Polygon layer representing United States counties with name attributes.About Natural EarthNatural Earth is a convenient resource for creating custom maps. Unlike other map data intended for analysis or detailed government mapping, it is designed to meet the needs of cartographers and designers to make generalized maps. Maximum flexibility is a goal.Natural Earth is a public domain collection of map datasets available at 1:10 million (larger scale/more detailed), 1:50 million (medium scale/moderate detail), and 1:110 million (small scale/coarse detail) scales. It features tightly integrated vector and raster data to create a variety of visually pleasing, well-crafted maps with cartography or GIS software. Natural Earth data is made possible by many volunteers and supported by the North American Cartographic Information Society (NACIS).Convenience – Natural Earth solves a problem: finding suitable data for making small-scale maps. In a time when the web is awash in geospatial data, cartographers are forced to waste time sifting through confusing tangles of poorly attributed data to make clean, legible maps. Because your time is valuable, Natural Earth data comes ready to use.Neatness Counts–The carefully generalized linework maintains consistent, recognizable geographic shapes at 1:10m, 1:50m, and 1:110m scales. Natural Earth was built from the ground up, so you will find that all data layers align precisely with one another. For example, where rivers and country borders are one and the same, the lines are coincident.GIS Attributes – Natural Earth, however, is more than just a collection of pretty lines. The data attributes are equally important for mapmaking. Most data contain embedded feature names, which are ranked by relative importance. Other attributes facilitate faster map production, such as width attributes assigned to river segments for creating tapers. Intelligent dataThe attributes assigned to Natural Earth vectors make for efficient mapmaking. Most lines and areas contain embedded feature names, which are ranked by relative importance. Up to eight rankings per data theme allow easy custom map “mashups” to emphasize your subject while de-emphasizing reference features. Other attributes focus on map design. For example, width attributes assigned to rivers allow you to create tapered drainages. Assigning different colors to contiguous country polygons is another task made easier thanks to data attribution.Other key featuresVector features include name attributes and bounding box extents. Know that the Rocky Mountains are larger than the Ozarks.Large polygons are split for more efficient data handling—such as bathymetric layers.Projection-friendly vectors precisely match at 180 degrees longitude. Lines contain enough data points for smooth bending in conic projections, but not so many that computer processing speed suffers.Raster data includes grayscale-shaded relief and cross-blended hypsometric tints derived from the latest NASA SRTM Plus elevation data and tailored to register with Natural Earth Vector.Optimized for use in web mapping applications, with built-in scale attributes to assist features to be shown at different zoom levels.

This article examines the significance of the world map in video games for the interpretation of spatial situations. An example is the popular role-playing game The Witcher 3: Wild Hunt. Nowadays, most video games are characterized by the presence of a spatial aspect. The game world map is the most important navigational element of the game that the gamer can use. To this end, the authors decided to test the importance of the game world map in the context of analyzing different examples of spatial situations that appear in The Witcher 3: Wild Hunt by the respondents. Eye movement tracking was chosen as the research method. The analysis was conducted using statistical tests. Both gamers and non-gamers of The Witcher 3: Wild Hunt, gamers and non-gamers in general, and people who identified themselves as women or men participated in the survey. Each subject was shown 5 movies (1 introductory movie, 4 movies in the main part of the study) from the gameplay of the game, in which the game world map was opened. After each video, a question was asked about both the gameplay and the game world map. It was found that familiarity with The Witcher 3: Wild Hunt, frequency of playing video games and gender influenced the correctness and time of answering the questions asked. In addition, it was found that the game world map and gameplay segments do not cognitively burden the users. Differences in visual strategy were observed between the groups of test subjects. The authors emphasized the importance of conducting further research on video games in relation to the analysis of spatial situations.

