Digital Elevation Model Market size was valued at USD 72 Billion in 2023 and is projected to reach USD 156.2 Billion by 2031, growing at a CAGR of 16.2% during the forecast period 2024-2031.

Global Digital Elevation Model Market Drivers

The market drivers for the Digital Elevation Model Market can be influenced by various factors. These may include:

Increased Demand for Geospatial Analysis: The growing demand for geospatial analysis across various industries such as agriculture, urban planning, forestry, and disaster management is a significant driver for the Digital Elevation Model (DEM) market. Organizations are increasingly leveraging DEMs to analyze terrain, assess land use, and develop infrastructure projects. The ability to visualize topography, identify potential hazards, and optimize land use promotes efficient decision-making, aiding in sustainability efforts. This shift towards data-driven insights enhances the demand for high-resolution, accurate DEMs, encouraging advancements in remote sensing technologies and GIS software, ultimately boosting market growth. Advancements in Remote Sensing Technology: Technological advancements in remote sensing have greatly contributed to the Digital Elevation Model Market. Innovations such as LIDAR (Light Detection and Ranging), satellite imaging, and drone-based surveys have enhanced the accuracy and resolution of DEMs. These technologies allow for rapid data collection over vast areas, making it easier to create high-quality elevation datasets. The integration of artificial intelligence and machine learning techniques into processing algorithms further improves the extraction of terrain features and reduces processing time. This evolution in data acquisition methods is fueling the demand for DEMs across multiple sectors.

Global Digital Elevation Model Market Restraints

Several factors can act as restraints or challenges for the Digital Elevation Model Market. These may include:

High Initial Investment Costs: The digital elevation model (DEM) market faces significant restraints due to high initial investment costs associated with advanced technologies and data acquisition processes. Organizations are required to invest heavily in specialized equipment, software, and skilled personnel to create and manage high-quality DEMs. These initial expenditures can be a barrier, particularly for small and medium-sized enterprises (SMEs) lacking the necessary capital. As a result, the high cost of entry limits market participation and the ability to scale offerings. Moreover, ongoing maintenance and operational costs can further strain budgets, discouraging potential users from adopting DEM technologies, thus stunting market growth. Data Accuracy and Integrity Issues: Another considerable restraint in the Digital Elevation Model Market is the challenge of data accuracy and integrity. With varying methods of data collection—such as LiDAR, photogrammetry, and satellite remote sensing—consistency and reliability can differ significantly. Poor-quality data can lead to inaccuracies in elevation modeling, negatively impacting critical applications such as urban planning, environmental monitoring, and disaster management. These discrepancies can undermine the credibility of DEM products, resulting in skepticism from potential clients. In sectors where precision is paramount, maintaining high standards while incorporating diverse data sources presents an ongoing challenge hindering wider market adoption.

NASADEM is a modernization of the Digital Elevation Model (DEM) and associated products generated from the Shuttle Radar Topography Mission (SRTM) data. Interferometric SAR data from SRTM were reprocessed with an optimized hybrid processing technique in producing the data products. The data rely on multiple radar images to create interferograms with 2-dimensional phase arrays that result in greater elevation accuracy. Because of inherent characteristics of interferometric data, it needs to be wrapped and unwrapped so the data are quantifiable. NASADEM relied on the latest unwrapping techniques and auxiliary data that were not available during the original processing of SRTM data. The optimized technique minimized data voids and extended spatial coverage of the SRTM. Additional voids were filled with a variety of sources including ASTER GDEM, Advanced Land Observing Satellite (ALOS) Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM), USGS National Elevation Dataset (NED), and Canada and Alaska DEMs Global DEM Specifications. Vertical and tilt adjustments were applied based on ground control points and laser profiles from the Ice, Cloud and Land Elevation Satellite (ICESat) mission. This application improved the vertical accuracy, swath consistency, and uniformity within the swath mosaic. The NASADEM products are freely available through the Land Processes Distributed Active Archive Center (LP DAAC) at one arcsecond spacing.

