Seasonal fix density data deduced from the large remote sensing data of GPS-collared Wild Forest Reindeer females on their transition routes.

The remote sensing technologies market is experiencing robust growth, driven by increasing demand across various sectors. While precise market size figures for the base year (2025) aren't provided, considering a typical CAGR in the high single digits to low double digits for this rapidly evolving sector (let's assume 8% for this analysis), and considering a plausible 2025 market size in the billions (e.g., $15 billion), we can project substantial expansion. This growth is fueled by advancements in sensor technology, the proliferation of satellite constellations, the increasing affordability of data analysis tools, and the growing need for real-time, accurate geospatial information in applications like precision agriculture, urban planning, environmental monitoring, and disaster management. Key players like Thales Group, Honeywell, and Lockheed Martin are leading innovation and market penetration, while emerging companies like Planet Labs are disrupting traditional approaches with new technologies and business models. The market is segmented by technology (e.g., optical, radar, hyperspectral), application (e.g., defense, agriculture, mapping), and region, offering diverse opportunities for growth and investment. Over the forecast period (2025-2033), the market is anticipated to maintain a healthy growth trajectory, with the CAGR likely to remain within the 8-10% range. This sustained growth will be influenced by factors such as the increasing integration of AI and machine learning into data processing, the development of smaller, more efficient sensors, and the expanding adoption of remote sensing in emerging economies. However, challenges such as the high initial investment costs associated with satellite systems and data processing infrastructure, as well as regulatory hurdles and data privacy concerns, could potentially moderate market expansion. Nevertheless, the long-term outlook for the remote sensing technologies market remains strongly positive, fueled by continuous technological advancements and escalating demand across multiple industries. With a projected market size significantly exceeding $15 Billion in 2025 and a CAGR of 8-10%, significant investment and innovation are expected in the coming years.

This dataset provides Rapid Science Orbits (RSO) from the Low Earth Orbiter (LEO) satellite CHAMP. It is part of the compilation of GFZ RSO products for various LEO missions and the appropriate GNSS constellation in sp3 format. The individual solutions for each satellite mission are published with individual DOI as part of the compilation (Schreiner et al., 2022). • The CHAMP RSO cover the period from 2000 202 to 2010 247 The LEO RSOs in version 1 are generated based on the 24-hour GPS RSOs in two pieces for the actual day with arc lengths of 14 hours and overlaps of 2 hours. One starting at 22:00 and ending at 12:00, one starting at 10:00 and ending at 24:00. For day overlapping arcs two 24h GNSS constellations are concatenated. The accuracy of the LEO RSOs is at the level of 1-2 cm in terms of SLR validation. Each solution in version 1 is given in the Conventional Terrestrial Reference System (CTS) based on the IERS 2003 conventions and related to the ITRF-2008 reference frame. The exact time covered by an arc is defined in the header of the files and indicated as well as in the filename.

This dataset provides Rapid Science Orbits (RSO) from the Low Earth Orbiter (LEO) satellite TerraSAR-X. It is part of the compilation of GFZ RSO products for various LEO missions and the appropriate GNSS constellation in sp3 format. The individual solutions for each satellite mission are published with individual DOI as part of the compilation (Schreiner et al., 2022). • The TerraSAR-X RSO cover the period - from 2007 264 to up-to-date The LEO RSOs in version 1 are generated based on the 24-hour GPS RSOs in two pieces for the actual day with arc lengths of 14 hours and overlaps of 2 hours. One starting at 22:00 and ending at 12:00, one starting at 10:00 and ending at 24:00. For day overlapping arcs two 24h GNSS constellations are concatenated. The accuracy of the LEO RSOs is at the level of 1-2 cm in terms of SLR validation. Each solution in version 1 is given in the Conventional Terrestrial Reference System (CTS) based on the IERS 2003 conventions and related to the ITRF-2008 reference frame. The exact time covered by an arc is defined in the header of the files and indicated as well as in the filename.

