataquality was indicated from the survey that was conducted. All received data will bedelivered to the CBP.

The counties that supplied their data were all very reasonable in responding to requestsfor their data. In some cases data agreements had to be signed indicating their data wouldnot be released to a third party. There were also cases where a small fee ($50 or less) hadto be paid to cover their costs in reproducing the data onto CD-ROM or DVD. Not all

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counties charged these fees. The vast majority of counties were agreeable to letting theCBP have access to the data as long as it was not released to third parties without priorconsultation with the respective county. Therefore it is specifically requested that theChesapeake Bay Program respect their wishes and only use this data for internalpurposes.

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Table 7: List of Counties That Supplied GIS Datasets

County

Stat

e County State

Kent DE Sullivan PABaltimore Co MD Tioga PA

Carroll MD Wyoming PA

Harford MD York PA

Howard MD Accomack VA

Broome NY Albemarle VA

Chemung NY Botetourt VA

Madison NY Clarke VA

Steuben NY Culpeper VA

Tioga NY Hanover VA

Tompkins NY Henrico VAAdams Pa James City VA

Cameron PA King William VA

Centre PA Loudoun VA

Chester PA Northampton VA

Clearfield PA Rockingham VA

Columbia PA Shenandoah VA

Cumberland PA Spotsylvania VA

Lancaster PA Warren VA

Mifflin PA York VA

Schuylkill PA Hampshire WV

One notable exception to this policy was aerial imagery from the state of Virginia. TheVirginia Geographic Information Network (VGIN) does not readily distribute their data.An agreement with Henrico County was signed for use of their aerial imagery sinceHenrico County viewed the Land Analysis Lab (LAL) as a subcontractor and LAL wasproducing a land use data layer for them as part of our overall project. This agreementprohibits LAL from giving the Chesapeake Bay Program these aerial images.Subsequent attempts to gain permission to use aerial images from other Virginia countieswere unsuccessful. Further information can be found at their website athttp://www.vgin.virginia.gov/.

Only one county refuse to provide available data to the project. This was ArlingtonCounty, Virginia, and the reason given was related to the fact that it was Federal project.Apparently that county has had some issues with data sharing with the Federalgovernment. Some of the other counties wanted more then $50 to release their data andthese fees were not paid, so no data was collected.

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It was noted that some counties where there are Federal lands, such as Harford County,Maryland (Aberdeen Proving Grounds), the county data sets do not cover the Federalareas. This was also noted in York County, Virginia, and for some datasets that weredownloaded from the District of Columbia (D.C.). The DC data was not included sinceany organization can download this data directly. The DC data has eliminated areas

around the White House, the Congressional Buildings, and the Naval Observatory. Toaccess these datasets go to http://dcgis.dc.gov/.

Appendix F contains a more detailed listing of the data that was received and some of theobservations on some of the datasets. Specific counties were selected where theirdatasets were used to help validate the impervious surface data that was produced byMaryland RESAC.

Appendix G contains specific details on some of the issues related to acquiring the data.This Appendix includes information on counties where data was received and also whereattempts were made to receive data. It may prove useful to someone who might try to

acquire updated GIS datasets in the future.

Objective #2: Comparison of locally produced landuse/land cover data to satellite derived data.

Methodologies

Land Cover Comparisons

One of the primary utilizations of the land use datasets developed by the Land AnalysisLab (LAL) was for reference datasets to assess the accuracy of remotely sensed landcover datasets. Comparisons were made with Maryland RESAC, NLCD 2000, and aPennsylvania land cover dataset compiled by the Penn State University's Office forRemote Sensing of Earth Resources under contract with the Pennsylvania Department ofEnvironment Protection. The RESAC dataset covers the entire Chesapeake BayWatershed. The NLCD 2000 data was only available for Baltimore County, YorkCounty, and Lancaster County during the period of our research project. The Penn Statedataset is a Pennsylvania statewide land cover map generated from Enhanced ThematicMapper satellite data and three other ancillary data sources. It is an update to the MRLCdata layer produced for the state in 1992.

