The choropleth map is a device used for the display of socioeconomic data associated with an areal partition of geographic space. Cartographers emphasize the need to standardize any raw count data by an area-based total before displaying the data in a choropleth map. The standardization process converts the raw data from an absolute measure into a relative measure. However, there is recognition that the standardizing process does not enable the map reader to distinguish between low–low and high–high numerator/denominator differences. This research uses concentration-based classification schemes using Lorenz curves to address some of these issues. A test data set of nonwhite birth rate by county in North Carolina is used to demonstrate how this approach differs from traditional mean–variance-based systems such as the Jenks’ optimal classification scheme.

This dataset is obtained from MapBiomas.

Landsat mosaics are used to generate classifications that produce thematic maps of land cover and land use for each year. Within the framework proposed by MapBiomas Amazonía, these maps will be updated whenever improvements are made to the classification algorithm. This classification method is dynamic, with the aim of improving the classification of each typology.

Here you can access annual land cover and land use maps of the Amazon, organized by country, map scale (1:250,000), and year.

Important: When creating a single mosaic or calculating statistics on the maps, you must consider that:

To calculate area, the use of an appropriate metric projection is required.
All data is in GeoTIFF format and uses LZW compression. To obtain class reference codes, visit:
LEGEND CODES – COLLECTION 6.0 Annual maps are combined into a single file with multiple bands, where each band represents one year from the historical series (the first band corresponds to the first year of the series). The international boundaries used by MapBiomas Amazonía are those used by RAISG and may differ from files from other sources.

This landcover map was produced with a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. Even though the project is concluded, the annual land cover classification product is continuously generated.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2020), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training areas were extracted as training data.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accuracy: 83.5%

class: user's accuracy / producer's accuracy (number of reference points n) forest: 90.6% / 91.9% (1410) low vegetation: 69.2% / 82.8% (844) water: 97.0% / 94.2% (69) built-up: 96.5% / 97.4% (983) bare soil: 8.5% / 68.3% (41) agriculture: 96.6% / 68.4% (1653)

Compared to the previous years, the overall accuracy and accuracies of some classes is reduced. 2021 was a rather cloudy year in Germany, which means that the detection of agricultural areas is hampered as it is based on the variance of the NDVI throughout the year. With fewer cloud-free images available, the NDVI variance is not fully covered and as no adaptations have been applied to the algorithm, agricultural fields may get classified as low vegetation or bare soil more often. Another reason for lower classification accuracy is the significant damage that occured to forest areas due to storm and bark beetle. The validation dataset was generated based on aerial imagery from the years 2018/2019 which and is slowly becoming obsolete. An up-to-date validation dataset will be applied.

Incora report with details on methods and results: pending

The main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules. A Map Sentiment Dataset with five discrete emotions is constructedMap emotional semantics are classified by deep learning approachesVisual variables Influencing map sentiment are analyzed. A Map Sentiment Dataset with five discrete emotions is constructed Map emotional semantics are classified by deep learning approaches Visual variables Influencing map sentiment are analyzed.

Abstract: This paper aimed to use the dasymetric mapping methods proposed by Mennis and Hultgreen (2006) and Strauch and Ajara (2015) to estimate the variation of the distribution in the population in the Jacarepaguá Watershed. For this, population data from the census tracts of 2010 and, as auxiliary data, the map of land use and land cover obtained from the supervised classification, were used - the auxiliary data were obtained using a maximum likelihood method with high resolution images. The method proposed by Mennis and Hultgreen (2006) preserved the pycnophylactic capacity of the dasymetric mapping; however, it resulted in a dasymetric map that distributes the population among the pixels, in accordance with the population variables, and in a more homogeneous way, since it considers only two classes of urban use and occupation. In the Strauch and Ajara (2015) method, there was a loss of 0.04% of the original population, but it emphasized the density differences, by distributing the population heterogeneously, because it allows the specialist to include other classes of land use and land cover and attribute different types of weights to these classes.

Mineral Land Classification studies are produced by the State Geologist as specified by the Surface Mining and Reclamation Act (SMARA, PRC 2710 et seq.) of 1975. To address mineral resource conservation, SMARA mandated a two-phase process called classification-designation. Classification is carried out by the State Geologist and designation is a function of the State Mining and Geology Board. The classification studies contained here evaluate the mineral resources and present this information in the form of Mineral Resource Zones. The objective of the classification-designation process is to ensure, through appropriate local lead agency policies and procedures, that mineral materials will be available when needed and do not become inaccessible as a result of inadequate information during the land-use decision-making process.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The ultimate goal of this project is to create an updated fine‐scale vegetation map for about 58,000 acres of Orange County, consisting of the 37,000‐acre Orange County Central and Coastal Subregions Natural Community Conservation Plan (NCCP)/Habitat Conservation Plan (HCP) Habitat Reserve System; approximately 9,500 acres of associated NCCP/HCP Special Linkages, Existing Use Areas, and Non‐Reserve Open Space; and approximately 11,000 acres of adjoining conserved open space (study area). The project consisted of three phases.Phase 1: To update vegetation mapping, the Natural Reserve of Orange County (NROC) proposes to use Manual of California Vegetation (MCV) methods (2009), which will be implemented in two stages: Stage 1 – Development of a vegetation classification system for the Central and Coastal Subregions of Orange County that is consistent with the MCV. Stage 2 – Application of the vegetation classification system to create a vegetation map through photointerpretation of available aerial imagery and ground reconnaissance. The MCV methods were developed by the California Department of Fish and Game (CDFG) Vegetation Classification and Mapping Program in collaboration with the California Native Plant Society (CNPS). This approach relies on the collection of quantifiable environmental data to identify and classify biological associations that repeat across the landscape. For areas where documentation is lacking to effectively define all of the vegetation patterns found in California, CDFG and CNPS developed the Vegetation Rapid Assessment Protocol. This protocol guides data collection and analysis to refine vegetation classifications that are consistent with CDFG and MCV standards. Based on an earlier classification by Gray and Bramlet (1992), Orange County is expected to have vegetation types not yet described in the MCV. Using the MCV approach, Rapid Assessment (RA) data was collected throughout the study area and analyzed to characterize these new vegetation types or show concurrence with existing MCV types.Phase 2: Aerial Information Systems, Inc. (AIS) was contracted by the Nature Reserve of Orange County (NROC) to create an updated fine-scale regional vegetation map consistent with the California Department of Fish & Wildlife (CDFW) classification methodology and mapping standards. The mapping area covers approximately 86,000 acres of open space and adjacent urban and agricultural lands including habitat located in both the Central and Coastal Subregions of Orange County. The map was prepared over a baseline digital image created in 2012 by the US Department of Agriculture – Farm Service Agency’s National Agricultural Imagery Program (NAIP). Vegetation units were mapped using the National Vegetation Classification System (NVCS) to the Alliance level as depicted in the second edition of the Manual of California Vegetation (MCV2). One of the most important data layers used to guide the conservation planning process for the 1996 Orange County Central & Coastal Subregion Natural Community Conservation Plan/Habitat Conservation Plan (NCCP/HCP) was the regional vegetation map created in the early 1990s by Dave Bramlett & Jones & Stokes Associates, Inc. (Jones & Stokes Associates, Inc. 1993). Up until now, this same map continues to be used to direct monitoring and management efforts in the NCCP/HCP Habitat Reserve. An updated map is necessary in order to address changes in vegetation makeup due to widespread and multiple burns in the mapping area, urban expansion, and broadly occurring vegetation succession that has occurred over the past 20 years since the original map was created. This update is further necessary in order to conform to the current NVCS, which is supported by the extensive acquisition of ground based field data and subsequent analysis that has ensued in those same 20 years over the region and adjacent similar habitats in the coastal and mountain foothills of Southern California. Vegetative and cartographic comparisons between the newly created 2012 image-based map and the original 1990s era vegetation map are documented in a separate report produced by the California Native Plant Society at the end of 2014.Phase 3: The California Native Plant Society (CNPS) Vegetation Program conducted an independent accuracy assessment of a new vegetation map completed for the natural lands of Orange County in collaboration with Aerial Information Systems (AIS), the California Department of Fish and Wildlife (CDFW), and the Nature Reserve of Orange County (NROC). This report provides a summary of the accuracy assessment allocation, field sampling methods, and analysis results; it also provides an in-depth crosswalk and comparison between the new map and the existing 1992 vegetation map. California state standards (CDFW 2007) require that a vegetation map should achieve an overall accuracy of 80%. After final scoring, the new Orange County vegetation map received an overall user’s accuracy of 87%. The new fine-scale vegetation map and supporting field survey data provide baseline information for long-term land management and conservation within the remaining natural lands of Orange County.Data made available in the OC Data Portal in partnership with UCI Libraries. Methods The project consisted of three phases, each with its own methodology.Phase 1: To update vegetation mapping, the Natural Reserve of Orange County (NROC) usedManual of California Vegetation (MCV) methods (2009), which will be implemented in two stages: Stage 1 – Development of a vegetation classification system for the Central and Coastal Subregions of Orange County that is consistent with the MCV. Stage 2 – Application of the vegetation classification system to create a vegetation map through photointerpretation of available aerial imagery and ground reconnaissance.Phase 2: Aerial Information Systems, Inc. (AIS) was contracted by the Nature Reserve of Orange County (NROC) to create an updated fine-scale regional vegetation map consistent with the California Department of Fish & Wildlife (CDFW) classification methodology and mapping standards.Phase 3: The California Native Plant Society (CNPS) Vegetation Program conducted an independent accuracy assessment of a new vegetation map completed for the natural lands of Orange County in collaboration with Aerial Information Systems (AIS), the California Department of Fish and Wildlife (CDFW), and the Nature Reserve of Orange County (NROC).For more detailed methodology information please consult the README.txt file included with dataset.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Spatial data from field observation points and quantitative plots were used to edit the formation-level maps of Petersburg National Battlefield to better reflect vegetation classes. Using ArcView 3.3, polygon boundaries were revised onscreen over leaf-off photography. Units used to label polygons on the map (i.e. map classes) are equivalent to one or more vegetation classes from the regional vegetation classification, or to a land-use class from the Anderson (Anderson et al. 1976) Level II classification system. Each polygon on the Petersburg National Battlefield map was assigned to one of twenty map classes based on plot data, field observations, aerial photography signatures, and topographic maps. The mapping boundary was based on park boundary data obtained from Petersburg National Battlefield in May 2006. Spatial data depicting the locations of earthworks was obtained from the park and used to identify polygons of the cultural map classes Open Earthworks and Forested Earthworks. One map class used to attribute polygons combines two similar associations that, in some circumstances, are difficult to distinguish in the field. The vegetation map was clipped at the park boundary because areas outside the park were not surveyed or included in the accuracy assessment. Twenty map classes were used in the vegetation map for Petersburg National Battlefield. Map classes are equivalent to one or more vegetation classes from the regional vegetation classification, or to a land-use class from the Anderson (Anderson et al. 1976) Level II classification system.

ABSTRACT Reliable environmental monitoring and evaluation require high-quality maps of land use and land cover. For the Amazon biome, the TerraClass and MapBiomas projects apply different methodologies to create these maps. We evaluated the agreement between land cover and land use maps generated by TerraClass and MapBiomas (Collections 2 and 3) for the Brazilian Amazon biome, from 2004 to 2014. Specifically, we: (1) described both project legends based on the LCCS (Land Cover Classification System); (2) analyzed the differences between their classes; and (3) compared the mapping differences among the Brazilian states that are totally or partially covered by the Amazon biome. We compared the classifications with a per-pixel approach and performed an evaluation based on agreement matrices. The overall agreement between the projects was 87.4% (TerraClass x MapBiomas 2) and 92.0% (TerraClass x MapBiomas 3). We analyzed methodological differences to explain the disagreements in class identification. We conclude that using these maps together without a properly adapted legend is not recommended for the analysis of land use and land cover change. Depending on the application, one mapping system may be more suitable than the other.

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 East Bay Regional Park District (EBRPD) initiated this project to map the topography, physical and biotic features, and diverse plant communities of the east bay region. This project was funded by the California Department of Forestry and Fire Protection (CAL FIRE), the California State Coastal Conservancy (SCC), and California Department of Fish and Wildlife (CDFW) grants. The mapping study area, consists of approximately 987,000 acres of Alameda and Contra Costa counties. This 115-class fine scale vegetation map was completed in May 2025 and contains 140,442 polygons. The map is based on summer 2020 National Aerial Imagery Program (NAIP) imagery. The map additionally contains lidar-derived information about stand height, canopy cover, and percentage of impervious cover as well as canopy mortality data for each polygon. The minimum mapping unit (MMU) for this project ranges from 1/5 to 1 acre depending on feature type, and is described in detail in the mapping report (Tukman Geospatial, 2025). Development of the Alameda and Contra Costa fine scale vegetation map was managed by EBRPD and staffed by personnel from Tukman Geospatial. Field surveys were completed by trained botanists from the California Native Plant Society (CNPS), who were assisted by botanists from Nomad Ecology Consulting. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the CNPS Vegetation Program, with support from the CDFW Vegetation Classification and Mapping Program (VegCAMP) to develop a county-specific vegetation classification. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS). For more information on the field sampling and vegetation classification work, refer to the final report issued by CNPS and corresponding floristic descriptions (Sikes et al., 2025), which are bundled with the vegetation map published for BIOS here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/3200_3299/ds3206.zipThe foundation for this vegetation map is an enhanced lifeform map produced in 2023 with funding from CAL FIRE. This lifeform map was developed using fine scale segmentation in Trimble® Ecognition® with machine learning and further manual image interpretation. In 2023-2025, Tukman Geospatial and Nomad Ecology staff conducted countywide reconnaissance field work. Field-collected data was used to train automated machine learning algorithms, which produced a semi-automated countywide fine scale vegetation and habitat map. Throughout 2024 and 2025, Tukman Geospatial manually edited the fine scale maps, and Tukman Geospatial and Nomad Ecology went to the field for validation trips to inform and improve the manual editing process. In 2025, input from Alameda and Contra Costa counties’ community of land managers and by the funders of the project was used to further refine the map.Accuracy assessment plot data were collected in 2025. Accuracy assessment results were compiled and analyzed May of 2025. The overall accuracy of the vegetation map by lifeform is 97%. The overall accuracy of the vegetation map by fine scale vegetation map class is 80.8%, with an overall ‘fuzzy’ accuracy of 93.1%.For a complete datasheet of the product, click here. Map class definitions, as well as a dichotomous key for the map classes, can be found in the Alameda and Contra Costa Fine Scale Vegetation Map Key (https://vegmap.press/alcc_mapping_key). A key to map class abbreviations is also available (https://vegmap.press/alcc_vegmap_abbrevs).

This is an API service classification table provided by NGTN (OpenAPI), which includes the functions, classification codes, and descriptions of each API, and is data that helps with API search and integrated management in disaster and spatial information utilization systems. 1. Format: CSV 2. Summary of contents ■ class_cd: API classification code (e.g. Q, R, I, etc.) ■ class_nm: API classification name (e.g. map control tool API, damage prediction information display 2D API, etc.) ■ class_dc: Description of API function (e.g. map movement, zoom in/out, damage prediction information display, etc.) ■ delete_at: Whether to delete the corresponding API classification (Y/N) ■ indict_at: Whether to display in the system (Y/N) ■ class_se: API classification (e.g. visualization and data processing type such as 2D, 3D, DP, etc.) 3. Usage examples ■ When public institutions or private companies classify various API services of NGTN by function and build an integrated linkage system, it is utilized for automatic classification and call management. ■ When building an automated API catalog documentation, map service and disaster information visualization platform, API classification criteria are used as a reference for function mapping and UI design. ■ When developing a new API or linking with open source, it can be utilized for function similarity analysis, duplication removal, and function classification re-establishment by referring to the existing API classification criteria.

This change map was produced on the basis of a classification method developed in the project incora 19F2079C) in cooperation with ILS (Institute for Regional and Urban Development Research gGmbH) and BBSR (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. The map indicates land cover changes between the years 2019 and 2020. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI — Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813 https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/9246503f-6adf-460b-a31e-73a649182d07 To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster: — Mode filter (3x3) to eliminate isolated pixels and edge effects — Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and so a change is likely. Gain ranges from 0 to 1, all changes

The following stream flow classifications on the map are final and include input provided during the public comment period.The State of Connecticut Stream Flow Standards and Regulations (Section 26-141b-1 to 26-141b-8 of the Regulations of Connecticut State Agencies) define four stream flow class standards:Class 1 is described as a free flowing streamClass 2 is described as minimally altered stream flowClass 3 is described as moderately altered stream flowClass 4 is described as substantially altered stream flowThe regulations include consideration of 18 factors related to the degree of alteration in stream flow when adopting river or stream system classifications. Spatial data for each of the factors was gathered from a variety of best available sources. These sources were available at varying scales. A methodology was developed to consider all 18 factors and determine the class for a particular stream segment. In addition, public comment was solicited and considered to identify the final stream flow classification.For additional information on the classification process, see Section 26-141b-5 RCSA Adoption of river or stream system classifications, Fact Sheet for Stream Flow Classification Process under Section 26-141b-1 to 26-141b-8 of the Regulations of Connecticut State Agencies, Technical Support Document Methodology for Defining Preliminary Stream Flow Classification, and Final Stream Flow Classifications and Statement of Reasons Document. These documents can be found on the CT DEEP website at www.ct.gov/deep/streamflow.

The forest cover data for the year 2000 is part of the WWF Deforestation Fronts Report. WCS for data download (Working with Web Coverage Service (WCS)): https://maps.globilportal.org/server/services/Forests/Global_Forest_Cover_2000/ImageServer/WCSServer?SERVICE=WCS&REQUEST=GetCapabilitiesResolution: A 250m x 250m (6.25ha) spatial resolution was selected for this analysis.Canopy threshold: When using data depicting percent tree cover, a threshold value of 25% was adopted.Multiple available remote sensing datasets were assessed in order to establish the likely extent of forests. After analysing the quality of various remote sensing products, it became evident that in order to obtain a global assessment of forest cover loss, no single approach/data source would work everywhere. This is because each available dataset adopts different definitions of forest, uses different thresholds of tree canopy cover to define forest, and comprises different timeframes. Those discrepancies lead to different estimates of forest cover. In addition, each remote sensing product has its own limitations in terms of area of coverage and timeframes of analysis. In order to address these limitations, an all-available data approach was used as a way to undertake the forest loss assessment by having all the datasets compensating one another. Using this approach, each dataset can complement the other datasets, thus contributing to achieve higher accuracy in classification.A majority vote based on the consensus theoretic classification method was developed to inform the condition of each location in terms of current forest presence.The global forest cover map of 2000 was derived by adding the global forest map for 2018 and the global deforested area map during the period from 2000 to 2017. Thereby, the forest cover map of 2000 is equal to the addition of the present (2018) forest cover map and the forest loss areas from 2000 to 2017. The rationales are that the main purpose of this study is to determine forest cover loss and due to the advancement of technology, the forest detection closer to the present is more accurate than that in the past.