The Remote Sensing Technology market is experiencing robust growth, driven by increasing demand for high-resolution imagery and data across diverse sectors. The market's expansion is fueled by advancements in satellite technology, miniaturization, and the decreasing cost of launching smaller satellites. This allows for greater accessibility and affordability of remote sensing data, leading to wider adoption across commercial, military, and government applications. The market is segmented by satellite mass, orbit class, and satellite subsystems, each exhibiting unique growth trajectories. For instance, the demand for smaller satellites (below 100kg) is surging due to their cost-effectiveness and ease of deployment, particularly in constellations for Earth observation. Meanwhile, segments like propulsion hardware and propellant, and satellite bus & subsystems, are experiencing significant growth driven by technological innovation and the need for improved performance and longevity of satellites. Geographically, North America and Europe currently hold significant market share, but the Asia-Pacific region is projected to witness substantial growth in the coming years, driven by increasing investments in space technology and infrastructure within countries like China and India. The market faces some restraints, including the high initial investment costs for satellite development and launch, as well as regulatory hurdles and data security concerns. However, ongoing technological advancements and the burgeoning demand for precise Earth observation data are expected to offset these limitations. Looking ahead to 2033, the market is poised for continued expansion, fueled by the increasing integration of remote sensing data into various applications, including precision agriculture, urban planning, environmental monitoring, disaster management, and defense. The development of advanced analytics capabilities to process and interpret the vast amounts of data generated by remote sensing technologies will further propel market growth. Competition among established players and new entrants is expected to intensify, driving innovation and potentially leading to more affordable and accessible remote sensing solutions. The continuous miniaturization of satellite technology, along with advancements in data processing and analytics, will continue to shape the market landscape, making remote sensing a critical technology for various industries in the coming years. Recent developments include: March 2023: The Japan Aerospace Exploration Agency (JAXA) had scheduled the launch of the first H3 Launch Vehicle with the Advanced Land Observing Satellite "Daichi 3" (ALOS-3) onboard from the Tanegashima Space Center.February 2023: NASA and geographic information service provider Esri will grant wider access to the space agency's geospatial content for research and exploration purposes through the Space Act Agreement.January 2023: Airbus Defence and Space has signed a contract with Poland to provide a geospatial intelligence system including the development, manufacture, launch and delivery in orbit of two high-performance optical Earth observation satellites.. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

Data files (meteorological data, sattelite derived velocity time series, and seismic station GPS data) and plotting codes to reproduce the dataset and plots presented in the paper

The geospatial analytics market, valued at $93.91 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 18.68% from 2025 to 2033. This significant expansion is fueled by several key factors. The increasing adoption of advanced technologies like GPS, GIS, and remote sensing across diverse sectors is a major driver. The BFSI (Banking, Financial Services, and Insurance), government and utilities, and telecom industries are particularly heavy users, leveraging geospatial analytics for improved risk assessment, resource management, and customer service. Furthermore, the rising demand for precise location-based services in manufacturing, automotive, and retail sectors is contributing to market growth. The integration of AI and machine learning into geospatial analytics platforms enhances analytical capabilities, further driving adoption. Government initiatives promoting digital transformation and smart city projects also significantly boost market demand. While data privacy and security concerns represent a potential restraint, the overall market outlook remains highly positive due to the expanding applications of geospatial analytics across various sectors and geographical regions. North America currently holds a dominant market share, primarily driven by the presence of major technology companies and significant investments in infrastructure development. However, the Asia-Pacific region is poised for rapid growth, fueled by increasing urbanization, rising digital adoption, and government investments in infrastructure development in countries like China and India. Europe also contributes significantly to market revenue, with strong growth expected in several key countries. The competitive landscape is characterized by a mix of large multinational corporations and specialized technology providers. Key players are focusing on strategic partnerships, acquisitions, and technological innovations to enhance their market position and cater to evolving customer demands. The market is expected to witness increased consolidation in the coming years as companies strive to expand their product offerings and geographical reach. The overall market dynamics indicate a bright future for geospatial analytics, with continued innovation and growth anticipated across all segments.

