20221115 15454600 1.0 FGDC CSDGM Metadata FALSE CropLegend -124.375251 -114.132542 32.540499 42.003613 1 002 346.726 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) <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/3.0.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>10000</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><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> WGS_1984_Web_Mercator_Auxiliary_Sphere 20221128 11153400 20221128 11153400 150000000 5000 FGDC Bekele Temesgen California Department of Water Resources Manager, Land and Water Use Section 916-651-9679 PO Box 942836 Sacramento CA 94236-0001 US Bekele.Temesgen@water.ca.gov 20220325 ArcGIS Metadata 1.0 Bekele Temesgen California Department of Water Resources Manager, Land and Water Use Section 916-651-9679 PO Box 942836 Sacramento CA 94236-0001 Bekele.Temesgen@water.ca.gov No Digital Data https://data.cnra.ca.gov/dataset/statewide-crop-mapping or https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#waterbudget File Geodatabase Feature Class 346.726 i15_Crop_Mapping_2019 2022-03-25 20220325 Joel Kimmelshue, Land IQ, LLC, Owner (Originator), Land IQ, LLC, Owner (Originator), Owner vector digital data Planning i15 CDWR Land Use Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer/. Statewide Crop Mapping on California Natural Resources Agency (CRNA) Open Data Portal: https://data.cnra.ca.gov/dataset/statewide-crop-mapping. SGMA Data Viewer: https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#waterbudget <DIV STYLE="text-align:Left;"><DIV><P><SPAN>Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2019 water year (WY 2019). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, and 2018 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018 and 2019 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.9% using the Land IQ legend and 98.1% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. A new column for the 2019 dataset is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.</SPAN></P><P><SPAN>The 2019 Crop Mapping dataset has been updated as of August 2022 and includes the following changes:</SPAN></P><P><SPAN>- Slightly shifted Urban polygons were relocated to their original correct positions.</SPAN></P><P><SPAN>- The following new rule has been included for ‘X’ Unclassified Fallow: “Unclassified Fallow is also used when indicating the planting of Alfalfa &amp; Alfalfa Mixtures or Miscellaneous Grasses. In these scenarios Unclassified fallow would be Crop1, and Alfalfa &amp; Alfalfa Mixtures or Miscellaneous Grasses would be Crop2.”</SPAN></P><P><SPAN>- Some UniqueID’s that were accidentally duplicated have been corrected back to their original UniqueID’s. </SPAN></P></DIV></DIV> Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management will be an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. For these purposes, as well as many others, a spatial mapping base layer is essential for effective decision-making and other applications. The primary objective of this effort was to produce a comprehensive and accurate spatial land use database with overall accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. DWR reviewed and revised the data in some cases. Detailed reviews and revisions of individual fields were determined by DWR Land Use staff in Regional Offices, therefore it is important to contact individual Senior Land Use Supervisors within Regional Offices for local details. For Northern Regional Office you may contact Tito Cervantes at Tito.Cervantes@water.ca.gov; North Central Regional Office, Jeff Smith at Jeff.A.Smith@water.ca.gov; South Central Regional Office, Steve Ewert at Steve.Ewert@water.ca.gov; and Southern Regional Office, Robert Fastenau at Robert.Fastenau@water.ca.gov. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.3, dated April 13, 2022. This data set was not produced by DWR. Data were originally developed and supplied by Land IQ, LLC, under contract to California Department of Water Resources. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. The official DWR GIS steward for the statewide compilation of this data is Bekele Temesgen, who may be contacted at 916-651-9679, or at... Land IQ, www.LandIQ.com, California Department of Water Resources, Division of Regional Assistance Regional Offices: Northern, North Central, South Central and Southern Regional Offices, and Water Use Efficiency Branch (Sacramento Headquarters). Stanley Mubako California Department of Water Resources Senior Environmental Scientist (Supervisor) (916) - 873 - 4784 715 P Street Sacramento CA 94236-0001 US Stanley.Mubako@water.ca.gov State of California Imagery Urban Planning Satellite imagery Ground truth Crop Raster Landsat Land cover Irrigated land 2019 Multispectral analysis Vector Digital imagery Image classification Aerial photography Land use Survey Boundaries Agriculture Imagery Urban Planning Satellite imagery Ground truth Crop Raster Landsat Land cover Irrigated land 2019 Multispectral analysis Vector Digital imagery Image classification Aerial photography Land use Survey Boundaries Agriculture State of California Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the California Department of Water Resources (DWR), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Version 6.2 (Build 9200); Esri ArcGIS 10.8.1 true -124.4692 -113.4995 42.0709 32.4317 0.000000 0.000000 publication date 2018-10-01 2019-09-30 0.000000 0.000000 Land-Use Data Quality Control This dataset is the final copy of Crop_Mapping_2019. Digital surveys published on this site are designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Each survey has been zipped into a downloadable folder which contains the survey shapefile, feature class, and other documentation that further explains how that specific survey was processed, and how the information is interpreted. 'Final' surveys are peer reviewed with extensive quality control methods to confirm that field polygon attributes reflect the most detailed and specific land-use classification available, using the standard DWR Land Use Legend specific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level. 'Provisional' data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data. 1 -124.375251 -114.132542 42.003613 32.540499 Data are considered logically consistent. Data are complete as of final delivery 2022/03/25 California Department of Water Resources, Division of Regional Assistance Regional Offices: Northern, North Central, South Central and Southern Regional Offices, and Water Use Efficiency Branch (Sacramento Headquarters). Land IQ California 2019 DWR 2022-03-25 Joel Kimmelshue, Land IQ, LLC, Owner (Originator), Land IQ, LLC, Owner (Originator), Owner vector digital data publication date 2018-10-01 2019-09-30 GROUND TRUTH DATA COLLECTION To generate and validate a land use map using remotely sensed imagery, a representative sample of reference points in the imagery with known class values (or known land use types) is needed for two reasons. Some of the data are used to calibrate or train the remote sensing model that generates land use results. The training process results in an algorithm that is used to determine land use on all analyzed fields. Another set of reference data is needed to independently validate the accuracy of the remote sensing model. Currently, Land IQ collects reference data for model training and validation from cropped areas in California by conducting “on-the-ground” survey. For this reason, Land IQ calls reference data collection “ground truthing.” Land IQ ground truth data collection seeks to gather information representing crop types with a target of 10 – 20% of mapped land. These data are split for purposes of training and validation as described in later sections. Ground truth data collection areas are selected to best represent the cropping systems in mapped areas across the state. In WY 2019, with the expansion of mapping to capture multiple cropping seasons, ground truth efforts were expanded to include Siskiyou, Modoc, Lassen and Shasta counties, the central coast, north coast, southern coast, and the low desert. Information from ground truth areas is compiled and applied regionally across the state. In this way the image classification approaches used in all areas are informed by training data from representative crops. Ground truth data collection approaches are continually refined as cropping and crop timing details are determined through mapping efforts. 