Metadata on Maryland RESAC can be found be found athttp://gis2.pasda.psu.edu/Pasda/UCI_Metadata/chesapeakebaylanduselandcover2000.htm

Metadata for the NLCD 2000 program is also readily available and can be found athttp://landcover.usgs.gov/index.asp

28
http://gis2.pasda.psu.edu/Pasda/UCI_Metadata/chesapeakebaylanduselandcover2000.htmhttp://landcover.usgs.gov/index.asphttp://gis2.pasda.psu.edu/Pasda/UCI_Metadata/chesapeakebaylanduselandcover2000.htmhttp://landcover.usgs.gov/index.asp


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Metadata describing the Penn State land cover dataset can be found athttp://www.pasda.psu.edu/documents.cgi/orser/psu-palulc_2000.xml

The three websites referenced can also be used to download these datasets.

All the LAL reference datasets will be posted at www.pasda.psu.eduat a later date andare being provided directly to the Chesapeake Bay program office.

The RESAC data were compared to eight county reference areas across the baywatershed as follows; Henrico, Baltimore, York, Lancaster, Mifflin, Centre, Clearfield,and Sullivan.

Key to this comparison was how the UMD codes were translated into Level I AndersonCodes. Table 8 indicates the translations used.

29
http://www.pasda.psu.edu/documents.cgi/orser/psu-palulc_2000.xmlhttp://www.pasda.psu.edu/http://www.pasda.psu.edu/http://www.pasda.psu.edu/documents.cgi/orser/psu-palulc_2000.xmlhttp://www.pasda.psu.edu/


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Table 8: UMD RESAC code translation to Anderson code.

RESAC

LU Codes RESAC Description

Anderson Level 1

LU/LC Codes Anderson Level 1 Description

1 Open water 500 Water 3 Low intensity development 100 Urban or Built-Up Land

4 Med intensity development 100 Urban or Built-Up Land

5 High intensity development 100 Urban or Built-Up Land

8 Transportation 140 Transportation/Communication

10 Urban/residential deciduous tree 100 Urban or Built-Up Land

11 Urban/residential evergreen tree 100 Urban or Built-Up Land

12 Urban/residential mixed trees 100 Urban or Built-Up Land

15Urban/residential/recreationalgrass 100 Urban or Built-Up Land

17 Extractive 700 Barren Lands

18 Barren 700 Barren Lands

20 Deciduous forests 400 Forestland

21 Evergreen forest 400 Forestland

22Mixed (deciduous-evergreen)forest 400 Forestland

25 Pasture/hay 200 Agriculture

26 Croplands 200 Agriculture

30 "Natural" grass 200 Agriculture

35 Deciduous wooded wetlands 600 Wetlands

36 Evergreen wooded wetland 600 Wetlands

37 Emergent (sedge-herb) wetland 600 Wetlands

38 Mixed wetland 600 Wetlands

Only the 100 (urban), 140 (transportation), 200 (agriculture), and 400 (forested) codeswere considered in the analysis. The 500 (water), 600 (wetlands), and 700 (barren) codeswere not considered to be reliable and also do not amount to significant percentages ofthe total land area in each study area.

Appendix H contains the general procedure that was used to compare the datasets. Theend result was that a confusion matrix (error matrix) was created for each comparisonshowing differences between the locally-produced data and the satellite-derived data.

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Impervious Surface Comparisons

In order to compare Maryland RESAC Impervious surface data some local countygovernments were identified that had good, high-resolution planimetric data that was

created at the same time as the Landsat TM images were captured. The best match to thisrequirement was Harford County, Maryland. Harford County captured severalimpervious surface layers from an aerial imagery flight that was flown in the spring of2000. These layers included road pavement, driveways, building structures, and parkinglots. They did not capture sidewalks which would have increased the impervious surfacepercentages. An example of the data is shown in Figure 11.

Figure 11: Harford County image showing planimetric impervious surface over color aerial

imagery.

In order to compare the Harford County data to the RESAC data the following procedurewas utilized.

1. All the relevant Harford County data layers were combined using the Unioncommand.

2. The resultant Harford County planimetric impervious data layer was thenreprojected into the same UTM projection as the RESAC data.

3. The Harford County data were then converted into a 5 meter raster data layer andclassified as 100 percent impervious where the cells were created. The remainingcells were reclassified as 0 percent impervious (Figure 12).

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Figure 12: Resample of Harford County planimetric to 5 meter cell raster

4. The 5 meter raster layer was then aggregated to the 30 meter cell resolution takingthe mean of all 5 meter cells within a 30 meter neighborhood to assign animpervious surface percentage for each 30 meter cell (Figure 13).

Figure 13: Aggregate of Harford County 5 meter to 30 meter impervious data layer.

5. A boundary file for the Harford County data was created that excluded theAberdeen Proving Grounds as the Harford County data did not include this area.