Knowledge of the habitat requirements for migratory species throughout their full annual cycle is necessary for comprehensive species protection plans. By describing seasonal shifts of space-use patterns in a key nonbreeding area, the Senegal Delta (Mauritania, Senegal), this study addresses a significant knowledge gap in the annual cycle of the rapidly declining continental Black-tailed Godwit Limosa limosa. We fitted continuous-time stochastic-process movement models with GPS location data to describe the core areas used by 22 GPS-tagged godwits over the 2022-2023 nonbreeding period. We mapped key habitat types, such as floodplain wetlands and rice fields, via supervised classification of satellite imagery. Godwits in the Senegal Delta show a distinct shift in habitat use over the nonbreeding period. The core areas of godwits in the early stages of the nonbreeding period (the wet season) were primarily in natural wetlands and fields with newly planted rice. As the rice crop matured and became too dense, godwits moved towards more recently sown rice fields. Later, as floodwaters receded and rice fields dried out, godwits abandoned rice fields and shifted toward natural wetlands with fewer invasive plants, particularly within the marshes and shallow floodplains of nature-protected areas in the lower Delta. Synthesis and Applications: Our findings illustrate the shifting importance of natural and agricultural wetlands for godwits at different stages of the nonbreeding season. Protected areas in the Senegal Delta, particularly the Djoudj National Bird Sanctuary (Senegal) and Diawling National Park (Mauritania), are crucial habitats during the dry season as godwits prepare for their northward migration, while rice fields take a key role during the wet season. Conservation efforts should prioritize eradicating invasive plants from the Djoudj and Diawling, as well as promote agroecological management in specific rice production complexes indicated in this study. Methods Land cover classification To differentiate between major habitat types utilized by godwits during their nonbreeding period in the Senegal Delta, we generated a land cover map of the study area. Although habitat descriptions of the Senegal Delta have been relatively well documented over the years, existing reports often lack specific spatio-temporal information or are outdated. Using Google Earth Engine (GEE), we processed Landsat 9 images acquired over the Senegal Delta between October 1st and November 30th, 2022, coinciding with the peak nonbreeding period for godwits in the region. This timeframe also corresponds to when most wet season rice fields exhibit high biomass, offering a distinct spectral signature. The eight available images were cloud-masked using bitwise operators on the Quality Assessment (QA) band of each image. Given that a single image may capture rice at different growth stages across various fields, we added a Normalized Difference Vegetation Index (NDVI) band into the image collection to create a greenest pixel composite (GEE guide at https://developers.google.com/earth-engine/tutorials/tutorial_api_06) which was then mosaicked and clipped to the boundary of the Delta du Fleuve Transboundary Biosphere Reserve, producing the final image for classification. A total of 1,760 ground truthing points were created from inspection of Landsat 9 and Sentinel 2 imagery. For areas which were not readily-discernable through satellite imagery alone, we cross-referenced several data sources, including multiple field expeditions to the region in Nov/Dec 2016 (Hooijmeijer et al., 2016), Nov 2017 (Hooijmeijer et al., 2017), Jul 2019 (Hooijmeijer et al., 2019b), and Oct/Nov 2019 (Hooijmeijer et al., 2019a), existing land cover maps (Zwarts et al., 2009; Bos et al., 2012), and open source products (ESA WorldCover 2021 v200; Google Earth). No permits were required for ground surveys, except in Djoudj National Park, where we were accompanied by park guides. Ground truthing points were randomly split into a training set comprising 70% of the points (n = 1,232) and a validation set containing the remaining 30% (n = 528). The training set was utilized to train a random forest (RF) classifier with 50 decision trees. The validation set was employed to assess the classification accuracy of the trained model through an error matrix, which tabulates the instances of correct and incorrect classifications. From this matrix, both overall accuracy and Cohen’s kappa coefficient (KC) were computed. Overall accuracy represents the proportion of correctly classified points, providing a general measure of the model's performance. The Serval plugin in QGIS version 3.22.4 (QGIS Development Team, 2009) was used to manually correct misclassified pixels in post-processing. The full GEE script is available at: https://code.earthengine.google.com/8085a86b503dc907fcf90eeeda411103 Land cover was categorized into eight major classes: (1) Open water: natural or