Research References

Few datasets serve as many fields as the shape of the ground. The same elevation model that shows a fault scarp to a seismologist shows a lava flow to a volcanologist, a moraine to a glaciologist, an abandoned meander to a river scientist, a sinkhole to an engineer and a buried causeway to an archaeologist. What changed in the last twenty years is the data. Airborne LiDAR sees the ground through forest canopy at half-metre detail, and whole countries now publish it openly: England, the Netherlands, France with LiDAR HD, Switzerland, the United States with 3DEP, and more on National Datasets. Where there is no LiDAR, global models such as Copernicus GLO-30 still give every place on Earth a 30 m surface.

Four things turn that grid of heights into evidence, and they recur through every section below:

• Relief visualizations (hillshade, sky-view factor, openness, local relief) make centimetre-to-metre features legible to the eye, whatever their orientation, which is how most features below were first spotted.
• Terrain derivatives (slope, curvature, TPI, ruggedness) put numbers on form, so landforms can be classified, compared, and mapped automatically.
• Differences between two surveys measure change: the uplift of an earthquake, the thickness of a lava flow, the ice a glacier lost, the rock an avalanche removed.
• Historical imagery adds the time axis for the surface itself: looting pits appearing year by year, a site disappearing under a new town, a coastline retreating.

The fields at a glance

• Faults and earthquakes. Scarps a few decimetres high, hard to follow on the ground under vegetation, run clearly across a hillshade; surveys before and after an earthquake measure how much the ground moved.
• Volcanoes. Lava flows have their own roughness and edges; a pre-eruption surface subtracted from a post-eruption one gives the thickness and volume of the lava, and from those the eruption rate.
• Landslides. Head scarps, hummocky deposits and cracks under forest stand out in sky-view factor and openness, and LiDAR inventories have replaced field mapping for whole regions.
• Glaciers and glacial landforms. Moraines, drumlins and eskers record where ice sheets stood; repeated models measure how fast today's glaciers thin.
• Rivers and palaeochannels. Meander scrolls and abandoned channels are metre-scale relief on nearly flat floodplains, lost in the colours of an elevation map and plain in a local relief model or a river relative elevation model.
• Karst. Sinkholes and dolines are closed depressions, the pits the mound and pit detector finds.
• Dunes, coasts and permafrost. Repeat surveys follow dunes moving, coasts retreating and ground sinking as permafrost thaws.
• Soils and ecology. Position on a slope, ruggedness and wetness predict soils, habitats and where species live.
• Landscape archaeology. Settlements under rainforest, mounds and tells, field systems, battlefields and industrial remains, all as relief.
• Heritage monitoring. Satellite imagery archives, read year by year, show looting, war damage, urban growth and erosion at sites that are hard or dangerous to visit.

How to read the entries

Each entry links the publisher's page and its DOI, and a free copy when an open-access version exists (from OpenAlex and the authors' repositories). [cites RVT] marks papers that cite the founding papers of the Relief Visualization Toolbox and [cites GLO-30] those citing the Copernicus DEM, from OpenAlex's citation records or the paper itself. Open … in Terrain Viewer opens the study area with the modes the paper used, on the same dataset when the app has it; See the elevation change opens the difference of two surveys the app has for that event. Where a paper covers a whole region or its exact sites could not be located, the link opens on the region.

Every reference was checked against its DOI or publisher record, not written from memory. To suggest one, open an issue on GitHub.

Earth surface processes

Faults and earthquakes

Also in the app without a paper here: the 2019 Ridgecrest earthquakes, as the difference of two stereo surface models from before and after. The slip was mostly horizontal, so look along the rupture rather than for broad change.

• Haugerud, R. A., Harding, D. J., Johnson, S. Y., et al. (2003). High-Resolution Lidar Topography of the Puget Lowland, Washington - A Bonanza for Earth Science. GSA Today. doi:10.1130/1052-5173(2003)13<0004:hltotp>2.0.co;2. Leaf-off lidar of the forested Puget Lowland revealed previously unknown postglacial fault scarps, deep-seated landslides, uplifted beaches and glacial landforms. Data: airborne lidar (Puget Sound Lidar Consortium), >10,000 km2. Open Puget Lowland, Washington, USA in Terrain Viewer ↗
• Chen, R.-F., Lin, C.-W., Chen, Y.-H., et al. (2015). Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM. Remote Sensing. Free copy. Uses grayscale slope, openness with tinted slope and a red relief image map from 1 m lidar to confirm the Meilongshan Fault as an east-dipping thrust and to map a deep-seated landslide under dense forest. Data: airborne LiDAR DEM 1 m. Open Meilongshan Fault near Xinfa, Laolung River, Liugui District, Kaohsiung, Taiwan in Terrain Viewer ↗
• Clark, K. J., Nissen, E. K., Howarth, J. D., et al. (2017). Highly variable coastal deformation in the 2016 MW7.8 Kaikōura earthquake reflects rupture complexity along a transpressional plate boundary. Earth and Planetary Science Letters. Free copy. Differences lidar from before and after the earthquake along about 110 km of coast and finds vertical displacements from -2.5 to +6.5 m, which require a revised multi-fault slip model. Data: pre- and post-earthquake airborne lidar (differenced). Open Kaikōura coast and Kaikōura Peninsula, New Zealand in Terrain Viewer ↗ · See the elevation change ↗
• Scott, C. P., Arrowsmith, J. R., Nissen, E., et al. (2018). The M7 2016 Kumamoto, Japan, Earthquake: 3-D Deformation Along the Fault and Within the Damage Zone Constrained From Differential Lidar Topography. Journal of Geophysical Research: Solid Earth. doi:10.1029/2018jb015581. 3-D differencing of lidar from before and after the earthquake shows that only about 36% of horizontal and 62% of vertical deformation happened on the main fault trace; the rest was spread through the damage zone. Data: pre- and post-event airborne lidar (differential topography). Open Futagawa-Hinagu Fault Zone at Mashiki, Kumamoto, Kyushu, Japan in Terrain Viewer ↗
• Bennett, S.E.K., DuRoss, C.B., Gold, R.D., et al. (2018). Paleoseismic Results from the Alpine Site, Wasatch Fault Zone: Timing and Displacement Data for Six Holocene Earthquakes at the Salt Lake City–Provo Segment Boundary. Bulletin of the Seismological Society of America. Lidar neotectonic mapping and a trench across an 8 m scarp near Alpine recorded six Holocene earthquakes with about 1.1 m of slip each, which suggests ruptures that crossed the segment boundary. Data: Lidar DEM (Wasatch Front, Utah). Open Alpine paleoseismic site, Wasatch fault zone, Utah in Terrain Viewer ↗
• Carena, S., Friedrich, A.M., Verdecchia, A., et al. (2023). Identification of Source Faults of Large Earthquakes in the Turkey-Syria Border Region Between 1000 CE and the Present, and Their Relevance for the 2023 Mw 7.8 Pazarcık Earthquake. Tectonics. Free copy. [cites GLO-30] Combining historical seismology, palaeoseismology and fault mapping on GLO-30 assigned source faults to fourteen M≥7 earthquakes since 1000 CE; the 2023 Pazarcık rupture could have been foreseen in location and timing but not in magnitude. Data: Copernicus GLO-30 DEM (texture shaded) + Sentinel-2 pixel tracking. Open East Anatolian Fault near Pazarcık, Türkiye in Terrain Viewer ↗
• Mere, A.M., Barth, N.C., Schwartz, J.J., et al. (2024). Slip History, Tectonic Evolution, and Fault Zone Structure Along the Southern Alpine Fault, New Zealand. Geochemistry, Geophysics, Geosystems. [cites RVT] Lidar-based mapping and zircon dating of offset basement rocks give ~70-90 km of right-lateral slip on the active strand of the southern Alpine Fault, which the authors take to be Plio-Quaternary. Data: Lidar DEM (Fiordland, New Zealand). Open Southern Alpine Fault between Lake McKerrow and the Kaipo River, Fiordland in Terrain Viewer ↗

Volcanoes

• Aufaristama, M., Höskuldsson, Á., Ulfarsson, M. O., et al. (2020). Lava Flow Roughness on the 2014-2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry. Geosciences. Free copy. Uses the Topographic Position Index and the Hurst exponent to characterise the roughness of spiny lava, a lava pond, blocky surfaces and an inflated channel. Data: airborne LiDAR and photogrammetric DEMs. Open Holuhraun lava field, Iceland in Terrain Viewer ↗
• Dietterich, H. R., Diefenbach, A. K., Soule, S. A., et al. (2021). Lava effusion rate evolution and erupted volume during the 2018 Kīlauea lower East Rift Zone eruption. Bulletin of Volcanology. Free copy. Repeat topography during the eruption gives 0.9-1.4 km3 of lava (dense-rock equivalent), 0.4 km3 of it on land, with effusion rates rising to 200 m3/s or more by mid-June. Data: repeat DEMs from small UAS, airborne lidar and airborne InSAR, plus bathymetry. Open Kīlauea lower East Rift Zone, Hawaii in Terrain Viewer ↗ · See the elevation change ↗
• Pedersen, G. B. M., Belart, J. M. C., Óskarsson, B. V., et al. (2022). Volume, Effusion Rate, and Lava Transport During the 2021 Fagradalsfjall Eruption: Results From Near Real-Time Photogrammetric Monitoring. Geophysical Research Letters. doi:10.1029/2021gl097125. Near-real-time DEM differencing over the six-month eruption gives a 4.8 km2 lava field with a bulk volume of 150 +/- 3 million m3 and a mean thickness over 30 m. Data: 33 repeat photogrammetric DEMs (satellite and airborne). Open Fagradalsfjall, Reykjanes Peninsula, Iceland in Terrain Viewer ↗ · See the elevation change ↗

Landslides and mass movements

• Van Den Eeckhaut, M., Kerle, N., Poesen, J., et al. (2012). Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data. Geomorphology. [cites RVT] Object-oriented classification of lidar slope, roughness and curvature recovered about 70% of an expert inventory of old deep-seated landslides under forest in the Flemish Ardennes. Data: Flemish single-pulse airborne lidar DTM. Open Flemish Ardennes, Belgium in Terrain Viewer ↗
• Iverson, R.M., George, D.L., Allstadt, K., et al. (2015). Landslide mobility and hazards: implications of the 2014 Oso disaster. Earth and Planetary Science Letters. Free copy. The ~8 million m³ Oso landslide started on a 180 m riverside bluff and crossed the whole ~1 km floodplain; the authors argue that liquefaction of saturated sediment at its base made it far more mobile than earlier slides at the site. Data: USGS 3DEP lidar (Washington). Open SR530 / Oso landslide, Stillaguamish valley, Washington in Terrain Viewer ↗
• Verbovšek, T., Popit, T., Kokalj, Ž. (2019). VAT Method for Visualization of Mass Movement Features: An Alternative to Hillshaded DEM. Remote Sensing. Free copy. [cites RVT] On five Vipava valley mass movements, the VAT blend (hillshade, slope, positive openness, sky-view factor) showed scarps, cracks and lobes more clearly than a hillshade alone and stays comparable between flat and steep terrain. Data: Slovenian national airborne lidar DTM (Vipava valley). Open Slano blato landslide, Vipava valley, Slovenia in Terrain Viewer ↗
• Bunn, M.D., Leshchinsky, B.A., Olsen, M.J., et al. (2019). A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives. Remote Sensing. Free copy. SICCM, a semi-automatic scarp-and-contour method, turned lidar DEMs into landslide inventories that matched DOGAMI's expert maps reasonably well across three geologically different areas of western Oregon. Data: DOGAMI / Oregon lidar DEMs (0.91 m, resampled to 6 m). Open Dixie Mountain quadrangle, Oregon in Terrain Viewer ↗
• Shugar, D. H., Jacquemart, M., Shean, D., et al. (2021). A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science. Free copy. Traces the February 2021 disaster to a rock and glacier-ice collapse from Ronti Peak that turned into a highly mobile debris flow and struck hydropower plants downstream. Data: satellite imagery and DEMs (data details not checked in this session). Open Ronti Peak north face and Ronti Gad, Chamoli, Uttarakhand, India in Terrain Viewer ↗ · See the elevation change ↗
• Dotta, G., Fornaciai, A., Bertolini, G., et al. (2023). Geomorphology of the upper sector of the Roncovetro active landslide (Emilia-Romagna Region, Italy). Journal of Maps. [cites RVT] A 1:1500 geomorphological map of an active landslide from 2014 lidar; differencing with a 1973 DEM shows about 6.2 × 10⁵ m³ of material left its upper sector in ~40 years. Data: 2014 airborne lidar DEM + 1973 Emilia-Romagna regional DEM. Open Roncovetro landslide, Enza valley, Emilia-Romagna in Terrain Viewer ↗

Glaciers and glacial landforms

Also in the app: Columbia Glacier, Alaska, 2012 to 2021, two ArcticDEM strips subtracted: tens of metres of ice lost near the front of one of the fastest-retreating tidewater glaciers.

• Yu, P., Eyles, N., Sookhan, S. (2015). Automated drumlin shape and volume estimation using high resolution LiDAR imagery (Curvature Based Relief Separation): A test from the Wadena Drumlin Field, Minnesota. Geomorphology. Separates drumlins from the regional surface with a curvature-based method on lidar and estimates the shape and volume of each drumlin automatically. Data: high-resolution LiDAR DEM. Open Wadena Drumlin Field, Minnesota, USA in Terrain Viewer ↗
• Haritashya, U.K., Kargel, J.S., Shugar, D.H., et al. (2018). Evolution and Controls of Large Glacial Lakes in the Nepal Himalaya. Remote Sensing. [cites RVT] Imja and Lower Barun lakes are much deeper than earlier measurements showed and still growing; differencing DEMs of their ice-cored moraine dams measured how fast the dams are lowering, which matters for outburst-flood hazard. Data: Multi-date DEMs + lake bathymetry. Open Imja Tsho, Khumbu, Nepal in Terrain Viewer ↗
• Middleton, M., Heikkonen, J., Nevalainen, P., et al. (2020). Machine learning-based mapping of micro-topographic earthquake-induced paleo-Pulju moraines and liquefaction spreads from a digital elevation model acquired through laser scanning. Geomorphology. [cites RVT] Principal-curvature features from a 2 m lidar DEM, fed to a neural network, recognised earthquake-induced Pulju moraines and liquefaction spreads with 94% accuracy, which supports mapping them nationwide. Data: NLS Finland 2 m airborne lidar DEM. Open Pulju moraine type area, Kittilä, Finnish Lapland in Terrain Viewer ↗
• Mannerfelt, E. S., Dehecq, A., Hugonnet, R., et al. (2022). Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry. The Cryosphere. Builds DEMs from about 21,700 archived terrestrial photographs (median date 1931) and differences them with modern DEMs, finding that Swiss glacier volume halved between 1931 and 2016. Data: historical DEMs from terrestrial photographs (1916-1947) differenced with modern DEMs. Open Swiss Alps in Terrain Viewer ↗
• Delaney, C.A., Adamson, K., Linch, L.D., et al. (2023). Reconstructing terrestrial ice sheet retreat dynamics from hummocky topography using multiscale evidence: An example from central Ireland. Quaternary Science Reviews. Free copy. [cites RVT] Much of the hummocky topography in the Brosna basin is fragmented mega-scale glacial lineations overlain by small moraine ridges, recording fast ice flow followed by retreat of the Irish Ice Sheet. Data: Irish airborne lidar DEMs + ALOS PALSAR 12.5 m (RVT-processed). Open Brosna basin between the Moate and Horseleap eskers, central Ireland in Terrain Viewer ↗
• Hofmann, F.M., Preusser, F. (2025). Revisiting ice-marginal positions north-east of Feldberg, southern Black Forest, south-west Germany. E&G Quaternary Science Journal. [cites RVT] Re-mapping from high-resolution elevation data and fieldwork largely confirmed the known ice-marginal positions NE of Feldberg, rejected a few, and added many previously undescribed moraines as targets for dating. Data: Airborne lidar DTM (Baden-Württemberg). Open Ice-marginal landforms NE of Feldberg, Black Forest in Terrain Viewer ↗