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Figure 15: Suburban sample area

Figure 16: Urban sample area

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Results and Discussion

Land Cover Comparisons

Firstly, comparisons between RESAC and LAL will be discussed.

Henrico County

Table 9 shows the confusion matrix for Henrico County locally-produced data comparedto RESAC. Overall RESAC underestimated total urban by 228 hectares, or a 1.3 %difference. While the total reported areas (hectares) were very close, the producersaccuracy and the users accuracy, as shown in the confusion matrix results, were not asgood.

Producer accuracy indicate the portion of land area within a given land cover category

that were classified correctly. For example, the producers accuracy was 49.9% for urban.This indicates that 49% of the urban areas were correctly classified by RESAC as urban.Therefore, 51.1 % of the urban areas were misclassified by RESAC as something otherthan urban.

User accuracy indicates the portion of cells classified by RESAC within a certain landcover category that are correctly classified. For example, the users accuracy for urbanwas 53.3 %. This indicates that 53.3% of the cells that were classified as urban byRESAC were actually urban. The remaining 46.7% of the urban cells are really not urbanaccording to LAL mapping.

When the producers accuracy is higher then the users accuracy, RESAC hasoverestimated the amount of land area for that category and proportionatelyunderestimated other categories. This is observed when looking at the agriculture landcover category. The producer accuracy is 79.2 % but the user accuracy is 61.4 %. In thiscase, RESAC overestimated agriculture by 2356 hectares or a 5.1 % difference.

As can be seen in the confusion matrix, there was significant misclassification found inmost categories. In particular, it is apparent that many agricultural and forested areaswere misclassified as some type of urban category.

An overall accuracy (produced by dividing the number of correctly classified cells by the

total number of cells) of 72.1% was found for this study area.

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Table 9: Henrico County versus RESAC Confusion Matrix

Henrico South 1998 - Anderson Level 1

RESAC

AndersonLevel1

All figures inHectares

HenricoCounty

(Urban)100

(Transportation)140

(Agriculture)200

(Forest)400

GrandTotal

(RESAC)

(Urban)100 3160 1186 387 1194 5928

(Transportation)140 261 793 153 155 1361

(Agriculture)200 1309 232 4245 1123 6909

(Forest)400 1602 351 574 13851 16377

Grand Total(Henrico) 6332 2562 5359 16322 30576

ProducersAccuracy

(UMD/LAL)

UsersAccuracy

(UMD/UMD)(Urban)100 49.9% (Urban)100 100 53.3%

(Transportation)140 30.9% (Transportation)140 140 58.2%

(Agriculture)200 79.2% (Agriculture)200 200 61.4%

(Forest)400 84.9% (Forest)400 400 84.6%

Land Use Code RESAC Total LAL Total Difference

(Urban)100 5928 6332 -404

(Transportation)140 1361 2562 -1201

(Agriculture)200 6909 5359 1550

(Forest)400 16377 16322 55

Examples from Henrico County illustrate common errors found in the residential RESACcategory. Figures 17-19 show LAL 10 meter data and RESAC data, respectively,centered on a relatively large residential subdivision. The RESAC 30 meter datamisclassify large portions of the subdivision as agriculture. As shown in Figures there isclearly a large amount of residential or urban (Anderson level 100) that is not captured.But overall in the entire study area the difference in urban between RESAC and LAL isonly 404 hectares The obvious conclusion is that RESAC must be over reporting urbanin another area of the county. In other words, on a cell-by-cell basis, the RESAC data areless accurate than they are on summary data produced from very large areas. Forestimates of land cover percentages on a county basis, the numbers may be accurate but

for small-area assessments, the numbers may be quite erroneous.

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Figure 17: Henrico County 10 meter LAL land cover data

Figure 18: Henrico County 30 meter RESAC land cover data

Since RESAC urban forest categories were reclassified as urban (100) in this analysis,this would result in higher urban areas. For example Figure 19 and 20 show an urbanforest area simplified in this analysis as urban. If RESAC data were not reclassified sothat urban forest was considered urban then RESAC would have significantlyunderestimated urban even more and differences would have been much larger then isevident after the reclassification (see Figure 21).

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Figure 19: Henrico County 10 meter LAL land cover data.

Figure 20: Henrico County 30 meter RESAC land cover data.

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Figure 21: RESAC land cover non-simplified.