2018-12-01 IMAGERY ACQUISITION Satellite data resources were used for the crop classification. Sentinel 2 and Landsat 8 were used for field delineation, classification, and QA/QC of the final product. Multiple Landsat 8 images were used for the initial crop classification. Imagery from the Landsat 8 satellite is free of charge, available every 16 days, and allowed temporal analysis throughout the growing season. Satellite-based imagery provided by Sentinel was also utilized during the QA/QC process. 2019-06-01 METHODS Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. The mapping approach employed advanced spatial statistical analysis approaches to determine prediction probabilities and inform Quality Assurance/Quality Control (QA/QC) efforts. A rigorous QA/QC and analysis refinement process was used to improve predictions on all lower confidence fields. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were defined so that each independent field could be analyzed independently and assigned to a crop class. The result represents the cropped area and not legal or other less detailed boundaries that may be available elsewhere. The classification legend was developed in coordination with DWR with consideration of the known crop variation, existing DWR legends used in current models, and Land IQ mapping classes. Two legend levels were selected to retain the detail in Land IQ’s base mapping while providing a more general legend consistent with DWR’s classification that groups some crops into categories. 2019-06-01 MODEL INPUT FEATURES The input features were produced using ground truth training samples and satellite imagery from Landsat 8 OLI/TIRS sensors collected during the growing season. 2019-10-31 CROP CLASSIFICATION MODEL Multiple geoprocessing tools and methods were employed to assess the model dataset, including ArcGIS, and other open-source statistical tools. These tools were used to generate spectral characteristics, textural characteristics, and temporal representations that are related to the specific attributes of each crop or land use. Supervised classification was used to classify delineated fields. Selected ground truth data and feature data were applied to the algorithm for model building and calibration. A portion of these data were used for model calibration and the remainder was used to train the models. Multiple remote sensing models are assessed and compared to determine the highest performing for classification. The preferred model was then applied to all delineated fields to predict land cover type, as well as prediction confidence, which was used to inform QA/QC efforts. 2019-10-31 ANALYSIS The Land IQ mapping scale is focused on all cropped fields 2 acres or greater across the state. In some cases, fields smaller than two acres were included if they were adjacent within 30 meters, the same crop and clearly associated as a part of a broader group of fields cultivated together. More than 446,000 delineated fields were classified in WY 2019 utilizing ground training examples and multiple image sources and dates. These images and ground truth data were used to develop classification algorithms for crop identification. Multiple selected image sources and timeframes served as input data for the remote sensing classification process, along with comprehensive ground truth training samples. In some more sparsely cropped counties, photo interpretation methods are used for initial crop mapping. 2019-10-31 GROUND TRUTH TRAINING DATA Field data from over 21% of all cropped land in California were collected, which represented 83,038 data points and 55,043 fields. The ground truthing data were stratified based on Land IQ’s classification schema, with approximately 75% of the data selected for model building and calibration, and the remaining approximately 25% dedicated to independent validation and accuracy assessment. These independent data were set aside from the modeling process and used in the final accuracy assessment discussed later. The two datasets were reviewed and evaluated statistically to identify any repeated points within a single field or samples considered unrepresentative (crops that were very stressed, intercropped or abandoned, for example). These data points were flagged and removed from the training and validation samples. 