Rivers and palaeochannels

• Singh, A., Thomsen, K.J., Sinha, R., et al. (2017). Counter-intuitive influence of Himalayan river morphodynamics on Indus Civilisation urban settlements. Nature Communications. Free copy. The Ghaggar-Hakra palaeochannel is a former course of the Sutlej that was abandoned shortly after ~8 ka, so Indus cities grew along an abandoned valley rather than beside a living Himalayan river. Data: SRTM DEM + Landsat (GLO-30 is the app's equivalent). Open Ghaggar-Hakra palaeochannel at Kalibangan, Rajasthan in Terrain Viewer ↗
• Strick, R.J.P., Ashworth, P.J., Awcock, G., et al. (2018). Morphology and spacing of river meander scrolls. Geomorphology. Free copy. Ten large Mississippi point bars in lidar show a hierarchy of scrolls, depositional packages and point-bar complexes; scrolls repeat every ~167 m, and across 19 rivers scroll spacing is about half the channel width. Data: Lower Mississippi lidar (5 m) + lidar/global DEMs for 19 other rivers. Open Lower Mississippi meander belt, Vicksburg to Baton Rouge in Terrain Viewer ↗
• Vayssière, A., Castanet, C., Gautier, E., et al. (2020). Readjustments of a sinuous river during the last 6000 years in northwestern Europe (Cher River, France): from an active meandering river to a stable river course under human forcing. Geomorphology. Free copy. Lidar DEM analysis plus geophysics and coring dated thirteen Holocene palaeomeanders, showing the Cher went from an actively meandering river to a stable course under human forcing. Data: Lidar DEM (middle Cher valley). Open Cher palaeomeanders at Bigny, France in Terrain Viewer ↗
• Schwendel, A.C., Cooper, A.H. (2021). Meander chute cutoff at an alluvial river facilitated by gypsum sinkholes. Geomorphology. Free copy. Gypsum collapse sinkholes on the floodplain guided a 2019 chute cutoff at Ripon Loop; openness and red-relief views of the lidar DTM showed the enclosed hollows best. Data: Environment Agency lidar DTM (2 m 2001; 2015) + SfM. Open Ripon Loop, River Ure, North Yorkshire in Terrain Viewer ↗
• Schmidt, J., Lindemann, S., Geißler, F., et al. (2025). Spatiotemporal dynamics of river channel patterns during the last 400 years south of Leipzig, Germany. E&G Quaternary Science Journal. [cites RVT] Oxbows, ridge-and-swale point bars, crevasse splays and levees in a 1 m lidar DTM record a once meandering and anabranching floodplain that was turned into stable mill races and engineered channels over 400 years. Data: Saxony lidar DTM 1 m (GeoSN) + historical maps. Open Elster-Pleisse floodplain, south Leipzig in Terrain Viewer ↗

Karst

• Zhu, J., Taylor, T. P., Currens, J. C., et al. (2014). Improved Karst Sinkhole Mapping in Kentucky Using Lidar Techniques: A Pilot Study in Floyds Fork Watershed. Journal of Cave and Karst Studies. doi:10.4311/2013es0135. Extracting closed depressions from a lidar DEM found 1,683 probable sinkholes where 383 had been mapped; field checks confirmed 106 of 121. Data: LiDAR DEM. Open Floyds Fork watershed, Jefferson County, Kentucky, USA in Terrain Viewer ↗
• Stefanovski, S., Kokalj, Ž., Stepišnik, U. (2024). Sky-view factor enhanced doline delineation: A comparative methodological review based on case studies in Slovenia. Geomorphology. Free copy. [cites RVT] Delineates dolines by combining hydrological tools with the sky-view factor on a 1 m DEM and compares the result with depression filling (fit for identification only) and U-Net segmentation. Data: lidar DEM 1 m. Open Slovenian karst in Terrain Viewer ↗

Dunes, coasts and permafrost

• Jones, B. M., Grosse, G., Arp, C. D., et al. (2015). Recent Arctic tundra fire initiates widespread thermokarst development. Scientific Reports. Free copy. Differencing lidar DTMs from two and seven years after the 2007 fire detects thaw subsidence across 34% of the burned tundra, compared with less than 1% in similar unburned terrain. Data: airborne LiDAR DTMs 1 m, 2009 and 2014 (differenced). Open Anaktuvuk River tundra fire scar, North Slope, Alaska in Terrain Viewer ↗
• Laporte-Fauret, Q., Castelle, B., Marieu, V., et al. (2020). Coastal Dune Morphology Evolution Combining Lidar and UAV Surveys, Truc Vert beach 2011-2019. Journal of Coastal Research (Special Issue 95). doi:10.2112/si95-032.1. Combines historical aerial photographs, airborne lidar and UAV surveys to track how the coastal dune at Truc Vert changed between 2011 and 2019. Data: airborne lidar and UAV photogrammetry, plus historical aerial photographs. Open Le Truc Vert, Lège-Cap-Ferret, Gironde, France in Terrain Viewer ↗ · See the elevation change ↗
• Łopuch, M., Zieliński, P., Jary, Z. (2023). Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt. Open Geosciences. [cites RVT] Lidar morphometry of a fixed cold-climate dune field in the European Sand Belt shows lengthwise zoning of dune types and several phases of Lateglacial wind activity, with streams limiting how far the dunes spread. Data: Polish airborne lidar DEM 1 m (resampled to 10 m). Open Bory Stobrawskie dune field, Opole Plain, Poland in Terrain Viewer ↗

Soils and ecology

• Zlinszky, A., Deák, B., Kania, A., et al. (2015). Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning. Remote Sensing. Free copy. Lidar-derived microrelief, vegetation height and wetness let the authors compute Natura 2000 conservation status for 24 km² of Pannonic salt steppe at 0.5 m resolution, replacing much of the fieldwork. Data: Full-waveform airborne lidar, 10 pt/m², leaf-on and leaf-off. Open Ágota-puszta, Hortobágy National Park, Hungary in Terrain Viewer ↗
• Fink, C. M., Drohan, P. J. (2016). High Resolution Hydric Soil Mapping using LiDAR Digital Terrain Modeling. Soil Science Society of America Journal. A logistic regression on 1 m lidar slope and 5 m lidar depressions predicts hydric soils better than the 10 m USGS DEM. Data: 1 m and 5 m LiDAR DEMs (Pennsylvania). Open Pennsylvania, northern Appalachians, USA in Terrain Viewer ↗
• Moeslund, J.E., Zlinszky, A., Ejrnæs, R., et al. (2019). Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent. Ecological Applications. Lidar measures of terrain and vegetation structure explained much of the species richness of plants, fungi, lichens and bryophytes at 130 sites across Denmark, from open grassland to forest. Data: Danish national airborne lidar point cloud (2014-2015). Open Denmark, 130 biodiversity sites in Terrain Viewer ↗
• Swift, T.P., Kennedy, L.M. (2021). Beaver-Driven Peatland Ecotone Dynamics: Impoundment Detection Using Lidar and Geomorphon Analysis. Land. Free copy. Geomorphons on a 1 m lidar DTM picked out active beaver ponds as noiseless flat patches just upstream of each dam, and three decades of imagery showed beaver activity cycling between active and fallow in this rare Appalachian peatland. Data: 2018 aerial lidar, 1 m bare-earth DTM (West Virginia). Open Cranberry Glades, Monongahela National Forest, West Virginia in Terrain Viewer ↗
• Khan, S., Bartley, R., Kinsey-Henderson, A., et al. (2024). Assessing gully erosion and rehabilitation using multi temporal LiDAR DEMs: Case study from the Great Barrier Reef catchments, Australia. International Soil and Water Conservation Research. Repeat 0.5 m lidar at three gullies gives untreated erosion rates of 14-53 t/ha/yr and shows that rehabilitation raises the share of fine sediment kept in the gully to about 60%. Data: multi-temporal airborne LiDAR DEMs 0.5 m (DEMs of Difference). Open Bowen-Bogie catchment, Burdekin Basin, Queensland, Australia in Terrain Viewer ↗
• Čonč, Š., Oliveira, T., Belotti, E., et al. (2024). Revealing functional responses in habitat selection of rocky features and rugged terrain by Eurasian lynx (Lynx lynx) using LiDAR data. Landscape Ecology. [cites RVT] A new lidar method found over a million rocky outcrops; GPS-collared lynx in Slovenia and the Bohemian Forest chose rugged, rocky terrain for day resting, and selected it more strongly where it was scarcer. Data: Airborne lidar DTMs (Slovenia; Bavarian Forest and Šumava NPs). Open Bohemian Forest Ecosystem in Terrain Viewer ↗

Landscape archaeology

LiDAR changed archaeological survey more than any method since aerial photography: whole cities under rainforest, thousands of mounds, field systems and roads mapped in months rather than decades.

