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

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

Abstract:

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

This database was compiled from the following sources:

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

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

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

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

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

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

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

source: https://glad.earthengine.app

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

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

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

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

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

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

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

Raw DEM and Soils data

Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)

DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.

DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.

Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)

Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).

Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).

ArcGIS Map Packages

Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).

Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.

Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).

Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019 Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA Department of Anthropology, Washington State University andrew.brown1234@gmail.com – Email andrewgillreathbrown.wordpress.com – Web

1. Cultural Ecosystem Services (CES) encompass a range of social, cultural and health benefits to local communities, for example recreation, spirituality, a sense of place and local identity. However, these complex and place-specific CES are often overlooked in rapid land management decisions and assessed using broad, top–down approaches. 2. We use the Toolkit for Ecosystem Service Site-based Assessment (TESSA) to examine a novel approach to rapid assessment of local CES provision using inductive, participatory methods. We combined free-listing and participatory geographic information systems (GIS) techniques to quantify and map perceptions of current CES provision of an urban green space. The results were then statistically compared with those of a proposed alternative scenario with the aim to inform future decision-making. 3. By identifying changes in the spatial hotspots of CES in our study area, we revealed a spatially-specific shift toward positive sentiment regarding several CES under the alternative state with variance across demographic and stakeholder groups. Response aggregations in areas of proposed development reveal previously unknown stakeholder preferences to local decision-makers and highlight potential trade-offs for conservation management. Free-listed responses revealed deeper insight into personal opinion and context. 4. This work serves as a useful case study on how the perceptions and opinions of local people regarding local CES could be accounted for in the future planning of an urban greenspace and how thorough analysis of CES provision is important to fully-inform local-scale conservation and planning for the mutual benefit of local communities and nature.

GIS Data Collector Market Outlook

According to our latest research, the global GIS Data Collector market size reached USD 6.8 billion in 2024, reflecting robust demand across multiple industries. The market is projected to grow at a healthy CAGR of 11.2% from 2025 to 2033, reaching an anticipated value of USD 19.7 billion by 2033. This significant expansion is driven by increasing adoption of geospatial technologies in urban planning, environmental monitoring, and the digital transformation strategies of enterprises worldwide. As per our findings, the surge in smart city initiatives and the proliferation of IoT-based mapping solutions are key contributors to the accelerating growth of the GIS Data Collector market globally.

The primary growth driver for the GIS Data Collector market is the escalating need for precise and real-time geospatial data across diverse sectors. Urbanization and the rapid expansion of metropolitan regions have intensified the demand for advanced mapping and surveying tools, enabling city planners and government agencies to make informed decisions. The integration of GIS data collectors with cutting-edge technologies such as artificial intelligence, machine learning, and cloud computing has further enhanced data accuracy and accessibility. As organizations seek to optimize resource allocation and improve operational efficiency, the utilization of GIS data collectors has become indispensable in applications ranging from infrastructure management to disaster response and land administration.

Another crucial factor propelling the market is the growing use of GIS data collectors in environmental monitoring and natural resource management. With the increasing frequency of climate-related events and the global emphasis on sustainability, accurate geospatial data is vital for tracking environmental changes, managing agricultural lands, and monitoring deforestation or water resources. Advanced GIS data collectors equipped with remote sensing and mobile mapping capabilities are enabling stakeholders to gather high-resolution data, analyze spatial patterns, and implement effective conservation strategies. The synergy between GIS and remote sensing technologies is empowering organizations to address environmental challenges more proactively and efficiently.

Technological advancements in data collection methods have also played a pivotal role in shaping the GIS Data Collector market landscape. The advent of unmanned aerial vehicles (UAVs), mobile mapping systems, and real-time kinematic (RTK) GPS has revolutionized the way geospatial data is captured and processed. These innovations have not only improved the accuracy and speed of data collection but have also reduced operational costs and enhanced safety in field surveys. The integration of GIS data collectors with cloud-based platforms allows seamless data sharing and collaboration, fostering a more connected and agile ecosystem for geospatial data management. As industries continue to digitize their operations, the demand for sophisticated and user-friendly GIS data collection solutions is expected to witness sustained growth.

