This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using United States Geological Survey (USGS) Landsat 8 and 9 Level 2, Collection 2, Tier 1 data, Harmonized Sentinel-2 Multispectral Instrument Level-2A data, USGS 3D Elevation Program (USGS 3DEP) data, and Height Above Nearest Drainage (HAND) data. Landsat 8, Landsat 9, and HAND data are at a 30-meter spatial resolution, and the Sentinel-2 and USGS 3DEP data are at a 10-meter spatial resolution. Sentinel-2 Normalized Difference Vegetation Index (NDVI) values and National Agriculture Imagery Program (NAIP) imagery from 2021 (the most recent available) were used to determine irrigation status for the manually classified training data points. Irrigated training point locations were first identified by the NAIP 2021 imagery. Those point locations were then used to sample all available Sentinel-2 NDVI images for the 2022 growing season, and the time series at each point location was reviewed. Only points whose NDVI values remained at or above 0.6 for the majority of the growing season retained their irrigation classification. All non-irrigated training points were reviewed with Sentinel-2 NDVI and false-color imagery to ensure no new crop fields had been established in those locations during the previous year.The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. A wetlands mask was applied using the U.S. Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data for areas without overlapping irrigation POUs or locations manually determined to have potential irrigation. “Speckling”, or small areas of incorrectly classified pixels, was reduced by using the Boundary Clean smoothing tool in ArcGIS with a descending sorting type.

This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 30-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data. A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using Level 2 (surface reflectance), Collection 2, Tier 1 data from Landsat 7 and Landsat 8, Mapping Evapotranspiration with Internalized Calibration (METRIC) data produced by IDWR, United States Geological Survey National Elevation Dataset (USGS NED) data, and Height Above Nearest Drainage (HAND) data. Landsat 7, Landsat 8, METRIC, and HAND data are at a 30-meter spatial resolution, and the USGS NED data are at a 10-meter spatial resolution. The Cropland Data Layer (CDL) from the United States Department of Agriculture (UDSA) National Agricultural Statistics Service (NASS), National Agriculture Imagery Program (NAIP) data from the USDA Farm Service Agency (FSA), Utah Water Related Land Use data from the Utah Division of Water Resources, and water rights data from IDWR were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. “Speckling”, or small areas of incorrectly classified pixels, was reduced by masking all pixels with a slope value of 10% or greater as “non-irrigated”, regardless of the status they were assigned by the Random Forest model. Speckling within irrigated areas was reduced by a majority filter smoothing technique using a kernel of 8 nearest neighbors. A limited amount of manual corrections were also made to the final results.

Report of Raft Fishing Reel is covering the summarized study of several factors encouraging the growth of the market such as market size, market type, major regions and end user applications. By using the report customer can recognize the several drivers that impact and govern the market. The report is describing the several types of Raft Fishing Reel Industry. Factors that are playing the major role for growth of specific type of product category and factors that are motivating the status of the market.

These data were used by the Idaho Department of Water Resources to support their business needs. The original imagery is from the USDA-Farm Service Agency - 1:20,000-scale black and white film. These data are not orthorectified.Individual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.

This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 30-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using Level 2 (surface reflectance), Collection 2, Tier 1 data from Landsat 5 and Landsat 7, Mapping Evapotranspiration with Internalized Calibration (METRIC) data produced by IDWR, United States Geological Survey National Elevation Dataset (USGS NED) data, and Height Above Nearest Drainage (HAND) data. Landsat 5, Landsat 7, and HAND data are at a 30-meter spatial resolution, and the USGS NED data are at a 10-meter spatial resolution. The National Land Cover Dataset (NLCD) from the USGS, National Agriculture Imagery Program (NAIP) data from the USDA Farm Service Agency (FSA), Utah Water Related Land Use data from the Utah Division of Water Resources, Mapping Evapotranspiration with Internalized Calibration (METRIC) data (where available), and water rights data from IDWR were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. “Speckling”, or small areas of incorrectly classified pixels, was reduced by masking all pixels with a slope value of 10% or greater as “non-irrigated”, regardless of the status they were assigned by the Random Forest model. Speckling within irrigated areas was reduced by a boundary clean smoothing technique.

This dataset was generated to determine a 2017 water budget. The boundary of the study area extends from Idaho into a portion of Utah.This layer depicts polygons representing land within the Raft River Study area boundary classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. Neither Irrigation status nor line work were verified by ground truthing. Field boundaries were refined using the 2017 Idaho National Agriculture Imagery Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, or other high resolution imagery. Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat and/or Sentinel satellite imagery as background reference. Landsat imagery is typically 30-meter (Landsat5) or 15-meter (Landsat7) resolution. Sentinel imagery is 10-meter resolution. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery is used for determining irrigation status and refining the polygon geometry. The interpretation and classification process is described in detail in the report, "2006 Irrigated Land Classification for the Eastern Snake Plain Aquifer" archived on the IDWR website: Legal Actions > Delivery Call Actions > SWC > Archived Matters > Technical Working Group Documents (https://idwr.idaho.gov/legal-actions/delivery-call-actions/SWC/archived-matters.html#twg-documents).