Kansas Applied Remote Sensing (KARS) Program June 2008 raster digital data withheld klcp2005l1k The 2005 Kansas Land Cover Patterns Level I map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly comparable to the 1990 Kansas Land Cover Patterns map. Using a similar methodology to produce the 2005 Kansas Land Cover Patterns map provides opportunities to identify and examine how the Kansas landscape has changed over a 15-year period. The map contains eleven land use/land cover classes. The positional accuracy and spatial resolution of the map are appropriate for producing 1:50,000 scale maps. The map is not intended to define precise boundaries between landscape features and while the source data has a spatial resolution of 30 m x 30 m, the minimum map unit varies by land cover class and ranges between 0.22 to 5.12 acres (see below). The formal accuracy assessment reports the map to have an overall accuracy level of 90.72%. User and Producer accuracies vary by land cover class and rural classes have higher accuracy levels (88-95%) than urban classes (48-78%). Users are encouraged to reference the reported accuracy levels in this report and/or metadata when using the 2005 Kansas Land Cover Patterns map. Digital versions of the map, metadata, and accuracy assessment can be accessed from the Data Access Support Center (DASC) website of the Kansas Geological Survey (http://www.kansasgis.org/) or the website of the Kansas Applied Remote Sensing Program (http://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 analyzes that involve land use and land cover. Land Cover Map Development Data Preprocessing: Although the imagery had already gone through some image pre-processing steps (i.e., terrain-correction), additional pre-processing was required in preparation for image classification. The pre-processing steps taken are listed below. Processing units were defined by Landsat TM path/rows. 1. For each path/row, subset bands 3, 4, 5, and 7 from each scene to combine in one file using the ERDAS Imagine Layer Stack Function, creating a 12-band multi-seasonal image. Previous experience has shown this band combination to be most effective in multi-date land cover classification. 2. Clean the edges of each 12-band image so that only pixels present in all three dates are preserved. This step was necessary due to slight positional offsets that occur from minor variations in the satellite's orbit over time. 3. Inspect the 12-band multi-seasonal image for cloud cover. If clouds exist: a. Digitize and mask clouds from the 12-band image. b. For cloud-contaminated areas, create a 2-date cloud-free layer stack as a separate sub-processing unit for image classification. c. If clouds overlap from two dates, create a 1-date cloud-free layer stack as a separate sub-processing unit for image classification. This was a rare occurrence and only represented a small percentage of the overall processing area. 4. Merge adjacent path/rows into one processing unit when they contain the same triplicate dates. 5. Subset the spatial extents of processing units to reduce the amount of overlap between processing units and then clip to the study area (the state boundary buffered by 300m or 10 TM pixels) where appropriate. Preference or priority was given (i.e. spatial extents were maximized) to processing units containing intra-annual triplicates (all within the same year) and to the 2005 TM data. There were a few instances when the overlap areas between adjacent scenes were combined to provide a cloud-free triplicate or more optimal dates for image classification. These combined areas were treated as separate processing units. 6. Using a heads-up digitizing approach, create an urban mask. Developed areas exceeding 40 acres areas were digitized on a displayed image of the 2005 NAIP using ArcGIS 9.1 software. The urban mask was then used to create both a rural layer stack and an urban layer stack for each processing unit for image classification. Image Classification: Image classification was broken into four tasks, conducted in parallel, with specific mapping objectives for each task. The 12-band image file was run through an unsupervised statistical clustering classifier because time-consuming and expensive signature development and training is not required and because our previous experience with land cover mapping has underscored the value of using unsupervised classification to identify these land cover classes. When all four tasks were completed, they were merged and the map generalized according to the minimum map unit defined for each class. The objectives for each task are listed below: 1. Produce a map of cropland and grassland; 2. Produce a map of woodland; 3. Produce a map of water and; 4. Produce a map of urban classes. For each task, the following processing steps were used: 1. Use the 12-band TM image file to generate N-spectral signatures (varied by class) using the ISODATA algorithm. a. Rural: 100 spectral signatures b. Woodland: 100 spectral signatures c. Water: 100 spectral signatures d. Urban: 50 spectral signatures 2. Use the spectral signatures and the Maximum Likelihood Classifier to create the specified number of spectral clusters. 