Field Data Collection Software has become an integral component in the realm of GIS data collection, providing users with the capability to efficiently gather, process, and analyze geospatial data in real time. This software facilitates seamless integration with various data collection devices, such as GPS receivers and mobile mapping systems, enabling field operatives to capture high-precision data with ease. The adoption of Field Data Collection Software is particularly beneficial in sectors like urban planning and environmental monitoring, where timely and accurate data is crucial for decision-making. By leveraging cloud-based platforms, this software ensures that data collected in the field is instantly accessible to stakeholders, promoting collaboration and enhancing the overall efficiency of geospatial projects. As the demand for real-time data insights grows, the role of Field Data Collection Software in supporting dynamic and responsive GIS operations continues to expand.

From a regional perspective, North America currently dominates the GIS Data Collector market, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, substantial investments in smart infrastructure, and suppo

PROCESS ID: "MS-CHATHAMCONSERVATION" 1. PurposeMap service dedicated to conservation data in Chatham County 2. Related Process ID'sWEBMAP-CONSERVATIONVIEWERWEBAPP-CONSERVATIONVIEWERSERVER-ARCGIS4 3. Data Output / RequirementsPublished to arcgis4.chathamcountync.gov 4. Server Location(s)REST Service URL:https://gisservices.chathamcountync.gov/webapps/rest/services/DedicatedDatasets/ChathamConservation/MapServer Map Document (.aprx) Server Location:\arcgis7\GIS Server Workspace\Web Application Services\ConservationViewer 5. FrequencyThe data only needs to be republished if the data source is changed. 6. Intended AudiencePublic data available for download or for use within staff web maps. 7. Important DatesImplemented: 6/30/2025Last SOP Revision: 6/30/2025Deprecated: N/A 8. Associated Diagrams / Screen Shots / NotesN/A

The Gap Analysis Program (GAP) is an element of the U.S. Geological Survey (USGS). GAP helps to implement the Department of Interior?s goals of inventory, monitoring, research, and information transfer. GAP has three primary goals: 1 Identify conservation gaps that help keep common species common; 2 Provide conservation information to the public so that informed resource management decisions can be made; and 3 Facilitate the application of GAP data and analysis to specific resource management activities. To implement these goals, GAP carries out the following objectives: --Map the land cover of the United States --Map predicted distributions of vertebrate species for the U.S. --Map the location, ownership and stewardship of protected areas --Document the representation of vertebrate species and land cover types in areas managed for the long-term maintenance of biodiversity --Provide this information to the public and those entities charged with land use research, policy, planning, and management --Build institutional cooperation in the application of this information to state and regional management activities. GAP provides the following data and web services: The Protected Areas Database of the United States (PAD-US) is a geodatabase that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The PADUS GAP Status Layer web service can be found at http://gis1.usgs.gov/arcgis/rest/services/gap/PADUS_Status/MapServer . The Land Cover Data creates a seamless data set for the contiguous United States from the four regional Gap Analysis Projects and the LANDFIRE project. The 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 . In addition 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 GAP species range data show a coarse representation of the total areal extent of a species or the geographic limits within which a species can be found (Morrison and Hall 2002). The GAP species distribution models represent the areas where species are predicted to occur based on habitat associations. A full report documenting the parameters used in each species model can be found via: http://gis1.usgs.gov/csas/gap/viewer/species/Map.aspx Web map services for species distribution models can be accessed from: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/

U.S. soil property maps in a raster format that meet the GlobalSoilMap standards. Services: GlobalSoilMap_v05/available_water_supply (MapServer) GlobalSoilMap_v05/bulk_density_lessthan_2mm (MapServer) GlobalSoilMap_v05/bulk_density_whole_soil (MapServer) GlobalSoilMap_v05/clay (MapServer) GlobalSoilMap_v05/effective_cation_exchange_capacity (MapServer) GlobalSoilMap_v05/electric_conductivity (MapServer) GlobalSoilMap_v05/gravel (MapServer) GlobalSoilMap_v05/pH (MapServer) GlobalSoilMap_v05/sand (MapServer) GlobalSoilMap_v05/silt (MapServer) GlobalSoilMap_v05/soil_depth (MapServer) GlobalSoilMap_v05/soil_organic_carbon (MapServer) Resources in this dataset:Resource Title: GlobalSoilMaps. File Name: Web Page, url: https://nrcsgeoservices.sc.egov.usda.gov/arcgis/rest/services/GlobalSoilMap_v05 ArcGIS REST Services Directory Folder: GlobalSoilMap_v05

