The vertical land change activity focuses on the detection, analysis, and explanation of topographic change. These detection techniques include both quantitative methods, for example, using difference metrics derived from multi-temporal topographic digital elevation models (DEMs), such as, light detection and ranging (lidar), National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and Interferometric Synthetic Aperture Radar (IFSAR), and qualitative methods, for example, using multi-temporal aerial photography to visualize topographic change. The geographic study area of this activity is Perry County, Kentucky. Available multi-temporal lidar, NED, SRTM, IFSAR, and other topographic elevation datasets, as well as aerial photography and multi-spectral image data were identified and downloaded for this study area county. Available mine maps and mine portal locations were obtained from the Kentucky Mine Mapping Information System, Division of Mine Safety, 300 Sower Boulevard, Frankfort, KY 40601 at http://minemaps.ky.gov/Default.aspx?Src=Downloads. These features were used to spatially locate the study areas within Perry County. Previously developed differencing methods (Gesch, 2006) were used to develop difference raster datasets of NED/SRTM (1950-2000 date range) and SRTM/IFSAR (2000-2008 date range). The difference rasters were evaluated to exclude difference values that were below a specified vertical change threshold, which was applied spatially by National Land Cover Dataset (NLCD) 1992 and 2006 land cover type, respectively. This spatial application of the vertical change threshold values improved the overall ability to detect vertical change because threshold values in bare earth areas were distinguished from threshold values in heavily vegetated areas. Lidar high-resolution (1.5 m) DEMs were acquired for Perry County, Kentucky from U.S. Department of Agriculture, Natural Resources Conservation Service Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGOrder.aspx#. ESRI Mosaic Datasets were generated from lidar point-cloud data and available topographic DEMs for the specified study area. These data were analyzed to estimate volumetric changes on the land surface at three different periods with lidar acquisitions collected for Perry County, KY on 3/29/12 to 4/6/12. A recent difference raster dataset time span (2008-2012 date range) was analyzed by differencing the Perry County lidar-derived DEM and an IFSAR-derived dataset. The IFSAR-derived data were resampled to the resolution of the lidar DEM (approximately 1-m resolution) and compared with the lidar-derived DEM. Land cover based threshold values were applied spatially to detect vertical change using the lidar/IFSAR difference dataset. Perry County lidar metadata reported that the acquisition required lidar to be collected with an average of 0.68 m point spacing or better and vertical accuracy of 15 cm root mean square error (RMSE) or better. References: Gesch, Dean B., 2006, An inventory and assessment of significant topographic changes in the United States Brookings, S. Dak., South Dakota State University, Ph.D. dissertation, 234 p, at https://topotools.cr.usgs.gov/pdfs/DGesch_dissertation_Nov2006.pdf.

All datasets except NSW land values and property sales information in this web maps are maintained by Spatial Service. Property NSW provides Land value and property Sales information. Update …Show full descriptionAll datasets except NSW land values and property sales information in this web maps are maintained by Spatial Service. Property NSW provides Land value and property Sales information. Update frequency for each dataset varies depending on the dataset. All these datasets are used in the land values and property sales map web map application. Please see individual metadata for each dataset below. Land value and property sales map can be found HERE.For more information regarding the Land valuation and Property Sales information data please contact : valuationenquiry@property.nsw.gov.auFor all other datasets, please contact ss-sds@customerservice.nsw.gov.au

Xverum’s Urban Planning Data is a comprehensive dataset of 230M+ verified locations, offering insights into commercial real estate, property trends, and urban development. Covering 5000 categories, our dataset supports real estate investors, urban planners, and policymakers in making data-driven decisions for infrastructure development, property market analysis, and zoning regulations.

With regular updates and continuous POI discovery, Xverum ensures your real estate and urban planning models have the latest property and commercial development data. Delivered in bulk via S3 Bucket or cloud storage, our dataset is ideal for GIS applications, market research, and smart city development.

🔥 Key Features:

Extensive Coverage for Urban Planning & Real Estate: ✅ 230M+ locations worldwide, spanning 5000 categories. ✅ Covers retail, office, industrial, hospitality, and mixed-use properties.

