Land Cover Data for Illinois, 1999-2000

In late 1999, the U. S. Department of Agriculture National Agricultural Statistics Service (NASS), the Illinois Department of Agriculture (IDA), and the Illinois Department of Natural Resources (IDNR) began a cooperative, interagency initiative to produce statewide land cover information on a recurring basis. The Illinois Interagency Landscape Classification Project (IILCP) completed Cycle 1 of this initiative in the summer of 2002, resulting in the Land Cover of Illinois 1999-2000 inventory and associated database. The IILCP is a cooperative effort with: United States Department of Agriculture, National Agricultural Statistics Services, Illinois Department of Agriculture, and Illinois Department of Natural Resources


The Land Cover of Illinois 1999-2000 database includes four distinct, but closely interrelated, products:

Land Cover of Illinois 1999-2000 Classification (Version 2, 11-15-03)
lcoi_geotiff.zip (GeoTIFF, 28.6Mb zip)
lcoi_imagine.zip (Erdas Imagine, 30 Mb zip); (with external pyramid layer)
lcoi_grid.zip (ESRI GRID, 31Mb zip)

Landsat 7 ETM+ Panchromatic Image Mosaic
TMPan.zip (MrSID, 97Mb zip)

TM Path/Row Pseudocolor Spectral Class Maps
Download files by Path/Row Landsat TM Scene Coverage Map
  Path 24 Path 23 Path 22 Landsat TM Scene Coverage Map
Row 31 Spring

Fall

Summer
Fall
 
Row 32 Spring
Summer
Fall
Spring
Summer

Spring

Fall
Row 33 Spring
Summer
Fall
Spring

Fall
Spring

Fall
Row 34   Spring

Fall
Spring

Fall


Statistical Summary of the Land Cover of Illinois for 1999-2000.
Statistical Summary by Category, State and County

Land Cover of Illinois 1999-2000 Classification

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A small portion of the Land Cover of Illinois 1999-2000 Classification for the Peoria, Illinois area.

This is the primary product of the IILCP initiative and is the result of integrating the directed supervised classification of agricultural lands and the unsupervised classification and spectral class labeling of non-agricultural lands. The primary source information for the computer classification is Landsat 5 TM and Landsat 7 ETM+ satellite imagery acquired for three dates (triplicates) during the spring, summer, and fall seasons of 1999 and 2000. Ten TM Path/Row scene areas are required to cover Illinois, and same year imagery was acquired for the TM/ETM+ triplicates to ensure seasonal consistency for the computer classification. The satellite imagery for four of the TM scene areas was acquired in 1999, and the remaining six scene areas were acquired in 2000 (1999-2000 TM Coverage Map).


All of the TM/ETM+ satellite imagery were geometrically corrected and co-registered to a transverse Mercator projection with a UTM zone 16 grid and NAD83 datum. The 1999 TM/ETM+ imagery is a mixture of simple geometric correction and terrain correction, while all of the 2000 imagery was terrain corrected using USGS 3 arc-second digital elevation model (DEM) data to correct relief displacement. The TM/ETM+ multispectral imagery possesses a 30x30 meter (98.4x98.4 feet) ground spatial resolution, which means that the resulting Land Cover of Illinois 1999-2000 Classification data is suitable for GIS and mapping applications at a scale of approximately 1:100,000 (1"=8,333') or smaller.


The computer classifications of the geometrically corrected and co-registered TM/ETM+ triplicates were conducted on a scene-by-scene basis. Spectral signatures were extracted from each TM/ETM+ triplicate data set utilizing an Isodata K-means clustering procedure (Duda and Hart 1973) and experimentation indicated that 200-250 spectral classes should be derived for each data set. The performance of several standard classifiers including minimum Euclidean distance, minimum Mahalanbois distance, and non-thresholding maximum likelihood have been evaluated and the results indicated greater improvement in classification accuracy can be achieved from the use of a maximum likelihood classifier, and this is supported by other research (Luman and Minhe 1995; and Gong and Howarth 1990). The labeling and reduction of the spectral classes from the unsupervised classification procedures into the final information classes (water, wetland, etc.) was accomplished by photo-interpretation of NAPP 3 aerial photography. The supervised classification of agricultural lands, which used USDA-NASS ground reference data as training data collected the same year for 425 sites across the state, directly resulted in information classes and therefore no additional class labeling was necessary. The resulting integrated classification contains 23 land cover categories, which are as follows:


Value Land Cover Category
10AGRICULTURAL LAND
11Corn
12Soybeans
13 Winter Wheat
14 Other Small Grains and Hay
15 Winter Wheat/Soybeans
16 Other Agriculture
17 Rural Grassland
 
20FORESTED LAND
21 Upland
25 Partial Canopy/Savannah Upland
26 Coniferous
 
30 URBAN LAND
31 High Density
32 Low/Medium Density
35 Urban Open Space
 
40 WETLAND
41 Shallow Marsh/Wet Meadow
42 Deep Marsh
43 Seasonally/Temporarily Flooded
44 Floodplain Forest
48 Swamp
49 Shallow Water
 
50OTHER
51 Surface Water
52 Barren and Exposed Land
53 Clouds
54 Cloud Shadows

Upon completion of the integrated classifications for each of the ten TM Path/Row scene areas, a seamless mosaic was created using image processing software developed by the USDA-NASS. Careful attention was made to develop the mosaic along county boundaries to ensure that the land cover information within each county area was derived from the same Landsat TM/ETM+ triplicate. Because of the orientation and overlap of adjacent Landsat TM scene areas, a few counties are a combination of 1999 and 2000 TM/ETM+ imagery. Compare the 1999-2000 TM Coverage Map with the Landsat TM Scene Coverage Map. Lastly, a formal assessment of the accuracy of the final integrated classification was conducted, and the detailed explanation of that procedure is contained in the Accuracy Assessment Section.