Habitat Fragmentation Mapping via Satellite Market Outlook

According to our latest research, the global habitat fragmentation mapping via satellite market size reached USD 1.47 billion in 2024, reflecting the growing need for advanced environmental monitoring solutions. The market is expected to expand at a robust CAGR of 11.2% from 2025 to 2033, reaching a projected value of USD 3.77 billion by the end of the forecast period. This growth trajectory is primarily driven by the increasing adoption of satellite-based technologies in biodiversity conservation and land management, as well as heightened regulatory focus on sustainable development and climate change mitigation.

One of the primary growth factors for the habitat fragmentation mapping via satellite market is the accelerating integration of advanced technologies such as remote sensing, GIS, and AI-based analytics. These technologies enable precise and large-scale monitoring of habitat changes, providing critical data for conservationists, policymakers, and researchers. The proliferation of high-resolution satellite imagery, coupled with the declining costs of satellite launches and data acquisition, has democratized access to real-time environmental data. As a result, stakeholders across the globe are leveraging these insights to identify and mitigate the adverse effects of habitat fragmentation, especially in biodiversity hotspots and ecologically sensitive regions.

Another significant driver is the increasing emphasis on biodiversity monitoring and conservation planning by governments and international organizations. As habitat fragmentation remains a leading cause of species decline and ecosystem degradation, there is a growing demand for actionable intelligence to guide land-use decisions and conservation interventions. Satellite-based habitat mapping offers unparalleled spatial and temporal coverage, allowing for the continuous tracking of land cover changes, habitat connectivity, and the effectiveness of restoration initiatives. The adoption of machine learning and AI-based analytics further enhances the accuracy and predictive capabilities of these mapping solutions, enabling proactive responses to emerging environmental threats.

Furthermore, the expanding role of public-private partnerships and funding initiatives is catalyzing market growth. Governments, environmental NGOs, and commercial enterprises are increasingly collaborating to deploy satellite technologies for habitat monitoring and environmental impact assessment. These partnerships facilitate the pooling of resources, expertise, and data, fostering innovation and accelerating the development of next-generation mapping solutions. The rise of open-access satellite data platforms and cloud-based analytics is also lowering entry barriers for smaller organizations and research institutes, broadening the market’s reach and impact.

From a regional perspective, North America and Europe currently dominate the habitat fragmentation mapping via satellite market, accounting for a combined market share of over 55% in 2024. These regions benefit from advanced technological infrastructure, substantial investments in environmental research, and stringent regulatory frameworks supporting sustainable land management. However, the Asia Pacific region is poised for the fastest growth during the forecast period, driven by rapid urbanization, deforestation, and increasing government initiatives for biodiversity conservation. Latin America and the Middle East & Africa are also witnessing rising adoption rates, particularly in areas facing acute habitat loss and ecosystem degradation.

Technology Analysis

The technology segment of the habitat fragmentation mapping via satellite market encompasses a diverse array of tools and methodologies, including remote sensing, GIS, machine learning, AI-based analytics, and other emerging technologies. Remote sensing remains the cornerstone of h

Open the Data Resource: https://gis.chesapeakebay.net/cross-git/overview/ This story map summarizes the data assembled and the scoring criteria recommended by the subject matter experts involved in the Chesapeake Bay Program's Cross-GIT Mapping Project. It also presents the composite results of the analyses. Access the Cross-GIT HUC-12 Conservation Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Cons_Composite/MapServer Access the Cross-GIT HUC-12 Restoration Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Rest_Composite/MapServer

