Drone Data Collection Service Market Outlook

The global market size for drone data collection services was valued at approximately USD 5.5 billion in 2023 and is projected to reach USD 21.4 billion by 2032, growing at a robust CAGR of 16.1% during the forecast period. This significant growth can be attributed to the increasing demand for advanced data analytics and the need for efficient data collection methods across various industries.

One of the major growth factors driving this market is the rapid advancement in drone technology. Innovations in drone hardware and software have significantly enhanced the capabilities of drones, making them more versatile and efficient in data collection tasks. Drones are now equipped with high-resolution cameras, LIDAR, and other advanced sensors that provide accurate and detailed data, which is invaluable for many industries. Additionally, improvements in battery life and flight stability have extended the operational range and endurance of drones, making them more practical for prolonged and large-scale data collection missions.

Another critical factor fueling the market's growth is the increasing adoption of drones in various applications such as agriculture, construction, mining, and oil & gas. In agriculture, drones are used for precision farming, crop monitoring, and soil analysis, which help in optimizing yields and reducing costs. Similarly, in construction, drones are utilized for site surveying, progress monitoring, and safety inspections, which enhance project efficiency and safety. The mining industry also benefits from drone data collection for exploration, mapping, and monitoring of mining operations, ensuring better resource management and operational safety.

The regulatory environment is another significant driver of market growth. Many countries are developing and implementing regulations that facilitate the integration of drones into commercial operations. These regulations are aimed at ensuring the safe and efficient use of drones while addressing privacy and security concerns. For instance, the Federal Aviation Administration (FAA) in the United States has established comprehensive guidelines for commercial drone operations, which have encouraged businesses to adopt drone technology for various data collection purposes.

Regionally, the North American market is expected to dominate the global drone data collection service market, followed by Europe and Asia Pacific. North America’s dominance can be attributed to the presence of major drone technology companies, a favorable regulatory environment, and high adoption rates across various industries. The Asia Pacific region, with its rapidly growing economies and increasing investments in drone technology, is projected to witness the highest growth rate during the forecast period. Europe is also expected to see significant growth, driven by technological advancements and increasing demand for efficient data collection methods in industries such as agriculture and construction.

Service Type Analysis

The drone data collection service market can be segmented by service type into aerial photography, mapping & surveying, inspection & monitoring, and others. Aerial photography is one of the most commonly used services in this market. High-resolution aerial photographs captured by drones are utilized in various industries, including real estate, tourism, and media. These photographs provide detailed and accurate visual data that can be used for marketing, planning, and documentation purposes. The advancements in camera technology and drone stability have further enhanced the quality and reliability of aerial photography.

Mapping & surveying is another critical segment in the drone data collection service market. Drones equipped with LIDAR, photogrammetry, and other advanced sensors are used to create detailed and accurate maps and surveys of large areas. This service is particularly beneficial in industries such as construction, mining, and agriculture, where precise data is crucial for planning and operational efficiency. The use of drones in mapping & surveying reduces the time and cost associated with traditional ground-based survey methods while providing high-quality and comprehensive data.

Inspection & monitoring services provided by drones are increasingly being adopted in industries such as utilities, oil & gas, and infrastructure. Drones are used to inspect and monitor assets such as power lines, pipelines, and bridges, ensuring their integrity and safety. The ability of drones to acce

DISCOVERAQ_Colorado_Ground_EPA_Data contains data collected by the Environmental Protection Agency (EPA) at ground sites around the study area, including Chatfield Park, Fort Collins, NREL-Golden, and Denver-I25 as part of the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Denver deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