Another factor in RESACsreporting of urban areas is found by analyzing some ehighways. RESAC utilized existing road networks as part of their decision treeand apparently assumed that urban development occurs along these roads. Asshown in Figure 22, this is not always the case.

Figure 22: RESAC simplified land cover showing major cloverleaf intersection

with grassy areas inside cloverleaf classified as urban.

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Baltimore County

Table 10 shows the confusion matrix for digitized data for Baltimore County compared toRESAC. Overall RESAC underestimated total urban by 623 hectares, or a 2.8 %difference. While these total reported hectares are close, the producers accuracy and the

users accuracy were also relatively low. For urban lands the producers accuracy was41.7% while the users accuracy was 52.3% and many of the same issues that affectedHenrico County appled to Baltimore County.

Agriculture was slightly overestimated by 407 hectares, or a difference of 3.5 %. Theproducers accuracy was 82.6 % while the users accuracy was 80.6 %.

As in Henrico County, there was significant misclassification of urban lands asagriculture and forest. Apparently, the satellite imagery and classification algorithms arenot able to discern low density residential development in open or forested lands.

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Table 10: Baltimore County versus RESAC Confusion Matrix.

Baltimore County 2002-2004 - Anderson Level 1

RESAC

AndersonLevel1

All figures are inHectares

BaltimoreCounty

RESAC(Urban)

100(Transportation)

140(Agriculture)

200(Forest)

400

Grand

Total(RESAC

(Urban)100 1092 96 213 574 1975

(Transportation)140 162 83 194 100 539

(Agriculture)200 898 60 5074 678 6710

(Forest)400 447 71 822 7032 8372

Grand Total(Baltimore) 2598 310 6303 8384 17595

Producers Accuracy (UMD/LAL) Users Accuracy (UMD/UMD)

(Urban)100 42.0% 100 55.3%

(Transportation)140 26.8% 140 15.4%

(Agriculture)200 80.5% 200 75.6%

(Forest)400 83.9% 400 84.0%

Land Use Code RESAC Total LAL Total Difference

(Urban)100 1975 2598 -623

(Transportation)140 539 310 229

(Agriculture)200 6710 6303 407

(Forest)400 8372 8384 -12

Figures 23 and 24 show the LAL and RESAC data , respectively, for an area of BaltimoreCounty along I-83. Note that the urban areas to the west of the highway look likebuffered zones versus the actual urban pattern. Also note that to the east of the highwaysmaller urban areas are largely misclassified by RESAC

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Figure 23: LAL land cover data for Baltimore County

Figure 24: RESAC simplified land cover data for Baltimore County

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An evaluation of original, non-simplified RESAC data (Figure 25), revealed that themajority of the urban area was classified as either urban forest (dark green) or urbanrecreational grass (light green). Developed areas (yellow) were small.

Figure 25: RESAC land cover data showing urban classifications

(forested lands and agriculture are shown in white).

Clearfield County

Table 11 shows the confusion matrix resulting from the comparison of RESAC data anddetailed data mapped for Clearfield County. Overall RESAC underestimated total urbanby -448 hectares, or a 0.2 % difference. While these total reported hectares are close, theproducers accuracy and the users accuracy were also relatively low on a cell-by-cellbasis.

The producers accuracy was 34.3% while the users accuracy was 35.3%. Whencompared to the more developed counties like Henrico and Baltimore there are morerural areas in Clearfield. As shown for Henrico and Baltimore RESAC data misses a lot

of rural development and this is why the producer and user accuracy percentages are low.The difference is very small because of the buffering of urban areas similar to what wasexplained in Baltimore County. An example of this will be discussed for Clearfield aswell.

Agriculture was overestimated by 5257 hectares, or a difference of 1.9 %. Theproducers accuracy was 57.8 % while the users accuracy was 46.6 %. The primary

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reason for the lower accuracy may be the large number of reclaimed strip mines inClearfield County that will be discussed later.

As in other counties, there was much confusion between urban, forest, and agriculture.Many agricultural and urban lands were mapped as forest. Many urban lands were

mapped as forest and agriculture.

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Table 11: Clearfield County versus RESAC Confusion Matrix.

