Kansas Applied Remote Sensing (KARS) Program
2009
2005
2005 Kansas Land Cover Patterns, Level IV, State of Kansas (300m buffer)
raster digital data
withheld
kclp2005l4k
The 2005 Kansas Land Cover Patterns (KLCP) Mapping Initiative was a two-phase mapping endeavor that occurred over a three-year period (2007-2009). Note that while the processing occurred during the 2207-2009 period, all satellite data used in the project was acquired in 2005. Concurrent with mapping the state of Kansas, the Kansas River Watershed was also mapped. The Kansas River Watershed extends into southern Nebraska and includes a portion of eastern Colorado. During Phase I a Modified Level I map was produced. In Phase II a series of maps, Modified Level II through IV, were produced. All KLCP 2005 map products were produced at four spatial extents: the state of Kansas plus a 300 meter buffer, a DEM-derived watershed boundary of the Kansas River, the Kansas River watershed boundary plus a 1,000 meter buffer, and a combined dataset of the state of Kansas plus 300 meter buffer and the watershed plus 1,000 meter buffer.These extents are annotated in the file names with the extentions k, w, wb, and kwb respectively.
The goal of Phase II was to map subclasses for grassland and cropland, classes which were mapped during Phase I. For the Level II map, cool- and warm-season grasslands were mapped along with Spring Crop, Summer Crop, Alfalfa, Fallow, and Double-Crop classes. For the Level III map, the Summer Crop subclasses Corn, Soybean, and Sorghum were mapped, and the Spring Crop class was reassigned to Winter Wheat. In the Level IV map, irrigation status was mapped and added to the Level III crop type map.The Kansas Land Cover Patterns Level IV map contains twenty-four land use/land cover classes and has a positional accuracy and spatial resolution appropriate for producing 1:50,000 scale maps. The minimum map unit (MMU) varies by land cover class and ranges from 0.22 to 5.12 acres.
In general, the mapping methodology used a hybrid, hierarchical classification of multi-temporal, multi-resolution imagery to develop modified Anderson Level II through Anderson Level IV land cover maps of the Kansas River Watershed and the State of Kansas. More specifically, multi-seasonal Landsat Thematic Mapper (TM) imagery from the 2004 and 2005 growing season was used to map the grassland subclasses (cool- and warm-season grasslands). while MODIS NDVI time-series imagery from the 2005 growing season was used to map cropland subclasses.
The land use/land cover classes in the Level IV map are coded hierarchically to allow aggregation of land use and land cover classes as needed by the end-user. For example, a user can aggregate the Level IV map classes to a Level III classification by ignoring or eliminating the last digit of each land use/land cover class. Likewise, a Level II and Level I map can be created from the Level IV map by eliminating the last two and three digits, respectively, from each Level IV land use/land cover class.
A formal accuracy assessment found the Level II, Level III, and Level IV maps to have overall accuracy levels of 86.3%, 82.0%, and 74.3%, respectively. User and Producer (per-class errors of commission and omission) accuracies vary by land cover class and users are encouraged to reference the reported accuracy levels in the final report and/or metadata when using the Kansas Land Cover Patterns map series. Digital versions of the map, metadata, and accuracy assessment can be accessed from the Data Access Support Center (http://www.kansasgis.org/) or the Kansas Applied Remote Sensing Program (http://www.kars.ku.edu/).
This database was developed as part of the Core Database for the State of Kansas. It is suited for county-level and watershed-level analyses that involve land use and land cover.
This dataset is a combination of the state of Kansas boundary with the a 300 m buffer and the Kansas River Watershed boundary with a 1 km buffer.
File naming conventions:
File name:
klcp2005l4k
Description
klcp=Kansas Land Cover Patterns
2005=data aquired
l4= modified Anderson classification level
k=state of Kansas plus 300m buffer
en
unknown
publication date
None planned
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40.151250
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1903924.000000
None
Land Cover
Crop land
Land Use
Grassland
Woodland
Urban
Water
Landsat
MODIS
NDVI
Land Use/Land Cover
Agriculture
Classification
Irrigated Cropland
Alfalfa
Corn
Sorghum
Soybeans
Wheat
Fallow
Double Cropped
Kansas Land Cover Patterns
KLCP
Kansas
KS
Nebraska
NE
Colorado
CO
2005
None
This database is not suited for site-specific analyses. Interpretations derived from its use are intended for planning purposes only.
Dana Peterson
Kansas Applied Remote Sensing (KARS) Program
mailing address
2101 Constant Avenue
Lawrence
Kansas
66047
USA
785-864-1500
785-864-1534
The 2005 Kansas Land Cover Patterns map was created at the Kansas Applied Remote Sensing Program of the Kansas Biological Survey, The University of Kansas.
Funding Sources: The land cover mapping project was co-funded by the Kansas GIS Policy Board with funds from the Kansas Water Plan that are administered by the Kansas Water Office under the grant titled "Kansas Next-Generation Land Use/Land Cover Mapping Initiative" and the National Science Foundation EPSCoR Program under the research project titled "Understanding and Forecasting Ecological Change: Causes, Trajectories and Consequences of Environmental Change in the Central Plains."
Unclassified
ESRI ArcCatalog 9.3.1.3000
Raster Dataset
More than sixteen thousand sample sites were used to generate the formal accuracy assessments. The formal accuracy assessments consist of an error matrix, an overall accuracy figure, Cohen?s Kappa statistic (1960), and for each class, omission accuracy (often referred to as producer accuracy) and commission accuracy (user accuracy). Accuracy assessments are reported for Level II through Level IV classifications. The error matrix is hierarchical in structure, meaning that the error matrix generated for the Level IV map was aggregated (by class) to derive error matrices and associated accuracy levels for the Level III and Level II maps.
The USDA Farm Service Agency (FSA) Common Land Unit (CLU) database (attributed; Kansas only) and Kansas GAP vegetation database were used as ground-truth to assess the accuracy of the cropland, grassland, and woodland classes. Manual photo interpretation of the 2005 NAIP served as ground-truth to assess the accuracy of the urban and water classes.
A stratified random sample by land use/land cover class was used to select sites for the accuracy assessment. A sample site was defined as a CLU field or area of contiguous pixels. Sample size was roughly proportionate to the percent area mapped for each land use/land cover class. According to Congalton and Green (1991), a minimum of 75-100 samples should be used per land use/land cover class when mapping large areas. Since the land cover map depicts landscape features (i.e., fields of cropland or grassland, stands of trees, etc.), polygon features were selected as the most appropriate sampling unit for the accuracy assessment. The MMU for each land cover class was used as an area threshold for site selection (i.e., polygon features less than the MMU were excluded from the accuracy assessment).
Level II: 86.3%; Level III: 82.0%; Level IV: 74.4%
The attribute accuracy value corresponds to the overall accuracy of the Modified Anderson Level II through IV maps. User and producer accuracies vary by land cover class.
Level II
LULC Class / LULC Code /UserAcc(%) / ProducerAcc(%)
Commercial Industrial / 11 / 74.36% / 70.73%
Residential / 12 / 77.19% / 64.23%
Urban Openland / 13 / 62.86% / 75.86%
Row Crop / 21 / 89.14% / 86.58%
Small Grains / 22 / 90.47% / 88.87%
Alfalfa / 23 / 81.52% / 67.57%
Fallow / 24 / 92.59% / 70.82%
Double-Crop / 25 / 50.86% / 25.43%
CRP / 31 / NA / NA
Warm Season Grassland / 32 / 86.48% / 93.73%
Cool Season Grassland / 33 / 67.76% / 65.31%
Periodic Emergent Vegetation / 34 / NA / NA
Urban and Rural Woodland / 40 / 89.01% / 98.04%
Urban and Rural Water / 50 / 94.29% / 96.35%
Other / 60 / NA / NA
Level III
LULC Class / LULC Code /UserAcc(%) / ProducerAcc(%)
Commercial Industrial / 110 / 74.36% / 70.73%
Residential / 120 / 77.19% / 64.23%
Urban Openland / 130 / 62.86% / 75.86%
Corn / 211 / 74.20% / 74.42%
Soybean / 212 / 71.82% / 56.37%
Sorghum / 213 / 57.14% / 67.91%
Wheat / 221 / 90.47% / 88.87%
Alfalfa / 231 / 81.52% / 67.57%
Fallow / 241 / 92.59% / 70.82%
Double-Crop / 251 / 50.86% / 25.43%