Settlements under forest

• Doneus, M., Briese, C., Fera, M., Janner, M. (2008). Archaeological prospection of forested areas using full-waveform airborne laser scanning. Journal of Archaeological Science 35. Shows full-waveform ALS can map an Iron Age hillfort and other earthworks under deciduous forest. Data: full-waveform airborne LiDAR. Open Leithagebirge near Mannersdorf, Lower Austria in Terrain Viewer ↗
• Chase, A. F., Chase, D. Z., Weishampel, J. F., et al. (2011). Airborne LiDAR, archaeology, and the ancient Maya landscape at Caracol, Belize. Journal of Archaeological Science 38. Free copy. Landmark lidar survey of the settlement, causeways and terraces of Caracol under rainforest canopy. Data: airborne LiDAR. Open Caracol, Cayo District, Belize in Terrain Viewer ↗
• Chase, A. F., Chase, D. Z., Fisher, C. T., et al. (2012). Geospatial revolution and remote sensing LiDAR in Mesoamerican archaeology. Proceedings of the National Academy of Sciences. Free copy. Uses new lidar data from Caracol (Belize) and Angamuco (Mexico) to show how lidar maps whole ancient cities hidden under forest canopy. Data: airborne lidar. Open Caracol, Cayo, Belize in Terrain Viewer ↗
• Evans, D. H., Fletcher, R. J., Pottier, C., et al. (2013). Uncovering archaeological landscapes at Angkor using lidar. Proceedings of the National Academy of Sciences 110. Free copy. Lidar reveals the forested urban grid and ceremonial centres of medieval Angkor. Data: airborne LiDAR. Open Angkor, Siem Reap, Cambodia in Terrain Viewer ↗
• Johnson, K. M., Ouimet, W. B. (2014). Rediscovering the lost archaeological landscape of southern New England using airborne light detection and ranging (LiDAR). Journal of Archaeological Science. [cites RVT] Uses airborne lidar to rediscover relict historical land-use features hidden under the forests of southern New England. Data: airborne LiDAR. Open Southern New England, USA in Terrain Viewer ↗
• Evans, D. (2016). Airborne laser scanning as a method for exploring long-term socio-ecological dynamics in Cambodia. Journal of Archaeological Science. Free copy. Reports on airborne laser scanning at Angkor and elsewhere in Cambodia, including the 2015 campaign, which the paper calls the largest ever made for archaeology. Data: airborne laser scanning (including the 2015 campaign). Open Angkor, Cambodia in Terrain Viewer ↗
• Canuto, M. A., Estrada-Belli, F., Garrison, T. G., et al. (2018). Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala. Science 361. Free copy. [cites RVT] Over 2,000 km2 of lidar across the Maya lowlands yields population estimates, agricultural works and defensive features. Data: airborne LiDAR (PACUNAM, over 2,000 km2). Open Maya Biosphere Reserve, Petén, Guatemala in Terrain Viewer ↗
• Inomata, T., Triadan, D., Vázquez López, V. A., et al. (2020). Monumental architecture at Aguada Fénix and the rise of Maya civilization. Nature 582. Lidar reveals Aguada Fénix, the largest and oldest known monumental Maya platform. Data: airborne LiDAR. Open Aguada Fénix, Tabasco, Mexico in Terrain Viewer ↗
• Inomata, T., Fernandez-Diaz, J. C., Triadan, D., et al. (2021). Origins and spread of formal ceremonial complexes in the Olmec and Maya regions revealed by airborne lidar. Nature Human Behaviour 5. Lidar over about 85,000 km2 of southern Mexico identifies 478 early ceremonial complexes sharing a common layout. Data: airborne LiDAR (about 85,000 km2). Open Tabasco and southern Veracruz, Mexico in Terrain Viewer ↗
• Prümers, H., Betancourt, C. J., Iriarte, J., et al. (2022). Lidar reveals pre-Hispanic low-density urbanism in the Bolivian Amazon. Nature. Free copy. [cites RVT] Lidar reveals two large Casarabe-culture settlements, Cotoca (147 ha) and Landívar (315 ha), with platforms, ramparts, causeways and canals in a four-tier settlement system. Data: airborne lidar (with TanDEM-X 12 m for context). Open Casarabe culture sites, Llanos de Mojos, Beni, Bolivia in Terrain Viewer ↗

Mounds, tells and field systems

• Menze, B. H., Ur, J. A., Sherratt, A. G. (2006). Detection of Ancient Settlement Mounds: Archaeological Survey Based on the SRTM Terrain Model. Photogrammetric Engineering & Remote Sensing 72(3). Uses the SRTM terrain model to detect and map tells (settlement mounds) across the Near East. Data: SRTM 3 arc-second.
• Kooistra, M. J., Maas, G. J. (2008). The widespread occurrence of Celtic field systems in the central part of the Netherlands. Journal of Archaeological Science. Free copy. AHN laser altimetry detected about 1,200 ha of Iron Age Celtic field systems, about 1,050 ha of them new, and suggests about 4,500 ha originally. Data: AHN (airborne laser altimetry). Open Central Netherlands just north of the Rhine in Terrain Viewer ↗
• Menze, B. H., Ur, J. A. (2012). Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale. Proceedings of the National Academy of Sciences 109(14). Free copy. Maps about 14,000 settlement sites, tells included, over 23,000 km2 and uses DEM-derived mound volume as a proxy for how long a site was occupied. Data: multispectral satellite time series plus a DEM (site volumes). Open Upper Khabur basin, northeastern Syria in Terrain Viewer ↗
• Casana, J. (2013). Radial route systems and agro-pastoral strategies in the Fertile Crescent: New discoveries from western Syria and southwestern Iran. Journal of Anthropological Archaeology. Reports newly found radial route systems (hollow ways) around sites in western Syria and southwestern Iran and discusses the agro-pastoral strategies they suggest. Data: not confirmed in this session (hollow-way mapping).
• Verschoof-van der Vaart, W. B., Lambers, K. (2019). Learning to Look at LiDAR: The Use of R-CNN in the Automated Detection of Archaeological Objects in LiDAR Data from the Netherlands. Journal of Computer Applications in Archaeology 2(1). Deep-learning detection of barrows and Celtic fields in Dutch lidar, in an area with over 1,000 recorded barrows. Data: airborne LiDAR (AHN). Open Veluwe, Gelderland, Netherlands in Terrain Viewer ↗
• Trier, Ø. D., Cowley, D. C., Waldeland, A. U. (2019). Using deep neural networks on airborne laser scanning data: Results from a case study of semi-automatic mapping of archaeological topography on Arran, Scotland. Archaeological Prospection 26(2), 165-175 (online 2018). [cites RVT] Tests whether a deep-learning detector developed in Norway transfers to Arran for roundhouses, shieling huts and clearance cairns; results range from usable to chaotic depending on the monument class. Data: airborne laser scanning. Open Isle of Arran, Scotland in Terrain Viewer ↗
• Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., et al. (2020). Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data. Proceedings of the National Academy of Sciences 117(31). Machine-learning classification of satellite imagery maps Indus-period mounded settlements in Cholistan, a non-DEM counterpart to relief-based mound detection. Data: Sentinel-1 SAR and Sentinel-2 multispectral time series (no DEM). Open Cholistan Desert, Punjab, Pakistan in Terrain Viewer ↗

Conflict landscapes

The First World War left millions of shell craters, trenches and dugouts, still preserved under the forests of the Western Front and the Alps.

• Magnini, L., Bettineschi, C., De Guio, A. (2016). Object-based Shell Craters Classification from LiDAR-derived Sky-view Factor. Archaeological Prospection. [cites RVT] Counts the remaining First World War shell craters on an Alpine plateau by object-based classification of a lidar sky-view factor image. Data: airborne LiDAR DTM. Open Vezzena/Luserna/Lavarone plateau, Trento, Italy in Terrain Viewer ↗
• Gheyle, W., Stichelbaut, B., Saey, T., et al. (2018). Scratching the surface of war. Airborne laser scans of the Great War conflict landscape in Flanders (Belgium). Applied Geography. [cites RVT] The Flanders lidar DTM reveals previously unknown Great War remains (trenches, dugouts, shelled ground) in the former front zone, using sky-view factor and relief-model visualizations. Data: DTM Flanders II (airborne LiDAR, 2013-2015). Open Former Ypres front zone, West Flanders, Belgium in Terrain Viewer ↗
• de Matos-Machado, R., Toumazet, J.-P., Bergès, J.-C., et al. (2019). War landform mapping and classification on the Verdun battlefield (France) using airborne LiDAR and multivariate analysis. Earth Surface Processes and Landforms. [cites RVT] Self-organising maps and clustering of lidar morphometry map more than a million First World War landforms (shell craters, shelters, gun positions) in eight shape classes; 93% of shell craters were correctly classified. Data: airborne LiDAR DTM of the whole battlefield. Open Verdun battlefield, Meuse, France in Terrain Viewer ↗