The dataset contains GIS data and JPEG maps of nature-based solution scenarios and related benefits in three case-study cities partners of the H2020 project Naturvation (https://naturvation.eu/): Barcelona (Spain), Malmö (Sweden), and Utrecht (the Netherlands). The data were produced as part of the research described in the article “Scaling up nature-based solutions for climate-change adaptation: potential and benefits in three European cities”, published in Urban Forestry & Urban Greening (doi:10.1016/j.ufug.2021.127450). The dataset is structured into three main folders, one for each city. Each folder contains six raster maps of land cover under different scenarios, a vector map with the results of the assessment of the selected benefits at the local level, and a sub-folder with the benefit maps printed in JPEG format. The six scenarios include the current condition (Baseline - LC); four scenarios that simulates the full-scale implementation of one specific type of nature-based solutions: installing green roofs (GreenRoofs - GR), de-sealing parking areas (ParkingAreas - PA), enhancing vegetation in urban parks (Parks - PK), and planting street trees (StreetTrees - ST); and a scenario considering the contemporaneous implementation of all four types of nature-based solutions (GreenDream - GD). The simulated full-scale implementation is based on space availability and technical feasibility: other constraints to the implementation of nature-based solutions are not considered. The five benefits assessed include two benefits related to climate change adaptation, i.e. heat mitigation (HM) and runoff reduction (RR), and three co-benefits, namely carbon storage (CS), biodiversity potential (BP), and overall greenness (OG). The vector maps and related JPEG prints show the results of the assessment at the block level. Blocks are based on a modified version of Urban Atlas polygons obtained by removing streets and railroads. Maps have coordinate reference system UTRS89 - LAEA Europe (EPSG:3035) and cover the whole administrative territory of the respective city, excluding the sea. Raster maps are provided in Geotiff format, UInt 16, with a resolution of 1 m. The legend includes eight land cover classes: water (0), trees (1), low vegetation (2), impervious (4), agriculture (5), buildings (10), green roofs (11), vegetation over water (13), permeable parking areas (14). The attribute tables of the vector maps store the value of the selected benefits for each block, together with the links to the original Urban Atlas polygons. Scenarios and benefits are identified by their two-letter codes as reported above. The printed JPEG maps of benefits have a common legend, to allow for comparison between cities.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society).

Natural Earth Vector comes in ESRI shapefile format, the de facto standard for vector geodata. Character encoding is Windows-1252.

Natural Earth Vector includes features corresponding to the following:

Cultural Vector Data Thremes:

• Countries: matched boundary lines and polygons with names attributes for countries and sovereign states. Includes dependencies (French Polynesia), map units (U.S. Pacific Island Territories) and sub-national map subunits (Corsica versus mainland Metropolitan France).
• Disputed areas and breakaway regions - From Kashmir to the Elemi Triangle, Northern Cyprus to Western Sahara.
• First order admin (provinces, departments, states, etc.): internal boundaries and polygons for all but a few tiny island nations. Includes names attributes and some statistical groupings of the same for smaller countries.
• Populated places: point symbols with name attributes. Includes capitals, major cities and towns, plus significant smaller towns in sparsely inhabited regions. We favor regional significance over population census in determining rankings.
• Urban polygons: derived from 2002-2003 MODIS satellite data.
• Parks and protected areas: US National Park Service units.
• Pacific nation groupings: boxes for keeping these far-flung islands tidy.
• Water boundary indicators: partial selection of key 200-mile nautical limits, plus some disputed, treaty, and median lines.

Physical Vector Data Themes:

• Coastline: ocean coastline, including major islands. Coastline is matched to land and water polygons.
• Land: Land polygons including major islands
• Ocean: Ocean polygon split into contiguous pieces.
• Minor Islands: additional small ocean islands ranked to two levels of relative importance.
• Reefs: major coral reefs from WDB2.
• Physical region features: polygon and point labels of major physical features.
• Rivers and Lake Centerlines: ranked by relative importance. Includes name and line width attributes. Don’t want minor lakes? Turn on their centerlines to avoid unseemly data gaps.
• Lakes: ranked by relative importance, coordinating with river ranking. Includes name attributes.
• Glaciated areas: polygons derived from DCW, except for Antarctica derived from MOA. Includes name attributes for major polar glaciers.
• Antarctic ice shelves: derived from 2003-2004 MOA. Reflects recent ice shelf collapses.
• Bathymetry: nested polygons at 0, -200, -1,000, -2,000, -3,000, -4,000, -5,000, -6,000, -7,000, -8,000, -9,000,and -10,000 meters. Created from SRTM Plus.
• Geographic lines: Polar circles, tropical circles, equator, and International Date Line.
• Graticules: 1-, 5-, 10-, 15-, 20-, and 30-degree increments. Includes WGS84 bounding box.