Landsat 7 ETM+ Panchromatic Image Mosaic

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A small portion of the Landsat 7 ETM+ Panchromatic Image Mosaic for the Peoria, Illinois area.

Seamless statewide mosaic produced from Landsat 7 ETM+, 15x15-meter resolution, continuous-tone panchromatic imagery. The Landsat 7 ETM+ sensor contains a Panchromatic channel sensitized to the green, red, and a portion of the near infrared reflected wavelengths, which corresponds to 0.52-0.90 micrometers within the electromagnetic spectrum (EMS). This broad band channel somewhat mimics a black-and-white (panchromatic) photograph, with the exception that traditional panchromatic photography is restricted to the visible portion of the EMS, extending from 0.38-0.74 micrometers.


As previously mentioned, the multispectral channels of the Landsat 5 TM and Landsat 7 ETM+ sensors possess a ground spatial resolution of 30x30 meters (98.4x98.4 feet). In contrast, the Landsat 7 ETM+ Panchromatic channel possesses a ground spatial resolution of 15x15 meters (49.2x49.2 feet), which means that it is suitable for GIS and mapping applications at a scale of approximately 1:50,000 (1"=4,167'') or smaller.


A review of the TM/ETM+ triplicates used for the IILCP initiative revealed that 1999 or 2000 Landsat 7 ETM+ Panchromatic imagery was available for all ten TM Path/Row scene areas within a one month fall period in 1999 or 2000 (compare fall season acquisition dates on the 1999-2000 TM Coverage Map). This narrow time span ensured consistency in ground conditions, creating the optimum conditions for development of a statewide mosaic. Wherever possible, the Panchromatic imagery was mosaicked along county boundaries, and each county area was individually contrast balanced. The final version of the Landsat 7 ETM+ Panchromatic Image Mosaic was compressed using MrSID for purposes of distribution.


TM Path/Row Pseudocolor Spectral Class Maps

These are single band, spectral class maps. The result of the unsupervised Isodata K-means clustering procedures imposed on each TM/ETM+ triplicate data set was a classification map containing 200-250 spectral classes. The resulting spectral classes have been color coded to simulate a false color infrared photograph for the spring, summer, and/or fall seasons. This has been accomplished by using an RGB 3-band rendition of either 4-3-2 or 4-3-1 from the Landsat TM/ETM+ sensor for each of the seasonal images.


These data sets are intermediate products between the original satellite imagery and the final classification. They are typically utilized only by remote sensing analysts for the class labeling procedures, a process of aggregating the 200-250 spectral classes to a fewer number of information classes that eventually results in the final classification.


These single band, spectral class maps can be color coded (pseudocolor) to simulate any 3-band RGB rendition from the original TM/ETM+ satellite imagery for the spring, summer, and fall seasons. The large number of spectral classes combined with the landscape complexity within each of the Path/Row scene areas results in Pseudocolor Spectral Class Maps that contain a large amount of detailed information. Since they mimic the original TM/ETM+ triplicates, this data product can also be used as an image map base for use in GIS and mapping applications.


An example will serve to demonstrate the potential for this data product.

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A small portion of the Pseudocolor Spectral Class Map for the Peoria, Illinois area. This October 10, 2000 image from the fall season has been color coded to simulate a false color infrared photograph. Note the amount of feature detail that is afforded with the 200+ spectral classes. Two spectral classes (#6 and #86) have been highlighted in bright blue and yellow, respectively. This was done to discriminate them from all of the remaining spectral classes. Spectral class #6 represents Peoria Lake, Upper Peoria Lake, and a portion of the mainstem of the Illinois River.

Spectral class #86 represents one category of upland forest.

Resource analysts can therefore derive their own variation of a land cover map for selected study areas by simply altering the colormap using a variety of GIS or graphics software.


Additional information about spectral bands and interpreting color infrared images is available.


References

Duda, R.D. and P.E. Hart, 1973. Pattern recognition and scene analysis, John Wiley and Sons, New York, 482 pp.


Luman, D.E. and J. Minhe, 1995. "The Lake Michigan Ozone Study: The Application of Satellite?Based Land Use and Land Cover Mapping to Large-Area Emissions Inventory Analysis", in Photogrammetric Engineering and Remote Sensing, Vol. 61, No. 8, pp. 1021-1032.


Gong, Peng and P.J. Howarth, 1990. An Assessment of Some Factors Influencing Multispectral Land Cover Classification@, in Photogrammetric Engineering and Remote Sensing, Vol. 56, No. 5, pp. 597?603.