Historic industry and mining

• Kincey, M., Batty, L., Chapman, H., et al. (2014). Assessing the changing condition of industrial archaeological remains on Alston Moor, UK, using multisensor remote sensing. Journal of Archaeological Science. Free copy. Lidar mapping plus Landsat change detection showed old lead-mining sites on Alston Moor are eroding, mostly by gullying, which moves heavily contaminated sediment into the wider catchment. Data: Airborne lidar (England) + Landsat. Open Alston Moor lead-mining landscape, North Pennines in Terrain Viewer ↗
• Raab, A., Bonhage, A., Schneider, A., et al. (2019). Spatial distribution of relict charcoal hearths in the former royal forest district Tauer (SE Brandenburg, Germany). Quaternary International. About 6,000 relict charcoal hearths were digitised from shaded-relief maps over 109 km2 of former royal forest that fuelled the Peitz ironworks (16th-19th century). Data: LiDAR-derived shaded-relief maps. Open Tauer forest district near Peitz, Spree-Neisse, Brandenburg, Germany in Terrain Viewer ↗
• Fernández-Lozano, J., Carrasco, R.M., Pedraza, J., et al. (2020). The anthropic landscape imprint around one of the largest Roman hydraulic gold mines in Europe: Sierra del Teleno (NW Spain). Geomorphology. [cites RVT] Roman hydraulic gold mining left river captures, widened drainages and reworked fans, moraines and rock ridges; the paper gives geomorphic criteria for telling these man-made landforms from natural ones. Data: Spanish PNOA lidar DEM. Open Sierra del Teleno Roman gold mines, León, Spain in Terrain Viewer ↗
• Suh, J. W., Anderson, E., Ouimet, W., et al. (2021). Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data. Remote Sensing. Free copy. [cites RVT] A U-Net fed with slope, hillshade and VAT rasters detects relict charcoal hearths, with F1 up to 95.5% in small test regions and 86% over a 493 km2 town. Data: regional open-access airborne LiDAR. Open New England, USA in Terrain Viewer ↗

Historical imagery

The app's historical mode shows, in split views by date, Esri World Imagery Wayback (since 2014), Google Earth's historical imagery (often back to the early 2000s), Bing, and Sentinel-2 and Landsat mosaics; see Basemaps and Historical Imagery. The studies below used the same archives, or declassified CORONA spy photographs of the 1960s, which the app does not have. Links open the site in historical mode, before and after side by side when the paper gives dates. No peer-reviewed study using the Wayback archive itself turned up yet.

Looting, damage and threats

• Contreras, D. A., Brodie, N. (2010). The Utility of Publicly-Available Satellite Imagery for Investigating Looting of Archaeological Sites in Jordan. Journal of Field Archaeology. It showed that free Google Earth imagery is good enough to identify, count and monitor looting pits at Jordanian sites such as the Early Bronze Age cemeteries of the Dead Sea Plain. Data: Google Earth imagery. Open Bab edh-Dhra cemetery, Dead Sea Plain, Jordan in Terrain Viewer ↗
• Lasaponara, R., Leucci, G., Masini, N., Persico, R. (2014). Investigating archaeological looting using satellite images and GEORADAR: the experience in Lambayeque in North Peru. Journal of Archaeological Science. Multitemporal GeoEye and Google Earth images, processed with autocorrelation statistics and classification, mapped the looting pits at Ventarron, checked on the ground with georadar. Data: Multitemporal GeoEye and Google Earth imagery. Open Ventarron, Lambayeque, Peru in Terrain Viewer ↗
• Parcak, S., Gathings, D., Childs, C., et al. (2016). Satellite evidence of archaeological site looting in Egypt: 2002-2013. Antiquity. Free copy. Across about 1100 Egyptian sitescapes, looting rose sharply from 2009 and again after the 2011 Arab Spring, with Lisht alone showing 873 new pits. Data: Google Earth Pro historical imagery 2002-2013 (0.5-0.8 m). Open Lisht, Egypt in Terrain Viewer ↗
• UNITAR-UNOSAT, UNESCO (2016). Satellite-Based Damage Assessment of Cultural Heritage Sites: 2015 Summary Report of Iraq, Nepal, Syria & Yemen. UNITAR-UNOSAT report (June 2016). Satellite images before and after August-October 2015 show ISIL/Daesh demolishing the Temple of Bel and other Palmyra monuments, alongside damage at Nimrud, Hatra and sites in Yemen and Nepal. Data: Very high-resolution commercial imagery (Airbus Pleiades for Palmyra, 2015). Open Temple of Bel, Palmyra, Syria in Terrain Viewer ↗
• Casana, J., Laugier, E. J. (2017). Satellite imagery-based monitoring of archaeological site damage in the Syrian civil war. PLOS ONE. Evaluating nearly 5000 sites in Syria, northern Iraq and southern Turkey, it logged the timing, severity and location of war-time looting and damage, with sites such as Dura-Europos and Mari riddled with new pits. Data: DigitalGlobe high-resolution imagery 2007-2017 (most observations 2014 or later). Open Dura-Europos, Syria in Terrain Viewer ↗
• Hammer, E., Seifried, R., Franklin, K., Lauricella, A. (2018). Remote assessments of the archaeological heritage situation in Afghanistan. Journal of Cultural Heritage. Over 1000 Afghan sites showed that much systematic looting predates the Taliban-era conflicts and that agriculture, urban growth and mining are now the greater threats. Data: Time-stamped DigitalGlobe satellite and BuckEye aerial imagery, plus CORONA and other historical imagery.
• Tapete, D., Cigna, F. (2019). Detection of Archaeological Looting from Space: Methods, Achievements and Challenges. Remote Sensing. Free copy. A review of 47 looting studies finds very high-resolution optical imagery dominates, SAR is emerging, and spectral indices remain little tested. Data: Review of VHR optical (panchromatic, pansharpened) and SAR imagery.
• Rayne, L., Gatto, M., Abdulaati, L., et al. (2020). Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery. Remote Sensing. Free copy. Sentinel-2 change detection in Egypt and Libya picked up construction, farming and dumping at sites with 85-91% accuracy, showing farm and urban growth as the main threat. Data: Sentinel-2 per-pixel change detection in Google Earth Engine (EAMENA project). Open Aswan and Kom Ombo area, Egypt in Terrain Viewer ↗
• Karaucak, M., Steiniger, D., Boroffka, N. (2021). A remote sensing-based survey of archaeological/heritage sites near Kandahar, Afghanistan through publicly available satellite imagery. PLOS ONE. Free satellite imagery around Kandahar yielded a new inventory of settlement mounds, fortresses, religious monuments and traditional water systems in an area closed to fieldwork. Data: Publicly available high-resolution satellite imagery (Google Earth / Bing class). Open Kandahar region, Afghanistan in Terrain Viewer ↗
• Payntar, N. D. (2023). A Multi-Temporal Analysis of Archaeological Site Destruction using Landsat Satellite Data and Machine Learning, Moche Valley, Peru. Journal on Computing and Cultural Heritage. Of more than 400 sites recorded in the 1970s in the lower Moche Valley, less than a quarter survived the urban and agricultural growth mapped from Landsat between 1985 and 2020. Data: Landsat time series 1985-2020 (Random Forest land-cover classification). Open Lower Moche Valley around Chan Chan, Peru in Terrain Viewer ↗
• Westley, K., Nikolaus, J., Emrage, A., et al. (2023). The impact of coastal erosion on the archaeology of the Cyrenaican coast of Eastern Libya. PLOS ONE. Shoreline change from Landsat and VHR imagery shows erosion is speeding up at the harbour sites of Apollonia, Ptolemais and Tocra, likely worsened by sand mining and urbanisation. Data: Landsat shoreline time series plus recent and historic VHR satellite imagery (DSAS). Open Apollonia, Libya in Terrain Viewer ↗
• Huang, Q., Jin, G., Xiong, X., et al. (2023). Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR. Remote Sensing. Free copy. Burned areas peaked around 22 March 2022, concentrated near Azovstal, and about 79% of Mariupol's buildings were severely damaged or destroyed. Data: Sentinel-2 and Landsat 8/9 optical, Sentinel-1 SAR (2022). Open Mariupol and the Azovstal works, Ukraine in Terrain Viewer ↗