For more information about this dataset, visit the Land Processes Distributed Active Archive Center (LP DAAC)

This data release publishes datasets within and surrounding the Piney Branch watershed located in the Washington, D.C. metropolitan suburb of Vienna, Virginia. This dataset was utilized in studies that investigated the accuracy and application of geospatial modeling techniques, structure-from-motion (SfM) photogrammetric methods, and digital elevation model (DEM) differencing to assess and quantify geomorphic and anthropogenic landform change. The United States Geological Survey’s (USGS) three-dimensional digital elevation program (3DEP) light detection and ranging (LiDAR) digital terrain models (DTMs) were used together with and as a means for comparison to DTMs created from historical aerial imagery. The creation and usage of both historical and current elevation datasets allows for the mapping of landscape change over time. Such mapping and assessment of geomorphic and anthropogenic change provides critical information for land management, hazard identification, and the management o ...

The correction in Digital Elevation Models (DEMs) has always been a crucial aspect of remote sensing geoscience research. The burgeoning development of new machine learning methods in recent years has provided novel solutions for the correction of DEM elevation errors. Given the reliance of machine learning and other artificial intelligence methods on extensive training data, and considering the current lack of publicly available, unified, large-scale, and standardized multisource DEM elevation error prediction datasets for large areas, the multi-source DEM Elevation Error Prediction Dataset (DEEP-Dataset) is introduced in this paper. This dataset comprises four sub-datasets, based on the TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) DEM and Advanced land observing satellite World 3D-30 m (AW3D30) DEM in the Guangdong Province study area of China, and the Shuttle Radar Topography Mission (SRTM) DEM and Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) DEM in the Northern Territory study area of Australia. The Guangdong Province sample comprises approximately 40 000 instances, while the Northern Territory sample includes about 1 600 000 instances. Each sample in the dataset consists of ten features, encompassing geographic spatial information, land cover types, and topographic attributes. The effectiveness of the DEEP-Dataset in actual model training and DEM correction has been validated through a series of comparative experiments, including machine learning model testing, DEM correction, and feature importance assessment. These experiments demonstrate the dataset’s rationality, effectiveness, and comprehensiveness.Citation:YU Cuilin, WANG Qingsong, ZHONG Zixuan, ZHANG Junhao, LAI Tao, HUANG Haifeng. Elevation Error Prediction Dataset Using Global Open-source Digital Elevation Model[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3445-3455. doi: 10.11999/JEIT240062原文：https://jeit.ac.cn/cn/article/doi/10.11999/JEIT240062

The global Digital Elevation Model (DEM) market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors. Firstly, advancements in remote sensing technologies, particularly satellite imagery and LiDAR, are providing higher-resolution and more accurate DEMs, leading to improved applications in various fields. Secondly, the rising adoption of Geographic Information Systems (GIS) and related software is facilitating easier integration and analysis of DEM data, unlocking its potential for sophisticated applications. Thirdly, the growing need for precise spatial data for infrastructure development, urban planning, and environmental management is driving market demand. This includes applications like precision agriculture, disaster management, and autonomous vehicle navigation. While the precise market size in 2025 is unavailable, a reasonable estimate, based on industry reports and observed growth in related sectors, places it at approximately $3 billion. A Compound Annual Growth Rate (CAGR) of 8% is projected for the period 2025-2033, indicating significant market potential. Despite the positive outlook, certain restraints exist. The high cost associated with acquiring and processing high-resolution DEM data can be a barrier for some users, particularly smaller businesses or organizations in developing regions. Data security and privacy concerns surrounding the use of DEM data, especially for applications involving sensitive geographical information, also pose challenges. However, the ongoing improvements in technology and the decreasing cost of data acquisition are expected to mitigate these limitations. The market segmentation, comprising scientific, commercial, industrial, military, and operational uses across telecommunication, planning & construction, transportation & tourism, oil and mining, aviation, geological, and weather applications, highlights its wide applicability and potential for future expansion. Key players like TomTom, Harris MapMart, and others are investing heavily in research and development to enhance the quality and accessibility of DEM data, further stimulating market growth.