This feature class contains reference data points with specific site information on vegetation dominance type and tree size for the existing vegetation type mapping for the Central portion of the Tongass National Forest. Reference data for this project came from numerous sources including: 1) Forest Service field crews collecting vegetation information specific to this project; 2) GO field crews collecting vegetation information for this project; 3) helicopter survey data; 4) Young-Growth Inventory data; 5) legacy data from previous Forest Service survey plots and the Forest Inventory and Analysis (FIA) program (FIA data are not included in this database); 6) legacy data from the prior Yakutat vegetation mapping project; and 7) image interpretation. This database contains reference data collected by GO staff for the Central Tongass Existing Vegetation Type Map. Tongass National Forest personnel collected most of the ground data that was targeted for this mapping effort using a variety of means—primarily by foot using existing trail and road infrastructure, or by boat—to collect samples that capture the diversity of vegetation across the project area. Helicopter survey data were collected over the course of three weeks in July 2024 for the Northern Tongass, with the goal of reaching difficult to access areas. The Young-Growth Inventory information was leveraged as reference data from actively managed forest stands. Legacy data was cross-referenced with the classification key to label each plot with a vegetation type. All sites were reviewed within the context of their corresponding segment using high-resolution imagery. For more detailed information on reference data methodology please see the Central and Northern Tongass Existing Vegetation Project Report.

The northeastern North Carolina coastal system, from False Cape, Virginia, to Cape Lookout, North Carolina, has been studied by a cooperative research program that mapped the Quaternary geologic framework of the estuaries, barrier islands, and inner continental shelf. This information provides a basis to understand the linkage between geologic framework, physical processes, and coastal evolution at time scales from storm events to millennia. The study area attracts significant tourism to its parks and beaches, contains a number of coastal communities, and supports a local fishing industry, all of which are impacted by coastal change. Knowledge derived from this research program can be used to mitigate hazards and facilitate effective management of this dynamic coastal system. This regional mapping project produced spatial datasets of high-resolution geophysical (bathymetry, backscatter intensity, and seismic reflection) and sedimentary (core and grab-sample) data. The high-resolution geophysical data were collected during numerous surveys within the back-barrier estuarine system, along the barrier island complex, in the nearshore, and along the inner continental shelf. Sediment cores were taken on the mainland and along the barrier islands, and both cores and grab samples were taken on the inner shelf. Data collection was a collaborative effort between the U.S. Geological Survey (USGS) and several other institutions including East Carolina University (ECU), the North Carolina Geological Survey, and the Virginia Institute of Marine Science (VIMS). The high-resolution geophysical data of the inner continental shelf were collected during six separate surveys conducted between 1999 and 2004 (four USGS surveys north of Cape Hatteras: 1999-045-FA, 2001-005-FA, 2002-012-FA, 2002-013-FA, and two USGS surveys south of Cape Hatteras: 2003-003-FA and 2004-003-FA) and cover more than 2600 square kilometers of the inner shelf. Single-beam bathymetry data were collected north of Cape Hatteras in 1999 using a Furuno fathometer. Swath bathymetry data were collected on all other inner shelf surveys using a SEA, Ltd. SwathPLUS 234-kHz bathymetric sonar. Chirp seismic data as well as sidescan-sonar data were collected with a Teledyne Benthos (Datasonics) SIS-1000 north of Cape Hatteras along with boomer seismic reflection data (cruises 1999-045-FA, 2001-005-FA, 2002-012-FA and 2002-013-FA). An Edgetech 512i was used to collect chirp seismic data south of Cape Hatteras (cruises 2003-003-FA and 2004-003-FA) along with a Klein 3000 sidescan-sonar system. Sediment samples were collected with a Van Veen grab sampler during four of the USGS surveys (1999-045-FA, 2001-005-FA, 2002-013-FA, and 2004-003-FA). Additional sediment core data along the inner shelf are provided from previously published studies. A cooperative study, between the North Carolina Geological Survey and the Minerals Management Service (MMS cores), collected vibracores along the inner continental shelf offshore of Nags Head, Kill Devils Hills and Kitty Hawk, North Carolina in 1996. The U.S. Army Corps of Engineers collected vibracores along the inner shelf offshore of Dare County in August 1995 (NDC cores) and July-August 1995 (SNL cores). These cores are curated by the North Carolina Geological Survey and were used as part of the ground validation process in this study. Nearshore geophysical and core data were collected by the Virginia Institute of Marine Science. The nearshore is defined here as the region between the 10-m isobath and the shoreline. High-resolution bathymetry, backscatter intensity, and chirp seismic data were collected between June 2002 and May 2004. Vibracore samples were collected in May and July 2005. Shallow subsurface geophysical data were acquired along the Outer Banks barrier islands using a ground-penetrating radar (GPR) system. Data were collected by East Carolina University from 2002 to 2005. Rotasonic cores (OBX cores) from five drilling operations were collected from 2002 to 2006 by the North Carolina Geological Survey as part of the cooperative study with the USGS. These cores are distributed throughout the Outer Banks as well as the mainland. The USGS collected seismic data for the Quaternary section within the Albemarle-Pamlico estuarine system between 2001 and 2004 during six surveys (2001-013-FA, 2002-015-FA, 2003-005-FA, 2003-042-FA, 2004-005-FA, and 2004-006-FA). These surveys used Geopulse Boomer and Knudsen Engineering Limited (KEL) 320BR Chirp systems, except cruise 2003-042-FA, which used an Edgetech 424 Chirp and a boomer system. The study area includes Albemarle Sound and selected tributary estuaries such as the South, Pungo, Alligator, and Pasquotank Rivers; Pamlico Sound and trunk estuaries including the Neuse and Pamlico Rivers; and back-barrier sounds including Currituck, Croatan, Roanoke, Core, and Bogue.