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 58.03 percent, this approach shows strong potential for generating crop type maps of current year in September.

America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.The Farmland Mapping and Monitoring Program (FMMP) provides data to decision makers for use in planning for the present and future use of California's agricultural land resources. The data is a current inventory of agricultural resources. This data is for general planning purposes and has a minimum mapping unit of ten acres.

This dataset may be a mix of two years and is updated as the data is released for each county. For example, one county may have data from 2014 while a neighboring county may have had a more recent release of 2016 data. For specific years, please check the service that specifies the year, i.e. California Important Farmland: 2016.Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.The Farmland Mapping and Monitoring Program (FMMP) provides data to decision makers for use in planning for the present and future use of California's agricultural land resources. The data is a current inventory of agricultural resources. This data is for general planning purposes and has a minimum mapping unit of ten acres.

Autonomous Vehicles HD Map Market Outlook

The global autonomous vehicles HD map market size in 2023 is valued at approximately USD 2.5 billion and is projected to reach USD 15.8 billion by 2032, growing at a CAGR of 22.5% during the forecast period. This market growth is primarily driven by the increasing demand for high-definition (HD) maps that provide real-time information to support the navigation and operational control of autonomous vehicles.

One of the primary growth factors for the autonomous vehicles HD map market is the rapid advancement in autonomous driving technologies. As major automotive manufacturers and tech companies invest heavily in developing autonomous vehicles, the need for precise and reliable HD maps has become crucial. These maps are essential for autonomous vehicles to navigate complex urban environments accurately and safely. Additionally, HD maps provide crucial data layers such as lane markings, road geometry, and traffic signals, which are vital for autonomous driving systems to make informed decisions.

Another significant growth factor is the increasing adoption of cloud-based solutions for HD mapping. Cloud-based HD maps offer several advantages, including real-time updates, scalability, and lower operating costs. These solutions enable autonomous vehicles to access the most up-to-date maps, ensuring that they can adapt to changing road conditions and traffic patterns. Moreover, cloud-based HD maps facilitate the integration of data from various sources, such as vehicle sensors and IoT devices, enhancing the map's accuracy and reliability.

The growing demand for enhanced safety features in vehicles is also driving the market for autonomous vehicles HD maps. HD maps play a crucial role in enabling advanced driver assistance systems (ADAS) and other safety features in both passenger and commercial vehicles. By providing detailed and accurate information about the road environment, HD maps help in reducing the risk of accidents and improving overall road safety. This has led to increased investments in HD mapping technologies by automotive OEMs and other stakeholders in the autonomous driving ecosystem.

Regionally, the Asia Pacific region is expected to witness significant growth in the autonomous vehicles HD map market. Countries like China, Japan, and South Korea are at the forefront of autonomous vehicle research and development. The strong presence of leading automotive manufacturers, coupled with supportive government policies and investments in smart city infrastructure, is driving the demand for HD maps in this region. Additionally, the increasing adoption of electric and autonomous vehicles in Asia Pacific is further propelling the market growth.

Solution Analysis

The autonomous vehicles HD map market is segmented into cloud-based and embedded solutions. Cloud-based HD mapping solutions are gaining popularity due to their numerous advantages, including real-time updates and scalability. These solutions allow autonomous vehicles to access the most current maps, ensuring that they can navigate accurately and safely. Moreover, cloud-based solutions facilitate the integration of various data sources, such as vehicle sensors and IoT devices, enhancing the map's accuracy and reliability. The lower operating costs associated with cloud-based solutions also make them an attractive option for automotive OEMs and fleet management companies.

Embedded HD mapping solutions, on the other hand, provide a robust alternative for autonomous vehicles that require high levels of data security and reliability. Unlike cloud-based solutions, embedded HD maps are stored locally within the vehicle's onboard systems, reducing the dependency on external data networks. This is particularly important for autonomous vehicles operating in remote or low-connectivity areas. Additionally, embedded solutions offer faster data processing and lower latency, which are critical for real-time decision-making in autonomous driving scenarios.

