교육데이터 활용•지원 서비스

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 Water Year 2018, covering over 9.4 million acres of Irrigable agriculture on a field scale and additional areas of urban extent. 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 and 2016 land use mapping, which classified over 14 million acres of land into Irrigable agriculture and urban area. Unlike the 2014 and 2016 datasets, the Water Year 2018 dataset includes multi-cropping and incorporates 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 true Irrigable 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, young perennials and wetland. 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 96.5% using the Land IQ legend and 98.3% 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. Revised crops and conditions 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_revised' data field. The value ‘n’ in the ‘DWR_REVISE’ data field indicates a Regional Office added a boundary and attributes where none was included in the Land IQ data set. 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, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition