LAND IQ 20160415 2016.06.16 vector digital data Please use this to cite the dataset: CDWR (2016). 2015 California Delta Agricultural Land Use , California Department of Water Resources, website: https://gis.water.ca.gov/app/CADWRLandUseViewer/ https://gis.water.ca.gov/app/CADWRLandUseViewer/ https://www.wildlife.ca.gov/Data/BIOS ftp://ftp.wildlife.ca.gov/BDB/GIS/BIOS/Public_Datasets/2700_2799/ds2791.zip Delta Landuse dataset for the extent of Delta Boundary. Delineated on Pleiades Image from July 13-14, 2015. The dataset is a comprehensive landuse for the period of July 13-14 2015 and also contains information on agricultural classes for spring 2015. Major assumption of this dataset is that the native vegetation is grouped into 5 level 2 categories and are grouped into 3 DWR native categories. This dataset presents the spatial extent of all agricultural, semi-agricultural, native, and urban lands within the legal Delta Boundary. 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 2.1, dated March 9, 2016. This data set was not produced by DWR. Data were originally developed and supplied by LandIQ, 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 this data set is John Lance, who may be contacted at 530-528-7442, or at John.Lance@water.ca.gov. Comments, problems, improvements, updates, or suggestions should be forwarded to the official GIS Steward as available and appropriate. 20150713 20150714 Legal Delta Boundary Complete Annually -121.955285 -121.195265 38.592842 37.613611 ISO 19115 Topic Categories inland Waters boundaries farming environment None Delta; Landuse; 2015;Agriculture;Urban;Native;Semi-Agricultural None Delta State of California None 2015 None None Dept. of Water Resources John Lance Research Analyst II (GIS) mailing

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Red Bluff CA 96080 530-528-7442 John.Lance@water.ca.gov M-F 0730-1600 California Department of Water Resources John Lance Data Steward mailing

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Red Bluff CA 96080 530-528-7442 john.lance@water.ca.gov N/A N/A N/A Land IQ, www.LandIQ.com Public domain Unclassified Available upon request Version 6.2 (Build 9200) ; Esri ArcGIS 10.5.1.7333 N/A Data are considered logically consistent. Data are complete as of final delivery 6/16/2016. Positional accuracy for this mapping dataset was determined to be +/- 2.0 meters horizontal accuracy at a 95% confidence level when registered against Pleiades reference imagery. The Pleiades reference image has a reported positional accuracy of 3.0 meters. Therefore, the combined horizontal accuracy is 5.0 meters. Positional accuracy was evaluated by review of all polygon boundaries in the subject dataset against the Pleiades reference dataset. Positional offset was measured in a randomly selected subset of approximately 5% of all fields. 5.0 The source image for Delta 2015 is Pleiades imagery, with a reported positional accuracy of 3 meters. (please refer to: https://www.satimagingcorp.com/satellite-sensors/pleiades-1/). LandIQ field boundary is delineated based on 2015 Pleiades imagery and the positional accuracy for field boundary was determined to be +/- 2.0 meters horizontal accuracy at a 95% confidence level when compared against Pleiades reference imagery. This is done by randomly selected 5% of total field boundaries and visually compared with Pleiades imagery. Therefore, the combined horizontal accuracy is 5.0 meters Matthew Twietmeyer 2015 onLine 20160616 publication date LandIQ producer 2.1 DATA COLLECTION Satellite imagery was captured and collected for purposes of this project. For 2015 analysis, a project-specific, high resolution (2 x 2 meter pixel size) image was captured with the Pleiades satellite platform on July 13-14th, 2015. These images were used for field delineation, classification and QA/QC of the final product. In addition, multiple Landsat 8 images were used for the initial crop classification and for temporal analysis throughout the growing season. Ground truth field crop classification data were collected from approximately 20% of all irrigated land in the Delta was collected. These data were used for training and validation of the mapping analysis. A subset of approximately 25% of these data were reserved for the purpose of independent validation of the mapping results. The remainder were used for training of the classification analysis algorithms. Example of 2015 Ground Truth Data Points Distributed Among Crop Types Crop Name - Ground Truth Count Alfalfa - 733, Pasture/Irrigated pasture - 401, Forage Grass - 28, Sudan grass - 45, Sorghum - 6, Sunflowers - 45, Safflower - 58, Tomato - 312, Other Truck Crop/Annuals - 4, Asparagus - 26, Annuals - 12, Peppers - 8, Carrots - 7, Strawberries - 2, Berries - 38, Blueberries - 5, Beans - 34, Onions - 2, Corn - 934, Squash - 49, Melons - 9, Melons - 3, Potato - 20, Almonds - 134, Pistachios - 5, Walnuts - 61, Pears - 229, Cherries - 62, Apricots - 3, Peaches/Nectarines - 3, Apples - 11, Plums - 2, Pecan - 1, Young Orchard - 1, Grapes - 555, Citrus - 2, Olives - 34, Rice - 38, Wheat - 153, Oats - 9, Fallow - 281, Fallow Prep - 22, Removed/Abandoned Orchard - 6, Rangeland - 123, Native Upland - 100 20150101 Land IQ, LLC mailing