2019-12-01 QUALITY REVIEW Photo interpretation methods were used to review imagery of classified fields with a low confidence level. Results were also cross-validated with ancillary data sources such as the coinciding USDA Crop Data Layer (CDL) and county agricultural surveys and county crop reports, to assess and evaluate significant differences. Differences do not always indicate incorrect classification but are used both to evaluate the classification result and explain deviation from other data sources if any exists. 2020-06-01 CROP FREQUENCY AND PEAK DATES Prior to WY 2018, only summer crops were classified. The WY 2019 dataset includes multiple crop attributes for some fields depending on the time of year. Mapping frequency was determined in coordination with DWR personnel to represent areas understood to have higher frequency crop production. These areas were delineated from existing DWR Detailed Analysis Unit (DAU) maps and incorporated all or portions of DAUs expected to have multiple cropping. Three levels of multiple cropping were defined (double, triple, and quadruple) using knowledge about regional production practices. All fields within a given multi-cropping area were mapped at the cropping frequency designated for that region. Unclassified fallow “X” reflect the following circumstances: (1) A field was fallow for a whole season (in this case, ‘X’ will be in the Crop2 ‘main crop’ segment and no other crops have been assigned for this field in this instance). (2) Fallow condition followed removal of a permanent crop (excluding Young Perennials), the permanent crop has been assigned in the Crop2 ‘main crop’ segment and the "X" will be in crop 3 segment indicating the orchard was removed. Permanent Crop Classes include Citrus, Deciduous, and Pasture (except Native Pasture). 3) Unclassified Fallow is also used when indicating the planting of Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses. In these scenarios Unclassified fallow would be Crop1, and Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses would be Crop2. The timing of multi cropping varies among crops and regions. Rather than defining set seasons such as spring, summer, fall and winter, an assessment of peak crop production date was conducted. Peak dates were determined for each crop by assessing field Normalized Difference Vegetative Index (NDVI), which represents crop vigor, and evaluating index patterns and maximum points. The average of the dates of these maximum points was determined to be the peak date for a particular crop season and a peak date was determined for each annual crop. A maximum of four crops per field were mapped depending on the targeted mapping frequency. Fields were attributed with more than one crop when more than one peak date was found. When a permanent crop such as alfalfa or almonds was removed, the mid-point between the removal date and the end date of the water year (October 2019) was recorded as the peak date, which can be used to calculate the crop’s timeframe for crop water use. 2020-07-24 PERCENT CROPPED A percent cropped (cover) attribute was added in 2016 and is included in the 2019 dataset for each field, and each crop in the case of double, triple, and quadruple cropped fields. This attribute represents the percentage of a field that is cropped. Staggered or strip planting, where a field is not planted uniformly but rather in stages during a season, is a common practice in crops such as lettuce/leafy greens, cole crops, carrots, and miscellaneous truck crops. To provide more accurate acreages for these crops, the percent cover attribute represents approximately what proportion of a field was in production for that mapped season. This is used to calculate more specific acreage for these crops while allowing outer field boundaries to remain relatively consistent over multiple years. 2020-07-24 CROP MAP PRODUCT The geospatial database was attributed with field size (acres), relevant county, and the appropriate crop classification categories per both the Land IQ and DWR legends. 