Crowdsourced price data from 15 pilot countries, namely, Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam; from December 2015 to August 2016 and covering 162 household good and service items. This database is a repository of information collected during a World Bank pilot study on the feasibility of crowdsourced price data collection utilizing modern information and communication technologies. The collected data can be used for a variety of spatial and temporal price studies and other price-related applications. The data was collected by leveraging a privately-operated network of paid on-the-ground contributors that had access to a smartphone application. Price collection tasks and related guidance was pushed through the application to specific geographical locations. The contributors carried out the requested collection tasks and submitted price data and other metadata using the application. The pilot was conducted in 15 pilot countries, namely, Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam from December 2015 to August 2016. The collected price data covers 162 tightly specified household good and service items, including food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine household maintenance; health; transport; communication; recreation and culture; education; restaurants and hotels; and miscellaneous goods and services. In total, the database includes 1,262,458 price observations, ranging from 196,188 observations for Brazil to 14,102 observations for Cambodia. The observations are accompanied by a rich set of metadata, including longitude and latitude coordinates and related geographical designations, time-stamps, outlet identifiers, volume and weight details, and brand and model information. Due to the pilot nature of this data, the survey coverage varies between and within countries. In addition, the comparability of price data for goods is typically more reliable than those for services. This database is a product of the World Bank Development Data Group. Use is subject to World Bank policies and procedures on access to information. Site-specific terms of use apply and are stated below.

KORUSAQ_Ground_EPA_Data are the Environmental Protection Agency (EPA) data collected at various ground sites as part of the KORUS-AQ field campaign. Contained in this dataset are measurements collected by the Teledyne CAPS analyzer, 2B Technologies Ozone Analyzer, Aerodyne QCL, and ceilometer. Data collection for this product is complete.The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.

Radiometric (gamma spectrometry) measurements were made during walking surveys in northern Maine using a GF Instruments Gamma Surveyor. These surveys involved recording measurement averages over 30-second intervals while holding the instrument approximately 1 meter above the ground. Locations were obtained via a handheld GPS. Data were collected over and in the vicinity of a radiometric thorium anomaly identified via an airborne survey. For more information on radiometric methods, please see the International Atomic Energy Agency publication "Guidelines for Radioelement Mapping Using Gamma Ray Spectrometry Data" (2003). Reference: International Atomic Energy Agency, 2003, Guidelines for Radioelement Mapping Using Gamma Ray Spectrometry Data, IAEA-TECDOC-1363, IAEA, Vienna, 173 pp.

TERN Ecosystem Data Services and Analytics

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Simplify your research data collection with the help of the research data repository managed by the Terrestrial Ecosystem Research Network. Our collection of ecosystem data includes ecoacustics, bio acoustics, lead area index information and much more.

The TERN research data collection provides analysis-ready environment data that facilitates a wide range of ecological research projects undertaken by established and emerging scientists from Australia and around the world. The resources which we provide support scientific investigation in a wide array of environment and climate research fields along with decision-making initiatives.