Prospection with Google Earth

• Myers, A. (2010). Camp Delta, Google Earth and the ethics of remote sensing in archaeology. World Archaeology. A virtual survey of the Camp Delta prison camp is used to discuss the method and ethics of remote archaeological survey with Google Earth. Data: Google Earth imagery. Open Camp Delta, Guantanamo Bay, Cuba in Terrain Viewer ↗
• Kennedy, D., Bishop, M. C. (2011). Google earth and the archaeology of Saudi Arabia. A case study from the Jeddah area. Journal of Archaeological Science. One Google Earth window east of Jeddah revealed 1977 stone structures (cairns, pendants, rings, wheels), showing how rich and unexplored the Arabian landscape is. Data: Google Earth high-resolution imagery windows. Open High-resolution window east of Jeddah, Saudi Arabia in Terrain Viewer ↗
• Kempe, S., Al-Malabeh, A. (2013). Desert kites in Jordan and Saudi Arabia: Structure, statistics and function, a Google Earth study. Quaternary International. Google Earth mapping counted at least 550 kites in Jordan and 252 in Saudi Arabia, separating chained star-shaped kites in the north from individual barbed kites in Harrat Khaybar. Data: Google Earth imagery. Open Harrat Khaybar, Saudi Arabia in Terrain Viewer ↗
• Hammer, E., Lauricella, A. (2017). Historical Imagery of Desert Kites in Eastern Jordan. Near Eastern Archaeology. Mapping kites on 1958-1960 U2 photographs shows how many have survived fifty years of change and how they relate to each other and to their setting. Data: Declassified U2 spy-plane photographs 1958-1960 compared with modern Google Earth imagery. Open Eastern Jordan badia in Terrain Viewer ↗
• Luo, L., Wang, X., Guo, H., et al. (2018). Google Earth as a Powerful Tool for Archaeological and Cultural Heritage Applications: A Review. Remote Sensing. Free copy. It reviews a decade of papers showing Google Earth's multi-temporal imagery used to discover, document and monitor archaeological and heritage sites, with case studies in five fields. Data: Google Earth multi-temporal very high-resolution imagery (review).

Declassified CORONA imagery

• Ur, J. (2003). CORONA Satellite Photography and Ancient Road Networks: A Northern Mesopotamian Case Study. Antiquity. Free copy. CORONA photos of north-eastern Syria reveal radiating 'hollow ways', the Early Bronze Age routes linking towns to their fields and pastures. Data: Declassified CORONA satellite photographs. Open Upper Khabur, north-eastern Syria in Terrain Viewer ↗
• Casana, J., Cothren, J., Kalayci, T. (2012). Swords into Ploughshares: Archaeological Applications of CORONA Satellite Imagery in the Near East. Internet Archaeology. Free copy. Orthorectified CORONA images across the Near East preserve sites and landscapes since lost to dams, cities and farming, and led to new discoveries (the app has no CORONA, so compare against modern imagery). Data: CORONA KH-4 series 1960-1972, orthorectified (CORONA Atlas). Open Tell Brak, Upper Khabur, Syria in Terrain Viewer ↗

Sentinel-2 and Landsat time series

• Agapiou, A. (2016). Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine applications. International Journal of Digital Earth. Multi-temporal Landsat data in Earth Engine helped detect buried Neolithic tells in Thessaly, and night-light time series tracked urban sprawl near World Heritage sites. Data: Multi-temporal Landsat series in Google Earth Engine; DMSP-OLS night-time lights. Open Thessaly plain around Larissa, Greece in Terrain Viewer ↗
• Orengo, H., Petrie, C. (2017). Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation. Remote Sensing. Free copy. Seasonal multi-temporal Landsat composites reconstructed more than 8000 km of palaeo-channels in a core area of the Indus Civilisation. Data: 28 years of Landsat 5 (1711 images) processed in Google Earth Engine. Open Sutlej-Yamuna interfluve, northwest India in Terrain Viewer ↗

Methods

Relief visualization

Sky-view factor, openness, local relief model and their blends: the toolbox of archaeological LiDAR interpretation, most of it collected in RVT. In the app: Relief Visualization.

• ZRC SAZU (EarthObservation) (). Relief Visualization Toolbox in Python (RVT_py). Software, GitHub. Open-source reference implementation of the RVT visualizations (hillshade, multidirectional hillshade, SVF, openness, LRM, VAT blends).
• Phong, B. T. (1975). Illumination for computer generated pictures. Communications of the ACM 18(6). The Phong reflection model (ambient, diffuse and specular terms) behind the app's Phong lighting mode.
• Yokoyama, R., Shirasawa, M., Pike, R. J. (2002). Visualizing Topography by Openness: A New Application of Image Processing to Digital Elevation Models. Photogrammetric Engineering & Remote Sensing 68(3), 257-265. Introduces positive and negative topographic openness, an angular measure of how exposed or enclosed each DEM cell is. Data: DEMs.
• Devereux, B. J., Amable, G. S., Crow, P. (2008). Visualisation of LiDAR terrain models for archaeological feature detection. Antiquity 82. Shows how hillshades lit from different directions can be combined into one image, the idea behind multidirectional hillshading for archaeology. Data: airborne LiDAR.
• Hesse, R. (2010). LiDAR-derived Local Relief Models - a new tool for archaeological prospection. Archaeological Prospection 17. Introduces the local relief model, which removes large-scale landforms so small shallow features can be seen and measured. Data: airborne LiDAR DEM.
• Zakšek, K., Oštir, K., Kokalj, Ž. (2011). Sky-View Factor as a Relief Visualization Technique. Remote Sensing 3(2). Free copy. [cites RVT] Introduces sky-view factor, a diffuse-illumination relief visualization that shows small features without the directional bias of hillshade. Data: high-resolution DEMs (airborne LiDAR).
• Kokalj, Ž., Zakšek, K., Oštir, K. (2011). Application of sky-view factor for the visualisation of historic landscape features in lidar-derived relief models. Antiquity 85. Shows on a Slovenian test site that sky-view factor reveals historic landscape features more clearly than single-source hillshade. Data: airborne LiDAR.
• Štular, B., Kokalj, Ž., Oštir, K., Nuninger, L. (2012). Visualization of lidar-derived relief models for detection of archaeological features. Journal of Archaeological Science 39. Free copy. [cites RVT] Systematically compares relief visualization techniques for detecting archaeological features in lidar DEMs. Data: airborne LiDAR.
• Bennett, R., Welham, K., Hill, R. A., Ford, A. (2012). A Comparison of Visualization Techniques for Models Created from Airborne Laser Scanned Data. Archaeological Prospection 19. [cites RVT] Quantitatively compares five techniques, from shaded relief to local relief and sky-view factor, against aerial-photo mapping of a UK study area. Data: airborne LiDAR.
• Doneus, M. (2013). Openness as Visualization Technique for Interpretative Mapping of Airborne Lidar Derived Digital Terrain Models. Remote Sensing 5(12). Free copy. [cites RVT] Argues openness is free of directional bias and horizontal displacement, making it well suited to outlining archaeological features. Data: airborne LiDAR DTM.
• Kokalj, Ž., Hesse, R. (2017). Airborne laser scanning raster data visualization: A guide to good practice. Prostor, kraj, čas 14 (ZRC SAZU, Založba ZRC). Open-access practical guide to choosing and parameterising relief visualizations for archaeological interpretation of LiDAR DEMs. Data: airborne LiDAR.
• Orengo, H. A., Petrie, C. A. (2018). Multi-scale relief model (MSRM): a new algorithm for the visualization of subtle topographic change of variable size in digital elevation models. Earth Surface Processes and Landforms 43. [cites RVT] Multi-scale relief model that brings out subtle landforms of any size in global 30 m-class DSMs, used to map palaeochannels on an alluvial plain. Data: medium-resolution global DSMs (e.g. SRTM, ASTER GDEM, ALOS, TanDEM-X). Open Sutlej-Yamuna interfluve, northwest India in Terrain Viewer ↗
• Guyot, A., Hubert-Moy, L., Lorho, T. (2018). Detecting Neolithic Burial Mounds from LiDAR-Derived Elevation Data Using a Multi-Scale Approach and Machine Learning Techniques. Remote Sensing 10(2). Free copy. [cites RVT] Combines multi-scale topographic position with random forests to map megalithic burial mounds, finding a previously unknown mound at Carnac. Data: airborne LiDAR DTM. Open Carnac and Gulf of Morbihan, Brittany, France in Terrain Viewer ↗
• Crutchley, S., Crow, P. (2018). Using Airborne Lidar in Archaeological Survey: The Light Fantastic. Historic England guidance (HEAG179), Swindon. Historic England's guidance on deciding whether airborne lidar suits an archaeological project and how to process, visualise and interpret the data; it revises the 2010 English Heritage edition. Data: airborne lidar (guidance).
• Kokalj, Ž., Somrak, M. (2019). Why Not a Single Image? Combining Visualizations to Facilitate Fieldwork and On-Screen Mapping. Remote Sensing 11(7). [cites RVT] Defines the VAT blend (visualization for archaeological topography), which combines several relief visualizations with blend modes into one image. Data: airborne LiDAR.
• Guyot, A., Lennon, M., Hubert-Moy, L. (2021). Objective comparison of relief visualization techniques with deep CNN for archaeology. Journal of Archaeological Science: Reports. [cites RVT] Benchmarks thirteen relief visualizations with a deep CNN that detects archaeological structures; e2MSTP, which builds on multiscale topographic position, performed best. Data: airborne laser scanning DTM.