The choice between cloud-based and embedded HD mapping solutions often depends on the specific requirements of the end-users. For instance, automotive OEMs and fleet management companies may prefer cloud-based solutions for their cost-effectiveness and ease of integration with existing systems. In contrast, mobility as a service providers might opt for embedded solutions to ensure high levels of reliability and data security. Both solution types are expected to see significant growth, driven by the increasing adoption of autonomous vehicles and the demand for advanced navig

Map Accuracy Monitoring Market Outlook

According to our latest research, the global Map Accuracy Monitoring Market size reached USD 1.62 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to grow at a CAGR of 12.4% during the forecast period, reaching an estimated USD 4.62 billion by 2033. This rapid growth is driven by the increasing demand for high-precision mapping solutions, which are essential for applications such as land surveying, urban planning, and navigation systems. The integration of advanced technologies like artificial intelligence, remote sensing, and IoT with mapping solutions is further enhancing the accuracy and reliability of geospatial data, catalyzing market expansion.

One of the primary growth factors propelling the Map Accuracy Monitoring Market is the rising need for accurate geospatial data in critical sectors such as transportation, urban development, and agriculture. As cities expand and infrastructure projects become more complex, the requirement for precise mapping to support planning, construction, and maintenance activities grows significantly. This has led to increased investments by governments and private organizations in advanced map accuracy monitoring solutions. Furthermore, the proliferation of smart city initiatives worldwide is accelerating the adoption of real-time monitoring technologies that ensure data integrity and support efficient resource management.

Technological advancements are also playing a pivotal role in shaping the market landscape. The integration of satellite imagery, UAVs (unmanned aerial vehicles), and machine learning algorithms into mapping workflows has substantially improved the accuracy and timeliness of data collection and analysis. These innovations enable organizations to monitor changes in land use, detect anomalies, and update maps more frequently and precisely. The transition from traditional surveying methods to digital, automated solutions is reducing human error and operational costs, making high-accuracy mapping accessible to a broader range of end-users, including smaller enterprises and academic institutions.

Another significant driver is the growing importance of map accuracy in navigation and logistics. With the expansion of e-commerce and the increasing reliance on autonomous vehicles and drones for last-mile delivery, the demand for highly accurate, real-time mapping data has surged. Companies in transportation and logistics are leveraging map accuracy monitoring solutions to optimize routes, reduce fuel consumption, and enhance delivery efficiency. Additionally, environmental monitoring and disaster management agencies are utilizing these solutions to assess risk, plan mitigation strategies, and respond swiftly to emergencies. The market is also benefiting from regulatory mandates that require organizations to maintain up-to-date and accurate geospatial records, particularly in sectors like defense, utilities, and land management.

From a regional perspective, North America currently dominates the Map Accuracy Monitoring Market, driven by significant investments in geospatial technologies, a strong presence of leading solution providers, and supportive government policies. Europe and Asia Pacific are also witnessing rapid growth, fueled by urbanization, infrastructure development, and increasing adoption of smart technologies. Emerging economies in Latin America and the Middle East & Africa are gradually catching up, as they recognize the value of accurate mapping for sustainable development and resource management. Each region presents unique opportunities and challenges, shaped by factors such as technological readiness, regulatory frameworks, and industry-specific requirements.

Component Analysis

The Component segment of the Map Accuracy Monitoring Market is categorized into software, hardware, and services, each playing a crucial role in delivering comprehensive mapping solutions. Software solutions form the backbone of map accura

Strategic noise map for road traffic with more than 3 million vehicle passes per year according to RL 2002/49/EC, together with impact of additional roads. The reference year for these data is 2016. The noise map indicates how much noise the environment is exposed to. The noise load is expressed in the parameter Lden. The Lden level is a weighted annual average sound pressure level over a 24-hour period, with the evening and night levels having relatively greater weight, which corresponds to the finding that noise pollution is generally experienced as more annoying in the evening and at night. European research shows that an Lden is a relatively good predictor of the extent to which local residents may experience nuisance. These noise maps are updated every 5 years. The strategic noise maps with reference years 2006, 2011 and 2016 were calculated using an old calculation method. From the strategic noise maps with reference year 2021, a new calculation method was used. This is a new joint European calculation method that is mandatory for all Member States from the mapping round with reference year 2021. Because this calculation method differs from that used in previous mapping rounds, it is not appropriate to compare the results of reference year 2021 with previous editions (2006, 2011 and 2016). After all, it cannot be ruled out that differences in calculated exposure are purely due to the application of this new calculation method and are not a consequence of increased or decreased exposure.