3. Display the three dates of TM imagery in separate windows with the spectral class image overlaid. 4. Highlight each spectral class and assign it to the appropriate LULC class: a. Rural: Grassland, Cropland, or Confused b. Woodland: Woodland, Non-woodland, or Confused c. Water: Water, Non-water, or Confused d. Urban: Urban Commercial/Industrial, Urban Residential, Urban Openland, or Confused 5. Use a cluster-busting technique on confused spectral classes. Create a TM image of the confused spectral classes (i.e. spectral clusters containing more than one land cover class), and break confused classes into additional spectral clusters (Typically, three times the number of confused classes). Highlight each class in the cluster-busted image on the TM imagery and assign a land cover class. Repeat the cluster busting process until all classes are interpreted. 6. Provide the spectral class assignments to a second analyst for QAQC. Evaluate feedback from the QAQC and resolve issues regarding the spectral class assignments. 7. Recode spectral classes from steps 4 and 5 to appropriate LULC classes. 8. Merge image classifications from steps 4 and 5 into one map for each processing unit. Results The end product for Phase I of the Next-Generation Kansas Land Cover mapping project is an updated digital Level 1 land cover map of Kansas. A summary of the land cover types, their area mapped in square kilometers, and the percent of the total area in Kansas represented by each type is presented in the table below. LULC area Summary table: Modified Anderson Level I Land Cover Types, Their Area Mapped (sq. km.), and the Percent of the State's Total Area Represented by Each Land Cover Type. Land Cover Type Code Percent Area ( km2) Commercial/Industri 11 0.34 720 Residential 12 0.69 1473 Urban Openland 13 0.59 1263 Urban Woodland 14 0.09 191 Urban Water 15 0.02 40 Cropland 20 45.98 98248 Grassland 30 41.97 89679 CRP Land 31 5.38 1496 Woodland 40 4.07 8705 Water 50 0.79 1688 Other 60 0.09 182 Total 100.00 213689 In the overall Kansas map, the broad patterns of land cover are readily apparent in the table and map. The effects of human activity upon the Kansas landscape are clearly reflected in the fact that nearly 46% of the state's land area is devoted to cropland while an additional 5% are CRP land. The major grassland areas of Kansas, including the Flint Hills of eastern Kansas, the Smokey Hills of north-central Kansas, and the Red Hills of south-central Kansas are easily distinguished, as are grasslands along the reaches of rivers and streams in western Kansas. Large areas of nearly continuous cropland dominate the western two thirds of the state, with large tracts of CRP land evident. Woodlands interspersed within grasslands and croplands characterize the heterogeneous eastern third of Kansas. The eastern third of Kansas also contains a major portion of the State's population and the major population centers of Kansas City, Lawrence, Manhattan, Pittsburgh, Topeka, and Wichita are seen, as well as numerous smaller towns. Cartographic generalization of the classified TM data was performed to eliminate "noise" in the classification and simplify the map. Noise is comprised of either extraneous misclassified pixels or small clumps of pixels that are insignificant at the suggested mapping scale of the map (1:50,000) (Figure 2a). Before designing and running the generalization procedures, the minimum mapping unit (MMU) was chosen for each land use/land cover class. The MMU size, or smallest number of contiguous pixels, chosen for a particular class was based on the following factors: 1) Is the class reliably detected by the classification? 2) Is the class accurately represented? 3) What level of thematic detail (i.e., how small of area) should be preserved at the suggested mapping scale? 4) Selecting class MMUs that would be comparable to the 1990 Kansas Land Cover Patterns database. Taking these factors into account, the following MMU for each land use/land cover class was established: Land Use/Land Cover Class MMU (pixels) MMU (acres) Urban classes o/t urban woods & water 15 3.114 Grassland and Cropland 23 5.115 Woodland (urban and rural) 3 0.667 Water (urban and rural) 1 0.222 Other 15 3.114 en 2005 2005 Complete As needed -102.298587 -94.553596 40.151250 36.845359 -532660.000000 122150.000000 1550344.000000 1903924.000000 None Land Cover Cropland Land Use Grassland Woodland Urban Water Kansas 2005 None This database is not suited for site-specific analyzes. Interpretations derived from its use are intended for planning purposes only. Kansas Applied Remote Sensing (KARS) Program Jerry Whistler

2101 Constant Ave.