Geomorphometry

The derivatives behind slope, aspect, curvature, TPI and roughness. The formulas as implemented are on Equations.

• Horn, B. K. P. (1981). Hill shading and the reflectance map. Proceedings of the IEEE 69(1). Classic treatment of hill shading, including the 3x3 gradient estimator widely used for slope and aspect.
• Zevenbergen, L. W., Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms 12. Fits a partial quartic surface to a 3x3 window to derive slope, aspect and profile and plan curvature. Data: grid DEMs.
• Florinsky, I. V. (1998). Accuracy of local topographic variables derived from digital elevation models. International Journal of Geographical Information Science 12. Analyses the errors of slope, aspect and curvature computed from gridded DEMs. Data: grid DEMs.
• Guisan, A., Weiss, S. B., Weiss, A. D. (1999). GLM versus CCA spatial modeling of plant species distribution. Plant Ecology 143. Early published use of the topographic position index (elevation relative to the neighbourhood mean) as a predictor. Data: DEM.
• Riley, S. J., DeGloria, S. D., Elliot, R. (1999). A Terrain Ruggedness Index That Quantifies Topographic Heterogeneity. Intermountain Journal of Sciences 5, 23-27. Defines the Terrain Ruggedness Index (TRI), which measures the elevation difference between a cell and its eight neighbours. Data: DEM.
• Weiss, A. D. (2001). Topographic Position and Landforms Analysis. Poster, ESRI User Conference, San Diego (The Nature Conservancy, Northwest Division). Introduces landform classification from the Topographic Position Index (TPI) at two neighbourhood scales, giving ten classes from canyons to mountain tops. Data: DEM.
• Sappington, J. M., Longshore, K. M., Thompson, D. B. (2007). Quantifying Landscape Ruggedness for Animal Habitat Analysis: A Case Study Using Bighorn Sheep in the Mojave Desert. The Journal of Wildlife Management. Defines the Vector Ruggedness Measure (VRM), which correlates less with slope than earlier indices, and shows that ruggedness and slope are separate parts of desert bighorn sheep habitat in three Mojave ranges. Data: DEM. Open Mojave Desert, southwestern USA in Terrain Viewer ↗
• Hengl, T., Reuter, H. I. (eds.) (2009). Geomorphometry: Concepts, Software, Applications. Developments in Soil Science 33, Elsevier. Standard reference book on land-surface parameters and objects extracted from DEMs.
• Wilson, J. P. (2012). Digital terrain modeling. Geomorphology 137. Review of digital terrain modelling and the land-surface parameters derived from DEMs.
• De Reu, J., Bourgeois, J., Bats, M., et al. (2013). Application of the topographic position index to heterogeneous landscapes. Geomorphology 186. Adapts TPI-based landform classification to landscapes of mixed relief. Data: DEM.
• Minár, J., Evans, I. S., Jenčo, M. (2020). A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modelling and prediction. Earth-Science Reviews 211. A systematic set of definitions for the many land-surface curvatures, with guidance on which to use.

Elevation data

Global DEMs

The elevation models a study reaches for when there is no LiDAR, and their accuracy. The app's global terrain is Mapterhorn, which merges them with national data; see Terrain sources. Mapterhorn is recent and no published study citing it turned up yet.

• Registry of Open Data on AWS (). Copernicus Digital Elevation Model (DEM). AWS Open Data registry. doi:10.60489/RODA.MXYOG0. Public cloud-optimised copy of the Copernicus DEM on AWS. Data: Copernicus DEM GLO-30 / GLO-90 (Cloud Optimized GeoTIFF).
• Registry of Open Data on AWS (). Terrain Tiles. AWS Open Data registry. doi:10.60489/RODA.6W7PSW. The Mapzen/Tilezen global Terrarium and GeoTIFF elevation tiles, one of the app's global sources. Data: Mapzen/Tilezen terrain tiles (composite of global and national DEMs).
• Mapterhorn (). Mapterhorn. Project website. Open global terrain tileset used as the app's default elevation source. Data: open global terrain tiles.
• Farr, T. G., Rosen, P. A., Caro, E., et al. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics 45. Reference description of SRTM, the first near-global 1 to 3 arc-second DEM. Data: SRTM.
• Guth, P. L., Geoffroy, T. M. (2021). LiDAR point cloud and ICESat-2 evaluation of 1 second global digital elevation models: Copernicus wins. Transactions in GIS 25. Finds the Copernicus DEM consistently beats the other 1 arc-second global DEMs against LiDAR point clouds. Data: Copernicus DEM GLO-30, ALOS, ASTER, NASADEM, SRTM vs LiDAR and ICESat-2.
• Marešová, J., Gdulová, K., Pracná, P., et al. (2021). Applicability of Data Acquisition Characteristics to the Identification of Local Artefacts in Global Digital Elevation Models: Comparison of the Copernicus and TanDEM-X DEMs. Remote Sensing 13(19). Evaluates the Copernicus DEM against the unedited TanDEM-X DEM in the Alps, Carpathians and Pyrenees, and how artefacts can be flagged. Data: Copernicus DEM, TanDEM-X DEM.
• European Space Agency (2022). Copernicus DEM - Global and European Digital Elevation Model. Copernicus Data Space Ecosystem (dataset). The Copernicus DEM (GLO-30 at 30 m worldwide), the surface model behind much of the app's global terrain. Data: Copernicus DEM GLO-30 / GLO-90.
• Hawker, L., Uhe, P., Paulo, L., et al. (2022). A 30 m global map of elevation with forests and buildings removed. Environmental Research Letters. Removes forests and buildings from the Copernicus GLO-30 DEM with machine learning (FABDEM), lowering mean absolute error from 1.61 to 1.12 m in built-up areas and from 5.15 to 2.88 m in forests. Data: Copernicus DEM GLO-30 (corrected with machine learning: FABDEM).
• Trevisani, S., Skrypitsyna, T. N., Florinsky, I. V. (2023). Global digital elevation models for terrain morphology analysis in mountain environments: insights on Copernicus GLO-30 and ALOS AW3D30 for a large Alpine area. Environmental Earth Sciences. Free copy. Across 6,210 km2 of Trentino, GLO-30 beats AW3D30 on gentle, unvegetated and urban terrain, AW3D30 does slightly better on rough, steep terrain, and neither captures fine-scale morphology on steep slopes. Data: Copernicus DEM GLO-30 and ALOS AW3D30 against an airborne lidar reference DEM. Open Trentino Province, Italian Alps in Terrain Viewer ↗
• Bielski, C., López-Vázquez, C., Grohmann, C. H., et al. (2024). Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography. IEEE Transactions on Geoscience and Remote Sensing 62. Free copy. Ranks open global DEMs with a multi-criteria method and confirms the Copernicus DEM as the best 1 arc-second option. Data: Copernicus DEM GLO-30 and other 1 arc-second global DEMs.

Citing the data

Datasets in the app with a DOI of their own:

• Mapzen, a Linux Foundation project (2018). Terrain Tiles (Version 1.0) [Dataset]. Registry of Open Data on AWS. doi:10.60489/RODA.6W7PSW.
• Earth Resources Observation and Science (EROS) Center (2018). Interferometric Synthetic Aperture Radar (IFSAR) Alaska [Dataset]. USGS. doi:10.5066/P9C064CO.
• OpenTopography (2022). Lidar Survey of Middle Usumacinta Region, Mexico. doi:10.5069/G95B00NF.
• Japan Aerospace Exploration Agency (JAXA) (2015). ALOS World 3D - 30m (AW3D30) Global DSM [Dataset]. doi:10.57746/EO.01KKFX0EJW8R5NDC9BG5NT13WS.
• OpenTopography (2021). Haiti Digital Terrain Model 2014 - 2016. doi:10.5069/G9GX48R8.
• Tarquini, S., Isola, I., Favalli, M., Battistini, A., & Dotta, G. (2023). TINITALY, a digital elevation model of Italy with a 10 meters cell size, version 1.1 [Dataset]. INGV. doi:10.13127/TINITALY/1.1.
• OpenTopography (2025). ANADEM: A Digital Terrain Model for South America. doi:10.5069/G9736P4G.
• European Space Agency (2022). Copernicus DEM [Dataset]. doi:10.5270/ESA-c5d3d65.
• EMODnet Bathymetry Consortium (2024). EMODnet Digital Bathymetry (DTM 2024) [Dataset]. doi:10.12770/cf51df64-56f9-4a99-b1aa-36b8d7b743a1.
• Ho, Y., & Hengl, T. (2026). Global Ensemble Digital Terrain Model 30m (GEDTM30) [Dataset]. Zenodo. doi:10.5281/zenodo.14900180.
• Porter, C., Howat, I., Noh, M.-J., et al. (2023). ArcticDEM - Mosaics, Version 4.1 [Dataset]. Harvard Dataverse. doi:10.7910/DVN/3VDC4W.
• Howat, I., Porter, C., Noh, M.-J., et al. (2022). The Reference Elevation Model of Antarctica - Mosaics, Version 2 [Dataset]. Harvard Dataverse. doi:10.7910/DVN/EBW8UC.
• Sentinel-2 Cloud-Optimized GeoTIFFs [Dataset]. Registry of Open Data on AWS. doi:10.60489/RODA.9J28F8.
• German Aerospace Center (DLR) (2020): TanDEM-X - PolarDEM - Antarctica, 90m. doi:10.15489/9jhr18jepi65.