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

GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were based on land cover data of ecological systems and--when applicable for the given taxon--on ancillary variables such as elevation, hydrologic characteristics, human avoidance characteristics, forest edge, ecotone widths, etc. Distribution models were generated using a python script that selects model variables based on literature cited information stored in a wildlife habitat relationship database (WHRdb); literature used includes primary and gray publications. Distribution models are 30 meter raster data and delimited by GAP species ranges. Distribution model data were attributed with information regarding seasonal use based on GAP regional projects (NWGAP, SWReGAP, SEGAP, AKGAP, HIGAP, PRGAP, and USVIGAP), NatureServe data, and IUCN data. A full report documenting the parameters used in each species model can be found via: http://gis1.usgs.gov/csas/gap/viewer/species/Map.aspx Web map services for species distribution models can be accessed from: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/ GAP used the best information available to create these species distribution models; however GAP seeks to improve and update these data as new information becomes available. Recommended citation: U.S. Geological Survey Gap Analysis Program (USGS-GAP). [Year]. National Species Distribution Models. Available: http://gapanalysis.usgs.gov. Accessed [date].

Wildlife-Vehicle Collision Hotspot Mapping Market Outlook

According to the latest research conducted in 2025, the global wildlife-vehicle collision hotspot mapping market size is valued at USD 312 million in 2024, reflecting a robust demand for advanced mapping and analytics solutions aimed at reducing wildlife-vehicle collisions worldwide. The market is projected to grow at a CAGR of 11.7% during the forecast period, reaching a forecasted market size of USD 860 million by 2033. This significant growth is primarily driven by the increasing adoption of data-driven road safety strategies, heightened awareness of biodiversity conservation, and the implementation of stricter transportation safety regulations across various regions.

The primary growth factor for the wildlife-vehicle collision hotspot mapping market is the escalating frequency and severity of wildlife-vehicle collisions globally. As urbanization and infrastructure development encroach upon natural wildlife habitats, the interface between transportation networks and animal migration routes has intensified, leading to a surge in collision incidents. Governments, transportation authorities, and environmental organizations are increasingly recognizing the human, economic, and ecological costs associated with these accidents. This has prompted a surge in investments in hotspot mapping technologies, which leverage advanced Geographic Information Systems (GIS), machine learning, and sensor-based data collection to proactively identify high-risk zones and inform mitigation strategies such as wildlife crossings, fencing, and dynamic warning systems.

Another crucial driver is the integration of cutting-edge technologies such as artificial intelligence, big data analytics, and Internet of Things (IoT) devices into wildlife-vehicle collision hotspot mapping solutions. The synergy of these technologies enables real-time data collection, predictive analytics, and automated reporting, significantly enhancing the accuracy and efficiency of hotspot identification. The growing trend of smart infrastructure and connected transportation systems is further accelerating the adoption of cloud-based mapping platforms, which offer scalability, interoperability, and ease of access for multiple stakeholders. This technological evolution is not only optimizing road safety management but also supporting broader wildlife conservation objectives by enabling data-driven decision-making and cross-sector collaboration.

Furthermore, the wildlife-vehicle collision hotspot mapping market is benefiting from increased funding and policy support from international organizations, governmental agencies, and non-governmental organizations (NGOs). Numerous countries have launched national and regional initiatives aimed at reducing roadkill and preserving biodiversity, which often mandate the use of advanced mapping solutions as part of comprehensive road safety and environmental management frameworks. In addition, growing public awareness and advocacy for wildlife protection are prompting both public and private sector entities to prioritize investments in preventive measures. These factors collectively create a conducive environment for sustained market expansion, with new entrants and established players alike focusing on innovation and customization to meet evolving end-user requirements.

From a regional perspective, North America currently dominates the wildlife-vehicle collision hotspot mapping market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States and Canada have implemented extensive road safety programs and wildlife monitoring systems, driving early adoption of mapping technologies. Europe’s market is characterized by strong regulatory frameworks and cross-border collaboration on wildlife corridors, while Asia Pacific is witnessing rapid growth due to expanding transportation networks and increasing environmental consciousness. Latin America and the Middle East & Africa are emerging markets, with growth opportunities tied to infrastructure development and international conservation partnerships. Regional variations in policy, technology adoption, and ecological challenges will continue to shape the market landscape throughout the forecast period.