artificial expanses of water, including lakes, rivers, and reservoirs. (2) Rice fields: areas under active wet season rice cultivation (July to November). (3) Uncultivated fields: bare fields primarily used for rice production during the dry season (March to June). (4) Mixed crop fields: agricultural areas for production of various crops, including sugar cane, millet, tomatoes, and other vegetables. (5) Cattail stands: dense stands of cattail in mostly permanent freshwater bodies. (6) Floodplain wetlands: broadly characterized by variably flooded floodplains, shallow water, diverse floodplain/marshland vegetation such as Sporobolus, Scirpus, Cyperus, and Phragmites. (7) Semi-arid grassland: Sahelian Acacia savanna with fixed dunes and plains of low-lying woody vegetation such as Acacia shrubs, grasses, and fallow fields. (8) Bare land: arid terrain consisting of dry sandy dunes, terrestrial hypersaline plains devoid of vegetative cover. Our field observations indicate that godwits abandon rice fields when the vegetation becomes excessively dense, similar to cattail-dominated areas, relocating to more recently planted fields or drainage areas between fields. To explore how godwits move between fields in relation to rice growth stages, we examined satellite imagery captured between June 2022 and February 2023. GPS locations of godwits were plotted onto the nearest temporally matched satellite image to assess their presence/absence at different rice growth stages. Tracking data During the 2021 and 2022 spring staging and breeding seasons we captured and fitted 87 adult godwits with GPS/GSM transmitters at six sites: 1) southwest Friesland, The Netherlands 2) the Krimpenerwaard in the province of South Holland, The Netherlands 3) Dümmer Nature Reserve, Lower Saxony, Germany 4) Unterelbe Nature Reserve, Lower Saxony, Germany 5) Rice fields in the middle basin of the River Guadiana in Extremadura, Spain 6) Tagus estuary, Portugal Godwits were captured using automated drop cages and claptraps at breeding sites (1-4), handheld nets and mist nets at Iberian staging sites (5, 6) and fitted with 4.5 g GPS/GSM transmitters (Hunan Global Messenger Technology, China) using leg-loop harnesses (Senner et al., 2015). Location fix rates varied between 1-6 fixes/day. All tracking data were stored in Movebank (Kays et al., 2022). Tracking data were filtered for individual tracks spanning a single nonbreeding period between June 2022 and March 2023, representing the approximate minimum arrival and maximum departure dates of adult godwits in the Senegal Delta (Verhoeven et al., 2021). As godwits use multiple sites along the extensive floodplains of the Senegal River, we narrowed our filtering to include only those that visited the lower Senegal Delta (16.8951°N, 15.7762°E, -15.5571°S, -16.5372°W), and removed in-flight locations with a ground speed filter over 0 km/h. The GPS tagging for this study was granted by the national Dutch committee for animal experiments following the Dutch Animal Welfare Act Articles 9 and 11. Home range estimation Features commonly seen in animal tracking data such as location autocorrelation, irregular sampling intervals, variable telemetry error, and data gaps violate the assumptions of many conventional statistical frameworks (Fleming et al., 2015). However, homogenization and reducing autocorrelation through restrictive subsampling comes at the cost of diminishing biologically meaningful results (De Solla et al., 1999). We therefore used a continuous-time stochastic-process (CTSP) modeling framework, based on the workflow described in Calabrese et al. (2016). CTSP models were fitted with GPS locations of godwits in the Senegal Delta using the continuous-time movement modeling (ctmm) package (Version 1.1.0) (Calabrese et al., 2016) in R (Version 4.2.2) (RStudio Team, 2020). Due to the high location accuracy (5-20 m) relative to the fix rate of our tags, where the movements of godwits exceed the location accuracy between fixes, we applied the default prior of 20 m for GPS error into the movement model. The ctmm framework depends on individuals exhibiting range residency, which we verified by visualizing the autocorrelation structure of each bird. Range residency can be determined through the existence of an asymptote in the semi-variogram (Supporting Information, Fig. S1), which indicates the distance and time beyond which there is no longer any correlation between observations. Meanwhile, deviances from the mean location (within a defined observation period) resulting from migration, dispersals, or other non-resident behavior, typically show up in the variograms as peaks, dips, and the absence of an asymptote (Calabrese et al., 2016). Godwits typically use multiple core areas throughout the nonbreeding period, oftentimes hundreds of kilometers away from their initial settlement area (Verhoeven et al., 2021). We therefore segmented tracks of each individual bird between different core