This part of SIM 3302 presents data for the seafloor-character map (see sheet 5, SIM 3302) of the Offshore of Coal Oil Point map area, California. The raster data file is included in "SeafloorCharacter_OffshoreCoalOilPoint_5m.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreCoalOilPoint/data_catalog_OffshoreCoalOilPoint.html. These metadata describe the seafloor-character data collected at 5-m resolution. See "SeafloorCharacter_OffshoreCoalOilPoint_2m_metadata.txt" metadata for a description of the seafloor-character data collected at 2-m resolution. The raster-format seafloor-character map shows five substrate classes of the Offshore of Coal Oil Point map area. The substrate classes mapped in this map area have been colored to indicate in which of the following California Marine Life Protection Act depth zones and slope classes they belong: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Slope Class 1, 0 degrees to 5 degrees (flat), Slope Class 2, 5 degrees to 0 degrees (sloping), and Slope Class 3, 30 degrees to 60 degrees (steeply sloping). Depth Zone 1 (intertidal), Depth Zone 5 (greater than 200 m), and Slope Classes 4 and 5, greater than 60 degrees (vertical to overhang) are not present in this map area. The map is created using a supervised classification method described by Cochrane (2008), available at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf. Bathymetry data were collected at two different resolutions: at 2-m resolution, down to approximately 80-m water depth (2006-2008 USGS data, and 2007 CSUMB data); and at 5-m resolution, in the deeper areas (2009 Fugro Pelagos data). The final resolution of the seafloor-character map is determined by the resolution of both the backscatter and bathymetry datasets; therefore, separate seafloor-character maps were generated to retain the maximum resolution of the source data. References Cited: California Department of Fish and Game, 2008, California Marine Life Protection Act master plan for marine protected areas--Revised draft: California Department of Fish and Game, accessed April 5, 2011, at http://www.dfg.ca.gov/mlpa/masterplan.asp. Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf. Sappington, J.M., Longshore, K.M., and Thompson, D.B., 2007, Quantifying landscape ruggedness for animal habitat analysis--A case study using bighorn sheep in the Mojave Desert: Journal of Wildlife Management, v. 71, p. 1,419-1,426.

This part of DS 781 presents data for the seafloor-character map of the Offshore of Monterey map area, California. Seafloor-character data are provided as two separate grids depending on resolution of the mapping system and processing method. The raster data file is included in "SeafloorCharacter_2m_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. This raster-format seafloor-character map shows four substrate classes in the Offshore of Monterey map area, California. The substrate classes mapped in this area have been colored to indicate which of the following California Marine Life Protection Act depth zones and slope classes they belong: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Depth Zone 5 (deeper than 200 m), Slope Class 1 (0 degrees - 5 degrees; flat), and Slope Class 2 (5 degrees - 30 degrees; sloping). Depth Zone 1 (intertidal), and Slopes Classes 3 and 4 (greater than 30 degrees) are not present in this map area. The map is created using a supervised classification method described by Cochrane (2008), using multibeam echosounder (MBES) bathymetry and backscatter data collected and processed between 1998 and 2014. Bathymetry data were collected at two different resolutions: at 2-m resolution, down to approximately 90-m water depth (1998-2012 CSUMB and MBARI data); and at 5-m resolution, in the deeper areas (1998-2012 MBARI data). The final resolution of the seafloor-character map is determined by the resolution of both the backscatter and bathymetry datasets; therefore, separate seafloor-character maps were generated to retain the maximum resolution of the source data. Reference Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf.