CRP / 310 / NA / NA
Warm Season Grassland / 320 / 86.48% / 93.73%
Cool Season Grassland / 330 / 67.76% / 65.31%
Periodic Emergent Vegetation / 340 / NA / NA
Urban and Rural Woodland / 400 / 89.01% / 98.04%
Urban and Rural Water / 500 / 94.29% / 96.35%
Other / 600 / NA / NA
Level IV
LULC Class / LULC Code /UserAcc(%) / ProducerAcc(%)
Commercial Industrial / 1100 / 74.36% / 70.73%
Residential / 1200 / 77.19% / 64.23%
Urban Openland / 1300 / 62.86% / 75.86%
Non-Irrigated Corn / 2110 / 29.20% / 56.08%
Irrigated Corn / 2111 / 65.52% / 33.93%
Non-Irrigated Soybean / 2120 / 56.84% / 49.27%
Irrigated Soybean / 2121 / 44.83% / 23.47%
Non-Irrigated Sorghum / 2130 / 53.44% / 65.63%
Irrigated Sorghum / 2131 / 13.33% / 10.96%
Non-Irrigated Wheat / 2210 / 84.19% / 84.34%
Irrigated Wheat / 2211 / 20.00% / 15.13%
Non-Irrigated Alfalfa / 2310 / 58.19% / 49.52%
Irrigated Alfalfa / 2311 / 72.73% / 57.60%
Fallow / 2410 / 92.59% / 70.82%
Double Crop / 2510 / 48.89% / 18.97%
Irrigated Double Crop / 2511 / 0.00% / 0.00%
CRP / 3100 / NA / NA
Warm Season Grassland / 3200 / 86.48% / 93.73%
Cool Season Grassland / 3300 / 67.76% / 65.31%
Periodic Emergent Vegetation / 340 / NA / NA
Quantitative_Attribute_Accuracy_Assessment Attribute_Accuracy_Value
Logical_Consistency_Report Datasets delivered to the Data Access and Support Center are quality assurance tested for major documentation and topological errors using macros. The databases are evaluated based on questions similar to the following: Does the database's documentation properly explain the dataset? Does the data import properly into its native dataset environment? Is consistent methodology used throughout the data? Are all of the database's fields and values legitimate compared to the documentation? Is the database's topology free of errors that could impair the functionality of the data? Once the dataset is tested a report is written describing the evaluation. A copy of the report is then forwarded to the data originator for his/her review. If errors are found, the data originator is expected to make any necessary corrections and then provide those corrected coverages. After these steps have been taken the dataset is considered archivable and made available for distribution. Data that is archived by DASC has been tested and corrected.
+/- 15 meters
15 meters
Landsat TM imagery from the 2004-2005 Kansas Satellite Image Database (KSID) (Whistler et al., 2006), a database previously developed by the Kansas Applied Remote Sensing Program and funded by the Kansas State GIS Policy Board with assistance from the USGS AmericaView program, augmented with additional TM imagery to cover the Kansas River Watershed funded by the NSF EPSCoR grant, was the primary data source used to map cool- and warm-season grassland. Imagery from nineteen path/row center points was required to provide complete coverage for the state of Kansas and the Kansas River Watershed. For each path/row, the image database contains terrain-corrected spring, summer, and fall TM triplicates. All imagery was selected based on the dates of available data and minimal cloud contamination. Additional scenes were purchased by the NSF EPSCoR grant to supplement KSID triplicates that had a cloud-contaminated scene, a non-optimal date for a scene, or an inter-annual triplicate (i.e., one or more scenes from a year other than 2005). NAIP imagery from 2005 was used as a backdrop to digitize Periodic Emergent Vegetation.
231-meter MODIS NDVI time-series data (16-day composites) from the 2005 growing season (March 22 to November 1) were used to produce the cropland subclasses for the Level II through IV maps. For each county in Kansas, and for counties in Colorado and Nebraska falling within the Kansas Watershed, USDA Quick Stats data and unattributed Common Land Unit (CLU) boundaries were used to develop the Level IV map of irrigation status by crop type.