Solution Type Analysis

The solution type segment of the wildlife-vehicle collision hotspot mapping market is categorized into software, hardware, and services, each playing a pivotal role in th

Ecosystem services provided by oceans and seas support most human needs but are threatened by human activities. Despite existing maps illustrating human impacts on marine ecosystems, information remains either large-scale but rough and insufficient for stakeholders (1 km² grid, lack of data along the coast) or fine-scale but fragmentary and heterogeneous in methodology. The objectives of this study are to map and quantify the main pressures exerted on near-coast marine ecosystems, at a large spatial scale though in fine and relevant resolution for managers (one pixel = 20 x 20 m). It focuses on the French Mediterranean coast (1,700 km of coastline including Corsica) at a depth of 0 to 80 m. After completing and homogenizing data presently available under GIS on the bathymetry and anthropogenic pressures but also on the seabed nature and ecosystem vulnerability, we provide a fine modeling of the extent and impacts of 10 anthropogenic pressures on marine habitats. The considered pressures are man-made coastline, boat anchoring, aquaculture, urban effluents, industrial effluents, urbanization, agriculture, coastline erosion, coastal population and fishing. A 1:10 000 continuous habitat map is provided considering 11 habitat classes. The marine bottom is mostly covered by three habitats: infralittoral soft bottom, Posidonia oceanica meadows and circalittoral soft bottom. Around two thirds of the bottoms are found within medium and medium high cumulative impact categories. Seagrass meadows are the most impacted habitats. The most important pressures (in area and intensity) are urbanization, coastal population, coastal erosion and man-made coastline. We also identified areas in need of a special management interest. This work should contribute to prioritize environmental needs, as well as enhance the development of indicators for the assessment of the ecological status of coastal systems. It could also help better apply and coordinate management measures at a relevant scale for biodiversity conservation.

Biodiversity Habitat Mapping via Satellite Market Outlook

According to our latest research, the global biodiversity habitat mapping via satellite market size reached USD 1.72 billion in 2024. The market is expected to expand at a robust CAGR of 12.4% during the forecast period, reaching USD 4.97 billion by 2033. This remarkable growth is propelled by increasing environmental awareness, the urgent need for conservation planning, and the rapid advancement in satellite imaging and data analytics technologies. The market’s expansion is further supported by significant investments from governments and private stakeholders in remote sensing and geospatial intelligence for natural resource management and climate resilience.

One of the primary growth factors for the biodiversity habitat mapping via satellite market is the intensifying focus on global biodiversity conservation. As ecosystems face mounting threats from deforestation, urbanization, and climate change, there is a growing demand for precise, high-resolution data to monitor and protect habitats. Satellite-based mapping technologies offer unparalleled coverage and temporal consistency, allowing for the detection of subtle changes in land cover and species distribution. This capability is especially critical for tracking endangered species, identifying habitat fragmentation, and informing conservation strategies at local, regional, and global scales. The integration of satellite data with ground-based observations is enabling more accurate and actionable insights, driving adoption among conservation organizations and research institutes.

Another significant driver is the advancement and convergence of enabling technologies such as remote sensing, Geographic Information Systems (GIS), and artificial intelligence (AI). The evolution of satellite sensors with higher spatial, spectral, and temporal resolution has dramatically improved the detail and accuracy of habitat mapping. Coupled with AI and machine learning algorithms, these technologies can process massive volumes of satellite imagery in near-real time, automating the identification and classification of land cover types, vegetation health, and ecosystem changes. This technological synergy is reducing operational costs, increasing analysis speed, and making biodiversity monitoring more accessible to a broader range of end-users, from government agencies to non-governmental organizations (NGOs) and private sector stakeholders.

A third key growth factor is the increasing application of biodiversity habitat mapping in policy-making, land use management, and climate change assessment. Governments and international bodies are leveraging satellite-based data to comply with environmental regulations, report on biodiversity targets, and plan sustainable land use. The ability to monitor vast and remote regions cost-effectively is particularly valuable for countries with limited ground-based monitoring infrastructure. Additionally, the private sector, including forestry, agriculture, and urban planning entities, is adopting satellite mapping to optimize resource management, minimize environmental impact, and enhance sustainability reporting. As global frameworks such as the Kunming-Montreal Global Biodiversity Framework and the United Nations Sustainable Development Goals gain traction, the demand for reliable habitat data will continue to grow.

