20140408
13431900
1.0
FGDC CSDGM Metadata
FALSE
OC.CompositeLandCover2010
13304895.945555
13469693.267373
340134.530842
508602.383222
1
Server=oakgisdb01; Service=sde:sqlserver:oakgisdb01\gispd5; Database=gisvec1; User=oc; Version=sde.DEFAULT
ArcSDE Connection
002
Projected
GCS_North_American_1983
Linear Unit: Foot (0.304800)
NAD_1983_StatePlane_Michigan_South_FIPS_2113_Feet_Intl
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.1'><WKT>PROJCS["NAD_1983_StatePlane_Michigan_South_FIPS_2113_Feet_Intl",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",13123359.58005249],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-84.36666666666666],PARAMETER["Standard_Parallel_1",42.1],PARAMETER["Standard_Parallel_2",43.66666666666666],PARAMETER["Latitude_Of_Origin",41.5],UNIT["Foot",0.3048],AUTHORITY["EPSG",2253]]</WKT><XOrigin>-107138599.99989653</XOrigin><YOrigin>-97193799.999930054</YOrigin><XYScale>15624.999985448077</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.0032808398950131233</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>2253</WKID><LatestWKID>2253</LatestWKID></ProjectedCoordinateSystem>
20151110
09035200
20151110
09035200
Please see document listed in Supplemental Information section for details.
535 Griswold Street
20121031
ArcGIS Metadata
1.0
Please see document listed in Supplemental Information section for details.
Offline
SDE Feature Class
Composite Land Cover 2010
Southeast Michigan Council of Governments
vector digital data
This dataset is a composite of the 5-class Green Infrastructure Land Cover, Building Footprints, and the 2-class Full Canopy datasets. The model to compile them accounts for features below the canopy by overlaying the GI classification with the Full Canopy. This dataset will enable the user to know where buildings, pavement, or open space exist beneath canopy. It is designed to be ingested into American Forests' CITYgreen software.A Green Infrastructure map for the 7-county SEMCOG region in southeastern Michigan was developed to support land cover and land use modeling. 4-band ADS-80 digital aerial imagery at 1-meter resolution was classified to a Green Infrastructure Level 1 classification scheme with the following classes: 1) Impervious Surface, 2) Open Space, 3) Trees, 4) Urban: Bare and 5) Water. The image was classified using Classification and Regression Tree techniques (CART analysis) and raster modeling.A standard accuracy assessment was performed on the final land cover. The overall accuracy statistic is 97.45%. Adjusted from the SEMCOG composite vector file, the data was reprojected to the Oakland County projection.
These data were developed in order to perform landcover and canopy assessments of the project area, and to provide the Southeast Michigan Council of Governments (SEMCOG) and adjacent communties with a current synopsis of the landscape. Please see document listed in Supplemental Information section for details.
Southeast Michigan Council of Governments (SEMCOG) 2011
Please see document listed in Supplemental Information section for details.
Suite 300
Michigan
Oakland County
USA
Full Canopy
Tree Canopy
Green Infrastructure
Level 1
Landcover
CityGREEN
Composite
transportation
climatologyMeteorologyAtmosphere
environment
structure
farming
imageryBaseMapsEarthCover
ISO 19115 Topic Categories
transportation
Michigan
Full Canopy
Oakland County
Tree Canopy
climatologyMeteorologyAtmosphere
environment
structure
Green Infrastructure
Level 1
Landcover
USA
CityGREEN
farming
Composite
imageryBaseMapsEarthCover
Planning and Regulatory
Data Store
This data has enhanced access or data sharing restrictions according to the Enhanced Access Policy (http://www.oakgov.com/it/gis/documents/EnhancedAccessPolicy.pdf). Please contact the One Stop Shop at 248.858.0720 for more information.
This data has enhanced access or data sharing restrictions according to the Geospatial Data Access, Distribution, and Use Policy (http://www.oakgov.com/it/gis/documents/GISDataPolicies.pdf). Please contact the One Stop Shop at 248.858.0720 for more information.
Microsoft Windows Server 2008 R2 Version 6.1 (Build 7600) ; ESRI ArcGIS 10.0.3.3600
-83.694195
-83.07423
42.426097
42.893618
For supplemental metadata information look on the web at: http://www.oakgov.com/it/gis/documents/metadata/SEMCOG_composite_vector.pdf.
1
-83.694203
-83.074239
42.893617
42.426098
Last metadata review date: 20121031
Data have been independently reviewed with respect to the SEMCOG 2010 imagery, and corrections made as a result of that review.
Overall Map Accuracy: 97.45% Minimum Mapping Unit for Assessment: 0.025 acres User's Accuracy: Impervious Surface: 100% Open Space: 99.35% Trees: 98.51% Urban: Bare: 79.45% Water: 100% Producer's Accuracy: Impervious Surface: 93.28% Open Space: 96.84% Trees: 100% Urban: Bare: 96.67% Water: 100%
Sanborn's experience tells us that no single software tool can do all the things that are required to create a good automated classification. We have a methodology that incorporates the most appropriate tools for the right output. We have written procedures that combine these tools effectively so that the output from one can be input into the other properly. We use a combination of statistical and remote sensing software packages, such as Feature Analyst, Erdas Imagine, See5, and Definiens Professional and other tools as necessary. Sanborn will classify the imagery into impervious and pervious classes, and segment the imagery into image objects that are then classified into the selected classification scheme. The See5 software creates a classification algorithm that ingests training samples of the land cover, creates a hierarchical classification ruleset, and can be applied to the entire project area. Once the classification ruleset is developed it is fairly simple to apply it at little cost to a much larger area. The SEMCOG 2010 imagery was captured during substantially leaf-off conditions, therefore if the classification were done purely using this imagery, the full extent of the canopy would be underestimated. The LiDAR data will help to mitigate this effect, as it will show a larger canopy extent when processed to create a Digital Canopy Model. The LiDAR data were also captured during leaf-off conditions, so even though the use of the LiDAR DCM will help give a fuller picture of the canopy, there will remain some underestimation. To mitigate this problem, Sanborn proposes an option to add an additional canopy/non-canopy classification derived from leaf-on National Agricultural Imagery Program (NAIP) imagery. This classification, when used in conjunction with the classification specified by the RFP, will give users a true picture of the state of the region’s Green Infrastructure. There is always a degree of confusion in the spectral classification; hence spatial models will be used to clean up misclassifications based on adjacency and ancillary datasets. These models are most effective when working with misclassification of shadows. When classifying using 8-bit imagery, shadows are often confused with water and woody vegetation, and these can be corrected based on surrounding pixels. Sanborn uses spatial models which account for a feature’s context when deciding whether or not to reclassify it. In this dataset, the tree canopy is prioritized over impervious. This is the most straightforward way to classify an aerial image, since the aerial sensor “sees” the tree crown rather than what is beneath. Most of the previous land cover datasets were mapped in this manner with the exception of the Alliance of Downriver Watersheds classification. In that case, impervious surfaces were prioritized over canopy. Sanborn has retained from previous production a version in which tree canopy was prioritized, and this can be used to create a more consistent classification.
To detect change in the landscape, Sanborn used the GeoCDX software application, developed by the University of Missouri. Using this methodology, spectral signatures and other features were compared between the two dates of imagery under consideration to create a potential change map. The change detection methodology compares several different aspects of both sets of imagery, 2008 and 2010. These attributes include, but are not limited to, spectral value, as well as spatial-spectral features, such as texture and linear correlation (good for finding roads, sidewalks, etc.). Data driven window parameters allow GeoCDX to consider pixel neighborhoods, which can aid in the discrimination of false change and true change, which most often occurs in larger, contiguous patches. Once the potential change map is complete, the area is broken into tiles, each tile is ranked according to the amount of potential change it contains, and the change-signatures are analyzed to group similar types of land cover change patterns. The change data are then ready for review by an imagery analyst and labeling of the change. The GeoCDX leverages an efficient and simple software web interface that allows an imagery analyst to cycle through the tiles according to the types of change and the amount of change detected by the routine. During this process, the analyst labels the type of change present in the tile for later use in creating the new classification. The amount of change can be customized and can be set for various minimum mapping units. Once the change labels are complete, Sanborn will use them to refine the potential change map, removing any “false positives” and classifying the true change to its proper 2010 class. This process is guided by the streamlined output provided by the GeoCDX system. The labels and delineation created in this step was thoroughly checked for quality and consistency by the project’s QC analyst.
Full - Canopy Dataset 1) Initial canopy processing was performed using training data from land cover classification described above. To enhance the coverage of the canopy data regionally, additional training sites were selected where needed (where there were inconsistencies in NAIP imagery across areas) and these were processed separately. The resulting canopy was modeled into the baseline canopy map. These models were saved and used on nearby areas with similar imagery characteristics. 2) Digital canopy models based on LIDAR data were used to better define canopy from non-canopy and particularly for distinguishing large trees from shrubs. 3) Individual production areas were edgematched. 4) QC was performed and any issues were resolved 5) Data was provided to SEMCOG for review and any valid issues identified by SEMCOG were also addressed. 6) All counties were edgematched into a SEMCOG coverage and then clipped to the various boundaries as needed.
Approximately 18% of the SEMCOG project area had been previously mapped using 2008 USGS imagery. The following process was used for the 2008 classification. 2008 ADS-40 imagery at 1 meter resolution was acquired for the area and imported into ERDAS Imagine for mosaicking and tiling. Image derivatives were made, including band ratios and texture of the NIR band. 6 of 18 mosaicked areas were selected for their diversity of cover types and training samples for each of the target cover types were delineated. These samples were then used to train the CART classifier (See5, Rulequest 2001), which resulted in a regression model that was applied to the mosaicked tiles. A preliminary classification was the result of this first step. Several spatial models were then created in AML and applied to the output of the CART classification. The output was a final automated classification that was ready for manual editing. At this point the classification was visually assessed against the imagery and errors were manually edited. An accuracy assessment protocol was run on the image to assess total and per class accuracy.
Matt Vernier
Sanborn
Senior Remote Sensing Analyst
734.213.1060
734.213.1085
Suite 80
320 Miller Ave.
Ann Arbor
MI
48103
US
mvernier@sanborn.com
M-F 9-5
Compilation: 1. Since the land cover and full canopy datasets originated in raster format, building footprints had to be converted to raster prior to incorporation into the model. This was accomplished in ArcMap through Vector to Raster Conversion with a raster resolution of 3.28084 feet (1 meter). 2. A raster model was constructed with the following rules: 1. If Full Canopy = “Canopy” a. If BFP = “Building” then Output = “Trees: Impervious Understory” b. If LC = “Impervious” then Output = “Trees: Impervious Understory” c. If LC = “Open Space” then Output = “Trees: Grass Understory, Grass 50- 75%” d. If LC = “Trees” then Output = “Trees: Grass Understory, Grass > 75%” e. If LC = “Urban: Bare” then Output = “Trees: Grass Understory, Grass 50- 75%” f. If LC = “Water” then Output = “Trees: Impervious Understory” 2. If BFP = “Building” then Output = “Impervious: Buildings/Structures” 3. If LC = “Trees” then Output = “Trees: Grass Understory, Grass 50- 75%” 4. If LC = “Impervious” then Output = “Impervious: Paved: Drain to sewer” 5. If LC = “Open Space” then Output = “Open Space: Grass/Scattered Trees” 6. If LC = “Urban: Bare” then Output = “Urban: Bare” 7. If LC = “Water” then Output = “Water Area” The result of the model was the 8-class raster in ESRI GRID format
Building Footprints 1) Vector building polygons were digitized heads up in a 2D environment using the following rules: 2) Buildings for all counties were captured even if there were existing datasets with building footprints for counties. For example, building footprints derived from LiDAR data were available for Washtenaw County but these were not used for this dataset due to the difference in methodology for this project. 3) Small residential buildings were digitized at the eve of the building roof whereas larger buildings with substantial height and offset from ground were captured at the base or footprint of the building structure. 4) Buildings were captured as an envelope delineating the periphery of the buildings and did not show different polygons for different heights. This was not possible due to the 2D data compilation and out of the scope. 5) Estimated building height was calculated using LiDAR data for each county – because LiDAR data vintage and specifications differed by counties, the building height attribution was done by county. 6) In order to estimate building heights, building footprint vectors were used to summarize a Digital Surface Model, or DSM. The DSM was created from the 2009-2010 LiDAR provided by SEMCOG by differencing the ground and non-ground points from each laser pulse, thereby deriving the height of the feature encountered by the pulse. Since those features would very often be trees, rather than structures underneath trees, high vegetation points were classified and removed from the LiDAR data prior to height derivation. 7) The summary routine returned the median height for a building, which was found to be a more robust measure of height than mean – the mean is usually influenced by large outliers, such as misclassified tree pixels. 8) Some buildings were not visible at all in the LiDAR, meaning the height could not be directly obtained from the DSM. This was particularly the case for buildings that were completely covered by high density trees or structures that were small enough to get few LiDAR points. Those buildings were assigned the median height of all buildings within their census block. If a census block contained too few buildings that were free of canopy cover, buildings without heights were assigned a global median height for the county. 9) Unusual building heights were flagged and reviewed. 10) All buildings where heights were calculated through interpolation using averages of census blocks, etc. were flagged for greater level of uncertainly and such information is captured in the data. For example, if a height extracted from the LIDAR data was incorrect based on the its’ ratio against the footprint shape area or if building seemed abnormally tall or it has a limited number of non-canopy LiDAR returns, the FLAG field was assigned a value of 1. This allowed the flagged buildings to be easily singled out for future improvements in the data for those buildings using other datasets such as tax and assessment databases, etc. 11) Both Sanborn and SEMCOG QCed the data and any identified issues were addressed. 12) Once all revisions were complete, the numbering system requested by SEMCOG was incorporated into the “IDLong” field in the building footprints attribute table. This field was populated using a model that insured that every object received a unique value while maintaining the requested numbering format. 1,000,000 range – Wayne County – not including Detroit 2,000,000 range – Oakland County 3,000,000 range – Macomb County 4,000,000 range – Washtenaw County 5,000,000 range – Monroe County 6,000,000 range – St. Clair County 7,000,000 range – Livingston County 8,000,000 range – Detroit
7962816
Oakland_comp_elim9_102912
Feature Class
7962816
Shape
Feature geometry.
ESRI
Coordinates defining the features.
SHAPE
Geometry
4
0
0
SubtypeCD
SubtypeCD
Double
8
38
8
CGFEATURE
CGFEATURE
String
255
0
0
OBJECTID
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
OBJECTID
OID
4
10
0
SHAPE.area
SHAPE.area
Double
0
0
0
SHAPE.len
SHAPE.len
Double
0
0
0
dataset
EPSG
8.2.6
Simple
FALSE
7962816
TRUE
FALSE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