This study investigated the impact of smartphone usage frequency on the effectiveness and ac-curacy of symbol location in a variety of spatial contexts on mobile maps using eye-tracking technology on the example of Mapy.cz. The scanning speed and symbol detection was also con-sidered. The use of mobile applications for navigation is discussed, emphasizing their populari-ty and convenience of use. The importance of eye-tracking as a valuable tool for testing the usa-bility of cartographic products, enabling the assessment of users' visual strategies and their abil-ity to memorize information, was highlighted. Frequency of smartphone use has been shown to be an important factor in users' ability to locate symbols in different spatial contexts. Everyday smartphone users have shown higher accuracy and efficiency in image processing, suggesting a potential link between habitual smartphone use and increased efficiency in mapping tasks. Par-ticipants who were dissatisfied with the legibility of the map looked longer at the symbols, suggesting that they put extra cognitive effort into decoding the symbols. In the present study, gender differences in pupil size were also observed during the study. The women consistently showed a larger pupil diameter, potentially indicating greater cognitive load on the partici-pants.

Recognizing the activities being performed on a map is crucial for adaptive map design based on user context. Despite eye tracking (ET) demonstrating potential in recognizing map activities and electroencephalography (EEG) measuring map users’ cognitive load, no studies have yet combined ET and EEG for recognition of the user’s activity on maps. Our study collected participants’ ET and EEG data during four types of map activities. After feature extraction and selection, we trained LightGBM (light Gradient-Boosting Machine) to classify these activities, and achieved 88.0% accuracy when combining ET and EEG features in the entire map usage trial, which is higher than using ET (85.9%) or EEG (53.9%) alone. Acceptable recognition accuracy could also be achieved with the early time windows (73.1% when using the first 3 seconds). Saccade features of ET were the most important for differentiating map activities, indicating selective map content for different tasks. Our findings demonstrate the feasibility and advantages of combining ET and EEG for activity recognition in map use. The results not only improve our understanding of visual patterns and cognitive processes in map use, but also enable the design of adaptive maps that can automatically adapt to the activities a map user is performing.

The scientifically based soil mapping (soil inventory) with the publication of soil maps is the most important basis for soil use, soil protection and soil research. These are surface data of soil-based mapping for the distribution and characteristics of the soils. They are the basis for the ground map 1: 50 000. The Ground Map of North Rhine-Westphalia 1: 50 000 (BK 50) represent the distribution of soils grouped into soil units in the leafy area. The map legend contains for each floor unit information on the soil type stratification up to 2 m depth, the soil types and the geological base rock. A special column shows the values of soil estimation, suitability for use, yieldability and workability, as well as the water conditions of the soils. The soil maps form an important basis for tasks in agriculture, forestry, land planning, land maintenance, water management and nature conservation, as well as for research, teaching and teaching. The soil maps of the Wiedenbrück district as well as the district and the city of Iserlohn reproduce the soil-related conditions across leaf boundaries. Each of them includes an explanatory book with a list of fonts and maps, in which, after an introduction, the factors of soil formation, the soil units, the land use, the usable rocks and ores, the building ground and the groundwater are discussed. There are: Ground map of the Wiedenbrück circle 1: 50 000. 19972 (with explanation) Ground map of the district and city of Iserlohn 1: 50 000. 1972 (with explanation)

Risk maps of points of particular importance corresponding to a scenario of average probability of flooding (return period of 100 years), taking into account information related to IPPC facilities, WWTP, Cultural Heritage and elements of special importance for Civil Protection.

Accurate cropland maps are important input for various proposes, such as ecosystem service and land cover change monitoring, and the representativeness of sample training samples influence the cropland mapping accuracy significantly. This study aims to propose a new sampling workflow based on unsupervised cluster and multi-temporal interpretation (UCMT) for cropland mapping. The monthly composited image time series were unsupervised clustered using the Iterative Self organizing Data Analysis (ISODATA) and optimal temporal phases were selected using the Gini importance score calculated from Ramdom Forest (RF). Training samples for each cluster were generated and visually interpreted for the optimal temporal phases. The cropland of each temporal phase was identified using the corresponding training samples, and the cropland maps were generated by merging multi-temporal cropland results. Results in two study regions showed that training samples generated using UCMT had good potential to identify cropland with overall accuracies higher than 94% in both study regions. In addition, comparing with randomly generated training samples, UCMT samples were less affected by training sample size as Producer’s accuracies and User’s accuracies were higher than 80% when 100 training samples used.