Process_Description Landsat Thematic Mapper imagery was acquired for the study area for each of the three periods of the 2005 growing season - spring, summer, and fall - and was supplemented with 2004 scenes where 2005 scenes were not available. A 12-band image stack was created for each path/row with bands 3, 4, 5 and 7 from each period.
A supervised classification approach using See5 decision tree software (Rulequest Research Pty Ltd., 2007) and the multi-seasonal TM imagery were used to map cool- and warm-season grassland. The Kansas Vegetation Survey and the attributed CLU data were used for training and validation of the decision tree classifier. The classification methodology used a hierarchical approach, where grassland pixels in the Level I map were used to extract grassland pixels from each TM path/row stack that were then classified into the two grassland subclasses. Once the classification for all path/rows was completed, the individual maps were mosaicked to produce a study-wide grassland map. The unattributed CLU field boundary data were then used for map generalization. The CLU data fit the grassland subclasses from the mosaic into fields delineated in the CLU data. The objective was to utilize the spatial precision of field boundaries provided by the CLU data to better depict the spatial extent of both grassland and cropland classes. Following the generalization, grassland pixels in the Level I map were reassigned to their respective grassland subclasses.
For the Level II cropland classification, a supervised classification approach using See5 decision tree software and the multi-temporal MODIS NDVI imagery were used to map the dominant crop types in the study area. The attributed CLU data were used for classification training and validation. The classification methodology utilized a hierarchical approach, where cropland pixels in the Level I map were used to extract cropland pixels from the MODIS NDVI time-series. The extracted crop pixels were then classified into the Level II crop subclasses Summer Crop, Spring Crop, Alfalfa, Fallow, and Double-Crop.
For the Level III cropland classification, another round of supervised classification using See5 decision tree software and the multi-temporal MODIS NDVI imagery was used to map three crop types within the Level II Row Crop class. The attributed CLU data were used for classification training and validation. Again, the classification methodology utilized a hierarchical approach, where Row Crop pixels in the Level II map were used to extract a subset of pixels from the MODIS NDVI imagery. The Row Crop pixels were then classified into three specific summer crop classes: Corn, Soybeans, and Sorghum. The Level III cropland map was then resampled from 231 meters to 30 meters to match the 30-meter footprint of the Level I map. Next, the Level III cropland map was generalized using the unattributed CLU field boundary data. The cropland pixels in the Level I map were then reassigned to their respective Level III and Level II crop subclasses.
The Level IV cropland classification depicts irrigation status by crop type and uses a different methodology than the supervised classification approach used to produce the crop classes found in the Level II and Level III maps. Irrigation status was mapped using 2005 time-series MODIS NDVI imagery, USDA Quick Stats data, unattributed CLU boundaries and the Level III cropland map. The methodology used to map irrigated croplands was based on Brown, et al., 2007. A basic assumption of the approach is that irrigated crops possess a peak, or maximum, NDVI value higher than non-irrigated crops. The maximum NDVI (?max NDVI?) was extracted from the 2005 MODIS NDVI time-series and then resampled from the original 231.6m to 30m to match the resolution of the Level III cropland map. The ?Cropland? class from the Level I LULC map was then used to extract ?max NDVI? values for cropland areas. Once extracted, the zonal average for ?max NDVI? was calculated using CLU fields as the zone grid. This procedure assigned the same ?max NDVI? value to all pixels comprising the CLU field (i.e., zone). ?max NDVI? values were then extracted for each crop type (i.e., alfalfa, corn, sorghum, soybeans, and wheat) using the Level III LULC map. For each county, a threshold value for ?max NDVI? for a crop type was used to classify individual CLUs as irrigated. The threshold value was determined by calculating the running total acres for a given crop type in descending ?max NDVI? order and then identifying the ?max NDVI? value (i.e., the target value) where the running total area most closed matched the irrigated area given in the USDA 2005 Quick Stats data. All CLUs with ?max NDVI? values greater than or equal to the target value were mapped as irrigated. Once all crop types had been processed, the individual irrigated crop types were merged to form an Irrigated Crops map. The Irrigated Crops map was then used to reassign crop type pixels in the Level III map as irrigated or non-irrigated to produce the Level IV map.
For detailed process information, the full project report is available through the Data Access Support Center (DASC) (http://www.kansasgis.org/) or the Kansas Applied Remote Sensing Program (http://www.kars.ku.edu/).