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Sacramento CA 95811 9162656330 jkimmelshue@landiq.com 2.2 ANALYSIS Multiple selected image sources and timeframes served as input data for the remote sensing classification process, along with comprehensive ground truth training samples. Ground truth data were reviewed and evaluated statistically to identify any samples considered. Image sources that encompass a range of spectral characteristics, spatial resolutions, and temporal representations were analyzed for crop determination. These images and ground truth data are used to develop classification algorithms for crop identification that are guided by an understanding of agricultural production systems, landscape processes, and crop phenology. Remotely sensed images were assembled to serve as input to the remote sensing classification process, during which each field was assigned a crop or other land cover class label. A supervised classification algorithm was applied to classify fields. The supervised classification used a random forest approach and is carried out county by county where training samples are available. Random Forest approaches are currently some of the highest performing for data classification and regression. They are advantageous because of their ability to classify large amounts of data with high accuracy. Random Forest approaches have other advantages over some more traditional classification methods like maximum likelihood algorithms and Classification and Regression Tree (CART). Random Forest algorithms are non-parametric and require no assumption of input data being normally distributed. They are flexible and can incorporate categorical and continuous input data and complex relationships within the dataset. Multiple geoprocessing tools were employed to assess the model dataset, including ArcGIS, ERDAS Imagine, and other open source statistical tools. These tools are used to generate spectral characteristics, textural characteristics, and temporal representations that are related to the specific attributes of each crop or land use. Fields classified with a low level of certainty were manually checked by photo interpretation of high resolution imagery. Errors identified in this QA/QC process were corrected. A final classification layer was generated and then legend classes were combined to produce rolled up classification level(s) that align with DWR’s existing and historical modeling. Both legend attributes were preserved in the database for every polygon. In addition, in 2015 young orchards (less than 2-4 years old) were distinguished as part of Land IQ’s mapping efforts and were annotated within the attributes of young orchard polygons. Separate, short-season spring crops produced outside the summer growing season were also identified using temporal analysis of Landsat image resources reflecting seasonal variation. Spring cropping and crop type were recorded for each field under a separate spring crop (“Sprg_Crop”) column in the database. 20150101 Land IQ, LLC mailing