From a regional perspective, North America and Europe currently command the largest shares of the biodiversity habitat mapping via satellite market, driven by mature technological infrastructure, robust funding for environmental initiatives, and stringent regulatory frameworks. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid economic development, increasing environmental challenges, and escalating investments in space and satellite technologies. Latin America and the Middle East & Africa are also witnessing rising adoption, particularly in countries with rich biodiversity and pressing conservation needs. The global market landscape is characterized by dynamic collaborations between governments, research institutions, and private technology providers, fostering innovation and expanding the reach of satellite-based habitat mapping solutions.

Technology Analysis

The technology segment is at the core of the biodiversity habitat mapping via satellite market, encompassing remote sensing, GIS, AI and machine learning, data analytics, and other su

◦Overview: A key principle of Landscape Conservation Design is that “Stakeholders design landscape configurations that promote resilient and sustainable social-ecological systems” (Campellone et al, 2018). From Campellone et al: (2018): “A beneficial aspect of stakeholder engagement in spatial design is the development of a deeper trust that the models used to identify priorities integrate their interests with other information and knowledge, which furthers social learning and collective agreement on resource allocation and landscape objectives” (Melillo et al., 2014). Overall, the co-development of a spatial design helps organize landscape elements while maintaining and improving stakeholder buy-in” (De Groot, Alkemade, Braat, Hein, & Willemen, 2009; Melillo et al., 2014).”◦Analytical Question: Create a prototype landscape design (blueprint) that integrates multiple values on the landscape including wildlife conservation, forest and agriculture production, recreation, cultural and human health. The prototype will be created based upon readily available data.This analysis will be used to understand landscape-scale conservation and working landscape priorities, while incorporating other important values.The blueprint will be used to represent a sustainable landscape in the future. ◦Desired Outcome: A map or maps that represents a balance of multiple values on the landscape, with a focus on conservation and working landscape values.

For each water body, the table describes its total area, its area covered by each cumulative impact score (IC) category (very low, low, medium, medium-high, high, very high impact), the average and standard deviation (SD) of IC and the sum of the IC obtained by each 20 x 20 m cell composing the water body. Coastal water bodies are numbered from West to East. Areas are indicated in ha.

The global wetland ecosystems management market is experiencing robust growth, driven by increasing awareness of the crucial ecological role of wetlands and the escalating need for their conservation and restoration. Government regulations aimed at protecting biodiversity and mitigating climate change are significant catalysts, alongside rising demand for sustainable water management solutions. The market's expansion is further fueled by advancements in technologies and techniques for wetland assessment, remediation, and restoration, including remote sensing, GIS mapping, and bioremediation. While precise market sizing requires specific data, considering a plausible CAGR (let's assume a conservative 5% based on industry trends) and a starting market size (let's assume $5 billion in 2025), the market could reach approximately $6.6 billion by 2033. Key market segments include services such as wetland restoration, creation, and mitigation, as well as consulting and planning services. Geographic variations exist, with regions possessing significant wetland areas (North America, Europe, and Asia-Pacific) exhibiting higher growth rates. Challenges remain, however, including the high cost of wetland management projects, limited skilled workforce availability, and the complexities of obtaining necessary permits and approvals. Nevertheless, the long-term outlook for this market is positive, driven by the enduring need for ecosystem preservation and sustainable resource management. The increasing adoption of nature-based solutions for climate change adaptation and mitigation will only further amplify demand for effective wetland ecosystem management services.

Ecosystem services offer a lens through which you can view the landscape and its natural resources. This lens may be helpful for those wanting to consider environmental impacts when needing to prioritize areas for management, restoration planning, urban planning, conservation and/or development.The goal of this map is to identify ecosystem services throughout New Hampshire as defined by the following:For the purposes of this project, ecosystem services can be thought of as:The ability to produce clean water (source: USFS Forest to Faucet database)Landscapes (geology, soil, climate etc.) that support biodiverse flora and fauna (source: TNC Resilient & Connected Landscapes --Resilient & Connected Network layer, NH Fish & Game WAP Tiers)Habitat for wildlife protection in order to live and reproduce (source: NH Fish & Game WAP Tiers)Connectivity for wildlife movement and migration in a changing climate (source: TNC Resilient & Connected Landscapes, Proximity to conserved & public lands)