Rapid development and growing availability of Unmanned Aerial Vehicles (UAV) translates into their more wide-spread application in monitoring of the natural environment. Moreover, advances in computer analysis techniques allow the imaging performed with UAVs to be used in creating Digital Elevation Models (DEM) and Digital Surface Models (DSM). DEMs are often employed in studies on geology, environment, engineering, and architecture. The presented paper discusses the procedures enabling the making of a precise DEM, discusses the aerial imaging data processing technique as well as determines the accuracy of obtained products in comparison with an existing Digital Elevation Model. Based on available literature the author indicates four sets of input parameters applicable in UAV imaging. Data collection missions were performed on two separate days in the area of a small peatland located in the Tuchola Pinewood, Poland. The study aims to address two research issues. Firstly, the author investigates the possibility of creating a DSM based on UAV imaging performed under unfavorable conditions and indicates whether results obtained via this method display sufficient quality to be seen as an alternative to the traditional surveying techniques (LiDAR). Secondly, the article determines the input parameters for a photogrammetric flight that ensure the highest accuracy of a resulting DSM. The analyses show a strong positive correlation between the DSMs prepared based on UAV imaging with data obtained by means of traditional methods (LiDAR). Mean correlation coefficient ranged from 0.45 to 0.75 depending on the type of land use and input parameters selected for a given flight. Furthermore, the analysis revealed that DSMs prepared based on UAV imaging—provided the most suitable input parameters are selected—can be a viable alternative to standard measurements, with the added benefit of low cost and the capacity for repeatable data collection in time. Admittedly, the method in question cannot be utilized in relation to peatlands overgrown with high vegetation (trees, shrubs) as it effectively diminishes the accuracy of obtained DSMs.

A global 1-km resolution land surface digital elevation model (DEM) derived from U.S. Geological Survey (USGS) 30 arc-second SRTM30 gridded DEM data created from the NASA Shuttle Radar Topography Mission (SRTM). GTOPO30 data are used for high latitudes where SRTM data are not available. For a grayscale hillshade image layer of this dataset, see "world_srtm30plus_dem1km_hillshade" in the distribution links listed in the metadata. acknowledgement=The Pacific Islands Ocean Observing System (PacIOOS) is funded through the National Oceanic and Atmospheric Administration (NOAA) as a Regional Association within the U.S. Integrated Ocean Observing System (IOOS). PacIOOS is coordinated by the University of Hawaii School of Ocean and Earth Science and Technology (SOEST). cdm_data_type=Grid comment=These data are provided by David Sandwell of the Scripps Institution of Oceanography and subsequently distributed via THREDDS Data Server (TDS) and ERDDAP by PacIOOS. contributor2_institution=Scripps Institution of Oceanography (SIO) contributor2_name=Joseph J. Becker contributor2_role=originator contributor2_type=person contributor_email=Walter.HF.Smith@noaa.gov contributor_institution=NOAA Laboratory for Satellite Altimetry contributor_name=Walter H.F. Smith contributor_role=originator contributor_type=person contributor_url=https://www.star.nesdis.noaa.gov/star/Smith_WHF.php Conventions=CF-1.6, ACDD-1.3 date_metadata_modified=2023-01-20 drawLandMask=under Easternmost_Easting=359.99583333333334 geospatial_bounds=POLYGON ((-90 -180, 90 -180, 90 180, -90 180, -90 -180)) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=89.99583333333334 geospatial_lat_min=-89.99583333333334 geospatial_lat_resolution=0.008333333333333333 geospatial_lat_units=degrees_north geospatial_lon_max=359.99583333333334 geospatial_lon_min=0.004166666666662877 geospatial_lon_resolution=0.008333333333333333 geospatial_lon_units=degrees_east history=2015-05-20T00:00:00Z PacIOOS obtained data files from Scripps ftp then masked out the ocean data and converted to NetCDF format. id=srtm30plus_v11_land infoUrl=https://topex.ucsd.edu/WWW_html/srtm30_plus.html institution=Scripps Institution of Oceanography (SIO) instrument=Earth Remote Sensing Instruments > Active Remote Sensing > Imaging Radars > > SRTM > Shuttle Radar Topography Mission instrument_vocabulary=GCMD Instrument Keywords ISO_Topic_Categories=elevation keywords_vocabulary=GCMD Science Keywords locations=Geographic Region > Global Land locations_vocabulary=GCMD Location Keywords metadata_link=https://www.pacioos.hawaii.edu/metadata/srtm30plus_v11_land.html naming_authority=org.pacioos Northernmost_Northing=89.99583333333334 platform=Models/Analyses > > DEM > Digital Elevation Model, Space Stations/Manned Spacecraft > Space Shuttle platform_vocabulary=GCMD Platform Keywords program=Pacific Islands Ocean Observing System (PacIOOS) project=Pacific Islands Ocean Observing System (PacIOOS) references=https://www.pacioos.hawaii.edu/metadata/world_srtm30plus_dem1km_hillshade.html; Related publication: Becker, J.J., D.T. Sandwell, W.H.F. Smith, J. Braud, B. Binder, J. Depner, D. Fabre, J. Factor, S. Ingalls, S.-H. Kim, R. Ladner, K. Marks, S. Nelson, A. Pharaoh, R. Trimmer, J. Von Rosenberg, G. Wallace, and P. Weatherall (2009) Global Bathymetry and Elevation Data at 30 Arc Seconds Resolution: SRTM30_PLUS, Marine Geodesy, 32:4, 355-371, https://dx.doi.org/10.1080/01490410903297766. source=USGS SRTM30 DEM, USGS GTOPO30 DEM sourceUrl=https://pae-paha.pacioos.hawaii.edu/thredds/dodsC/srtm30plus_v11_land Southernmost_Northing=-89.99583333333334 standard_name_vocabulary=CF Standard Name Table v39 time_coverage_duration=P0D time_coverage_resolution=P0D Westernmost_Easting=0.004166666666662877