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The Soil and Landscape Grid of Australia provides relevant, consistent, comprehensive, nation-wide data in an easily-accessible format. It provides detailed digital maps of the country’s soil and landscape attributes at a finer resolution than ever before in Australia.

The annual Australia’s Environment products summarise a large amount of observations on the trajectory of our natural resources and ecosystems. Use the data explorer to view and download maps, accounts or charts by region and land use type. The website also has national summary reports and report cards for different types of administrative and geographical regions.

TERN’s ausplotsR is an R Studio package for extracting, preparing, visualising and analysing TERN’s Ecosystem Surveillance monitoring data. Users can use the package to directly access plot-based data on vegetation and soils across Australia, with simple function calls to extract the data and merge them into species occurrence matrices for analysis or to calculate things like basal area and fractional cover.

The Australian Cosmic-Ray Neutron Soil Moisture Monitoring Network (CosmOz) delivers soil moisture data for 16 sites over an area of about 30 hectares to depths in the soil of between 10 to 50 cm. In 2020, the CosmOz soil moisture network, which is led by CSIRO, is set to be expanded to 23 sites.

The TERN Mangrove Data Portal provides a diverse range of historical and contemporary remotely-sensed datasets on extent and change of mangrove ecosystems across Australia. It includes multi-scale field measurements of mangrove floristics, structure and biomass, a diverse range of airborne imagery collected since the 1950s, and multispectral and hyperspectral imagery captured by drones, aircraft and satellites.

The TERN Wetlands and Riparian Zones Data Portal provides access to relevant national to local remotely-sensed datasets and also facilitates the collation and collection of on-ground data that support validation.

ecocloud provides easy access to large volumes of curated ecosystem science data and tools, a computing platform and resources and tools for innovative research. ecocloud gives you 10GB of persistent storage to keep your code/notebooks so they are ready to go when you start up a server (R or Python environment). It uses the JupyterLabs interface, which includes connections to GitHub, Google Drive and Dropbox.

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The TERN data portal provides open access ecosystem data. Our tools support data discovery, analysis, and re-use. The services which we provide facilitate research, education, and management. We maintain a network of monitoring site and sensor data streams for long-term research as part of our research data repository.

Aerial Mapping System Market Outlook

The global aerial mapping system market size is estimated to reach USD 4.5 billion in 2023 and is projected to grow to USD 10.2 billion by 2032, at a compound annual growth rate (CAGR) of 9.7% during the forecast period. The primary growth drivers for this market include advancements in geospatial technology, rising demand for accurate and cost-effective location-based services, and increased governmental and commercial investments in infrastructure and urban planning.

One of the most significant growth factors in the aerial mapping system market is the rapid technological advancements in geospatial data collection and processing. Innovations in hardware, such as high-resolution cameras and LiDAR sensors, combined with sophisticated software algorithms for data analysis, have dramatically improved the accuracy and efficiency of aerial mapping. These advancements have made it possible to capture highly detailed and precise geospatial data, which is essential for a wide range of applications, from urban planning to environmental monitoring.

Increasing demand for cost-effective and accurate location-based services is another crucial factor driving market growth. As industries such as agriculture, construction, and disaster management become more reliant on precise geospatial information, the need for advanced aerial mapping systems has surged. These systems offer a significant advantage over traditional ground-based survey methods by providing comprehensive, real-time data that can be used for various decision-making processes. This trend is expected to continue as more sectors recognize the value of accurate geospatial data.

Additionally, substantial investments from both governmental and commercial entities in infrastructure and urban planning are fueling the growth of the aerial mapping system market. Governments worldwide are increasingly adopting aerial mapping technologies for city planning, infrastructure development, and environmental monitoring. In the commercial sector, industries such as real estate, mining, and utilities are leveraging aerial mapping systems for site assessment, resource management, and operational efficiency. These investments are expected to drive the market further, as they underscore the critical role of aerial mapping in modern infrastructure development.

From a regional perspective, North America holds a significant share of the aerial mapping system market, primarily due to the presence of major technology companies and extensive governmental initiatives focused on infrastructure and environmental monitoring. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, infrastructural development, and increasing adoption of advanced technologies in countries like China and India.