National datasets whose producer publishes no DOI, but whose copy on OpenTopography has one:

• Natural Resources Canada (2025). Medium Resolution Digital Elevation Model (MRDEM) - CanElevation Series. Distributed by OpenTopography,. doi:10.5069/G9N8780J (OpenTopography; producer page: open.canada.ca).
• ©swisstopo (or 'Federal Office of Topography swisstopo'). swissALTI3D. doi:10.5069/G9513WFJ (OpenTopography; producer page: www.swisstopo.admin.ch).
• AHN (Actueel Hoogtebestand Nederland). Via PDOK. doi:10.5069/G9CN725M (OpenTopography; producer page: www.ahn.nl).
• © Kartverket. doi:10.5069/G9DF6PFH (OpenTopography; producer page: www.kartverket.no).
• Toitū Te Whenua Land Information New Zealand (n.d.). New Zealand LiDAR 1m DEM. CC BY 4.0. doi:10.5069/G9ZK5DW0 (OpenTopography; producer page: www.linz.govt.nz).
• U.S. Geological Survey (n.d.). 3D Elevation Program (3DEP) seamless DEM. Public domain. doi:10.5069/G98K778D (OpenTopography; producer page: www.usgs.gov).

The rest have no DOI; cite the producer, as they ask, and link their page:

• Mapterhorn (built-in default terrain): Mapterhorn (2025). Public terrain tiles.
• Land Tirol DGM 5m: Land Tirol (n.d.). Digitales Geländemodell Tirol 5 m. data.gv.at. CC BY 4.0.
• Digitaal Vlaanderen DHMV II DTM 1m: Digitaal Vlaanderen (n.d.). Digitaal Hoogtemodel Vlaanderen II, DTM, raster, 1 m.
• swisstopo swissALTIRegio 10m: Bundesamt für Landestopografie swisstopo, swissALTIRegio (plus the contributing datasets swisstopo lists: TINITALY 10.13127/tinitaly/1.1, Austrian DGM, IGN RGE ALTI, German DGM1).
• ČÚZK DMP 1G 0.5m (surface): ČÚZK (n.d.). Digitální model povrchu (DMP 1G). CC BY 4.0.
• ČÚZK DMR 5G: ČÚZK (n.d.). Digitální model reliéfu České republiky 5. generace (DMR 5G). CC BY 4.0.
• IGN/CNIG XYZ Modelo Digital de Elevaciones (TerrainRGB): CC BY 4.0 scne.es (form CNIG uses for its MDT products).
• Andalucía MDE del PNOA-LiDAR 2020-21 (REDIAM COG): Junta de Andalucía, REDIAM (2021). MDE del PNOA-LiDAR de Andalucía 2020-21.
• Maa-amet LiDAR DTM 1m: Elevation data <years>, Republic of Estonia Land and Spatial Development Board (page asks to cite source and date).
• NLS Elevation model 10 m (via CSC Paituli): Contains data from the National Land Survey of Finland Elevation model 10 m <MM/YYYY>.
• IGN LiDAR HD MNS (DSM): IGN (2021-). LiDAR HD - Modèle numérique de surface. Licence Ouverte 2.0.
• IGN RGE ALTI 1-5m: IGN (n.d.). RGE ALTI®. Licence Ouverte 2.0.
• Lands Department DTM 5m: Lands Department, HKSAR Government (n.d.). Digital Terrain Model (DTM) 5 m grid. DATA.GOV.HK (attribution to the Government, LandsD and DATA.GOV.HK required).
• Hawaii statewide LiDAR DTM 1m: Hawaii Statewide GIS Program (n.d.). Hawaii statewide LiDAR DTM 1 m.
• New Brunswick GeoNB LiDAR DEM 1m: Government of New Brunswick / Service New Brunswick (n.d.). LiDAR Digital Elevation Model (DEM). GeoNB.
• SA River Murray 2021 LiDAR DTM 0.5m: SA Department for Environment and Water (2021). River Murray LiDAR 2021, DTM 50 cm. Via ELVIS / MDBA.
• NSW 5m statewide DEM: NSW Spatial Services (DCS) (n.d.). NSW Elevation Data Service, 5 m.
• Queensland QldDem best-available: © State of Queensland (n.d.). Queensland Elevation DEM Public Time Series Service. CC BY 4.0.
• Msimbazi LiDAR DTM 0.5m 2019: Resilience Academy (2020). Msimbazi Digital Elevation Model_ LiDAR 0.50m 2019. Aerial survey data, Resilience Academy.
• Msimbazi UAV DSM 0.5m 2019: Resilience Academy (2020). Msimbazi Digital Elevation Model_ UAV 2019. Aerial survey data, Resilience Academy.
• New York statewide LiDAR DEM 1m: NYS GIS Program Office / NYS Elevation Program (n.d.). Latest DEM, 1 m.
• Klaipėda city LiDAR DTM 0.5m: Klaipėda City Municipality (2017). Reljefo modelis (city LiDAR DTM).
• Umhvørvisstovan FO_DSM_2017 25m: Data from the Faroese Environment Agency, Umhvørvisstovan: FO_DSM_2017, <date> (webservice).
• England EA LIDAR Composite DTM 1m: © Environment Agency copyright and/or database right 2022. All rights reserved.
• Bhotekoshi 2026 post-event DSM 0.5m, Rasuwagadhi-Timure: geo-pera (2026). bhotekoshi-2026-reconstruction v1.1 [GitHub release]. CC BY 4.0.
• BIG DEMNAS ~8m: Badan Informasi Geospasial (n.d.). DEMNAS - DEM Nasional.
• GSI/GSJ seamless elevation tiles (Terrain-RGB): 産総研地質調査総合センター シームレス標高タイル (GSJ, AIST); source data 出典: 国土地理院 (GSI).
• INEGI Continuo de Elevaciones Mexicano 3.0: INEGI (n.d.). Continuo de Elevaciones Mexicano 3.0 (CEM 3.0).
• IDEuy MDT nacional 30m: IDEuy / AGESIC (2021). Ortoimagen y modelo digital del terreno de cobertura nacional.
• Esri World Elevation 3D (Terrain3D): Esri (n.d.). World Elevation 3D / Terrain 3D. Sources: see Esri item.
• EGM96 geoid undulation 15': NGA. EGM96 geoid undulation grid (us_nga_egm96_15.tif), via PROJ CDN.
• Smart Maps GEL NASADEM 30m Terrain-RGB: UN Smart Maps Group, Global Elevation Tiles (source.coop/smartmaps/gel), from NASADEM: NASA JPL (2020). NASADEM Merged DEM Global 1 arc second V001.
• Open Waters Seascape bathymetry: Open Waters, Seascape (github.com/openwatersio/seascape); main input GEBCO_2025 Grid.
• Re:Earth Terrain (Terrarium, ellipsoid): Re:Earth, reearth-terrain (github.com/reearth/reearth-terrain), with the attributions its TileJSON carries for each source.
• Google Open Buildings 2.5D Temporal 2023 (BRA sample): Google Research (n.d.). Open Buildings 2.5D Temporal Dataset.
• Ontario LiDAR-derived DTM 0.5m: Ontario Ministry of Natural Resources (n.d.). Ontario Digital Terrain Model (Lidar-Derived). © King's/Queen's Printer for Ontario.
• Geobasis NRW DGM1 1m: Geobasis NRW (n.d.). Digitales Geländemodell (DGM1). dl-de/zero-2.0 (no attribution required).

Using this with an assistant

Each "Open in Terrain Viewer" link is an ordinary URL. An assistant can build the same for any place with the Terrain Viewer skill or llms.txt: "show me the sky-view factor over the area of this paper".