Lawrene 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. 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 The CLU database and Kansas GAP vegetation database were used as ground-truth to assess the accuracy levels of cropland, grassland, and woodland classes. Manual photo interpretation of the 2005 NAIP served as ground-truth to assess the accuracy levels of urban and water classes. A stratified random sampling design was used for site selection. The sample size for each land cover class was proportionate to the area mapped for each class. The CRP and Other classes were not represented in the accuracy assessment. Polygons or features were used as the sampling unit for the accuracy assessment. More than thirty-one thousand sample sites were used to generate the formal accuracy assessment. The formal accuracy assessment consists of an error matrix, an overall accuracy level, Cohen's Kappa statistic, and for each class, omission accuracy (often referred to as producer accuracy) and commission accuracy (user accuracy). Attribute Accuracy Value = 90.72% The attribute accuracy value corresponds to the overall accuracy of the Modified Anderson Level 1 classification. User and producer accuracies vary by land cover class. LULC Class LULC Code UserAcc(%) ProducerAcc(%) Urban Commercial Industrial 11 61.05 74.36 Urban Residential 12 48.35 77.19 Urban Openland 13 78.43 64.17 Cropland 20 90.92 93.37 Grassland 30 91.23 88.58 CRP 31 NA NA Woodland (rural and urban) 14 & 40 95.77 80.68 Water (rural and urban) 15 & 50 95.81 92.93 Other 60 NA NA 90.72 Field campaigns for accuracy assessments can be costly and time-consuming endeavors. Rather than conducting an independent field campaign for the accuracy assessment, two existing databases were used to assess the accuracy of the 2005 land cover map. The 2005 Common Land Unit (CLU) dataset was used to assess the accuracy of mapped grassland and cropland and the Kansas GAP vegetation database was used to assess the accuracy of mapped woodlands. The Kansas GAP vegetation database is a digital database of sample sites used for training and validation of the Kansas Vegetation Map (Egbert et al., 2001). Urban and water databases were unavailable, and therefore, manual photo interpretation techniques of digital imagery were used to assess the accuracy of these land cover classes. More than thirty thousand sample sites were used to generate the formal accuracy assessment. The formal accuracy assessment consists of an error matrix, an overall accuracy level, Cohen's Kappa statistic, and for each class, omission accuracy (often referred to as producer accuracy) and commission accuracy (user accuracy). Sampling Unit: According to Congalton and Green (1991), the sampling unit dictates the level of detail in the accuracy assessment and the same MMU used for map development should also be used for reference data development. The MMU and the spatial detail of the map were the two factors considered for selecting the appropriate sampling unit. With the exception of the urban and rural water classes, the land use/land cover classes had MMU's greater than a single pixel. Therefore, single pixels were deemed inappropriate sampling units for the accuracy assessment. 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). Site Selection and Sample Size: A stratified random sample by land use/land cover class was used to select sites for the accuracy assessment. 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. Seventy-eight sites were selected from the smallest class mapped (Commercial/Industrial, representing 0.28% of the total study area). The number of samples selected for the additional map classes were determined using roughly the same sample-size-to-area-mapped ratio, with the exception the woodland class, which lacked the available data to maintain a similar proportion. A total of 31,298 sites were used to assess the accuracy of the land cover map.The approach and methods used to generate ground reference data for land cover class are described below. Urban: Polygons within urban areas were randomly selected from the land cover map. Randomly selected polygons were visually interpreted using the 2005 NAIP as ground reference and assigned an urban class. For the accuracy assessment, urban water and urban woodland were grouped with rural water and woodland classes. Water: Two hundred two-square mile areas were randomly selected from the statewide public land survey system (PLSS) data layer. For each selected area, water bodies larger than 30 m x 30 m (one TM pixel) were identified on the NAIP and digitized. Because many streams in the study area are ephemeral, only standing water bodies were represented in the accuracy assessment. Grasslands: Dominant grass types (e.g. Fescue, Brome, Native, Big Blue, etc.) specified by the attribute "TYPE_ABBR" in the CLU database were subset from the CLU database. Several grassland features and types were excluded from the accuracy assessment. The description and rationale for the exclusions follow. ·Uncommon grass types, defined as representing less than 100 acres in the state (as determined by the CLU database), and grasses grown in a crop type fashion for sod (e.g. Crabgrass, Turf, Zoysia), were excluded from the site selection process. ·CRP land was excluded from the grassland accuracy assessment since this land cover class was derived directly from the CRP layer in the CLU database. ·The 30m spatial resolution of Landsat Thematic Mapper was too coarse to map many grass waterway features. Because of this limitation, grass waterway features were excluded from the accuracy assessment. These features were identified as having a relatively high perimeter-to-area ratio. Specifically, grassland features with a perimeter-to-area threshold greater than 45.2 were eliminated from the accuracy assessment site selection process. Woodlands: Accuracy levels for the rural and urban woodland classes were assessed using the Kansas GAP vegetation database. The woodland from the GAP database was overlaid on 2005 NAIP and sample sites with positional accuracy problems or sample sites that fell on non-woodland, were modified or deleted from the database. To ensure adequate class representation, additional urban woodland sites were collected using manual photo interpretation techniques. Other: The other class was not included in the accuracy assessment since the class represents such a small percentage (0.07%) of the study area and is a rare, catch-all class, (e.g. the class represents bare earth, rock outcrops, sand bars, and man-made features). A random, non-clustered dataset for use in the accuracy assessment could not be developed. Accuracy Assessment Results: The overall accuracy of the map was 90.72%, on target with the goal of achieving an accuracy level greater than 85%. The Cohen KAPPA statistic was equal to 83.54%. These results represent the highest overall accuracy level for a Level I map produced by the Kansas Applied Remote Sensing Program to date. Confusion between cropland and grassland classes raised concern regarding the accuracy of the CLU data, which was used as ground reference data for the accuracy assessment. To determine whether the reported accuracy levels were reflecting error in the CLU database, CLU features contributing to errors of omission and commission in the grassland and cropland classes were visually interpreted using the 2005 NAIP as reference. A total of 2020 grassland features and 1205 cropland features were visually interpreted. Of the 2020 grassland sites evaluated in the CLU database, 166 were visually interpreted to be cropland or woodland and 137 features were labeled as confused or undecided. These 303 sites were eliminated from the final accuracy assessment. Likewise, 124 cropland fields in the CLU database were interpreted as grassland, 3 as woodland, 1 as water and 34 undecided. These 162 sites were also eliminated from the final accuracy assessment. Interestingly, a large number of problematic CLU's were clustered in one county. Combining the grassland and cropland CLU assessments, this particular county had an error rate of 43%. The high error rate may be a function of database mismanagement, specifically, how the attribute database was joined to the polygon feature database. Because of the high error rate, all sample sites from this county were dropped from the accuracy assessment. After the errors in the CLU database were identified and eliminated, accuracy levels were recalculated. Even though user and producer accuracy levels for cropland and grassland were relatively high (88-93%), there was some misclassification errors (omission and commission errors) between these two land cover classes (Table 8). The sections below explore and discuss multiple scenarios in which these misclassification errors occur. Difficulties Mapping Grasslands: There were 1,457 fields, or features, mapped as cropland that the CLU database identified as grassland. Approximately 47% of these grassland areas were cool season grasslands and 53% were warm season grasslands. The majority of these fields were relatively small. Misclassifying cool-season grasslands as cropland - Seventy-six percent (527 fields) of the cool-season grasslands misclassified as cropland were either brome or smooth brome fields and were located primarily in the eastern half of the state. Common management practices on cool season grasslands (brome and fescue) include fertilizing, grazing, and haying and the frequency and duration of these management practices varies. It is likely that these management practices caused spectral confusion between cool season grasslands and cropland in the image classification. Using three dates of TM imagery, the lush spring vegetation in brome fields were spectrally similar to winter wheat fields, while haying events generated a spectral response similar to a harvested spring or summer crop, depending on the timing of the haying event. Misclassifying warm-season grasslands as cropland - There were 768 fields of warm season grassland inaccurately mapped as cropland. Eighty percent of these warm season grasslands were located in western half of the state. Upon further investigation, we found that a large number of these features were corners of center pivot fields (Figure 4). While some of these small corners were not detectable or mapped using TM data, others were mapped, but eliminated during the generalization process. Center pivot corners less than the MMU (5 acres or 23 pixels) were eliminated in stage 2 of our generalization procedure. And some of the center pivot corner features exceeding the MMU were eliminated during stage 3 of the generalization procedure. During stage 3 of the generalization procedure, which used the unattributed CLU data, the zonal majority within each CLU polygon was calculated and pixels within the CLU were reassigned to the zonal majority value. Unfortunately, there were discrepancies in the level of detail between the 63-county attributed data (used for the accuracy assessment) and the statewide unattributed data (used for stage 3 of the generalization). More specifically, there were instances where center pivot corners were delineated in the attributed CLU data but not in the unattributed CLU database. Although the use of the unattributed CLU data in the generalization process caused some undesirable outcomes, we believe the overall benefits of its use in terms of cleaning up misclassified areas and improving class cartographic representation far exceeds loss of these smaller features. Difficulties Mapping Cropland Features: Misclassifying cropland as grassland. Of 15,836 cropland reference sites used in the accuracy assessment, 1,012 were misclassified as grassland (Table 8). Over 70% of these sites were soybean, fallow land, or winter wheat. According to the CLU database, 172 (17%) soybean sites were misclassified as grassland with half of those sites falling within TM path/row 27/33 in northeastern Kansas. The TM triplicate for this processing unit had a summer date of June 22, 2005, a date potentially too early in the growing season to classify all instances of late summer crops such as soybeans. Of the 1,012 cropland sites misclassified as grassland, 246 (24%) were labeled as fallow land in the CLU database. The potential for fallow land to be defined as a land use and also as a land cover type explains a large portion of these "classification errors". From a land use perspective, fallow land has been temporarily removed from cultivation as a land management strategy for weed control and/or to conserve soil nutrients and soil moisture. However, from a land cover perspective, during the first year fallow land is composed of crop stubble and bare soil, with little or no vegetation cover. Lands removed from cultivation for one or more years, would no longer be bare soil but would be in the early stages of plant succession. Therefore, bare fields were typically mapped as cropland, while fields idle or fallow for one or more years with established vegetation cover were mapped based on the dominant land cover type. And in many instances the dominant vegetation cover was (weedy) grassland and in rare instances, woodlands. The CLU data lacks information regarding the timing or duration of fallow status. Approximately 316 (31%) cropland features misclassified as grassland were non-irrigated winter wheat fields. In some areas, the date of the spring image may have been too early (mid- to late-March) to differentiate all non-irrigated winter wheat from grassland. In contrast, irrigated wheat fields in these same areas were lush and consequently were more spectrally distinct from grassland. Difficulties Mapping Urban Features: The inherent heterogeneity and fragmentation typical of urban landscapes can make image classification of urban areas using 30 m TM data challenging. For example, four-lane highways traverse residential and commercial areas alike. Small strip malls reside in the midst of residential areas. Wooded streams flow along highways and through industrial bottomlands. Compounding the mapping challenge, most urban land use classes are a mix of land cover types. For example, the residential land use class is composed of several cover types; concrete/asphalt roads, drives, and sidewalks, grass and tree covered lawns, and a variety of rooftop types. In a mature neighborhood, the overall mix of these cover types will generally be detected by a TM pixel as a distinct land use class. However, this ideal falls apart very quickly in low density residential areas such as suburbs and small towns, where the spacing between the various cover types tends to increase and grass and tree cover become the dominant features detected within the TM pixel. Similar situations of classification confusion can be drawn for other urban land use classes. Therefore it should not be altogether surprising that user and producer accuracy levels for the urban classes were relatively low. In spite of these challenges, the classification of urban areas holds up quite well when a visual comparison is made with the 2005 NAIP photography. Misclassification of Openland as Residential - Commission errors for the residential class were relatively high, meaning residential areas were overestimated. The relatively low user accuracy level (48.35%) for the residential class was largely the result of two scenarios: 1) urban openland areas possessing moderate densities of roads were often misclassified as residential and 2) very low density residential areas were often classified as urban residential rather than