This hosted feature layer has been published in RI State Plane Feet NAD 83.This hosted view layer has been filtered to include only Prime and Important Farmland Soils. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the Rhode Island Soil Survey Program in partnership with the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped.

The Web Service Map of points of special importance in river flood risk T=100 years allows the visualisation and consultation of the data set of the environmental risk maps (points) corresponding to a scenario of average probability of flood (return period of 100 years), taking into account the information related to IPPC facilities, WWTP, Cultural Heritage and elements of particular importance for Civil Protection. The URL of the WMS Service Map of Points of Special Importance at River Flood Risk T=100 years is: https://wms.mapama.gob.es/sig/Agua/Riesgo/RiesgoPto_100/wms.aspx The reference systems offered by this service are: — For geographical coordinates: CRS: 84, EPSG: 4230 (ED50), EPSG: 4326 (WGS 84), EPSG: 4258 (ETRS 89). — For U.T.M coordinates: EPSG:32628 (WGS 84/UTM zone 28N), EPSG:32629 (WGS 84/UTM zone 29N), EPSG:32630 (WGS 84/UTM zone 30N), EPSG:32631 (WGS 84/UTM zone 31N), EPSG:25828 (ETRS 89/UTM zone 28N), EPSG:25829 (ETRS 89/UTM zone 29N), EPSG:25830 (ETRS 89/UTM zone 30N), EPSG:25831 (ETRS 89/UTM zone 31N), EPSG:23028 (ED50/UTM zone 28N), EPSG:23029 (ED50/UTM zone 29N), EPSG:23030 (ED50/UTM zone 30N), EPSG:23031 (ED50/UTM zone 31N).