Geographic & Property Market Data: ✅ Latitude & longitude coordinates for precise mapping & real estate valuation. ✅ Property classifications, including commercial & mixed-use assets. ✅ Country, state, city, and postal code classifications for regional analysis.

Comprehensive Real Estate & Property Data: ✅ Property metadata, including location type, size, and market value insights. ✅ Business & commercial property listings for competitive analysis. ✅ Zoning data & regulatory insights for urban expansion & infrastructure planning.

Optimized for Real Estate & Urban Development: ✅ Supports market research, investment analysis & infrastructure development. ✅ Enhances real estate forecasting & planning applications. ✅ Provides in-depth insights for land use and smart city initiatives.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in a structured format (.json) for seamless integration.

🏆 Primary Use Cases:

Urban Planning & Infrastructure Development 🔹 Optimize land use planning, zoning, and city expansion projects. 🔹 Enhance GIS mapping with real estate & infrastructure data.

Real Estate Market Analysis & Investment Research: 🔹 Track commercial property trends & investment opportunities.

Smart City & Economic Growth Planning: 🔹 Identify high-growth regions for real estate & commercial expansion.

💡 Why Choose Xverum’s Urban Planning Data? - 230M+ Verified Locations – One of the largest & most structured real estate datasets available. - Global Coverage – Spanning 249+ countries, covering all real estate & property sectors. - Regular Updates & New Property Discoveries – Ensuring the highest accuracy. - Comprehensive Geographic & Market Metadata – Coordinates, zoning insights & property classifications. - Bulk Dataset Delivery – Direct access via S3 Bucket or cloud storage. - 100% Compliant – Ethically sourced & legally compliant.

Property currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.

The following columns were added 4/14/2023:

• Sales Status
• Sale Offering Status
• Sale Offering Reason
• Square Footage - City Estimate
• Land Value (2022) -- Note: The year will change over time.

The following columns were added 3/19/2024:

• Application Use
• Grouped Parcels
• Application Deadline
• Offer Round
• Application URL

Interagency Wildland Fire Perimeter History (IFPH) Overview This national fire history perimeter data layer of conglomerated agency perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2024 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer, links are provided where possible below. In addition, many agencies are now using WFIGS as their authoritative source, beginning in mid-2020.Alaska fire history (WFIGS pull for updates began 2022)USDA FS Regional Fire History Data (WFIGS pull for updates began 2024)BLM Fire Planning and Fuels (WFIGS pull for updates began 2020)National Park Service - Includes Prescribed Burns (WFIGS pull for updates began 2020)Fish and Wildlife Service (WFIGS pull for updates began 2024)Bureau of Indian Affairs (Incomplete, 2017-2018 from BIA, WFIGS pull for updates began 2020)CalFire FRAS - Includes Prescribed Burns (CALFIRE only source, non-fed fires)WFIGS - updates included since mid-2020, unless otherwise noted Data LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoritative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.Attributes This dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer maintained by IrWIN. (This unique identifier may NOT replace the GeometryID core attribute) FORID - Unique identifier assigned to each incident record in the Fire Occurence Data Records system. (This unique identifier may NOT replace the GeometryID core attribute) INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name. FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT). AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin. SOURCE - System/agency source of record from which the perimeter came. DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy. MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Other GIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9 UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001 LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456. UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMP COMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or Unknown GEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID). Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4,781 Records thru 2021), other federal sources for AK data removed. No RX data included.CA: GEOID = OBJECT ID of downloaded file geodatabase (8,480 Records, federal fires removed, includes RX. Significant cleanup occurred between 2023 and 2024 data pulls resulting in fewer perimeters).FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2,959 Records), includes RX.BIA: GEOID = "FireID" 2017/2018 data (382 records). No RX data included.NPS: GEOID = EVENT ID 15,237 records, includes RX. In 2024/2023 dataset was reduced by combining singlepart to multpart based on valid Irwin, FORID or Unique Fire IDs. RX data included.BLM: GEOID = GUID from BLM FPER (23,730 features). No RX data included.USFS: GEOID=GLOBALID from EDW records (48,569 features), includes RXWFIGS: GEOID=polySourceGlobalID (9724 records added or replaced agency record since mid-2020)Attempts to repair Unique Fire ID not made. Attempts to repair dates not made. Verified all IrWIN IDs and FODRIDs present via joins and cross checks to the respective dataset. Stripped leading and trailing spaces, fixed empty values to