Component Analysis

The aerial mapping system market is segmented by components into hardware, software, and services. Hardware components, such as cameras, sensors, and drones, are essential for collecting high-resolution aerial imagery and data. The advancements in these hardware components have significantly enhanced the efficiency and accuracy of aerial mapping systems. High-resolution cameras and LiDAR sensors, for example, provide detailed and precise geospatial data, which is crucial for various applications, including urban planning and environmental monitoring.

Software components play a pivotal role in processing and analyzing the data collected by hardware. Sophisticated software algorithms can convert raw data into actionable insights, making it easier for users to interpret and utilize the information. The development of advanced data processing and analysis software has been a major driver for the market, as it allows for the efficient handling of large volumes of geospatial data. This software is essential for generating accurate maps, 3D models, and other valuable outputs from aerial imagery.

Services, which include data collection, processing, analysis, and consulting, are also a significant segment of the aerial mapping system market. These services are often provided by specialized companies that have the expertise and equipment to conduct aerial surveys and produce high-quality geospatial data. The demand for these services is driven by the need for accurate and timely information for various applications, such as disaster management, environmental monitoring, and infrastructure development. Service providers play a crucial role in the market by offering end-to-end solutions, from dat

This feature class contains reference data points with specific site information on vegetation dominance type and tree size for the existing vegetation type mapping for the Northern portion of the Tongass National Forest. Reference data for this project came from numerous sources including: 1) Forest Service field crews collecting vegetation information specific to this project; 2) GO field crews collecting vegetation information for this project; 3) helicopter survey data; 4) Young-Growth Inventory data; 5) legacy data from previous Forest Service survey plots and the Forest Inventory and Analysis (FIA) program (FIA data are not included in this database); 6) legacy data from the prior Yakutat vegetation mapping project; and 7) image interpretation. This database contains reviewed legacy data for the Northern Tongass Existing Vegetation Type Map. Tongass National Forest personnel collected most of the ground data that was targeted for this mapping effort using a variety of means—primarily by foot using existing trail and road infrastructure, or by boat—to collect samples that capture the diversity of vegetation across the project area. Helicopter survey data were collected over the course of three weeks in July 2024 for the Northern Tongass, with the goal of reaching difficult to access areas. The Young-Growth Inventory information was leveraged as reference data from actively managed forest stands. Legacy data was cross-referenced with the classification key to label each plot with a vegetation type. All sites were reviewed within the context of their corresponding segment using high-resolution imagery. For more detailed information on reference data methodology please see the Central and Northern Tongass Existing Vegetation Project Report.

DISCOVERAQ_Texas_Ground_EPA_Data contains data collected by the Environmental Protection Agency (EPA) at various ground sites around the study area, including LaPorte, Smith Point, and Texas Avenue as part of the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

LISTOS_Ground_Rutgers_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) Rutgers ground site data collected during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

This dataset contains air temperature, relative humidity, precipitation, solar radiation, wind speed, soil temperature, and soil moisture data from the Soil Climate Analysis Network (SCAN) site 2026, "Walnut Gulch #1," located in Cochise County, Arizona. The dataset links to a National Resources Conservation Service data request form, from which available data can be queried. The data collection site is at an elevation of 4500 feet; data has been continuously collected there since 1999-03-19. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/WalnutGulch1_eaa_2015_February_23_023

To help enhance USA soil health, and ensure a robust living soil component that sustains essential functions for healthy plants, animals, and environment, and ultimately provides food for a healthy society, the GRACEnet Soil Biology group are working together with the larger USDA-ARS GRACEnet community to provide soil biology component measurements across regions and to eliminate data gaps for GRACEnet and REAP efforts. The Soil Biology group is focused on efforts that foster method comparison and meta-analyses to allow researchers to better assess soil biology and soil health indicators that are most responsive to agricultural management and that reflect the ecosystems services associated with a healthy, functioning soil. The GRACEnet Soil Biology mission is to produce the soil biology data, including methods of identifying and quantifying specific organisms and processes they govern, that are needed to evaluate impacts on agroecosystems and sustainable agricultural practices. This data collection effort is being accomplished in a highly structured manner to support current and future soil health and antimicrobial resistance research initiatives. The outcomes of the efforts of this team will provide a common biological data platform for several ARS databases, including: GRACEnet/REAP, Nutrient Use and Outcome Network (NUOnet), Long-Term Agroecosystem Research (LTAR) network, soil biology (e.g., MyPhyloDB) databases, and others. Resources in this dataset:Resource Title: Soil Biology Data Search. File Name: Web Page, url: https://agcros-usdaars.opendata.arcgis.com/datasets?group_ids=091b86e9e44a4e948ef2aeae3c916ca5

This dataset collection contains one or more dataset tables sourced from the website of the Geological Survey of Finland (Geologian tutkimuskeskus) in Finland.

The Archive''s soil specimens are invaluable time capsules for assessing temporal changes in soil properties. Physical samples are a basic element for reference, study, and experimentation in research. There is an urgent need for better integrating these physical objects into the digital research data ecosystem, both in a global and in an interdisciplinary context to support scientific reuse. The CREA collection, located at the Experimental Farm of Fagna, Scarperia (FI), stores specimens and associated metadata. It covers all major agricultural and forestry soil landscapes in Italy for organic and mineral horizons. Parameters include water impedance, rooting depth, stoniness, Coarse fraction, particle size, pH, organic carbon, and total carbonates, World Reference Base classification. Part of collected samples was recently received and is temporarily stored unordered. With the present work, a tool was developed to expose both metadata, digital research data, displacement to support FAIR principles. The tool was developed by means of Ms Power BI. The original local Ms Access database was stored on the cloud and connected to the tool to allow automatic updates. Geographic and semantic queries are graphically implemented through drop-down menus and pie charts on administrative units- Soil districts- European Environments- Land use- WRB- and Project. The tool expose data collected by 13 different projects from 1986 to 2017. Contains 13,231 analyzed observations (pedological profiles, minipits, or augerings) for a total of 33,523 samples. Soil properties resulted in ranging for Clay 0.1-93.5 (29,9 average)- Sand 0.0-99.4 (17.9), pH (water) 3.9-9.7 (7.5) - Organic carbon 0.0-53.4 (7.8) - Total carbonates 0.0-91.4 (5.5) for the whole dataset. Textural composition of every Reference Soil Group (24 out of 32) is presented as Bar Histogram. A navigation panel allows to preview the site location and storing collocation. Although samples access is restricted, data and storing displacement are exposed to support use of the data and specimen''s reuse. The developed tool represents a first attempt to expose both metadata, soil data and filtering capabilities.

Context

US Forest Service Forest Inventory and Analysis National Program.

The Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service provides the information needed to assess America's forests.

https://www.fia.fs.fed.us/

Content

As the Nation's continuous forest census, our program projects how forests are likely to appear 10 to 50 years from now. This enables us to evaluate whether current forest management practices are sustainable in the long run and to assess whether current policies will allow the next generation to enjoy America's forests as we do today.

FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership.

The Forest Service has significantly enhanced the FIA program by changing from a periodic survey to an annual survey, by increasing our capacity to analyze and publish data, and by expanding the scope of our data collection to include soil, under story vegetation, tree crown conditions, coarse woody debris, and lichen community composition on a subsample of our plots. The FIA program has also expanded to include the sampling of urban trees on all land use types in select cities.

For more details, see: https://www.fia.fs.fed.us/library/database-documentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.fia.fs.fed.us/

https://cloud.google.com/blog/big-data/2017/10/get-to-know-your-trees-us-forest-service-fia-dataset-now-available-in-bigquery

FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with State and Private Forestry and National Forest Systems. FIA traces it's origin back to the McSweeney - McNary Forest Research Act of 1928 (P.L. 70-466). This law initiated the first inventories starting in 1930.

Banner Photo by @rmorton3 from Unplash.

Inspiration

Estimating timberland and forest land acres by state.

https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png

Centralized stormwater capture facilities are engineered features located in specific locations that perform well at capturing large flows when available. In general, these facilities can capture and infiltrate more than 100 acre-feet per year.

This dataset shows the monthly yield for centralized stormwater capture across LA city for use in the stormwater dashboard and sustainable city pLAn.

Statewide AEM Surveys Project Overview

The Department of Water Resources’ (DWR’s) Statewide Airborne Electromagnetic (AEM) Surveys Project is funded through California’s Proposition 68 and the General Fund. The goal of the project is to improve the understanding of groundwater aquifer structure to support the state and local goal of sustainable groundwater management and the implementation of the Sustainable Groundwater Management Act (SGMA).

During an AEM survey, a helicopter tows electronic equipment that sends signals into the ground which bounce back. The data collected are used to create continuous images showing the distribution of electrical resistivity values of the subsurface materials that can be interpreted for lithologic properties. The resulting information will provide a standardized, statewide dataset that improves the understanding of large-scale aquifer structures and supports the development or refinement of hydrogeologic conceptual models and can help identify areas for recharging groundwater.

DWR collected AEM data in all of California’s high- and medium-priority groundwater basins, where data collection is feasible. Data were collected in a coarsely spaced grid, with a line spacing of approximately 2-miles by 8-miles. AEM data collection started in 2021 and was completed in 2023. Additional information about the project can be found on the Statewide AEM Survey website. See the publication below for an overview of the project and a preliminary analysis of the AEM data.

California’s Statewide Airborne Electromagnetic Surveys and Preliminary Hydrogeologic Interpretations

Survey Areas

AEM data are being collected in groups of groundwater basins, defined as a Survey Area. See Survey Area Map for groundwater subbasins within a Survey Area:

• Survey Area 1: 180/400 Foot Aquifer (partial), East Side (partial), Upper Valley, Forebay Aquifer, Paso Robles, Atascadero (limited), Adelaida (limited), Cuyama Valley.
• Survey Area 2: Scott River Valley, Shasta Valley, Butte Valley, Tulelake, Fall River Valley (limited), Big Valley (Modoc/Lassen County).
• Survey Area 3: Big Valley (Lake County), Ukiah Valley, Santa Rosa Plain, Petaluma Valley, Sonoma Valley.
• Survey Area 4: White Wolf, Kern County, Tulare Lake, Tule, Kaweah.
• Survey Area 5: Pleasant Valley, Westside, Kings, Madera, Chowchilla, Merced, Turlock, Modesto, Delta-Mendota
• Survey Area 6: Cosumnes, Tracy, Eastern San Joaquin, East Contra Costa, Solano, Livermore, South American, North American, Yolo, Sutter, South Yuba, North Yuba
• Survey Area 7: Colusa, Butte, Wyandotte Creek, Vina, Los Molinos, Corning, Red Bluff, Antelope, Bowman, Bend, Millville, South Battle Creek, Anderson, Enterprise, Eel River, Sierra Valley
• Survey Area 8: Seaside, Monterey, 180/400 (partially surveyed Summer 2021), Eastside (partially surveyed Summer 2021), Langley, Pajaro, Santa Cruz Mid-County, Santa Margarita, San Benito, and Llagas (partial).
• Survey Area 9: Basin Characterization Pilot Study 1 - Madera and Kings.
• Survey Area 10: San Antonio Creek Valley, Arroyo Grande, Santa Maria, San Luis Obispo, Los Osos Area, Warden Creek, Chorro Valley (limited), Morro Valley (limited)
• Survey Area 11: Indian Wells Valley, Rose Valley, Owens Valley, Fish Slough, Indio, Mission Creek, West Salton Sea (limited), East Salton Sea (limited), Ocotillo-Clark Valley (limited), Imperial Valley (limited),Chocolate Valley (limited), Borrego Springs, and San Jacinto

Data Reports

Data reports detail the AEM data collection, processing, inversion, interpretation, and uncertainty analyses methods and procedures. Data reports also describe additional datasets used to support the AEM surveys, including digitized lithology and geophysical logs. Multiple data reports may be provided for a single Survey Area, depending on the Survey Area coverage.

Data Availability and Types

All data collected as a part of the Statewide AEM Surveys will be made publicly available, by survey area, approximately six to twelve months after individual surveys are complete (depending on survey area size). Datasets that will be publicly available include:

AEM Datasets

• Raw AEM Data
• Processed AEM Data
• Inverted AEM Data
• Inverted AEM Data Uncertainty Analysis
• Interpreted AEM Data (for coarse fraction)
• Interpreted AEM Data Uncertainty Analysis

Supporting Datasets

• Flown Survey Lines
• Digitized Lithology Logs
• Digitized Geophysical Logs

AEM Data Viewers

DWR has developed AEM Data Viewers to provides a quick and easy way to visualize the AEM electrical resistivity data and the AEM data interpretations (as texture) in a three-dimensional space. The most recent data available are shown, which my be the provisional data for some areas that are not yet finalized. The Data Viewers can be accessed by direct link, below, or from the Data Viewer Landing Page.

• AEM 3D Viewer (Beta) (computer only)

• AEM Profile Viewer

• SGMA Data Viewer (Basin Characterization tab)

AEM Depth Slice and Shallow Subsurface Average Maps

As a part of DWR’s upcoming Basin Characterization Program, DWR will be publishing a series of maps and tools to support advanced data analyses. The first of these maps have now been published and provide analyses of the Statewide AEM Survey data to support the identification of potential recharge areas. The maps are located on the SGMA Data Viewer (under the Hydrogeologic Conceptual Model tab) and show the AEM electrical resistivity and AEM-derived texture data as the following:

• Shallow Subsurface Average: Maps showing the average electrical resistivity and AEM-derived texture in the shallow subsurface (the top approximately 50 feet below ground surface). These maps support identification of potential recharge areas, where the top 50 feet is dominated by high resistivity or coarse-grained materials.

• Depth Slices: Depth slice automations showing changes in electrical resistivity and AEM-derived texture with depth. These maps aid in delineating the geometry of large-scale features (for example, incised valley fills).

Shapefiles for the formatted AEM electrical resistivity data and AEM derived texture data as depth slices and the shallow subsurface average can be downloaded here:

• Electrical Resistivity Depth Slices and Shallow Subsurface Average Maps

• Texture Interpretation (Coarse Fraction) Depth Slices and Shallow Subsurface Average Maps

Technical Memos

Technical memos are developed by DWR's consultant team (Ramboll Consulting) to describe research related to AEM survey planning or data collection. Research described in the technical memos may also be formally published in a journal publication.

• AEM Test Flights to Evaluate the Bias Signal Caused by Vineyard Trellises Containing Metal

• SkyTEM Instrument Comparison for Airborne EM

2018-2020 AEM Pilot Studies

Three pilot studies were conducted in California from 2018-2020 to support the development of the Statewide AEM Survey Project. The AEM Pilot Studies were conducted in the Sacramento Valley in Colusa and Butte county groundwater basins, the Salinas Valley in Paso Robles groundwater basin, and in the Indian Wells Valley groundwater basin.

Provisional Statement

Data Reports and datasets labeled as provisional may be incomplete and are subject to revision until they have been thoroughly reviewed and received final approval. Provisional data and reports may be inaccurate and subsequent review may result in revisions to the data and reports. Data users are cautioned to consider carefully the provisional nature of the information before using it for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences.

The data in this release map Marconi Beach, Head of the Meadow Beach, and Nauset Light Beach, in Cape Cod National Seashore (CACO), Massachusetts, before and after Hurricane Lee in September 2023. U.S Geological Survey personnel conducted field surveys to collect topographic data using global navigation satellite systems (GNSS) at all three beaches. In addition, at Nauset Light Beach, an uncrewed aerial system (UAS) was used to collect images with a Ricoh GRII camera for use in structure from motion photogrammetry. High-precision GNSS targets (AeroPoints) were used as ground control points (GCPs) for the UAS photogrammetry. Agisoft Metashape (v. 2.0.1) software was used to create a digital surface model and an orthomosaic from the collected imagery and GCPs. Photos were taken with smartphones for environmental context. This work was conducted under National Park Service Research Permit CACO-2020-SCI-0021.