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend’s big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species’ distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species’ responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions. Methods Study area and survey data The study area covers the U.S. state of California, which has steep environmental gradients that support an array of species (Dobrowski et al. 2011). Because California is ecologically diverse, with regions ranging from forested mountain ranges to deserts, we examined local environmental needs by modeling at both the state-wide and ecoregion scale, using U.S. Environmental Protection Agency (EPA) Level III ecoregion designations and there are thirteen Level III ecoregions in California (Table S1.1) (Griffith et al. 2016). Species occurrence data used in this study were from a statewide survey of C. townsendii in California conducted by Harris et al. (2019). Briefly, methods included field surveys from 2014-2017 following a modified bat survey protocol to create a stratified random sampling scheme. Corynorhinus townsendii presence at roost sites was based on visual bat sightings. From these survey efforts, we have visual occurrence data for 65 maternity roosts, 82 hibernation roosts (hibernacula), and 91 active-season non-maternity roosts (transition roosts) for a total of 238 occurrence records (Figure 1, Table S1.1). Ecogeographical factors We downloaded climatic variables from WorldClim 2.0 bioclimatic variables (Fick & Hijmans, 2017) at a resolution of 5 arcmin for broad-scale analysis and 30 arcsec for our ecoregion-specific analyses. To calculate elevation and slope, we used a digital elevation model (USGS 2022) in ArcGIS 10.8.1 (ESRI, 2006). The chosen set of environmental variables reflects knowledge on climatic conditions and habitat relevant to bat physiology, phenology, and life history (Rebelo et al. 2010, Razgour et al. 2011, Loeb and Winters 2013, Razgour 2015, Ancillotto et al. 2016). To trim the global environmental variables to the same extent (the state of California), we used the R package “raster” (Hijmans et al. 2022). We performed a correlation analysis on the raster layers using the “layerStats” function and removed variables with a Pearson’s coefficient > 0.7 (see Table 1 for final model variables). For future climate conditions, we selected three general circulation models (GCMs) based on previous species distribution models of temperate bat species (Razgour et al. 2019) [Hadley Centre Global Environment Model version 2 Earth Systems model (HadGEM3-GC31_LL; Webb, 2019), Institut Pierre-Simon Laplace Coupled Model 6th Assessment Low Resolution (IPSL-CM6A-LR; Boucher et al., 2018), and Max Planck Institute for Meteorology Earth System Model Low Resolution (MPI-ESM1-2-LR; Brovkin et al., 2019)] and two contrasting greenhouse concentration trajectories (Shared Socio-economic Pathways (SSPs): a steady decline pathway with CO2 concentrations of 360 ppmv (SSP1-2.6) and an increasing pathway with CO2 reaching around 2,000 ppmv (SSP5-8.5) (IPCC6). We modeled distribution for present conditions future (2061-2080) time periods. Because one aim of our study was to determine the consequences of changing climate, we changed only the climatic data when projecting future distributions, while keeping the other variables constant over time (elevation, slope). Species distribution modeling We generated distribution maps for total occurrences (maternity + hibernacula + transition, hereafter defined as “combined models”), maternity colonies , hibernacula, and transition roosts. To estimate the present and future habitat suitability for C. townsendii in California, we used the maximum entropy (MaxEnt) algorithm in the “dismo” R package (Hijmans et al. 2021) through the advanced computing resources provided by Texas A&M High Performance Research Computing. We chose MaxEnt to aid in the comparisons of state-wide and ecoregion-specific models as MaxEnt outperforms other approaches when using small datasets (as is the case in our ecoregion-specific models). We created 1,000 background points from random points in the environmental layers and performed a 5-fold cross validation approach, which divided the occurrence records into training (80%) and testing (20%) datasets. We assessed the performance of our models by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, 1982), where values >0.5 indicate that the model is performing better than random, values 0.5-0.7 indicating poor performance, 0.7-0.9 moderate performance and values of 0.9-1 excellent performance (BCCVL, Hallgren et al., 2016). We also measured the maximum true skill statistic (TSS; Allouche, Tsoar, & Kadmon, 2006) to assess model performance. The maxTSS ranges from -1 to +1:values <0.4 indicate a model that performs no better than random, 0.4-0.55 indicates poor performance, (0.55-0.7) moderate performance, (0.7-0.85) good performance, and values >0.80 indicate excellent performance (Samadi et al. 2022). Final distribution maps were generated using all occurrence records for each region (rather than the training/testing subset), and the models were projected onto present and future climate conditions. Additionally, because the climatic conditions of the different ecoregions of California vary widely, we generated separate models for each ecoregion in an attempt to capture potential local effects of climate change. A general rule in species distribution modeling is that the occurrence points should be 10 times the number of predictors included in the model, meaning that we would need 50 occurrences in each ecoregion. One common way to overcome this limitation is through the ensemble of small models (ESMs) (Breiner et al. 2015., 2018; Virtanen et al. 2018; Scherrer et al. 2019; Song et al. 2019) included in ecospat R package (references). For our ESMs we implemented MaxEnt modeling, and the final ensemble model was created by averaging individual bivariate models by weighted performance (AUC > 0.5). We also used null model significance testing with to evaluate the performance of our ESMs (Raes and Ter Steege 2007). To perform null model testing we compared AUC scores from 100 null models using randomly generated presence locations equal to the number used in the developed distribution model. All ecoregion models outperformed the null expectation (p<0.002). Estimating range shifts For each of the three GCMs and each RCP scenario, we converted the probability distribution map into a binary map (0=unsuitable, 1=suitable) using the threshold that maximizes sensitivity and specificity (Liu et al. 2016). To create the final maps for each SSP scenario, we summed the three binary GCM layers and took a consensus approach, meaning climatically suitable areas were pixels where at least two of the three models predicted species presence (Araújo and New 2007, Piccioli Cappelli et al. 2021). We combined the future binary maps (fmap) and the present binary maps (pmap) following the formula fmap x 2 + pmap (from Huang et al., 2017) to produce maps with values of 0 (areas not suitable), 1 (areas that are suitable in the present but not the future), 2 (areas that are not suitable in the present but suitable in the future), and 3 (areas currently suitable that will remain suitable) using the raster calculator function in QGIS. We then calculated the total area of suitability, area of maintenance, area of expansion, and area of contraction for each binary model using the “BIOMOD_RangeSize” function in R package “biomod2” (Thuiller et al. 2021).