The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries. This site provides links to download ESRI shape and symbology layer files, as well as low resolution JPEG or higher resolution PDF images for each state. Also included in the FAQ are how to cite the data set, time period, how geographic features are represented and described, originators and contributors, contacts to address questions about the data, how the data set was created (previous works, e.g. USGS topographic quadrangles, US Census Bureau, space imagery, etc.), data generation-, processing-, and modification methods, and similar or related data. Applicable legal restrictions on access or use of the data and disclaimers are provided. Resources in this dataset:Resource Title: Land Use Strata - Selected States. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/stratafront2b.php This site provides links to download ESRI shape and symbology layer files, as well as low resolution JPEG or higher resolution PDF images for each state.

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.

While seasonal maximum vegetation index values from general cropland have been found to have some utility for identifying irrigated locations, the diverse agricultural landscape and pronounced climatic gradients across Kansas undermine the general effectiveness of approaches based solely on this information. Consequently, we developed an alternative procedure for creating a binary irrigated lands map for Kansas focused on 2007 but applicable to the 2003-2012 time period and which utilizes multiple datasets and methods facilitative of this task. Five crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) dominate the Kansas agricultural landscape, and these crop types were considered for this exercise. The mapping effort is summarized in the following steps.

“Place of Use” (POU) spatial information provided by the Division of Water Resources – Kansas Department of Agriculture, which indicates where high-volume irrigation is permissible in Kansas, was used to restrict the mapping exercise to locations contained therein. All irrigated locations in the final dataset fall within POU boundaries.Based on spatial variability in agricultural management tendencies, crop-specific MODIS NDVI profile distributions, and irrigation use extensiveness, we split the state into two regions, west-central (WC) and east (E), following Agricultural Statistics District (ASD) boundaries. Region-specific boosted decision tree models developed using MODIS NDVI time series were used to provide initial annual estimates for irrigated locations. Models were trained using ground reference data consisting of USDA Farm Service Agency (FSA) annual cropping records from 2003-2007 that were spatially linkable to FSA Common Land Unit (CLU) polygons (c.2007). Models for 2003-2005 were constructed to simultaneously map both crop type and irrigation status, whereas models for 2006-2012 were constructed to map crop-specific irrigation status. For the latter period, USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) spatial information was used to map individual crop types prior to irrigation status model development. Classified raster data were generalized to field boundaries. Field boundaries were created by combining multi-year CLU data and manual delineations using heads-up digitizing and NAIP imagery.Initializing the irrigated locations map using model output from 2007, land use/land cover (LULC) trajectories were examined for 2007 non-irrigated cropland locations within the POU. A rule set was developed based on mapped irrigation frequency during 2003-2012 that was used to “flip” irrigation status from non-irrigated to irrigated for fields in the 2007 layer that met particular criteria.Statewide irrigated area, by crop, was computed for the 2007 map and compared to USDA NASS irrigated cropland area estimates from 2007. Mapped irrigated area for alfalfa and corn were found to exceed the USDA numbers (by 14% and 3%, respectively), so no further irrigation status changes were applied to locations mapped to those crop types in 2007. It was determined that 2007 total mapped soybean area within the POU fell 15% short of the 2007 USDA estimate for irrigated soybean area in Kansas, so all soybeans within the POU were assigned a status of irrigated, and no further irrigation status changes were applied to fields mapped to this crop in 2007. Mapped irrigated area for 2007 sorghum and winter wheat were found to fall short of 2007 USDA irrigated area estimates (by 44% and 26%, respectively), and non-irrigated areas for these crops were sufficiently prevalent within the POU to consider further irrigation status changes.Non-irrigated 2007 sorghum and winter wheat parcels were ranked according to 10 size classes reasoned to be decreasingly reflective of field sizes commonly associated with center pivot irrigation. The same fields were then ranked according to 2007 maximum MODIS NDVI, and then again by mapped irrigation frequency during 2003-2012. A crop-specific weighted sum of these three rankings was calculated to provide each parcel with a score reasoned to reflect its likelihood of being irrigated. For each crop, county-level NASS irrigated area estimates from 2007 were compared with respective county specific, mapped irrigated area totals from 2007. For each county whereby the mapped irrigated area exceeded the NASS irrigated area, no action was taken. For each county whereby the mapped total area within the POU fell short of the NASS irrigated area, all relevant parcels within the POU were assigned a status of irrigated. For the final case, whereby mapped irrigated area fell short of NASS irrigated area but total mapped area within the POU exceeded NASS irrigated area, one by one, the highest ranked parcels were reassigned to irrigated status until NASS totals were met or exceeded. The irrigated location map obtained at the end of this step is the final map. Processing was completed on 28-April-2014.

NSF BACC-FLUD Kansas Land Cover 2003-2012 (ZIP download)

REFERENCES

Gao, J., A.Y. Sheshukov, H. Yen, J.H. Kastens, and D.L. Peterson (2017). Impacts of Incorporating Dominant Crop Rotation Patterns as Primary Land Use Change on Hydrologic Model Performance. Agriculture, Ecosystems and Environment, 247: 33-42. DOI: 10.1016/j.agee.2017.06.019

MardanDoost, B., A.E. Brookfield, J. Feddema, B. Sturm, J. Kastens, D. Peterson, and C. Bishop (2019). Estimating irrigation demand with geospatial and in-situ data: Application to the High Plains Aquifer, Kansas, USA. Agriculture Water Management, 223(2019): 105675. DOI: 10.1016/j.agwat.2019.06.010

This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales. A QWRA considers several different components, each resolved spatially across the region, including:likelihood of a fire burning, the intensity of a fire if one should occur,the exposure of assets and resources based on their locations, and the susceptibility of those assets and resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. WildEST results were modified for risk calculations in the PNW QWRA 2023 using an irrigated agriculture mask to assign FLPs to pixels that are likely to be irrigated during fire season. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”), and data was collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels and pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). FSim is a comprehensive fire occurrence, growth, behavior, and suppression simulation system that uses locally relevant fuel, weather, topography, and historical fire occurrence information to generate spatially resolved estimates of the contemporary likelihood and intensity of wildfire events. FSim generates stochastic simulation data based on many thousands of iterations and then integrates those into a probabilistic result. These FSim model results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE). which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. This simulation is calibrated to the 2022 trend in wildfire occurrence. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified period (1 year).The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Agriculture eNVC represents risk integrated across all Agriculture sub-HVRAs. The Agriculture HVRA is intended to evaluate wildfire risk to cropland and associated infrastructure. We mapped the extent of croplands using the last five years of Cropscape data from the U.S. Department of Agriculture (USDA-NASS, 2022). All pixels that were considered cultivated between 2018 and 2022 were included in the HVRA extent and the most common crop type associated with each cultivated pixel was used to classify the sub-HVRA as either perennial or annual. Sub-HVRAs included:Perennial CropsAnnual CropsRisk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise it may be possible to experience beneficial fire in areas with negative NVC values. Citations:USDA-NASS, 2022. USDA National Agricultural Statistics Service Cropland Layer (2022). https://nassgeodata.gmu.edu/CropScape/Ketchum, D., Jencso, K., Maneta, M.P., Melton, F., Jones, M.O., Huntington, J., 2020. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing 12, 2328. https://doi.org/10.3390/rs12142328Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-zPrimary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov