A set of three estimates of land-cover types and annual transformations of land use are provided on a global 0.5 x0.5 degree lat/lon grid at annual time steps. The longest of the three estimates spans 1770-2010. The dataset presented here takes into account land-cover change due to four major land-use/management activities: (1) cropland expansion and abandonment, (2) pastureland expansion and abandonment, (3) urbanization, and (4) secondary forest regrowth due to wood harvest. Due to uncertainties associated with estimating historical agricultural (crops and pastures) land use, the study uses three widely accepted global reconstruction of cropland and pastureland in combination with common wood harvest and urban land data set to provide three distinct estimates of historical land-cover change and underlying land-use conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and extent to which different ecosystem have undergone changes. The three estimates use a consistent methodology, and start with a common land-cover map during pre-industrial conditions (year 1765), taking different courses as determined by the land-use/management datasets (cropland, pastureland, urbanization and wood harvest) to attain forest area distributions close to satellite estimates of forests for contemporary period. The satellite based estimates of forest area are based on MODIS sensor. All data uses the WGS84 spatial coordinate system for mapping.

This dataset compiles the international, peer-reviewed literature on land use conflicts that was published between 2005 and 2020. It lists 306 publications and provides for each of them information regarding the research methods, research aims, covered land use types, covered geographical region, subject area, definition of "land use conflict", and conceptual approach. It can be used to identify publications on specified topics or combinations of topics, such as literature on land use conflicts in Africa regarding the land use type pastoralism. The ROSES for systematic map reports file provides information of how the systematic map was compiled and the list of unobtainable and excluded publications lists those publications that appeared in the initial database search but were not included in the mapping, with reasons for exclusion. More information on the dataset's creation, a detailed analysis, and interpretation are provided in the corresponding journal article:

Fienitz, Meike. 2023. "Taking stock of land use conflict research: A systematic map with special focus on conceptual approaches." Society & Natural Resources. http://dx.doi.org/10.1080/08941920.2023.2199380

The following data is provided as a public service, for informational purposes only. This data should not be construed as legal advice. Users of this data should independently verify its determinations prior to taking any action under the California Environmental Quality Act (CEQA) or any other law. The State of California makes no warranties as to accuracy of this data. General plan land use element data was collected from 532 of California's 539 jurisdictions. An effort was made to contact each jurisdiction in the state and request general plan data in whatever form available. In the event that general plan maps were not available in a GIS format, those maps were converted from PDF or image maps using geo-referencing techniques and then transposing map information to parcel geometries sourced from county assessor data. Collection efforts began in late 2021 and were mostly finished in late 2022. Some data has been updated in 2023. Sources and dates are documented in the "Source" and "Date" columns with more detail available in the accompanying sources table. Data from a CNRA funded project, performed at UC Davis was used for 7 jurisdictions that had no current general plan land use maps available. Information about that CNRA funded project is available here: https://databasin.org/datasets/8d5da7200f4c4c2e927dafb8931fe75dIndividual general plan maps were combined for this statewide dataset. As part of the aggregation process, contiguous areas with identical use designations, within jurisdictions, were merged or dissolved. Some features representing roads with right-of-way or Null zone designations were removed from this data. Features less than 4 square meters in area were also removed.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022). All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References: Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719. Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

This product contains plot location data for LCMAP Hawaii Reference Data in a .shp format as well as annual land cover, land use, and change process variables for each reference data plot in a separate .csv table. The same information available in the.csv file is also provided in a .xlsx format. The LCMAP Hawaii Reference Data Product was utilized for evaluation and validation of the Land Change Monitoring, Assessment, and Projection (LCMAP) land cover and land cover change products. The LCMAP Hawaii Reference Data Product includes the collection of an independent dataset of 600 30-meter by 30-meter plots across the island chain of Hawaii. The LCMAP Hawaii Reference Data Products collected variables related to primary and secondary land use, primary and secondary land cover(s), change processes, and other ancillary variables annually across Hawaii from 2000-2019. The sites in this dataset were collected via manual image interpretation. These samples were selected using a stratified random sampling process via stratification using a hybrid NLCD 2001 Hawaii and NOAA C-Cap 2011 land cover map.

Land Cover Mapping Analysis And Urban Growth Modelling Using Remote Sensing Techniques In Greater Cairo RegionEgypt
This dataset falls under the category Traffic Generating Parameters Land Cover.
It contains the following data: This study modelled the urban growth in the Greater Cairo Region (GCR), one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC) maps (1984, 2003 and 2014) were produced from satellite images by using Support Vector Machines (SVM). Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change) and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modelled to predict the future scenarios for year 2025 using Land Change Modeller (LCM) embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.
This dataset was scouted on 2022-02-03 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.researchgate.net/publication/282321895_Land_Cover_Mapping_Analysis_and_Urban_Growth_Modelling_Using_Remote_Sensing_Techniques_in_Greater_Cairo_Region-Egypt Please note: This link leads to an external resource. If you experience any issues with its availability, please try again later.

Overview: Land cover mapping represents the coverage of vegetation, bare, wet and built surfaces (developed and natural surfaces) at a given point in time. The existing land cover map was developed by Whatcom County Planning and Development Services (PDS) during spring of 2012 for the Lower Nooksack Water Budget. The dataset represents ground conditions between 2006 and 2010. The project team created the existing condition land cover dataset by combining local and regional datasets to get the most accurate and current data for the U.S. and Canadian portions of WRIA 1. The development of the existing land cover map includes 14 land cover categories; each has a unique impact on the water balance. The agricultural land cover class was further classified into crop types.

Land cover and crop types influence evapotranspiration and infiltration, playing an important role in determining the watershed’s water balance. Land cover data provides information used to parameterize the water movement through the vegetation canopy and water demand of plant evapotranspiration in the estimation of the water budget by the hydrology model.

Land cover changes over time, as exemplified by comparing the existing and historic land cover data in WRIA 1, displayed in Figure 1 and Figure 2. Historic land cover mapping developed by Utah State University (Winkelaar, 2004) as part of the WRIA 1 Watershed Management Project was used to represent land cover/land use for the undepleted flow simulations. This work was done using a suite of studies and ancillary datasets, including turn of the century GLO maps and NRCS soils data. Methods and sources more thoroughly described in Mapping Methodology and Data Sources for Historic Conditions Landuse/ Landcover Within Water Resource Inventory Area 1 (WRIA1) Washington, U.S.A. The historic land cover map includes 10 land cover classes.

Purpose: Within the Topnet-WM hydrologic model used to estimate the Lower Nooksack Water Budget, the local land cover type is used to parameterize the water movement through the vegetation canopy and water demand for plant evapotranspiration, as described in detail in Chapter 2: Water Budget Model. Water input to the canopy comes from rainfall, snowmelt, and irrigation. The process of some water retention by the canopy is known as interception. Potential evapotranspiration is first satisfied from the canopy interception storage. Water that passes through the canopy to the soil becomes input to the vadose zone soil storage. The vadose zone is the unsaturated soil region above the water table. Potential evapotranspiration not satisfied from the interception storage becomes potential evapotranspiration from the vadose zone soil storage. The model calculates crop evapotranspiration using the Penman-Monteith method. Irrigation requirements are calculated using potential crop evapotranspiration and irrigation efficiency. Land cover mapping also identifies impervious surfaces where water directly runs off, as well as lakes and wetlands where water is stored and evaporates.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

Source of Imagery

Our strategy is to reuse images from existing benchmark datasets as much as possible and manually annotate new land cover labels. We selected xBD, Inria, Open Cities AI, SpaceNet, Landcover.ai, AIRS, GeoNRW, and HTCD datasets. For countries and regions not covered by the existing datasets, aerial images publicly available in such countries or regions were collected to mitigate the regional gap, which is an issue in most of the existing benchmark datasets. The open data were downloaded from OpenAerialMap and geospatial agencies in Peru and Japan. The attribution of source data is summarized here.

Classes and Annotations

We provide annotations with eight classes: bareland, rangeland, developed space, road, tree, water, agriculture land, and building. Their color and proportion of pixels are summarized below. All the labeling was done manually, and it took 2.5 hours per image on average.

License of Labels in OpenEarthMap

Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data here. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Reference

@inproceedings{xia_2023_openearthmap,

title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping},

author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako},

booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

month = {January},

year = {2023},

pages = {6254-6264}

}

High resolution land cover dataset for Virginia Beach, VA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3x3 square feet.

The primary sources used to derive this land cover layer were (1) NAIP 2008 imagery derived from the compressed county mosaic, (2) Normalized digital surface model (nDSM) for Virginia Beach, VA, generated from 2004 light detection and ranging (LiDAR) data representing a 2ft surface. Ancillary data sources used were GIS vector planimetrics layers containing buildings, roads, utility lines, and surface water, provided by the City of Virginia Beach to aid in classification. This land cover dataset is considered current as of November, 2009.

Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.

No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. 18194 corrections were made to the classification.

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

The map was developed using available parcel polygons attributed with tax assessment data as of project initiation in early 2020, Computer-Assisted Mass Appraisal (CAMA) data dated February 2020, and the Chesapeake Bay Program’s 2017/18 Land Use Land Cover data (2022 edition), subsequently referred to as “CBP LULC.” The map also incorporates land use datasets provided by county and municipal jurisdictions to the extent possible while maintaining standard statewide classification definitions and rules. The product was developed to be consistent with the 2018 National Agriculture Imagery Program (NAIP) imagery and CBP LULC dataset. MDP’s draft updated land use classification scheme is available as a separate document. This product is a beta release for public use and further testing. Methods for developing subsequent releases beyond this 2018 baseline will be refined based on feedback from the user community. Urban Land Uses 11 Low-density residential - Detached single-family/duplex dwelling units, yards, and associated areas. Includes generalized areas with lot sizes of less than five acres but at least one-half acre (0.2 to 2 dwelling units/acre). 12 Medium-density residential - Detached single-family/duplex, attached single-unit row housing, yards, and associated areas Includes generalized areas with lot sizes of less than one-half acre but at least one-eighth acre (2 to 8 dwelling units/acre). 13 High-density residential - Attached single-unit row housing, garden apartments, high-rise apartments/condominiums, mobile home and trailer parks, yards, and associated areas. Includes generalized areas with more than eight dwelling units per acre. This may include subsidized housing. 14 Commercial - Retail and wholesale services. Areas used primarily for the sale of products and services, including associated yards and parking areas. This category may include airports, welcome houses, telecommunication towers, and boat marinas. 15 Industrial - Manufacturing and industrial parks, including associated warehouses, storage yards, research laboratories, and parking areas. Warehouses that are returned by a commercial query should be categorized as industrial. This also includes power plants. 16 Institutional - Elementary and secondary schools, middle schools, junior and senior high schools, public and private colleges and universities, military installations (built-up areas only, including buildings and storage, training, and similar areas), churches, medical and health facilities, correctional facilities, government offices and facilities that are clearly separable from any surrounding natural or agricultural land cover, and other non-profit uses. 17 Extractive - Surface mining operations, including sand and gravel pits, quarries, coal surface mines, and deep coal mines. Status of activity (active vs. abandoned) is not distinguished. 18 Open urban land - Includes parks, open spaces, recreational areas not classified as institutional, golf courses, and cemeteries. Includes only built-up and turf-dominated areas that are clearly separable from any surrounding natural or agricultural land cover. 190 – Very Low Density Residential – Clustered residential parcels that have lot sizes less than 20 acres but at least five acres (0.2 to 0.05 dwelling units/acre) 50 – Water 80 Transportation - Transportation features include impervious roads, roadway rights-of-way, and parcels primarily containing light rail or metro stations and park-and-ride lots. 99 – Other Land - Remaining land not covered under another category. Examples include but are not limited to unbuilt lots, rural land, single-family residential parcels greater than or equal to 20 acres in size, and undeveloped portions of large parcels containing urban uses. May include undeveloped land that is either developable or constrained from further development.Note: Urban Land Use classifications encompass the entire parcel on parcels less than five acres that contain a structure as of 2018 based on the Maryland Department of Planning and Maryland State Department of Assessment and Taxation’s Computer-Assisted Mass Appraisal (CAMA) Building dataset. Elsewhere, the Chesapeake Bay Program’s 2017/18 Land Use Land Cover dataset (2022 edition) is used to delineate the extent of development on a parcel. For more information, see Methodology Documentation.Feature Service Link: https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_LandUse/MapServer/1This copy has been projected to "WGS 1984 Web Mercator (auxiliary sphere)" and, therefore, is for illustrative purposes only. To use the data for geospatial analysis or area calculations, please download the copy projected to "NAD_1983 StatePlane Maryland FIPS 1900" from MDP's website at https://planning.maryland.gov/pages/ourproducts/downloadfiles.aspx.

Version 1.1 of a thematic grid of Land Use Systems (LUS) and its attributes with a spatial resolution of 5 arc minutes or 0.083333 decimal degrees. This dataset is developed in the framework of the LADA project (Land degradation Assessment in Drylands) by the Land Tenure and Management Unit of the Food and Agriculture Organization of the United Nations and is copyright of FAO/UNEP GEF. The LUS map implementation is based on a innovative methodology combining more than 10 global datasets. Due to the map generation method, the quality of the map can never be uniform. The overall quality of the map depends heavily on the individual quality of the data for the different countries.

The Victorian Land Use Information System (VLUIS) dataset has been created by the Spatial Information Sciences Group of the Agriculture Research Division in the Department of Economic Development, Jobs, Transport, and Resources. The method used to create VLUIS is significantly different to traditional methods used to create land use information and has been designed to create regular and consistent data over time. It covers the entire landmass of Victoria and separately describes the land tenure, land use and land cover for each cadastral parcel across the state, biennially for land tenure and use and annually for land cover; for each year from 2006 to 2015. The data is in the form of a feature class.

To use the VLUIS data correctly it is important to understand the difference between the three components of VLUIS. The Guidelines for land use mapping in Australia: principles, procedures and definitions, Edition 3 published in 2006 by the Commonwealth of Australia, defines them as follows:
Land tenure is the ownership and leasehold interests in land (VLUIS only reports ownership). Land use means the purpose to which the land cover is committed or the property type. Land cover refers to the physical surface of the earth, including various combinations of vegetation types, soils, exposed rocks and water bodies as well as anthropogenic elements, such as agriculture and built environments.
The Victorian Land Use Information System (VLUIS) is an ongoing project designed to maintain and manage the Victorian land use mapping dataset.

The methodology is still being refined and as such the dataset is subject to improvements and the release of later versions. It is important you speak to the custodian to be advised of the technical details of the dataset and its utility for your desired use.

Land cover classification accuracy varies between classes and the overall classification accuracy may be misleading in terms of the accuracy of an individual class. Users are asked to contact the data custodians for detailed class accuracy information if required for their purposes.

Irrigation activity is included when available. The data was not available in 2006-07 and there was incomplete coverage in 2012-13 and therefore the irrigation activity was not included in either of those datasets.

The dataset does not replace LandUse100 which is still valid for the time in which it was created (1996 - 2005).

A metadata statement, for the VLUIS 2006/07 product, and ESRI symbology files for the data can be freely downloaded from the VLUIS project page:
http://vro.depi.vic.gov.au/dpi/vro/vrosite.nsf/pages/vluis

DOI: http://dx.doi.org/10.5061/dryad.n08t0

The Land Use (2005) datalayer is a Massachusetts statewide, seamless digital dataset of land cover / land use, created using semi-automated methods, and based on 0.5 meter resolution digital ortho imagery captured in April 2005.The classification scheme is based on the coding schema used for previous Massachusetts land use datasets, with modifications.These data were prepared by Sanborn. The minimum mapping unit (MMU) is generally 1 acre, but a MMU as low as ¼ acre may be found in some areas, e.g. in urban areas where assessor parcels were used to enhance the mapping of multi-family residential areas.The formerly used “MacConnell” schema combined land cover and land use categories, and was designed for manual interpretation of aerial photos. In this project, that protocol was modified so that it was useful in an automated environment, but it still maintains much compatibility with the older system. The spatial accuracy of the current method is excellent, since the land use map is derived directly from the ortho image.Please see https://www.mass.gov/info-details/massgis-data-land-use-2005 for more details.Map service also available.

Land cover refers to the physical and biological cover over the surface of land, including water, vegetation, bare soil, and/or artificial structures. Land use usually refers to signs of human activities such as agriculture, forestry and building construction that altered the original land surface processes. The Land Use / Land Cover (LULC) maps were developed using remotely sensed data (i.e., satellite imagery) of different resolution and vintage and validated with the aid of some ground truthing, virtual truthing (using high-resolution imagery of more recent vintage and other internet resources), agriculture census, and other ancillary data collected during the course of the project. Preliminary interpretation of the Land Use / Land Cover (LULC) data from the satellite imagery was carried out by using an image classification algorithm and was enhanced by using imagery parameters such as tone, texture, pattern, size, shape and contextual association. This process was further improved using onscreen digitization of the known crops on high resolution satellite imagery. A Normalized Difference Vegetation Index (NDVI) was generated and used for classifying forest areas classification into dense, open, or shrub forests. Given the methodology adopted, it is to be expected that in some instances the information included in the LULC maps may be obsolete and inaccurate. The LULC maps developed are suitable for the scope of assessing wind and flood hazard (roughness factors and precipitation runoff percentages) as well establishing a crop exposure database. Data Sources: Publically available EO-1, LandSat imagery, partial coverage 10m SPOT, others. Compiled by AIR Worldwide.

This hosted feature layer has been published in RI State Plane Feet NAD83.This is a statewide, seamless digital dataset of the land cover/land use for the State of Rhode Island derived using semi-automated methods and based on imagery captured in 2003-2004. The project area encompasses the State of Rhode Island and also extends 1/2 mile into the neighboring states of Connecticut and Massachusetts or to the limits of source orthophotography. Geographic feature accuracy meets the National Mapping Standards for 1:5000 scale mapping with respect to base level data (roads, hydrography, and orthos). The minimum mapping unit for this dataset is .5 acre. The land use classification scheme used for these data was based on the Anderson Level III modified coding schema used in previous land use datasets in Rhode Island (1988 & 1995) with some modifications for the 2003 classification.The dataset is also intended to be incorporated into the Rhode Island Geographic Information System database for use by federal, state and local government and made available to the general public under established RIGIS licensing procedures.This hosted feature service layer replaces the map service https://maps.edc.uri.edu/arcgis/rest/services/Atlas_PLAN/Land_Use_and_Land_Cover_0304/MapServer/0

Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020) -

This dataset provides high-resolution, nationwide land use/land cover (LULC) and terrestrial carbon stock maps of Pakistan for four epochs: 1990, 2000, 2010, and 2020. Developed using multi-sensor satellite imagery and advanced classification techniques in Google Earth Engine (GEE), the dataset presents a comprehensive analysis of land cover changes driven by urbanization and their impacts on carbon storage capacity over 30 years.

The LULC data includes nine distinct classes, covering key land cover types such as forest cover, agriculture, rangeland, wetlands, barren lands, water bodies, built-up areas, and snow/ice. Classification was performed using a hybrid machine learning approach, and the accuracy of the land cover maps was validated using a stratified random sampling approach.

The carbon stock maps were derived using the InVEST model, which estimated carbon storage in four major carbon pools (above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter) based on the LULC maps. The results showed a significant decline in carbon storage due to rapid urban expansion, particularly in major cities like Karachi and Lahore, where substantial forest and agricultural lands were converted into urban areas. The study estimates that Pakistan lost approximately -5% of its carbon storage capacity over this period, with urban areas growing by over ~1040%.

This dataset is a valuable resource for researchers, policymakers, and environmental managers, providing crucial insights into the long-term impacts of urbanization on land cover and carbon sequestration. It is expected to support future land management strategies, urban planning, and climate change mitigation efforts. The high temporal and spatial resolution of the dataset makes it ideal for monitoring land cover dynamics and assessing ecosystem services over time.

This dataset is aslo available as Google Earth Engine application. For more details check:

> Github Project repository: https://github.com/waleedgeo/lulc_pk
> Paper DOI: https://doi.org/10.1016/j.eiar.2023.107396
> Paper PDF: https://waleedgeo.com/papers/waleed2024_paklulc.pdf

If you find this work useful, please consider citing it as

Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105, 107396.

Contributors:
Mirza Waleed (email) (Linkedin)
Muhammad Sajjad (email) (Linkedin)
Muhammad Shareef Shazil

To check other work, please check:
My Webpage & Google Scholar

HILDA+ version 2.0 is an updated version of the HILDA+ (HIstoric Land Dynamics Assessment+) doi:10.1594/PANGAEA.921846. It is a global dataset on annual land use/cover change between 1960-2020 at 1 km spatial resolution. It is based on a data-driven reconstruction approach and integrates multiple open data streams (from high-resolution remote sensing, long-term land use reconstructions and statistics). Compared to the previous version, this new HILDA+ version 2.0 uses a base map from the year 2020 (based on ESA World Cover), integrates new remote sensing-based land cover datasets (see documentation sheet in the uploaded data), is calibrated on updated national land use statistics from FAO and includes additional cropland-related land use categories: tree crops, agroforestry and annual crops. See the documentation and the paper reference for the method of cropland mapping. Forests are subdivided into different forest types based ESA CCI Land Cover (1992-2020). HILDA+ version 2.0 covers the following land use/cover categories (given with their respective code numbers in the dataset): 11: Urban areas, 22: Annual crops, 23: Tree crops, 24: Agroforestry, 33: Pasture/rangeland, 40: Forest (unknown/other), 41: Forest (evergreen, needle leaf), 42: Forest (evergreen, broad leaf), 43: Forest (deciduous, needle leaf), 44: Forest (deciduous, broad leaf), 45: Forest (mixed), 55: Unmanaged grass/shrubland, 66: Sparse/no vegetation.

This landuse / landcover (LULC) map displays a basic depiction of the Los Planes watershed in Baja California Sur, Mexico. This simplified, 7-class LULC map displays classes that are useful for hydrologic modeling and broad vegetation mapping in the region. It was created from analysis of six Sentinel-2 satellite images and other existing geospatial datasets. These satellite images are provided at 10-meter spatial resolution and were calibrated for topographic illumination effects to enhance its accuracy in rugged, mountainous terrain like that seen in the watershed. A novel filtering methodology was also applied to minimize the "salt-and-pepper effect" from the principle component analysis (PCA) and image classification methodology. See "lulc_los_planes_watershed_final_2_legend.jpg" for a low-resolution overview of the image and legend.

Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2021 water year (WY 2021), covering over 10.7 million acres of agriculture on a field scale and additional areas of urban extent.The primary objective of this effort was to produce a spatial land use database with an accuracy exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the land use mapping which began in the 2014 crop year, which classified over 15 million acres of land into agricultural and urban areas. Unlike the 2014 and 2016 datasets, the annual WY datasets from and including 2018, 2019, 2020, and 2021 include multi-cropping.Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing true cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification process using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multicropped fields, peak growth dates were determined for each field of annual crops. Fields were attributed with DWR crop categories, which included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset.Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 97% using the Land IQ legend (Land IQ Subclass) and 98% using the DWR legend (DWR Class). Accuracy and error results varied among crop types. Some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered, could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor.Revisions were made if:- DWR corrected the original crop classification based on local knowledge and analysis,-PARTIALLY IRRIGATED CROPS Crops, irrigated for only part of their normal irrigation season were given the special condition of ‘X’,-In certain areas, DWR changed the irrigation status to non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge,- young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes),- DWR determined that a field originally classified ‘Idle’ or 'Unclassified' were actually cropped one or more times during the year,- the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column),- DWR determined that the field boundary should have been changed to better reflect the cropped area of the polygon and is identified by a 'b' in the DWR_REVISED column,- DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column,- The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found,- DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop).This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the WY (Water Year begins October 1 and ends September 30 of the following year), the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the WY, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column.A new column for the 2019, 2020, and 2021 datasets is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the WY representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019, 2020, and 2021 datasets, indicates the Normalized Difference Vegetation Index (NDVI) peak date for this main season crop. The column 'EMRG_CROP' for 2019, 2020, and 2021 indicates an emerging crop at the end of the WY. Crops listed indicate that at the end of the WY, September 2021, crop activity was detected from a crop that reached peak NDVI in the following WY (2022 WY). This attribute is included to account for water use of crops that span multiple WYs and are not exclusive to a single WY. It is indicative of early crop growth and initial water use in the current WY, but a majority of crop development and water use in the following WY. Crops listed in the ‘EMRG_CROP’ attribute will also be captured as the first crop (not necessarily Crop 1) in the following WY (2022 WY). These crops are not included in the 2021 UCF_ATT code as their peak date occurred in the following WY.For the 2021 dataset new columns added are: 'YR_PLANTED' which represent the year orchard / grove was planted. 'SEN_CROP' indicates a senescing crop at the beginning of the WY. Crops listed indicate that at the beginning of the WY, October 2020, crop activity was detected from a crop that reached peak NDVI in the previous WY (2020 WY), thus was a senescing crop. This is included to account for water use of crop growth periods that span multiple WYs and are not exclusive to a WY. Crops listed in the ‘SEN_CROP’ attribute are also captured in the CROPTYP 1 through 4 sequence of the previous WY (2020 WY). These crops are not included in the 2021 UCF_ATT code as their peak NDVI occurred in the previous WY. CTYP#_NOTE: indicates a more specific land use subclassification from the DWR Standard Land Use Legend that is not included in the primary, DWR Remote Sensing Land Use Legend.DWR reviewed and revised the data in some cases. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.6, dated September 27, 2023. This data set was not produced by DWR. Data were originally developed and supplied by Land IQ, LLC, under contract to California Department of Water Resources. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Detailed compilation and reviews of Statewide Crop Mapping and metadata development were performed by DWR Land Use Unit staff, therefore you may forward your questions to Landuse@water.ca.gov.This dataset is current as of 2021.