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Sacramento California 95811 9162656330 jkimmelshue@landiq.com Vector GT-polygon composed of chains 40185 NAD 1983 California Teale Albers 34.0 40.5 -120.0 0.0 0.0 -4000000.0 coordinate pair 0.0001 0.0001 meter D North American 1983 GRS 1980 6378137.0 298.257222101 ds2791 Shape Feature geometry. Esri Coordinates defining the features. 2015 2015 100 N/A N/A OBJECTID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Sprg_Crop Spring 2015 Crop Type LandIQ Alfalfa Alfalfa was grown in the Spring LandIQ Corn Corn was grown in the Spring LandIQ Cucurbit Cucurbit was grown in the Spring LandIQ Fallow field was fallowed LandIQ Forage grass Field was used for forage grass LandIQ Grapes Grapes were grown in the Spring LandIQ Small Grains Small grain crops were grown in the Spring LandIQ Truck Crops Field was used for a specific truck type crop LandIQ Turf Turf was grown in the Spring LandIQ 20150713 20150715 90 Actual attribute accuracies are not 100% or known and accuracy statement may need future revision. None planned Yng_Class Young Other Decidous Classification Land IQ Young Grapes Indicates a young crop(less than 2-4 years old) and what crop type it was classified as LandIQ Young Almonds Indicates a young crop(less than 2-4 years old) and what crop type it was classified as LandIQ Young Cherries Indicates a young crop(less than 2-4 years old) and what crop type it was classified as LandIQ Young Orchards Indicates a young crop(less than 2-4 years old) and what crop type it was classified as LandIQ 20150713 20150715 100 Actual attribute accuracies are not 100% or known and accuracy statement may need future revision. None planned SA DWR Crop Classification Code DWR C7 Subtropical Fruits Misc. DWR C8 Kiwis DWR D** Deciduous DWR D1 Apples DWR D10 Deciduous-Misc DWR D11 Deciduous-Mixed DWR D3 Cherries DWR D5 Peaches & Nectarines DWR D6 Pears DWR E** Entry Denied DWR F** Field Crops DWR F11 Field Misc. DWR F2 Safflower DWR F4 Hops DWR F6 Corn (field) DWR F7 Grain Sorghum DWR F8 Sudan DWR G** Grain and Hay Crops DWR G6 Grain - Misc. DWR I1 Idle Lands-cropped w/last 3 yrs (not cultivated) DWR NR1 Native Riparian-Marsh Lands, Tules and Sedges DWR NR4 Native Riparian-Seasonal Duck Marsh DWR NR5 Native Riparian-Permanent Duck Marsh DWR NV** Native Veg DWR P1 Alfalfa DWR P3 Pasture-Mixed DWR P6 Pasture-Miscellaneous Grasses DWR R** Rice-Misc DWR R2 Rice-Wild DWR T10 Onions & Garlic DWR T15 Tomatoes DWR T12 Potatoes DWR T17 Truck Crop (four or more)-Mixed DWR T18 Truck - Misc. DWR T19 Bushberries DWR T2 Asparagus DWR T20 Strawberries DWR T6 Carrots DWR T9 Melons, Squash, & Cucumbers. DWR V** Vineyard DWR 2015 2015 100 determined accurate at time of creation None planned Prior_SA15 Sacramento County Land Use Survey Crop Type DWR Alfalfa Alfalfa DWR Annuals Annuals DWR Cherries Cherries DWR Corn Corn DWR Cotton Cotton DWR Fallow Fallow DWR Forage Grass Forage Grass DWR Grapes Grapes DWR Herbaceous Upland Herbaceous Upland DWR Irrigated Pasture Irrigated Pasture DWR Native Upland Native Upland DWR Other Fruit/Nut Tree Other Fruit/Nut Tree DWR Pasture Pasture DWR Pears Pears DWR Potatoes Potatoes DWR Rice Rice DWR Safflower Safflower DWR Tomatoes Tomatoes DWR Truck Crop Truck Crop DWR Walnuts Walnuts DWR Young Cherries Young Cherries DWR Young Orchards Young Orchards DWR 20150101 20150101 100 determined accurate at time of creation None planned Acres Acreage of Polygon extent Land IQ acreages 2015 2015 100 Acreages are accurate insofar as linework is accurate. None planned Level_2 Detailed Land IQ land use classification legend level with some additional, more detailed crop classes segregated Land IQ Alfalfa Alfalfa LandIQ Almonds Almonds LandIQ Bush Berries Bush Berries LandIQ Cherries Cherries LandIQ Citrus Citrus LandIQ Corn Corn LandIQ Cucurbit Cucurbit LandIQ Fallow Fallow LandIQ Floating Vegetation Floating Vegetation LandIQ Forage Grass Forage Grass LandIQ Olives Olives LandIQ Other Deciduous Other Deciduous LandIQ Pasture Pasture LandIQ Pears Pears LandIQ Pistachios Pistachios LandIQ Potatoes Potatoes LandIQ Rice Rice LandIQ Riparian Riparian LandIQ Safflower Safflower LandIQ Semi-agricultural/ROW Semi-agricultural/ROW LandIQ Sunflower Sunflower LandIQ Tomatoes Tomatoes LandIQ Truck Crops Truck Crops LandIQ Turf Turf LandIQ Upland Herbaceous Upland Herbaceous LandIQ Urban Urban LandIQ Vineyards Vineyards LandIQ Walnuts Walnuts LandIQ Water Water LandIQ Wet herbaceous/sub irrigated pasture Wet herbaceous/sub irrigated pasture LandIQ 2015 2015 100 determined accurate at time of creation None planned Shape_Area ESRI-defined area of feature in internal units squared. Esri Positive real numbers that are automatically generated. Software-defined 2015 2015 100 software-defined None planned Level_1 Main “rolled up” land use classification categories consistent with historical DWR classification legend groupings DWR Citrus/Subtropical Citrus/Subtropical DWR Field Crops Field Crops DWR Idle Idle DWR Native Native DWR Native Riparian Native Riparian DWR Other Deciduous Other Deciduous DWR Pasture Pasture DWR Rice Rice DWR Semi-agricultural Semi-agricultural DWR Truck Crops Truck Crops DWR Urban Urban DWR Vineyards Vineyards DWR Water Water DWR 2015 2015 100 determined accurate at time of creation None planned Comments Any user-provided comments DWR comments 2015 2015 100 comments None planned Source Original source of the boundary and attribute information DWR LandIQ, LLC Name of source LandIQ, LLC 2015 2015 100 LandIQ is source for all delivered data None planned DDRT Date the data refers to DWR July, 2015 20150713 20150715 100 determined accurate at time of creation None planned DWR_Standa Shape_Length Length of feature in internal units. Esri Positive real numbers that are automatically generated. 2015 2015 100 software defined None planned N/A DWR California Department of Water Resources John Lance Data Steward mailing

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Red Bluff CA 96080 530-528-7442 john.lance@water.ca.gov None 20180516 California Department of Water Resources John Lance Data Steward mailing

2440 Main Street

Red Bluff CA 96080 530-528-7442 john.lance@water.ca.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time