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20091019
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kclp2005l4kll.vat
Table
24
Rowid
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
Rowid
OID
4
0
0
VALUE
VALUE
Integer
4
0
0
COUNT
COUNT
Integer
4
0
0
ACRES
ACRES
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50
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LEVEL2_LULC_CODE
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4
0
0
Level IV land use/land cover codes, classes, and definitions:
1100 Urban Commercial/Industrial
1200 Urban Residential
1300 Urban-Openland
1400 Urban-Woodland
1500 Urban-Water
2110 Non-Irrigated Corn
2111 Irrigated Corn
2120 Non-Irrigated Soybean
2121 Irrigated Soybean
2130 Non-Irrigated Sorghum
2131 Irrigated Sorghum
2210 Non-Irrigated Winter Wheat
2211 Irrigated Winter Wheat
2310 Non-Irrigated Alfalfa
2311 Irrigated Alfalfa
2400 Fallow
2500 Double-Crop
3100 Conservation Reserve Program
3200 Warm-Season Grassland
3300 Cool-Season Grassland
3400 Periodic Emergent Vegetation
4000 Woodland
5000 Water
6000 Other
Level III land use/land cover codes, classes, and definitions:
110 Urban Commercial/Industrial
120 Urban Residential
130 Urban-Openland
140 Urban-Woodland
150 Urban-Water
211 Corn
212 Soybean
213 Sorghum
221 Winter Wheat
231 Alfalfa
240 Fallow
250 Double-Crop
310 Conservation Reserve Program
320 Warm-Season Grassland
330 Cool-Season Grassland
340 Periodic Emergent Vegetation
400 Woodland
500 Water
600 Other
Level II land use/land cover codes, classes, and definitions:
11 Urban Commercial/Industrial
12 Urban Residential
13 Urban-Openland
14 Urban-Woodland
15 Urban-Water
21 Summer Crop
22 Spring Crop
23 Alfalfa
24 Fallow
25 Double-Crop
31 Conservation Reserve Program
32 Warm-Season Grassland
33 Cool-Season Grassland
34 Periodic Emergent Vegetation
40 Woodland
50 Water
60 Other
Entity_and_Attribute_Detail_Citation Urban Commercial/Industrial (class code 11)- Same as Level IV class code 1100 description.
Entity_and_Attribute_Detail_Citation Urban Residential (class code 12)- Same as Level IV class code 1200 description.
Entity_and_Attribute_Detail_Citation Urban-Openland (class code 13)- Same as Level IV class code 1300 description.
Entity_and_Attribute_Detail_Citation Urban-Woodland (class code 14)- Same as Level IV class code 1400 description.
Entity_and_Attribute_Detail_Citation Urban-Water (class code 15)- Same as Level IV class code 1500 description.
Entity_and_Attribute_Detail_Citation Summer Crop (class code 21)- Areas used for the production of Corn, Soybean and Sorghum crops.
Entity_and_Attribute_Detail_Citation Spring Crop (class code 22)- Areas used for the production of small grains.
Entity_and_Attribute_Detail_Citatation: Alfalfa (class code 23) ? Same as Level III class code 231 description.
Entity_and_Attribute_Detail_Citation Fallow (class code 24)- Same as Level IV class code 2400 description.
Entity_and_Attribute_Detail_Citation Double-Crop (class code 25)- Same as Level IV class code 2500 description.
Entity_and_Attribute_Detail_Citation Conservation Reserve Program (CRP) Land (class code 31)- Same as Level IV class code 3100 description.
Entity_and_Attribute_Detail_Citation Warm-Season Grassland (class code 32)- Same as Level IV class code 3200 description.
Entity_and_Attribute_Detail_Citation Cool-Season Grassland (class code 33)- Same as Level IV class code 3300 description.
Entity_and_Attribute_Detail_Citation Periodic Emergent Vegetation(class code 34)- Same as Level IV class code 3400 description.
Entity_and_Attribute_Detail_Citation Woodland (class code 40)- Same as Level IV class code 4000 description.
Entity_and_Attribute_Detail_Citation Water (class code 50)- Same as Level IV class code 5000 description.
Entity_and_Attribute_Detail_Citation Other (class code 60)- Same as Level IV class code 6000 description.
Downloadable Data
The Kansas Biological Survey (KBS) and its representatives make no warranty or representation, either expressed or implied, with respect to the digital data and their documentation, their quality, performance, merchantability, or fitness for a particular purpose. The digital data are distributed on "as is" basis, and the user assumes all risk as to their quality, the results obtained from their use, and the performance of the data. In no event will the KBS or its representatives be liable for any direct, indirect, special, incidental or consequential damages resulting from and defect in the KBS's digital data or in their documentation. This disclaimer of warranty is exclusive and in lieu of all others, oral or written, expressed or implied. No agent or employee is authorized to make any modification, extension, or addition to this warranty.
53.591
53.591
20100603
Kansas Applied Remote Sensing (KARS) Program
Dana Peterson
mailing address
2101 Constant Avenue
Lawrence
Kansas
66047
USA
785-864-1500
785-864-1534
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
local time
http://www.esri.com/metadata/esriprof80.html
ESRI Metadata Profile
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ESRI Metadata Profile
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The 2005 Kansas Land Cover Patterns (KLCP) Mapping Initiative was a two-phase mapping endeavor that occurred over a three-year period (2007-2009). Note that while the processing occurred during the 2207-2009 period, all satellite data used in the project was acquired in 2005. Concurrent with mapping the state of Kansas, the Kansas River Watershed was also mapped. The Kansas River Watershed extends into southern Nebraska and includes a portion of eastern Colorado. During Phase I a Modified Level I map was produced. In Phase II a series of maps, Modified Level II through IV, were produced. All KLCP 2005 map products were produced at four spatial extents: the state of Kansas plus a 300 meter buffer, a DEM-derived watershed boundary of the Kansas River, the Kansas River watershed boundary plus a 1,000 meter buffer, and a combined dataset of the state of Kansas plus 300 meter buffer and the watershed plus 1,000 meter buffer.These extents are annotated in the file names with the extentions k, w, wb, and kwb respectively.
The goal of Phase II was to map subclasses for grassland and cropland, classes which were mapped during Phase I. For the Level II map, cool- and warm-season grasslands were mapped along with Spring Crop, Summer Crop, Alfalfa, Fallow, and Double-Crop classes. For the Level III map, the Summer Crop subclasses Corn, Soybean, and Sorghum were mapped, and the Spring Crop class was reassigned to Winter Wheat. In the Level IV map, irrigation status was mapped and added to the Level III crop type map.The Kansas Land Cover Patterns Level IV map contains twenty-four land use/land cover classes and has a positional accuracy and spatial resolution appropriate for producing 1:50,000 scale maps. The minimum map unit (MMU) varies by land cover class and ranges from 0.22 to 5.12 acres.
In general, the mapping methodology used a hybrid, hierarchical classification of multi-temporal, multi-resolution imagery to develop modified Anderson Level II through Anderson Level IV land cover maps of the Kansas River Watershed and the State of Kansas. More specifically, multi-seasonal Landsat Thematic Mapper (TM) imagery from the 2004 and 2005 growing season was used to map the grassland subclasses (cool- and warm-season grasslands). while MODIS NDVI time-series imagery from the 2005 growing season was used to map cropland subclasses.
The land use/land cover classes in the Level IV map are coded hierarchically to allow aggregation of land use and land cover classes as needed by the end-user. For example, a user can aggregate the Level IV map classes to a Level III classification by ignoring or eliminating the last digit of each land use/land cover class. Likewise, a Level II and Level I map can be created from the Level IV map by eliminating the last two and three digits, respectively, from each Level IV land use/land cover class.
A formal accuracy assessment found the Level II, Level III, and Level IV maps to have overall accuracy levels of 86.3%, 82.0%, and 74.3%, respectively. User and Producer (per-class errors of commission and omission) accuracies vary by land cover class and users are encouraged to reference the reported accuracy levels in the final report and/or metadata when using the Kansas Land Cover Patterns map series. Digital versions of the map, metadata, and accuracy assessment can be accessed from the Data Access Support Center (http://www.kansasgis.org/) or the Kansas Applied Remote Sensing Program (http://www.kars.ku.edu/).
002
This database was developed as part of the Core Database for the State of Kansas. It is suited for county-level and watershed-level analyses that involve land...
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16
Degree
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