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

The datasets were produced during the doctoral research titled "A modelling framework to estimate the effects of future transport interventions on land values". Specifically, the datasets were produced and prepared with the purpose of training a geostatistical model and construct a residential land value map for Guatemala City using a predictive approach. The file "gc_log" contains a set of spatial points representing parcel centroids of the observations that were used to train a regression-kriging model. A spatialized variable selection was implemented in order to retain only important variables. The "grid_prediction.csv" contains a set of spatial points representing the centroids of an hexagonal tessellation containing values for all the required predictors of land value. The "research_code.R" file contains the scripts developed to implement the spatial variable selection, train the regression-kriging model and construct the land value map.

Descriptions Excel Application Tool for Statewide Agricultural Water Use Data 2016 - 2020 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2016 – 2020 statewide agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using Cal_simetaw model for updating the information in the California Water Plan Updates-2023. Therefore, this current Excel application tool just covers agricultural water use data from the period of 2016 - 2020 water years. It should also be mentioned that there are 3 other similar Excel applications that cover 1998 - 2005 and 2006 – 2010, & 2011 - 2015 agricultural water use data for the California Water plan Updates 2005/2009, 2013, and 2018 respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2016 – 2020 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu. Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised.

Following are definitions of terminology and listing of 20 crop categories used in this Excel application.

1. Study Area Maps
   The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR),
   The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation.

2. Irrigated Crop Area (ICA) in acres
   The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres)

3- Multi-cropping (MC) in acres
A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field.
Please note that there are no double cropping acreages for 2017. Because on a normal year when Regional Offices (RO) receive data from Land IQ, they were able to provide double cropping acreages. Since the 2017 land use data was derived from average crop acres between water years 2016 and 2018,2019, & 2020, they lost spatial and temporal data necessary to calculate double cropping.

1. Evapotranspiration (ET)
   Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity.

2. Reference Evapotranspiration (ETo)
   Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988).

3. Penman-Monteith Equation (PM)
   Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo.

4. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet)
   Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed
   as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc.
   One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep.

5. Crop Coefficient (Kc)
   Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration.

6. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet)
   Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation

7. Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet)
   Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw.

8. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet)
   Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency.

9. Consumed Fraction (CF) in percentage (%)
   An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year.

10. Crop category numbers and descriptions
    Crop Category Crop category description.

1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay)
2 Rice (rice, rice_wild, rice_flooded, rice-upland)
3 Cotton
4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early)
5 Corn
6 Dry beans
7 Safflower
8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane
9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual)
10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue)
11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc)
12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc)
13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon)
14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic)
15 Potatoes (potatoes, potatoes_sweet)
16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower)
17 Almond & pistachios
18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis)
19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba)
20 Vineyards (grape_table, grape_raisin, grape_wine)

Excel Application Tool for Agricultural Water Use Data 1998 - 2005 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 1998 – 2005 agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using California Ag Water Use model for updating the information in the California Water Plan Updates-2003 & 2009. Therefore, this current Excel application tool just covers agricultural water use data from the period of 1998 - 2005 water years. It should also be mentioned that there are 2 other similar Excel application tools that cover 2006 - 2010 and 2011 - 2015 agricultural water use data for the California Water plan Updates - 2013 and 2018, respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 1998 – 2005 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu.
Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets were included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised.

Following are definitions of terminology and listing of 20 crop categories used in this Excel application.

1. Study Area Maps The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR),
   The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation.

2. Irrigated Crop Area (ICA) in acres The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres)

3. Multi-cropping (MC) in acres A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field.

4. Evapotranspiration (ET) Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity.

5. Reference Evapotranspiration (ETo) Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988).

6. Penman-Monteith Equation (PM) Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo.

7. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet) Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep.

8. Crop Coefficient (Kc) Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration.

9. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet) Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation

10. Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet) Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw.

11. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet) Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency.