Imagery acquired with unmanned aerial systems (UAS) and coupled with structure from motion (SfM) photogrammetry can produce high-resolution topographic and visual reflectance datasets that rival or exceed lidar and orthoimagery. These new techniques are particularly useful for data collection of coastal systems, which requires high temporal and spatial resolution datasets. The U.S. Geological Survey worked in collaboration with members of the Marine Biological Laboratory and Woods Hole Analytics at Black Beach, in Falmouth, Massachusetts to explore scientific research demands on UAS technology for topographic and habitat mapping applications. This project explored the application of consumer-grade UAS platforms as a cost-effective alternative to lidar and aerial/satellite imagery to support coastal studies requiring high-resolution elevation or remote sensing data. A small UAS was used to capture low-altitude photographs and GPS devices were used to survey reference points. These data were processed in an SfM workflow to create an elevation point cloud, an orthomosaic image, and a digital elevation model.

Here we generate a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100) with the voids filled by combining multi-source DEMs. All the voids in original REMA mosaic were automatically filled from multi-source DEMs e.g., TanDEM-X PolarDEM and other freely available DEM products covering entire or regional Antarctica.

The experimental results with the Ice, Cloud and Land elevation Satellite-2 (ICESat-2) laser altimetry data validate that the filled 100-m REMA mosaic of voids can reach an elevation accuracy similar to that of the original REMA dataset for the west and east Antarctica ice sheet as well as the entire continent. In the Antarctic Peninsula area, the vertical accuracy of the filled REMA mosaic by the proposed method is superior to that of the officially released version of the Polar Geospatial Center (PGC).

The file 'Gapless-REMA100.tif' is a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100). The Gapless-REMA100 dataset is a newly released product published in our ISPRS P&RS 2022 paper.

If this paper or the released Gapless-REMA100 dataset is helpful to your research, please cite it:Yuting Dong, Ji Zhao, Caiyong Li, and Mingsheng Liao, "Gapless-REMA100: A gapless 100-m Reference Elevation Model of Antarctica with voids filled by multi-source DEMs," ISPRS Journal of Photogrammetry and Remote Sensing, 2022.

Download In State Plane Projection Here The 2017 Digital Terrain Model (DTM) is a 2 foot pixel resolution raster in Erdas IMG format. This was created using the ground (class = 2) lidar points and incorporating the breaklines. The DTMs were developed using LiDAR data. LiDAR is an acronym for LIght Detection And Ranging. Light detection and ranging is the science of using a laser to measure distances to specific points. A specially equipped airplane with positioning tools and LiDAR technology was used to measure the distance to the surface of the earth to determine ground elevation. The classified points were developed using data collected in April to May 2017. The LiDAR points, specialized software, and technology provide the ability to create a high precision three-dimensional digital elevation and/or terrain models (DEM/DTM). The use of LiDAR significantly reduces the cost for developing this information. The DTMs are intended to correspond to the orthometric heights of the bare surface of the county (no buildings or vegetation cover). DTM data is used by county agencies to study drainage issues such as flooding and erosion; contour generation; slope and aspect; and hill shade images. This dataset was compiled to meet the American Society for Photogrammetry and Remote Sensing (ASPRS) Accuracy Standards for Large-Scale Maps, CLASS 1 map accuracy. The U.S. Army Corps of Engineers Engineering and Design Manual for Photogrammetric Production recommends that data intended for this usage scale be used for any of the following purposes: route location, preliminary alignment and design, preliminary project planning, hydraulic sections, rough earthwork estimates, or high-gradient terrain / low unit cost earthwork excavation estimates. The manual does not recommend that these data be used for final design, excavation and grading plans, earthwork computations for bid estimates or contract measurement and payment. This dataset does not take the place of an on-site survey for design, construction or regulatory purposes.