2022-05-06 0 i15_Crop_Mapping_2019 Table containing attribute information associated with the data set. Producer defined Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Feature geometry. Esri Coordinates defining the features. Shape Geometry 0 0 0 CLASS2 The Main Season (summer) crop classified on the field for Single cropped fields. The second crop in the water year for Double, Triple, and Quadruple cropped fields. California Department of Water Resources See CLASS1 for detail CLASS2 String 2 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. dataset EPSG 6.18.3(9.3.1.2) Simple FALSE 0 TRUE TRUE /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAjeaOOSKN3CtKSqA/xEAnH5An8KkqteWsNwsbyJGzxNuj8wnAY8fnzgfWpXZFCxuf9ZlQP Xgn+QNAElQPe20YO6UDGSRg5UDGSR2AyMk9Koa3Lf20dtNYKhUTBZh5W8hSeSAKzNOsbyx8T3ct1 M5sruJmQyyM2enytn7pAz+BI+gBoyeKNGiZ1e85Q4YCJzj8hVyz1Szv7VLm2m3wu21XKMuTnHcet Ja6TZWSukECrG6hTH1XAz2988+uBTSLfTIGitYM+YS6QRYUds46ADOPxNAFuWeOAKZWChmCgnuSQ APzIpY5Y5QTG6uAcEqc81zhvbq8vbSyazayjjQSzB5gxKtuj2lejZPOcnsa1tEZm0mDzEVJFBSRV GAGU7W/VTQBoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBl6tfzQT2lpZKkl7N JkIxwAg+8x9Bjj64q5KZo51dCpjYbWDH7pzwRx7nPPp+Ne+0+2uLmK4uIYT5eCJWO10IPGD369P5 1aRsxkLudozg7gV3H8vftxQA55QhVQGLN0+Ukfj6Cq9zPZCSK3u5IS0gJRJCMt24H44qaG1hgeR4 41VpH3sQOp9a4/xZHp9nNZ3NxNK0kcgwGj80FdxLA7jyMdvp0oA6Ka5mvIZGtT/o6MwkZHxIQF42 ds5z1x0qtF4ct2ube4umluWhQbGkfaVO7cDhQBkcD6VdsdPjtog9syAMCUwmFAPPTOccDv2ovp76 FXkghjZIyDyXZiMjdhVHPGce/agBJ9xvC+2CZAuFV1+ZW3dc4PA+X6cmr6KFBO0Kzctg55xWHNZ3 c921za3SPZ3DDzoZLdXxgAeoJ6HuSPQ1tQTLcQLKn3WoAkooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAqk16ZGl8gqFtpQs7SggFduTtPqMj27e9XazLoXUV2TbCOKPIcs/Ilcggrg HI4VTnHvg80AXLNg9sGE/nKSWVyRkgnI6AdAQK4bx/H5sCvFCHK3BV2UnIOwcEZ5+vbj1qxHc32m 3k0dlCBOHaf7H5gzKhGSOmMqScAHkduKm1SIeJ/DUUrypbXkb5dF+bDglQp5+UZxk/TPSgDm/Dni y4shDZ3U7/ZkkVgepC91wASc+lenn7NewfPGksZwdrrkHoeh/CvCfMZVCbVG1iwIHOeO/XtXX+Gd Tke8jdlkhhtbcso8xhEcE9c+vzD22jrjFAHeX8OoypiyuDAScbnwdgxndjndyBwcdW68VatIliiK Kwcbsl8glj3LYHWlSWC9t2UPFICNrqrBwOxH55H4VWsTFZItid6lW8tGYYWQ7d3yDsAPwHIoA0KK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKQgMCCAQeCDS0UAcL420+WKGyNtF5m JcId37wHkhV7nAHv2rntG17ULbURI6y3Fu6rDPG4Lhgc4GO5IzjPX6V6xLEk0ZSRQynsf51wGraY sV1daXb3RAL+eI5TxI7LjA6AkYzj6Y9KTA4y6iey1BzJCqMr7hE68dehHp7Gt3StYF9eSLdQ224A GDdGAkQB6E8DaB69easpoF14hhkIgmE0QIjuGl+RuQQrA8hsHquRmuZmt7vTb8xTx+Rcxc7XUH6e xosnqB6boaS6S3lTLEWnGUhhbLk5ycjCqPvA8ADHWumCjuF4ORx0/wA815/o+p23iVfIvWkS+XCh YiEWRScZyMMcbixGecn8O7SWKPy4A6FgMYUgYwOeM/T86YEVq628q2AWYkI0gd+RjcRjd69OOwq5 SMSqkgZx2FQSXDRwySPHtEbc5I5Xufy/lQBYoppdVZVLAM3Cgnr9KdQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABWJr+mG6VroRvN5ULAQxsVZ26ryM57jGO9bdFAHA2uvf2Lcw+dEyeY i+fbfZzC+MkeZz95vUDt3ODi34msbfxBYWerWMJu1jJMiRnDvHyOPcEfzqzrWgalqGqpfpcQgwK3 kDYSPXa4PUEZBI9uKTw7fW9r9otlsvss4l/f2+/JjJwAVHdCST1459aSSSsgPN7S/u9JvGksZmic jaSrA7hnOOPwru7PxVZ6/ZR213OlhdeYvmMA3zjn7jA8HoOfXvWf4t8ICJJtW0474ixaaFV+5/eY e2e3auKjuJYY5UjbCyrtcY6jOf6UwPdbGRpLSPfvLgYLOAN3+1wSORzwe9F3ZxXcRV9yttKh0Yqy 564I+grzTwb4muNPvUsJlee3uHCqByyN0yPUdOK9ToAhhUvFE0yr5qj+E5APTipqjRFjkdVIG879 vv3P+e5PrUlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXP69p8E1zFfuQJL ZS2ScYAwfxXnnIPHp36CqmoafDqFuUkUeYAfKkHDRsR1B6j+vSgDmv8AhINK1VbjTLwxSRC48vcH 2CTqQykH1BJJwORya4690bT4tUayjuyrYKxscFWbI2ljnCg8554xnjOB0mlWFhaa1Jp1/ZIl2zAg hSVkTHDABvlORnGDgk4xnA6a8063v7RtMv5klG7zBlQrBenGD1Gevv05oA8bt7aa4vI7aBS07uEU Kepz617hCUsbJTcOy9DI8jlvmPXk9s/gBXmGo6c3hrX0exgu5fLJZGdOB1AwQOfWuy8N6zdX+kWr 6hHLNI8jESQxBlwDgb8D5SCc+uAD60AdBLfW8FsLl5AICQPM/h56HPpyOalSVZVR4zuR13K46Edv 50w3Ceb5eGwMhmK/KOnGfxFOh2Lbx+WVMe0BSg4I7Yx2oAkoppbDquV5z1PP4Cl5yOBjvQAisWBy rLgkc4596dSFQSCQMjofSlJAGScUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByni3 QLvVJbeexghMkZy77tkh5yMNjt/Wp7K+32IsvEFsBLtCfNC7hh05bbtzn0JrpKQ5x8oBPucUAY8N 7pVnbJAt8FgjXcjhiFQElcFvu8HIAPTHtVK41KwtrdYdP1YqfL/diOPzkJ3DH3RnPbAI61d1zw3Z a6IzO0kciHh4yAT7H1/+ufWsa18LTaQDLPfRz2acPHIg4X1BPTAP144NTJtJtAO0h9YS9iW4sN9l HKwhYqQysTgk55GBnBAC4z6jFibxRZXd2lksU08boBIsIwdxbAGTgjABJx6in3uoTW1rNLptwLiK FMsiTBiCDzyQew9fwrnL/W7pWh89rq3uWcK298xhSQQRsCk8c8dcd+KzhWU1dIbVjuLeys2dHg88 G3faAzvjIGMYbgjB6/l0rQrmbLWXOgTagtrJvxl2jk8xTzjKgsSODnBH1rR0mRbyztpE1KS4aLPm H5AWz2cDge2K2Eamfmxg9OtJJGssbRvnawwcHBpWZVxuIGTgZPeloAKKYg3ESbmwRwp6DOKfQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR1oooAoahpFrqNu0TqY227VkjO1l/8Arex4 rg/GFl/ZunafaShppUaTZMEAVlLH5fUEZHHPWvS6w/FmmNqmiNEiszRuJfkXc2ADnaM8n270vMCH w5E8unWd35k8UDDeIyQC7MTksAOeTkHI4A4A6yWuj28OpDWLYSaerIVnt8BVbB+8R0HT9c8HrneG dNuIrd2WKOK0ZwyRvAwdyh4Y5YbfmGRkHjHPFdcpyBnrjnjFNAZqa5p02rHSxMrTtGHUZBVwRnAI 745wex707WtNk1bTZLNLhoBKVDMP7oOTj69K43/hFdbHi4aksVusQuhLmNwo255468iu+lM4jfyU jL/wb3IB474FAC28CW1tFbxDEcSBFHsBgVJTY/M8pPNKmTA3bRxnvinUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/Z