Water Related Land Use Statewide (2017) (Features)

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 ArcMap 10.X with NAIP or Google 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. 2018 marked the first year a comparison could be made using the CDL methodology. The comparison between 2017 and 2018 showed a large change in agricultural land use to other land use. It was determined this shift was due to crop land being allowed to sit fallow for a season and did not represent a shift away from agricultural land. The following code amended the data: ************************************************************************************************************************************* ####On 02/07/2020 this dataset was amended with the following R script to better reflect agricultural land changes: require(arcgisbinding) arc.check_product() ####Bring in layers LU17<-arc.open("Path to data") LU17<-arc.select(LU17) #####Amend data LU17$Landuse[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Agricultural" LU17$CropGroup[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Fallow/Idle" LU17$IRR_Method[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Dry Crop" arc.write("Path to data", LU17) ************************************************************************************************************************************* LUID -Unique ID number for each polygon in the final dataset, matches object. Landuse - Land use type, similar to land cover and represents our own categories of how the land is used. 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 Methods. Acres - Calculated acreage of the polygon. State - Spatial intersection identifying the State where the polygons are found. County - Spatial intersection identifying the County where the polygons are found. Basin - Spatial intersection identifying the Basin where the polygons are found. Basins, or Utah Hydrologic Basins are large watersheds created by DWRe. SubArea - Spatial intersection identifying the Subarea where the polygons are found. 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 specific cover and use types. LABEL - Old shorthand descriptive label for each crop and irrigation type or land use type. Class_Name - Zonal Statistics majority value derived from the USDA CDL Cropscape raster layer, may differ from final crop determination. OldLanduse - This is the old short code found under landuse in past datasets and is kept to maintain connectivity with historical data. 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. Is useful when comparing to past layers.要素类（Feature Classes）被加载至平板电脑，外业人员被指派对选定的部分田块或多边形开展作物、土地覆被类型与灌溉方式的标注工作。每台平板电脑均连接GPS设备以实现实时追踪，在外业标注过程中持续更新作业人员的位置信息。以NAIP或谷歌影像作为底图，并添加其他图层作为参考，基于ArcMap 10.X将其数字化为地理数据库（Geodatabase）要素类。对单个农田、城市区域等现有田块边界进行精准数字化，并在此过程中记录灌溉类型与土地利用的变化情况。 作物数据图层（Cropland Data Layer，CDL）栅格数据由美国农业部（United States Department of Agriculture，USDA）下属的国家农业统计服务中心（National Agricultural Statistics Service，NASS）提供，将根据对应年份下载该数据，下载地址为：https://nassgeodata.gmu.edu/CropScape/。使用分区统计（Zonal Statistics）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。随后将数据与历史清查数据开展多阶段比对，并进行质量检查，以确定并生成最终属性信息。 2018年是首次可采用CDL方法开展比对的年份。2017年与2018年的比对结果显示，农业用地向其他用地类型发生了大规模转变。经研判，该转变是由于耕地被休耕一季所致，并非农业用地整体转出。 以下代码对数据集进行了修正： ************************************************************************************************************************************* ####2020年2月7日，本数据集通过以下R脚本进行修正，以更准确地反映农业用地变化情况： require(arcgisbinding) arc.check_product() ####导入图层 LU17<-arc.open("Path to data") LU17<-arc.select(LU17) #####修正数据 LU17$Landuse[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Agricultural" LU17$CropGroup[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Fallow/Idle" LU17$IRR_Method[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Dry Crop" arc.write("Path to data", LU17) ************************************************************************************************************************************* 各字段说明如下： LUID：最终数据集中每个多边形的唯一标识符，与对象ID一致。 Landuse：土地利用类型，与土地覆被类型概念相近，代表本数据集自定义的土地利用分类体系。 CropGroup：大类作物分组，便于快速访问或查询所有果园、谷物类等作物类型。 Description：属性字段，用于描述或标注通过GIS流程确定的各类作物类型与土地利用类型。 IRR_Method：作物灌溉方式。 Acres：多边形对应的测算面积（英亩）。 State：通过空间相交分析得到的多边形所在州。 County：通过空间相交分析得到的多边形所在县。 Basin：通过空间相交分析得到的多边形所在流域。此处流域即犹他州水文流域，为由DWRe划定的大型汇水区。 SubArea：通过空间相交分析得到的多边形所在次级区域。次级区域为DWRe划定的大型水文流域的细分单元。 Label_Class：处理过程中生成的Label与Class_Name字段的组合值，用于标识具体的覆被与利用类型。 LABEL：用于描述各类作物、灌溉方式或土地利用类型的旧版简写标签。 Class_Name：由USDA CDL Cropscape栅格图层通过分区统计得到的众数值，可能与最终确定的作物类型存在差异。 OldLanduse：旧版数据中landuse字段下的旧短编码，保留该字段以维持与历史数据的关联性。 LU_Group：代表部分内部分组的编码，可用于符号化及其他汇总分析场景。 SURV_YEAR：标识数据对应的年份或生长季，便于与历史图层开展比对。