Overcoming the obstacle of frequent cloud coverage in optical remote sensing data is essential for monitoring dynamic land surface processes from space. APiC, a novel adaptable pixel-based compositing and classification approach, is especially designed to use high resolution spatio-temporal space-borne data. Here, pixel-based compositing is used separately for training data and prediction data. First, cloud-free pixels covered by reference data are used within adapted composite periods to compile a training dataset. The compiled training dataset contains samples of spectral reflectances for respective land cover classes at each composite period. For land cover prediction, pixel-based compositing is then applied region-wide. Multiple prediction models are used based on temporal subsets of the compiled training dataset to dynamically account for cloud coverage at pixel level. Thus we present a data-driven classification approach which is applicable in regions with different weather conditions, species composition and phenology. The capability of our method is demonstrated by mapping 19 land cover classes across Germany for the year 2016 based on Sentinel-2A data. Since climatic conditions and thus plant phenology change on a large scale, the classification was carried out separately in six landscape regions of different biogeographical characteristics. The study drew on extensive ground validation data provided by the federal states of Germany. For each landscape region, composite periods of different lengths have been established, which differ regionally in their temporal arrangement as well as in their total number, emphasising the advantage of a flexible regionalised classification procedure. Using a random forest classifier and evaluating outcomes with independent reference data, an overall accuracy of 88% was achieved, with particularly high classification accuracy of around 90% for the major land cover types. We found that class imbalances have significant influence on classification accuracy. Based on multiple temporal subsets of the compiled training dataset, over 10,000 random forest models were calculated and their performance varied considerably across and within landscape regions. The calculated importance of composite periods show that a high temporal resolution of the compiled training dataset is necessary to better capture the different phenology of land cover types. In this study we demonstrate that APiC, due to its data-driven nature, is a very flexible compositing and classification approach making efficient use of dense satellite time series in areas with frequent cloud coverage. Hence, regionalisation can be given greater focus in future broad-scale classifications in order to facilitate better integration of small-scale biophysical conditions and achieve even better results in detailed land cover mapping.

These products were developed to provide scientific and correspondingly spatially explicit information regarding the distribution and abundance of conifers (namely, singleleaf pinyon (Pinus monophylla), Utah juniper (Juniperus osteosperma), and western juniper (Juniperus occidentalis)) in Nevada and portions of northeastern California. Encroachment of these trees into sagebrush ecosystems of the Great Basin can present a threat to populations of greater sage-grouse (Centrocercus urophasianus). These data provide land managers and other interested parties with a high-resolution representation of conifers across the range of sage-grouse habitat in Nevada and northeastern California that can be used for a variety of management and research applications. We mapped conifer trees at 1 x 1 meter resolution across the extent of all Nevada Department of Wildlife Sage-grouse Population Management Units plus a 10 km buffer. Using 2010 and 2013 National Agriculture Imagery Program digital orthophoto quads (DOQQs) as our reference imagery, we applied object-based image analysis with Feature Analyst software (Overwatch, 2013) to classify conifer features across our study extent. This method relies on machine learning algorithms that extract features from imagery based on their spectral and spatial signatures. Conifers in 6230 DOQQs were classified and outputs were then tested for errors of omission and commission using stratified random sampling. Results of the random sampling were used to populate a confusion matrix and calculate the overall map accuracy of 84.3 percent. We provide 5 sets of products for this mapping process across the entire mapping extent: (1) a shapefile representing accuracy results linked to our mapping subunits; (2) binary rasters representing conifer presence or absence at a 1 x 1 meter resolution; (3) a 30 x 30 meter resolution raster representing percentage of conifer canopy cover within each cell from 0 to 100; (4) 1 x 1 meter resolution canopy cover classification rasters derived from a 50 meter radius moving window analysis; and (5) a raster prioritizing pinyon-juniper management for sage-grouse habitat restoration efforts. The latter three products can be reclassified into user-specified bins to meet different management or study objectives, which include approximations for phases of encroachment. These products complement, and in some cases improve upon, existing conifer maps in the western United States, and will help facilitate sage-grouse habitat management and sagebrush ecosystem restoration. These data support the following publication: Coates, P.S., Gustafson, K.B., Roth, C.L., Chenaille, M.P., Ricca, M.A., Mauch, Kimberly, Sanchez-Chopitea, Erika, Kroger, T.J., Perry, W.M., and Casazza, M.L., 2017, Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales: U.S. Geological Survey Open-File Report 2017-1093, 40 p., https://doi.org/10.3133/ofr20171093. References: ESRI, 2013, ArcGIS Desktop: Release 10.2: Environmental Systems Research Institute. Overwatch, 2013, Feature Analyst Version 5.1.2.0 for ArcGIS: Overwatch Systems Ltd.