Water Related Land Use Statewide (2024) (Features)

These data include the types and extent of irrigated crops as well as information concerning dry agriculture, wetlands, open water, and urban areas. The annual data published by the program includes a finalized WRLU geospatial information systems (GIS) dataset, geospatial maps, and this report. Once published, the data are used for various planning purposes including approximating cropland water use, evaluating irrigated land losses, conversion to urban lands, planning for new water development, estimating irrigated acreages, and developing water budgets. Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made each subsequent year. While this improves the data we report, it makes comparisons to historical data difficult. Increases or decreases in acres reported should not be construed to represent definitive trends. In 2024 the USDA Cropland Data Layer (CDL) underwent several methodological changes. These changes included switching from a decision tree classifier to using a random forest classifier, implemented the use of Google Earth Engine (GEE) for data processing, and changed several crop classifications assigned values. Analysis was conducted to adjust and normalize these data as close to the prior methodologies as possible. For more detailed information regarding these changes please refer to the USDA CDL metadata. For a file geodatabase (.gdb) Click Here (includes files used to create data). For the final report, full documentation, and metadata Click Here. Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. Digitizing is done as Geodatabase feature classes using ArcPro 3.5.1 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process. Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/ Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes. Attributes Landuse– A general land cover classification differentiating how the land is used Agriculture: Land managed for crop or livestock purposes Other: A broad classification of wildland Riparian/Wetland: Wildland influenced by a high water table, often close to surface water Urban: Developed areas, includes urban greenspace such as parks. Water: Surface water such as wet flats, streams, and lakes. CropGroup– Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc. Description– Attribute that describes/indicates the various crop types and land use types determined by the GIS process. IRR_Method– Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks. Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the crop Dry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes. Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plot None: Associated with non-agricultural land Sprinkler: Water is applied above the crop via sprinklers that generally move across the field. Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian area Acres– Calculated acreage of the polygon. State– State where the polygons are found. County– County where the polygons are found. Basin– The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes. SubArea– The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe. Label_Class– Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop. LABEL– A shorthand descriptive label for each crop description and irrigation type. Class_Name– The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description). OldLanduse– Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse. LU_Group– These codes represent some in-house groupings that are useful for symbology and other summarizing. SURV_YEAR– Indicates which year/growing season the data represents.本数据集涵盖灌溉作物的类型与覆盖范围，同时包含旱作农业、湿地、开阔水域及城市区域的相关信息。本项目发布的年度数据包含最终版的WRLU地理信息系统（GIS）数据集、地理空间地图及本报告。数据发布后可用于多种规划场景，例如估算农田用水量、评估灌溉土地损失量、土地向城市用地的转化情况、新水资源开发规划、灌溉面积测算以及水资源预算编制等。 由于方法学调整、影像分辨率提升以及软硬件升级，后续每年的清查工作精度均有所提升。尽管这优化了我们发布的数据质量，但也使得历史数据对比变得困难。因此，报告中提及的耕地面积增减不应被解读为确定性的趋势变化。 2024年，美国农业部（USDA）农田数据层（CDL）进行了多项方法学调整：包括从决策树分类器切换为随机森林分类器、启用谷歌地球引擎（Google Earth Engine, GEE）进行数据处理，以及调整了若干作物分类的赋值规则。研究团队开展了分析工作，以尽可能贴合过往方法学对这些数据进行校正与归一化处理。如需了解此次调整的详细信息，请参阅USDA CDL元数据。点击此处获取文件地理数据库（.gdb，包含构建数据集所用的全部文件）；点击此处获取最终报告、完整文档及元数据。 要素类（Feature Classes）被加载至平板电脑，野外作业人员会对选定的部分地块或多边形进行作物、土地覆盖类型及灌溉方式的标注。每台平板电脑均配套全球定位系统（GPS）设备，可在野外标注过程中实时追踪并持续更新作业人员的位置。数据数字化工作采用ArcPro 3.5.1软件构建地理数据库要素类，以哨兵（Sentinel）影像作为底图，并叠加其他参考图层。针对单个农田、城市区域等现有地块边界的更新工作将被精准数字化，在此过程中同步记录灌溉类型与土地利用的变化情况。 针对对应年份，我们会下载美国农业部（USDA）国家农业统计服务局（National Agricultural Statistics Service, NASS）发布的农田数据层（CDL）栅格数据，相关数据可通过 https://nassgeodata.gmu.edu/CropScape/ 获取。随后使用分区统计地理处理工具，为多边形赋予来自CDL的更新后作物类型属性。之后，数据将经过多轮与历史清查数据的对比及质量检查流程，以确定并生成最终属性字段。 ### 属性字段说明 1. **土地利用类型（Landuse）**：通用土地覆盖分类，用于区分土地利用方式： - 农业（Agriculture）：用于作物或畜牧生产的土地 - 其他（Other）：野生地的宽泛分类 - 河岸/湿地（Riparian/Wetland）：受高地下水位影响的野生区域，通常邻近地表水 - 城市用地（Urban）：已开发区域，包含公园等城市绿地 - 水域（Water）：地表水域，如滩涂、溪流与湖泊 2. **作物组别（CropGroup）**：对作物大类进行分组，以便快速检索所有果园、谷物等作物类型。 3. **描述字段（Description）**：用于描述或标识经GIS流程确定的各类作物类型与土地利用类型。 4. **灌溉方式（IRR_Method）**：作物灌溉方式，数据源自2015年结束的全州野外调查，并基于影像与年度野外核查进行更新。各类灌溉方式说明如下： - 滴灌（Drip）：通过管道缓慢向作物表层或地下层供水 - 旱作（Dry Crop）：该农田未施加人工灌溉，作物依靠自然过程获取水分 - 漫灌（Flood）：从作物上游的沟渠或管道引水，以足够水量淹没灌溉地块 - 无灌溉（None）：对应非农业用地 - 喷灌（Sprinkler）：通过移动喷头向作物上方喷洒供水 - 地下水位补给灌溉（Sub-irrigated）：该地块未施加人工灌溉，但因地下水位较高可获得充足水分，通常邻近河岸区域 5. **面积（Acres）**：多边形地块的测算面积 6. **州（State）**：多边形地块所在的州 7. **县（County）**：多边形地块所在的县 8. **水文流域（Basin）**：多边形地块所在的水文流域，与HUC 6分级密切相关。该流域边界由DWRe划定，为便于管理纳入了存在跨流域水流的其他流域部分区域 9. **子区域（SubArea）**：多边形地块所在的子区域，与HUC 8分级密切相关。子区域为DWRe划定的大型水文流域的细分单元 10. **标注类别（Label_Class）**：处理过程中生成的Label与Class_Name字段组合，用于标识具体作物、灌溉方式，以及CDL是否将该地块归类为相似作物或"Other"作物 11. **简写标签（LABEL）**：针对每种作物描述与灌溉类型的简短描述性标签 12. **类别名称（Class_Name）**：多边形内来自USDA CDL CropScape栅格图层的多数像元值，可能与最终作物判定结果（Description）存在差异 13. **旧土地利用类型（OldLanduse）**：与Landuse类似，但会根据灌溉方式进一步细分农业用地。2017年之前的数据集将该字段定义为Landuse 14. **土地利用分组（LU_Group）**：此类编码为内部自定义分组，可用于符号化展示及其他汇总统计工作 15. **调查年份（SURV_YEAR）**：标识数据对应的年份或生长季