Clearfield County, PA 1997/2003 - Anderson Level 1

UMD

2000-

AndersonLevel1

All figures are inHectares

ClearfieldCounty

RESAC

(Urban)

100

(Transportation)

140

(Agriculture)

200

(Rangeland)

300

(Forest)

400

GraTo

(RES(Urban)100 5414 172 1012 1462 7290 15

(Transportation)140 804 131 316 298 1137 2

(Agriculture)200 2073 22 12580 8384 3962 27

(Forest)400 7508 684 7856 15447 205267 236

Grand Total(Clearfield) 15798 1008 21764 25591 217657 281

Producers Accuracy(UMD/LAL)

Users Accuracy (UMD/UMD)

(Urban)100 34.3% 100 35.3%

(Transportation)140 13.0% 140 4.9%

(Agriculture)200 57.8% 200 46.6%

(Forest)400 94.3% 400 86.7%

Land UseCode

RESACTotal

LALTotal Difference

(Urban)100 15350 15798 -448

(Transportation)140 2388 1008 1380

(Agriculture)200 27021 21764 5257

(Forest)400 221314 217657 3657

The Treasure Lake area of Clearfield County illustrates the overestimation of urban areas

probably resulting from RESACs buffering of street data. Figure 26 shows the LALland cover data for Treasure Lake. Large portions of Treasure Lake are not developedyet streets do exist. Figure 27 shows the RESAC data where one can clearly observe theeffects of buffering the streets. Note that many small areas of urban development aremissed in RESAC. It should also be noted that most of the urban shown in RESAC isurban forest.

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Figure 26: LAL land cover for Treasure Lake area.

Figure 27: RESAC land cover for Treasure Lake area.

In regards to the accuracy of the RESAC agriculture, the main issue here may be thelarge number of reclaimed strip mines in Clearfield County. The reclaimed land is notused generally for agriculture and was classified as rangeland, or Anderson code 300.Figure 28 shows a sample area with a high concentration of reclaimed strip mines (red).Figure 29 shows the same area classified by RESAC as agriculture and forest.

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Figure 28: LAL land cover showing reclaimed strip mine area.

Figure 29: RESAC land cover in reclaimed strip mine area.

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NLCD 2000 Data

The NLCD 2000 land cover data has some of the same issues as the RESAC data. Thedata underestimates urban/residential development, especially in rural areas, and the dataoverestimates agricultural areas. As with RESAC, NLCD is reasonably accurate in

mapping woodlands, especially large contiguous woodland areas. The NLCD codes weretranslated as shown in Table 12.

Table 12: NLCD code translation to Anderson code.

NLCD2001

LU CodesNLCD 2001 Description

AndersonLevel 2LU/LCCodes

Anderson Level 1 Description

11 Open Water 500 Water

21 Developed, Open Space 100 Urban or Built-Up

22 Developed, Low Intensity 100 Urban or Built-Up

23 Developed, Medium Intensity 100 Urban or Built-Up

24 Developed, High Intensity 100 Urban or Built-Up

31 Barren Land (Rock/Sand/Clay) 700 Barren Lands

41 Deciduous Forest 400 Forestland

42 Evergreen Forest 400 Forestland

43 Mixed Forest 400 Forestland

81 Pasture/Hay 200 Agriculture

82 Cultivated Crops 200 Agriculture

90 Woody Wetlands 600 Wetland

95 Emergent Herbaceous Wetlands 600 Wetland

Table 13 shows the confusion matrix for LAL versus NLCD. For the Level 1 Anderson100 urban built-up land there is a difference of 10577 hectares, or 9.1 %. NLCDunderreported urban/residential development. The larger difference in the two datasets ascompared to RESAC versus LAL is explained by the fact that NLCD did not have anurban forest category nor does it appear that NLCD used planimetric street centerlines intheir decision tree. A comparison between Figure 28 and Figure 29 shows some visualexamples of the differences between LAL and NLCD in regard to urban/residentialdevelopment.

For Level 1 Anderson 200 agriculture there is a difference of 22,537 hectares, or 19.5 %.NLCD over-reported agricultural lands. Much of this difference can be explained by the

underreporting of rural residential land. This land was classified as agriculture instead.

The confusion matrix illustrates significant mapping of developed lands as agriculture.This region contains much residential development at relatively low densities. Satelliteimagery is typically unable to differentiate low-density residential development fromagricultural lands. Figures 30 and 31 show a portion of the county where NLCDmisclassifies residential lands (yellow) as agriculture.

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Table 13: York County, PA, LAL versus NLCD Confusion Matrix.

York County, PA 2002-2003 - Anderson Level 1

NLCD

2000-

AndersonLevel1

All figures inhectares

YorkCounty

NLCD 2000(Urban)

100(Trans)

140(Agriculture)

200(Rangeland)

300(Forest)

400

GrandTotal

(NLCD)

(Urban)100 3901 1013 1077 305 292 6588

(Agriculture)200 11616 1273 50610 5994 6137 75629

(Forest)400 1648 287 1405 1040 29253 33634

Grand Total(York) 17165 2572 53092 7339 35682 115850

Producers Accuracy(NLCD/LAL)

Users Accuracy(NLCD/NLCD)

(Urban)100 22.7% 100 59.2%

(Agriculture)200 95.3% 200 66.9%

(Forest)400 82.0% 400 87.0%

Land UseCode NRLC Total

LALTotal Difference

(Urban)100 6588 17165 -10577

(Agriculture)200 75629 53092 22537

(Forest)400 33634 35682 -2048

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Figure 30: LAL land cover for York County, PA.

Figure 31: NLCD land cover for York County, PA.

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Agricultural Comparisons

The reported agriculture categories by both RESAC and NLCD were also examined forhay/pasture versus row crops. In Lancaster County it was observed that there was a largeamount of classified hay/pasture which did not correlate with agricultural conditions in

Lancaster County as described by the Pa Agricultural Statistics Service.

Tables 14 and 15 show RESAC, NLCD and Ag Statistics for Lancaster County. The datashow striking differences in the relative proportions of cultivated and uncultivatedcroplands. In fact, the relative proportions are nearly opposite.

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Table 14: UMD RESAC and NLCD 2000 Pasture and Cropland acreage Totals.

UMD Classifications Acres

25 Pasture/hay 258,923

26 Croplands 84,832

Total 343,756

NLCD 2001 Classifications

81 Pasture/Hay 286,968

82 Cultivated Crops 111,819

Total 398,787

Table 15: Pennsylvania Department of Agriculture 2002 Pasture and Cropland Acreage Totals.

PA Ag Stats (Year 2002) Acres

Wheat 10,830

Hay 73,264

Barley 9,071

Haylage/Silage 35,222

Rye 2,449

Oats 591

Pasture 25,574

Total Hay/Pasture 157,001

Corn/Grain 94,421

Corn/Silage 69,829

Soybeans 28,223

Tobacco 4,496

Vegetables 5,606

Total Row Crops 202,575

Total Agriculture 359,576

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Impervious Surface Comparisons

The results of comparisons of impervious surface areas mapped using county-level dataand satellite-derived products (RESAC) indicate that the RESAC impervious layer may

significantly underestimate total impervious surface area. Table 16 shows data for acomparison for Harford County.

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Table 16: Impervious Surface Comparison between RESAC and Harford County.

Data SourceOverall

ImperviousRural

ImperviousSuburbanImpervious

UrbanImpervious

RESAC 3.8 % 0.5 % 4.0 % 19.5 %

Harford CountyGIS 6.0 % 2.3 % 7.8 % 23.3 %

Area (sq miles) 379.0 7.8 7.8 7.8

Notes

1. Aberdeen Proving Grounds not part of study area

2. Harford County Planimetric data dated from Spring of 2000

3. Resac data cbw2000imperv_v1.2

The RESAC data was most accurate in urban areas and least accurate in rural areas.Figure 32 shows an example of missed impervious areas. The black cells representwhere RESAC indicated an impervious surface area greater then 0 while the coloredareas indicate where impervious surfaces actually exist.

Figure 32: Impervious Surface areas not mapped by RESAC.

In urban areas, RESAC data often overestimate specific cells percent impervious whileoverall slightly underestimating the actual impervious. In Figure 33 the blue cellsrepresent overestimates of impervious surface areas while the yellow/red colors representunderestimates. Figure 34 shows the Harford County planimetric impervious dataoverlay for comparison.

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Figure 33: Impervious Surface overestimated urban areas.

Figure 34: Planimetric overlaid onto RESAC data shown in Figure 31.

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Appendix A: Data Dictionary of GIS survey questions

Part I

ID of the jurisdiction

Survey StatusDate Survey Completed

Name of Data Recorder

Entity Coverage (FIPS)

Name of jurisdiction

Name of State

Preferred Salutation

First Name

Last Name

Preferred Suffix

Business Title

Organization Name

Department or UnitBuilding Name/ Mail Stop

Street Address

City

Zip Code

Telephone Number

FAX Number

E-mail

Organization Web Page URL

Name of Planning Director

Telephone Number

E-mail

Name of 911 DirectorTelephone Number

E-mail

Name of Public Works Director

Telephone Number

E-mail

Are you responding to this questionnaire for Entire County including mostMunicipalities?

Are you responding to this questionnaire for Single County Government Agency?

Are you responding to this questionnaire for Single Municipal GovernmentAgency?

Are you responding to this questionnaire for State Agency Only?

Are you responding to this questionnaire for Entire County Government Only?Are you responding to this questionnaire for Entire Municipal Government Only?

Are you responding to this questionnaire for Regional Government Agency?

Are you responding to this questionnaire for Other?

Please estimate the total number of people working in jurisdiction

Please estimate the number of Dedicated GIS Operators in the jurisdiction

Please estimate the total number of GIS users working in the jurisdiction

Are the GIS functions Centrally Managed?

Are the GIS functions Split Among Different Departments?

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Does the Planning Department have GIS functions?

Does the Public Works Department have GIS functions?

Does the Environmental Department have GIS functions?

Does the Health Department have GIS functions?

Does the Fire Department have GIS functions?

Does the Police Department have GIS functions?

Does the Information Technology Department have GIS functions?Do other departments have GIS functions? (list)

Are your GIS Operating Systems UNIX?

Are your GIS Operating Systems Windows NT?

Are your GIS Operating Systems Windows (Yr.)?

Is your GIS Software ArcInfo?

Is your GIS Software ArcView?

Is your GIS Software GenaMap?

Is your GIS Software Imagine?

Is your GIS Software Intergraph?

Is your GIS Software MapInfo?

Is your GIS Software Smallworld?

Is your GIS Software IDRISI?

Is your GIS Software Maptitude?

Is your GIS Software Other (list)?

Is your CAD Software AutoCAD?

Is your CAD Software TurboCAD?

Is your CAD Software ArcCAD?

Is your CAD Software Microstation?

Is your CAD Software Other (list)?

Is your RDBMS Software Oracle?

Is your RDBMS Software Informix?

Is your RDBMS Software SQL Server?

Is your RDBMS Software MS Access?

Is your RDBMS Software dBase?

Is your RDBMS Software Other (list)?

Do you collect data with at GPS receiver?

If you do collect data with a GPS receiver, what grade receiver do you use?

If you do collect data with a GPS receiver, are your readings differentiallycorrected?

Is your Map Coordinate System VA State Plane North?

Is your Map Coordinate System VA State Plane South?

Is your Map Coordinate System Pennsylvania State Plane North?

Is your Map Coordinate System Pennsylvania State Plane South?

Is your Map Coordinate System West Virginia State Plane?

Is your Map Coordinate System New York State Plane West?

Is your Map Coordinate System New York State Plane Central?

Is your Map Coordinate System New York State Plane East?

Is your Map Coordinate System Delaware State Plane?

Is your Map Coordinate System Maryland State Plane?

Is your Map Coordinate System Geographic?

Is your Map Coordinate System UTM?

Is your Map Coordinate System Other (list)?

Is your unit of measure US Survey Feet?

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Will you allow these agencies to post these data to a related web application orInternet Map Server that does not allow the raw data to be downloaded?

Will you allow your data to be used by State and Federal agencies for HomelandSecurity Purposes?

If yes, will you provide the data with full charges?

If yes, will you provide the data with no charges?

If yes, will you provide the data with reduced charges?Will you allow these agencies to redistribute these data?


Will you allow your data to be used by State and Federal agencies to improve theMAF/TIGER data produced by the US Census Bureau?

If yes, will you provide the data with full charges?

If yes, will you provide the data with no charges?

If yes, will you provide the data with reduced charges?

Will you allow these agencies to redistribute these data?


Can these agencies obtain a copy of your price schedules by hardcopy?

Can these agencies obtain a copy of your price schedules by mail?

Can these agencies obtain a copy of your price schedules by fax?

Is it ok to list your contact information and data in a statewide GIS resourceguide?

Part II

What was the population of your jurisdiction in the year 2000?

Does your jurisdiction link building permit data to the parcel layer?

Does your jurisdiction assign a land use code to the parcel layer?

Does your jurisdiction

Evaluation of Local Land Cover Land Use Mapping Approachs for the Chesapeake Bay Watershed, Final...

Documents

Transcript of Evaluation of Local Land Cover Land Use Mapping Approachs for the Chesapeake Bay Watershed, Final...