Remote Sensing Technologies and Global Markets describes the fundamentals of remote sensing technology and provides 2012 through 2017 forecasts for what BCC Research envisions developing into a $12.4 billion industry. The report is divided into 27 chapters, 20 of which individually focus on the 20 largest and most robust technologies.

Since the early 1990s, the GFZ has operated a global GNSS station network with currently about 70 stations for precise satellite clock & orbit determination, realization of the terrestrial reference frame, radio occultation measurements or studies on crust dynamics. A subset of these stations contributes also to the tracking networks of the International GNSS Service (IGS) and the EUREF Permanent GNSS Network (EPN). Other stations contribute to GFZ observatories (IPOC, DESERVE, TERENO), to the GPS Atmosphere Sounding Project (GASP), to WMO Global Climate Observing System Reference Upper-Air Network (GRUAN) or to other external cooperations. We offer data of 51 GFZ GNSS stations under this DOI. Nearly all stations are equipped with Javad or Septentrio hardware. Depending on the location and hardware they provide data of GPS (L1 / L2 / L5), GLONASS (L1 / L2 / L3), Galileo (E1 / E5a / E5b / E6), BeiDou (B1 / B2 / B3), QZSS (L1 / L2 / L5 / L6), NAVIC (L5), and SBAS (L1 / L5). The GNSS Station Nework Site (https://isdc.gfz-potsdam.de/gnss-station-network/) provides direct access to the 1s and 30s sampled RINEX data (near real-time, file based) and to real-time streams. Real-time streams are available for stations contributing to the IGS. Raw data GNSS binary raw observations are available upon request. All GFZ Stations follow the site guidelines of the International GNSS Service (https://kb.igs.org/hc/en-us/articles/202011433-Current-IGS-Site-Guidelines) Station specific metadata can be found at our metadata portal SEMISYS. An overview of the list of stations with direct links to the station specific metadata in semisys is available via ftp://datapub.gfz-potsdam.de/download/10.5880.GFZ.1.1.2020.001/2020-001_Ramatschi-et-al_List-of-GFZ-GNSS-Stations-with-links-to-SEMISYS.pdf.

Geographic Information System (GIS) Tools Market Outlook

The global Geographic Information System (GIS) tools market size was valued at approximately USD 10.8 billion in 2023, and it is projected to reach USD 21.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2032. The increasing demand for spatial data analytics and the rising adoption of GIS tools across various industries are significant growth factors propelling the market forward.

One of the primary growth factors for the GIS tools market is the surging demand for spatial data analytics. Spatial data plays a critical role in numerous sectors, including urban planning, environmental monitoring, disaster management, and natural resource exploration. The ability to visualize and analyze spatial data provides organizations with valuable insights, enabling them to make informed decisions. Advances in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with GIS, are enhancing the capabilities of these tools, further driving market growth.

Moreover, the increasing adoption of GIS tools in the construction and agriculture sectors is fueling market expansion. In construction, GIS tools are used for site selection, route planning, and resource management, enhancing operational efficiency and reducing costs. Similarly, in agriculture, GIS tools aid in precision farming, crop monitoring, and soil analysis, leading to improved crop yields and sustainable farming practices. The ability of GIS tools to provide real-time data and analytics is particularly beneficial in these industries, contributing to their widespread adoption.