12. Consumed Fraction (CF) in percentage (%) An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year.

13. Crop category numbers and descriptions
    Crop Category Crop category description.

1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay)
2 Rice (rice, rice_wild, rice_flooded, rice-upland)
3 Cotton
4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early)
5 Corn
6 Dry beans
7 Safflower
8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane
9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual)
10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue)
11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc)
12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc)
13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon)
14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic)
15 Potatoes (potatoes, potatoes_sweet)
16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower)
17 Almond & pistachios
18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis)
19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba)
20 Vineyards (grape_table, grape_raisin, grape_wine)

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover (TCC) data to serve needs of multiple user communities. These datasets encompass the conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: - The raw model outputs referred to as the annual Science data; and - A modified version built for the National Land Cover Database referred to as NLCD data. They are available at the following locations: Science: https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife NLCD: https://www.mrlc.gov/data https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata.

Land consolidation has been used to reduce the effects of land fragmentation in many parts of the world in order to increase food productivity and food security, amongst other benefits. However, its use on customary lands has been limited. Land fragmentation on customary lands has two main causes ? the nature of the customary land tenure system, and the somewhat linked agricultural system. The attempts to increase food productivity on customary lands have mostly involved fertilisation, and mechanisation. But with the small and scattered farmlands, these approaches fall short of increasing food productivity. Previous attempts at land consolidation have involved directly transferring land consolidation approaches from other regions of the world. These have failed as little consideration were given to the local conditions of customary lands. This thesis suggests the use of responsible approaches; ones that continuously consider and align technical and administrative conditions, and internal processes inherent to land consolidation, to the dynamic local societal demands, economic conditions, and cultural and legal requirements. To this end, this thesis aimed to develop a responsible land consolidation approach for customary lands. For this purpose, using Ghana as a case, four specific objectives are addressed. First, to explore how the factors need to be addressed to develop a responsible land consolidation approach for customary lands, a comparative study was conducted between Ghana and three other countries. The factors when selecting a land consolidation strategy were identified for three countries with existing land consolidation strategies: The Netherlands, Lithuania, and Rwanda. These were set against Ghana, which has no land consolidation strategy, but has customary lands. The comparison found that certain factors in the countries with land consolidation - the government support; the prior existence of conventional land markets; an individual land tenure system; and the coverage of a functioning land information system ? were all absent in Ghana. The comparison concluded that these factors that differ require ways to be addressed and adapted in order to develop a responsible land consolidation strategy for Ghana?s customary areas. Second, in order get a land information to support responsible land consolidation on customary lands, the next chapter develops and assesses an approach for collecting land information to support responsible land consolidation on customary lands. The concept of Participatory Land Administration (PLA) is then developed in the context of crowdsourced, voluntary, and participatory approaches alongside newly related insights into neogeography and neo-cadastre, and fit-for-purpose and pro-poor land administration. The PLA concept is experimented in Northern Ghana, where the process was developed together with the local farming community. The experiment involved collecting land information relating to farms over a two- week period, using a mobile app and a satellite image, based on PLA. The results show that PLA can potentially support land consolidation as the land information collected supports land consolidation, and the local people?s involvement gave them a sense of ownership of the results. Third, as there is no land market to provide support to land consolidation as a basis for comparison of farmland parcels, a land valuation approach to support responsible land consolidation on customary lands is developed and assessed. Using a Multiple Attribute Decision-Making (MADM) approach, the Land Value Indices is developed to assign quid pro quo land values to customary farmland parcels, based on the local people?s view of land value. In a case study of Nanton, Ghana, key land value factors were identified and weighted by the local community. The weights were integrated into the framework that produced a Land Value Index for each farmland parcel. The strength of the approach is found in scenario and sensitivity analyses. However, the prime weakness of this approach is that it is more expensive to use than automatic valuation models. The presence of the customary land tenure system limits the use of conventional land reallocation techniques available. Hence the framework of a process model for a reallocation approach to support responsible land consolidation on customary lands is developed and assessed. Using the process model approach, the key characteristics of customary land tenure and the general requirements for land re-allocation of these lands were identified; from which a land re-allocation approach is developed. This is subsequently applied to a case study in Northern Ghana. The results show that even though the approach is successful to the extent that land fragmentation (physical and legal) is significantly reduced in the study area within family lands; social land mobility, land tenure and cultural practices hinder the application of the land re-allocation between families, as this would either increase legal or physical land fragmentation. In conclusion, land consolidation can be used to reduce land fragmentation and increase food productivity on customary lands. However, this is limited by the balance between reducing physical or land tenure fragmentation, as the reduction of the former leads to an increase in the later and vice versa. Further studies have to be conducted to overcome the balance between reducing physical land fragmentation on one hand and land tenure fragmentation on the other hand. The results of this thesis contribute to knowledge and literature, by broadening the knowledge on the transfer of land management activities to customary lands. To policy formulation and implementation, the results show that the need for land policies to consider the gap between land information collection or the building of a cadastre on the one hand and sustainable development on the other. In terms of food policy, the results enrich the need for a stronger link between food policy and land policy, especially in terms of food productivity. In terms of meeting societal challenges and needs, though the focus of this work is on food security, the developed land administration processes provide support for other societal needs and goals such as economic and infrastructural development, disaster risk management, climate change adaptation, and large-scale land acquisitions.