urban openland. These scenarios also explain the relatively low producer accuracy level (high omission error) for the urban openland class. The confusion between urban residential and openland largely hinges on difficulties developing mutually exclusive spectral class signatures for these two land use classes, especially for rural residential areas that include varying proportions of both residential and openland. Contributing to the poor classification accuracy for the residential and urban openland classes was the decision to delineate suburban residential areas as an urbanized area. Because there are numerous suburban residential areas in many parts of Kansas, the decision was made that they constitute an import feature to map. However, the lot size in these developments are often 5 acres or greater. Consequently, the dominant cover types are often grasses and these areas were classed as urban openland. In hindsight, it could be argued whether these areas should have been included in the urban delineation. 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 data 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 data 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, was the primary data source used for map development. Sixteen path/row scenes were required to provide complete coverage of the state. For each scene center in Kansas, the database contains terrain-corrected spring, summer, and fall TM triplicates. Imagery in the 2004-2005 KSID was selected based on the dates of available data and cloud contamination. Additional scenes were purchased by the NSF EPSCoR grant to supplement triplicates that had cloud-contaminated scenes, a non-optimal date for a scene, or an inter-annual triplicate. Dataset copied. withheld Metadata imported. withheld 20080813 12412000 Metadata imported. withheld 20091116 16224200 Dataset copied. withheld 20100621 14214200 Not Appicable Raster Grid Cell 11786 21827 1 30.000000 30.000000 8 Upper Left FALSE Default 1 matrix values FALSE GRID row and column 30.000000 30.000000 meters North American Datum of 1983 Geodetic Reference System 80 6378137.000000 298.257222 GCS_North_American_1983 NAD_1983_Albers klcp2005l1k.vat Table 11 Count Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Rowid OID 4 0 0 Rowid Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. VALUE Integer 4 0 0 VALUE COUNT Integer 4 0 0 Value LULC_NAME String 50 0 0 COUNT The eleven mapped classes are defined as: Commercial/Industrial (class code 11)- commercial/industrial land consists of areas of intensive use with much of the land covered by structures or other hard surfaces. These areas are used predominantly for the manufacture and sale of products and/or services. This category includes the central business districts of cities, towns, and villages; suburban shopping centers and strip developments; educational, governmental, religious, health, correctional and institutional facilities; industrial and commercial complexes; and communications, power, and transportation facilities. The main buildings, secondary structures, and areas supporting the basic use are all included - office buildings, warehouses, driveways, parking lots, landscaped areas, streets, etc. Highways or interstate systems running through the core of urban areas, are also included in this class. Residential (class code 12)- residential land consists of areas of medium density housing characterized by a more or less even distribution of vegetative cover and houses/garages, to high density housing characterized by multi-unit structures such as apartment complexes. Linear residential developments along transportation routes extending outward from urban areas are included. Rural subdivisions not directly connected to the core of an urbanized area are also included. The main buildings, secondary structures, and immediate surrounding landscape are all included (i.e., house, apartment complexes, streets, garages, driveways, parking areas, lawns, trees, etc.). Urban-Openland (class code 13)- urban-openland consists of areas with uses such as golf courses, zoos, urban parks, cemeteries, and undeveloped land within an urban setting. Low density rural residential areas may also be included in this category. This category also includes tracts of land that have been zoned residential or commercial, but have yet to be developed. Urban-Woodland (class code 14)- urban-woodland consists of wooded tracts within a town or city. These wooded tracts maybe associated with golf courses, zoos, urban parks, and other undeveloped land. Urban-Water (class code 15)- urban-water consists of any open surface water within a town or city. This includes ponds, lakes, sewage settling ponds, etc. Cropland (class code 20)- cropland includes all areas with actively growing row crop and small grains, as well as harvested lands, fallow land, and large, uniform areas of bare, plowed ground. Grassland (class code 30)- this category includes all pasture (hayed land), rangeland, and other grasslands having insufficient trees and/or shrubs to be classified as "woodland". It does NOT include conservation reserve program (CRP) land. Conservation Reserve Program (CRP) Land (class code 31)- this category includes all lands enrolled in the conservation reserve program as determined by the 2005 Common Land Unit (CLU) database. Woodland (class code 40) - this class includes any wooded areas having a canopy closure of 50% and greater. Water (class code 50) - all open water bodies, including reservoirs, lakes, ponds, rivers and streams. Ephemeral streams may not be represented. Other (class code 60)- the "other" class is used to identify land cover land use classes not previously defined. In general, this class is used for exposed bare ground other than cropland. Examples include rock quarries, sand and gravel pits, sandbars, and built-up areas less than 40 acres. Downloadable Data 31.753 31.753 20100112 REQUIRED: The organization responsible for the metadata information. REQUIRED: The person responsible for the metadata information. REQUIRED: The mailing and/or physical address for the organization or individual. REQUIRED: The city of the address. REQUIRED: The state or province of the address. REQUIRED: The ZIP or other postal code of the address. REQUIRED: The telephone number by which individuals can speak to the organization or individual. FGDC Content Standards for Digital Geospatial Metadata FGDC-STD-001-1998 local time http://www.esri.com/metadata/esriprof80.html ESRI Metadata Profile http://www.esri.com/metadata/esriprof80.html ESRI Metadata Profile en 20100621 14214200 FALSE 20210715 14373500 20210715 14373500 {FF4171BB-450C-48AE-80F4-6413306A46E6} klcp2005l1k 002 -532660.000000 122150.000000 1550344.000000 1903924.000000 1 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_Albers <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/10.4'><WKT>PROJCS[&quot;NAD_1983_Albers&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Albers&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-96.0],PARAMETER[&quot;Standard_Parallel_1&quot;,29.5],PARAMETER[&quot;Standard_Parallel_2&quot;,45.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,23.0],UNIT[&quot;Meter&quot;,1.0]]</WKT><XOrigin>-16901100</XOrigin><YOrigin>-6972200</YOrigin><XYScale>266467840.99085236</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem> 8 FALSE RLE 1 GRID TRUE continuous unsigned integer 0 150000000 5000 FGDC Version 6.2 (Build 9200) ; Esri ArcGIS 10.4.1.5686 klcp2005l1k -532660 122150 1903924 1550344 1 -102.298587 -94.553596 40.15125 36.845359 1 1 -102.298587 -94.553596 40.151250 36.845359 This database was developed as part of the Core Database for the State of Kansas. It is suited for county-level and watershed-level analyzes that involve land... <DIV STYLE="text-align:Left;"><DIV><P><SPAN>The 2005 Kansas Land Cover Patterns Level I map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly comparable to the 1990 Kansas Land Cover Patterns map. Using a similar methodology to produce the 2005 Kansas Land Cover Patterns map provides opportunities to identify and examine how the Kansas landscape has changed over a 15-year period. The map contains eleven land use/land cover classes. The positional accuracy and spatial resolution of the map are appropriate for producing 1:50,000 scale maps. The map is not intended to define precise boundaries between landscape features and while the source data has a spatial resolution of 30 m x 30 m, the minimum map unit varies by land cover class and ranges between 0.22 to 5.12 acres (see below). The formal accuracy assessment reports the map to have an overall accuracy level of 90.72%. User and Producer accuracies vary by land cover class and rural classes have higher accuracy levels (88-95%) than urban classes (48-78%). Users are encouraged to reference the reported accuracy levels in this report and/or metadata when using the 2005 Kansas Land Cover Patterns map. Digital versions of the map, metadata, and accuracy assessment can be accessed from the Data Access Support Center (DASC) website of the Kansas Geological Survey (http://www.kansasgis.org/) or the website of the Kansas Applied Remote Sensing Program (http://kars.ku.edu/).</SPAN></P></DIV></DIV> The 2005 Kansas Land Cover Patterns map was created at the Kansas Applied Remote Sensing Program of the Kansas Biological Survey. 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." Land Cover Cropland Land Use Grassland Woodland Urban Water <DIV STYLE="text-align:Left;"><DIV><P><SPAN>This database is not suited for site-specific analyzes. Interpretations derived from its use are intended for planning purposes only.</SPAN></P></DIV></DIV> ISO 19115 Geographic Information - Metadata DIS_ESRI1.0 dataset withheld Local Area Network 002 31.753 Raster Dataset Raster Dataset NAD_1983_Albers 2 1 21827 30 Meter 1 Meter = 1 Meter(s) 11786 30 Meter 1 Meter = 1 Meter(s) 2 1 0 -532660.000000 1550344.000000 -532660.000000 1903924.000000 122150.000000 1903924.000000 122150.000000 1550344.000000 -205255.000000 1727134.000000 21827 30.000000 11786 30.000000 20210715 klcp2005l1k 60.000000 11.000000 8 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