The growing importance of location-based services (LBS) in various applications is another key driver for the GIS tools market. LBS are extensively used in navigation, logistics, and transportation, providing real-time location information and route optimization. The proliferation of smartphones and the development of advanced GPS technologies have significantly increased the demand for LBS, thereby boosting the GIS tools market. Additionally, the integration of GIS with other technologies, such as the Internet of Things (IoT) and Big Data, is creating new opportunities for market growth.

Regionally, North America holds a significant share of the GIS tools market, driven by the high adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to increasing investments in infrastructure development, smart city projects, and the growing use of GIS tools in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, driven by various government initiatives and increasing awareness of the benefits of GIS tools.

Component Analysis

The GIS tools market can be segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market due to the increasing demand for advanced GIS software solutions that offer enhanced data visualization, spatial analysis, and decision-making capabilities. GIS software encompasses a wide range of applications, including mapping, spatial data analysis, and geospatial data management, making it indispensable for various industries. The continuous development of user-friendly and feature-rich software solutions is expected to drive the growth of this segment.

Hardware components in the GIS tools market include devices such as GPS units, remote sensing devices, and plotting and digitizing tools. The hardware segment is also expected to witness substantial growth, driven by the increasing use of advanced hardware devices that provide accurate and real-time spatial data. The advancements in GPS technology and the development of sophisticated remote sensing devices are key factors contributing to the growth of the hardware segment. Additionally, the integration of hardware with IoT and AI technologies is enhancing the capabilities of GIS tools, further propelling market expansion.

The services segment includes consulting, integration, maintenance, and support services related to GIS tools. This segment is expected to grow significantly, driven by the increasing demand for specialized services that help organizations effectively implement and manage GIS solutions. Consulting services assist organizations in selecting the right GIS tools and optimizing their use, while integration services ensure seamless integr

Comma-separated values (.csv) file containing data (and derived data) from GPS tracking of free-flying bald eagles (Haliaeetus leucocephalus), Iowa, USA.

Greenland borehole waterlevel and ice motion data from two sites in the ablation area of the ice sheet. Boreholes were drilled to the ice bed interface and instrumented with pressure sensors that froze in. GPS data were acquired with Trimble R9s. Surface weather data also included at one site. The two sites are nicknamed Gull and Foxx.

HSR image, POI data, taxi GPS data for Zhuhai UFZ classification project.

Seasonal fix density data deduced from the large remote sensing data of GPS-collared Wild Forest Reindeer females during summer time.

This dataset provides Rapid Science Orbits (RSO) from GNSS satellites. It is part of the compilation of GFZ RSO products for various LEO missions and the appropriate GNSS constellation in sp3 format. The individual solutions for each satellite mission are published with individual DOI as part of the compilation (Schreiner et al., 2022- Dach DOI).

GNSS Constellation: GPS 30h The GPS RSOs of version 2 are 30-hour long arcs starting at 21:00 the day before and ending at 03:00 the day after. The accuracy of the GPS RSO sizes at the 3-cm level in terms of RMS values of residuals after Helmert transformation onto IGS combined orbit solutions.

This study investigates seasonal ice dynamics of Nioghalvfjerdsfjorden or 79°N Glacier, one of the major outlet glaciers of the North East Greenland Ice Stream. Based on remote sensing data and in-situ GPS measurements we show that surface melt water is quickly routed to the ice-bed interface with a direct response on ice velocities measured at the surface. From the temporally highly resolved GPS time series we found summer peak velocities of up to 22% faster than their winter baseline. These average out to 9% above winter velocities when relying on temporally lower resolved velocity estimates from TerraSAR-X intensity offset tracking. From our GPS time series we also found short term ice acceleration after the melt season. By utilizing optical satellite imagery and interferometrically derived digital elevation models we were able to link the post melt season speed-up to a rapid lake drainage event (

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

Covers a broad range of remote sensing technology, describing in detail historical and current market data, five-year market projections, technological advancements, important patents, and the major companies involved in the industry.

Hyperspectral Remote Sensing Processing Incorporating coremicro IMU and GPS Data, Phase I