Data sets containing information from the application for and disbursement of production subsidies to agricultural undertakings during the 2019 application period. The data was retrieved in mid-June 2020, and the data set contains both applications that have been paid and applications that were currently being processed. For the applications that are shown with payout, the approved values for the individual codes are displayed, for other applications there are applied values. This dataset has replaced the data set that was extracted in February 2020. For each enterprise, the organisation number, name, municipality number and number of animals and acres listed in the individual code are shown in the application form.In addition, the calculated subsidy per scheme, the basic allowance, as well as the sum calculated subsidy according to the basic allowance are shown. Any deductions other than bottom deductions are not shown. The 2019 application period consists of two parts: • Part 1 — March: O Contains application information on the number of animals on the counting date 1 March. Part 2-October: O Contains application information on the number of animals on the date of counting on 1 October (including conservation livestock breeds), animals on pasture, green and potato production as well as available areas and which crops are grown on the land. — Purpose: Production and replacement subsidy for agricultural enterprises — application period 2019.Data sets containing information from the application for and disbursement of production subsidies to agricultural undertakings during the 2019 application period. The data was retrieved in mid-June 2020, and the data set contains both applications that have been paid and applications that were currently being processed.For the applications that are shown with payout, the approved values for the individual codes are displayed, for other applications there are applied values. This dataset has replaced the data set that was extracted in February 2020. For each enterprise, the organisation number, name, municipality number and number of animals and acres listed in the individual code are shown in the application form. In addition, the calculated subsidy per scheme, the basic allowance, as well as the sum calculated subsidy according to the basic allowance are shown. Any deductions other than bottom deductions are not shown. The 2019 application period consists of two parts: • Part 1 — March: O Contains application information on the number of animals on the counting date 1 March. Part 2-October: O Contains application information on the number of animals on the date of counting on 1 October (including conservation livestock breeds), animals on pasture, green and potato production as well as available areas and which crops are grown on the land. — Purpose: Production and replacement subsidy for agricultural enterprises — application period 2019.

Excel Application Tool for Agricultural Water Use Data 2006 - 2010

Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2006 – 2010 agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using California Ag Water Use model for updating the information in the California Water Plan Updates-2013. Therefore, this current Excel application just covers agricultural water use data from the period of 2006 -2010 water years. It should also be mentioned that there are 2 other similar Excel application tools that cover 1998 - 2005 and 2011 - 2015 agricultural water use data for the California Water plan Updates - 2003/2009 and 2018, respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2006 – 2010 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu.
Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised.

Following are definitions of terminology and listing of 20 crop categories used in this Excel application.

1. Study Area Maps
   The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR),
   The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation.

2. Irrigated Crop Area (ICA) in acres
   The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres)

3. Multi-cropping (MC) in acres
   A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field.

4. Evapotranspiration (ET)
   Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity.

5. Reference Evapotranspiration (ETo)
   Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988).

6. Penman-Monteith Equation (PM)
   Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo.

7. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet)
   Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep.