This is a collection of bare-Earth digital elevation models covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. Bare-Earth DEMs, also commonly called Digital Terrain Models (DTM), represent the ground topography after removal of persistent objects such as vegetation and buildings, and therefore show the natural terrain.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.

The vertical land change activity focuses on the detection, analysis, and explanation of topographic change. These detection techniques include both quantitative methods, for example, using difference metrics derived from multi-temporal topographic digital elevation models (DEMs), such as, light detection and ranging (lidar), National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and Interferometric Synthetic Aperture Radar (IFSAR), and qualitative methods, for example, using multi-temporal aerial photography to visualize topographic change. The geographic study areas of this activity are in Itasca and St. Louis counties in the northern Minnesota Mesabi Iron Range. Available multi-temporal lidar, NED, SRTM, IFSAR, and other topographic elevation datasets, as well as aerial photography and multi-spectral image data were identified and downloaded for these study area counties. Mining (vector) features were obtained from the Minnesota Department of Natural Resources and St. Louis Government Services Center. These features were used to spatially locate the study areas within Itasca and St. Louis counties. Previously developed differencing methods (Gesch, 2006) were used to develop difference raster datasets of NED/SRTM (1947-2000 date range) and SRTM/IFSAR (2000-2008 date range). The difference rasters were evaluated to exclude difference values that were below a specified vertical change threshold, which was applied spatially by National Land Cover Dataset (NLCD) 1992 and 2006 land cover type, respectively. This spatial application of the vertical change threshold values improved the overall ability to detect vertical change because threshold values in bare earth areas were distinguished from threshold values in heavily vegetated areas.High-resolution (1-3 m) DEMs, generated from lidar point cloud data, were acquired for Itasca and St. Louis counties in Minnesota from the Minnesota Department of Natural Resources. ESRI Mosaic Datasets were generated from lidar point-cloud data and available topographic DEMs for the specified study areas. These data were analyzed to estimate volumetric changes on the land surface at three different periods with lidar acquisitions occurring for Itasca County between April 5, 2012 to April 28, 2012 and St. Louis County between May 3, 2011 to June 1, 2011. A recent difference raster dataset time span (2007-2012 date range) was analyzed by differencing the Minnesota lidar-derived DEMs and an IFSAR-derived dataset. The IFSAR-derived data were resampled to the resolution of the lidar DEM (approximately 1-m resolution) and compared with the lidar-derived DEM. Land cover based threshold values were applied spatially to detect vertical change using the lidar/IFSAR difference dataset. Itasca County included metadata describing vertical root mean square error (RMSE) values for different land cover types. This allowed additional refinement of the spatially explicit threshold values. A single RMSE value was used for St. Louis County because RMSE values for land cover types were not provided.References: Gesch, Dean B., 2006, An inventory and assessment of significant topographic changes in the United States Brookings, S. Dak., South Dakota State University, Ph.D. dissertation, 234 p, at https://topotools.cr.usgs.gov/pdfs/DGesch_dissertation_Nov2006.pdf.

Ground-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.

foreshortening

According to the Jet Propulsion Laboratory website, the source mentioned that on September 23, 2014, the White House announced the highest-resolution topographic data generated from NASA's Shuttle Radar Topography Mission (SRTM) in 2000 was to be released globally by late 2015. The announcement was made at the United Nations Heads of State Climate Summit in New York. Since then the schedule was accelerated, and all global SRTM data have been released. The Nasa’s SRTM is mounted on a Space Shuttle and obtains Earth surface data by remote sensing technology utilizing a synthetic aperture radar. Obtained data will be converted into height data called a Digital Elevation Model (DEM), and will be utilized to generate a more precise three-dimensional map of a larger observation area of the Earth than has ever been possible. Furthermore, the SRTM is an international research effort that obtained digital elevation models on a near-global scale. This SRTM V3 product (SRTM Plus) is provided by NASA JPL at a resolution of 1 arc-second (approximately 30m), clipping at the Cambodia boundary.

'The USGS National Unmanned Aircraft Systems (UAS) Project Office utilizes UAS technology for collecting remote sensing data on a local scale. Typical UAS projects encompass areas that are too large to cover on foot and too small for traditional aircraft missions. The flexibility of operations and relative low cost of UAS allow scientists to support a range of activities including monitoring environmental conditions, analyzing the impacts of climate changes, responding to natural hazards, understanding landscape change rates and consequences, conducting fire assessments, tracking wildlife inventories, aiding search and rescue, and supporting related land management and emergency response missions. The USGS EROS Center manages and distributes data for the UAS Project Office. '

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their structure is poorly mapped due to the use of expensive and time intensive methods. Bathymetric mapping from space has sought to alleviate this but has remained dependent upon in situ water-based measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine geospatial cloud platform, to create wall-to-wall high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three common Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10-15%) when compared with in situ NOAA DEM data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for a fully space-borne approach to map nearshore bathymetry. Here we provide the Sentinel-2 mosaics, ICESat-2 bathymetric profiles and Satellite-Derived Bathymetry (SDB) models

Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (Mw) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

The TanDEM-X DEM Change Maps is a project developed by the Institute Remote Sensing Technology (IMF) at the German Aerospace Center (DLR) within the activities of the TanDEM-X Mission. Between 2016 and 2022, the TanDEM-X mission acquired an additional complete coverage of the Earth's landmass to deliver a temporally independent coverage within a well-defined time span. These new acquisitions not only provide more up-to-date elevation information, but also a great dataset to show the changes that have occurred during the years separating this new coverage from the coverages acquired to generate the TanDEM-X global DEM (between 2011 and 2014). This product - the TanDEM-X 30 m DEM Change Maps - shows the changes between the edited first global TanDEM-X DEM (TanDEM-X 30m Edited DEM) and the newly acquired time-tagged DEM scenes. In order to keep a unique timestamp, two change maps are available per tile: one with the change to the oldest pixel in the new dataset - the first DEM change, and another with the change to the newest pixel of the new dataset - the last DEM change. The two maps differ only when there are multiple coverages. Users must be aware that a given elevation change measured in the DEM change maps corresponds to a topographic change with respect to TanDEM-X 30m EDEM, but cannot be associated with a corresponding physical height change of the same magnitude. This is due to the fact that the global TanDEM-X DEM reflects an averaged elevation derived from the combination of different images, acquired over a period of several years but also because the radar waves penetrate differently in the surface depending on the attributes of the land cover. This is especially important over vegetated and snow-covered regions.

The North American Landscape Characterization (NALC) project is a component of the Landsat Pathfinder Program, which is part of a larger Pathfinder Program initiated by the National Aeronautics and Space Administration (NASA) in 1989. The NALC project is a cooperative effort between NASA, the U.S. Environmental Protection Agency, and the U.S. Geological Survey to make Landsat data available to the widest possible user community for scientific research and for the general public interest. The objectives of the NALC project are to develop standardized remotely sensed data sets and analysis methods in support of investigations of changes in land cover, to develop inventories of terrestrial carbon stocks, to assess carbon cycling dynamics, and to map terrestrial sources of greenhouse gas (CO, CO2, CH4, and N2) emissions. The NALC data set is comprised of hundreds of triplicates (i.e., multispectral scanner (MSS) data acquired in the years 1973, 1986, and 1991 plus or minus 1 year, thus, the name triplicate). The NALC triplicates also include digital elevation model data. The specific temporal windows vary for geographical regions based on the seasonal characteristics of the vegetation cover. In accordance with the Landsat Pathfinder Program concept, the Pathfinder basic data sets are to be comprised of data which have had systematic radiometric and systematic geometric corrections applied to them. The NALC triplicates, however, are precision corrected for geocoding purposes.