Each of the inputs (water, wildlife tiers, resilient & connected network, and proximity to conserved & public lands) were given equal weight in the model design. Scores ranked from 2-15. Relative Rank was binned into Low, Moderate, and High categories. By selecting an area on the map, you can see the score and rank for that area as well as the inputs for how those were determined.This map of information is intended to guide regional decision making related to the above criteria and can be combined with other data to create a fuller picture for the users' needs. Please note, in highly urbanized areas and at very local scale, this map should be interpreted with caution. Not intended for city-level planning.For more information, please contact the NH Division of Forests & Lands (Dept of Natural & Cultural Resources).

The Coastal Barrier Resources System data layers can be used to help property owners, local, State, and Federal stakeholders, and the public determine whether or not properties or project sites may be affected by CBRA. The Coastal Barrier Resources System (CBRS) boundaries depicted in the mapper are representations of the controlling CBRS boundaries, which are shown on the official CBRS maps. The boundaries depicted in the mapper are not to be considered authoritative for in/out determinations close to a CBRS boundary (i.e. the area depicted within the “CBRS Buffer Zone”). Users are advised to read the data disclaimer and use constraints located at www.fws.gov/ecological-services/habitat-conservation/cbra/maps/Data_Disclaimer_Mapper.htmlThese data have been made publicly available from an authoritative source other than this Atlas and data should be obtained directly from that source for any re-use. See the original metadata from the authoritative source for more information about these data and use limitations. The authoritative source of these data can be found at the following location: Digital Coastal Barrier Resources System Boundaries

GAP species range data show a coarse representation of the total areal extent of a species or the geographic limits within which a species can be found (Morrison and Hall 2002). To represent these geographic limits, GAP compiled existing GAP data, where available, and NatureServe data (Patterson et al. 2003, Ridgely et al. 2007, NatureServe 2010) IUCN data (IUCN 2004), where needed. Data provided by GAP in collaboration with the Northwest Gap Analysis Project (NWGAP), the Southwest Regional Gap Analysis Project (SWReGAP), the Southeast Gap Analysis Project (SEGAP), the Alaska Gap Analysis Project (AKGAP), the Hawaii Gap Analysis Project (HIGAP), the Puerto Rico Gap Analysis Project (PRGAP), and the U.S. Virgin Islands Gap Analysis Project (USVIGAP). Web map services for species ranges can be accessed via: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/ Bird data provided by NatureServe in collaboration with Robert Ridgely, James Zook, The Nature Conservancy's Migratory Bird Program, Conservation International's Center for Applied Biodiversity Science (CABS), World Wildlife Fund US, and Environment Canada's WILDSPACE. Mammal data provided by NatureServe in collaboration with Bruce Patterson, Wes Sechrest, Marcelo Tognelli, Gerardo Ceballos, The Nature Conservancy's Migratory Bird Program, Conservation International's CABS, World Wildlife Fund US, and Environment Canada's WILDSPACE. Reptile data were provided by the International Union for Conservation of Nature and Natural Resources (IUCN). Amphibian data developed as part of the Global Amphibian Assessment and provided by IUCN-World Conservation Union, Conservation International and NatureServe. Once the needed range data were compiled it was intersected with Natural Resource Conservation Service National Watershed Boundary dataset of 12-digit hydrological units for the US (U.S. Geological Survey and U.S. Department of Agriculture, Natural Resources Conservation Service 2009). Range data were attributed with information regarding occurrence/presence, origin, reproductive use, and seasonal use from GAP regional projects (SWReGAP, SEGAP, NWGAP, AKGAP, HIGAP, PRGAP, and USVIGAP), NatureServe data, and IUCN data. GAP used the best information available to create these species ranges; however GAP seeks to improve and update these data as new information becomes available. These species range data provide the biological context within which to build our species distribution models. Recommended citation: U.S. Geological Survey Gap Analysis Program (USGS-GAP). [Year]. National Species Ranges. Available: http://gapanalysis.usgs.gov. Accessed [date].