8. Crop Coefficient (Kc)
   Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration.

9. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet)
   Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation

10. Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet)
    Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw.

11. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet)
    Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency.

12. Consumed Fraction (CF) in percentage (%)
    An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year.

13. Crop category numbers and descriptions
    Crop Category Crop category description.

1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay)
2 Rice (rice, rice_wild, rice_flooded, rice-upland)
3 Cotton
4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early)
5 Corn
6 Dry beans
7 Safflower
8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane
9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual)
10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue)
11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc)
12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc)
13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon)
14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic)
15 Potatoes (potatoes, potatoes_sweet)
16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower)
17 Almond & pistachios
18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis)
19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba)
20 Vineyards (grape_table, grape_raisin, grape_wine)

This product summarizes fertilizer consumption in the United States by plant nutrient and major fertilizer products—as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients—for 1960 through the latest year for which statistics are available. The share of planted crop acreage receiving fertilizer, and fertilizer applications per receiving acre (by nutrient), are presented for major producing States for corn, cotton, soybeans, and wheat (data on nutrient consumption by crop start in 1964). Fertilizer farm prices and indices of wholesale fertilizer prices are also available.

Important Note: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation,
clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

In accordance with Decree No 2018-1350 of 28 December 2018 on the publication in electronic form of information relating to property values declared in connection with real estate transfers, this DVF file is now available in open data. The publication of this data meets the objective of transparency of land and real estate markets. The DVF file contains personal data and DGFiP draws your attention to the legal obligations arising therefrom. * Article R112 A-3 of the Tax Procedures Book provides that the use of these files shall not permit the re-identification of the persons concerned, indirectly. * Re-use of data should not allow indexing of data from external search engines. Please read the detailed and downloadable terms and conditions of use carefully. ### What is DVF? This dataset “Requests for Land Values”, published and produced by the Directorate-General for Public Finance, makes it possible to know the real estate transactions that took place over the last five years in the metropolitan territory and the DOM-TOM, with the exception of Alsace-Moselle and Mayotte. The data contained are derived from notarial acts and cadastral information. The files each corresponding to a vintage are made available in.txt. format over 5 years. The files for the restitution of land values are organised as follows: * identification of each mutation by its disposition number; * display of one line per room; * for each room (each line), the lots of the corresponding building (within the limit of 5) are displayed as well as the total number of lots for that building; * the actual surface is associated with the room; * return of the CARREZ surface associated with the lot when indicated. The list of DVF data strictly respects the variables provided for by the decree of 28 December 2018. No additional data can be transmitted at this stage. When the file is opened in a spreadsheet, the following separator should be used: | (AltGr 6) A downloadable explanatory note accompanies the files, as well as the general conditions of use and the information of the persons concerned by the computer processing implemented. > Warning, the files are large ## Update files The files made available are subject to a biannual update, in April and October. Each update removes and replaces all previously released files. Point of attention: the files are fed by data entered in DGFiP’s land advertising services and with each update of new transactions are likely to be added in all vintages depending on the date of transfer. Contact: for any information requests or errors detected, please contact DGFIP’s CL2A office at the following address: bureau.cl2a-fdl@dgfip.finances.gouv.fr

Transaction Data shows how many customer applications we completed in the previous month. We publish 5 different versions of this dataset. They're normally available to download on the 15th day of the month. There are 2 datasets listed by location: * number of applications in England and Wales divided by location * number of applications in England and Wales divided by local authority There are 3 datasets listed by customer name. They show the applications we get from solicitors, conveyancers and lenders that use an HM Land Registry account number: * number and types of applications by all account customers * number and types of transactions for value by all account customers * number of searches by all account customers The data excludes: * bankruptcy applications * bulk applications * applications that we've received but not yet completed ## Geographic coverage England and Wales ## License Statement The data is available free of charge for use and re-use under the Open Government Licence (OGL). Make sure you understand the terms of the OGL before using the data. If you use or publish this data, you must add the following attribution statement: Contains HM Land Registry data © Crown copyright and database right [year of supply or date of publication]. This data is licensed under the Open Government Licence v3.0. ## Frequency of update Monthly