S. Hassan Alavi’s Post

A highly recommended method for survey of rivers which are susceptible to flooding and in high risk position.

🌟Underwater LiDAR:🌟 💡Underwater LiDAR is marine research and exploration by providing high-resolution 3D maps of underwater environments with high detail and accuracy. 🌟 Key Benefits of Underwater LiDAR: 💡1-Precision Mapping: Captures detailed 3D models of underwater landscapes and structures with high-level accuracy. 💡2-Enhanced Exploration: Enables scientists and researchers to study monitor changes over time, and discover hidden treasures with unparalleled clarity. 💡3-Efficient Surveys: Streamlines underwater surveys and inspections, reducing time and costs associated with traditional methods. 💡4-Archaeological Discoveries: Unveils submerged archaeological sites and artifacts, shedding light on our maritime history and cultural heritage. Video credit: Geospatial World #UnderwaterLiDAR #MarineExploration #OceanResearch #Innovation #Technology #MarineConservation #LiDAR #UnderwaterMapping

This technology is very interesting since LiDAR problem is penetrating water bodies. Although it has the ability to penetrate water, its range is very shallow. I am very curious and interested on how the mechanics of the LiDAR and how accurate the depth that it can produce.

Geologic maps form the basis for a multitude of natural resource-related decisions. Geologic mapping is a rapidly evolving discipline that employs a range of modern techniques including satellite and drone imagery, multispectral analysis, 3D visualization, custom lidar (high-resolution topography), complex relational databases, and the new and evolving field of machine learning, a subfield of artificial intelligence (AI). Learn how our Geologic Mapping Program is employing these new tools in the latest Mapping News–https://ow.ly/Wgx850Qt8au #utahgeology #geologicmaps Photo: Drone photograph of unconsolidated deposits and a rock glacier in upper Gad Valley scanned with new drone-based lidar. The view is to the northeast.

By using LiDAR technology, we are able to support national mapping programs by capturing accurate data across various applications such as 🗺️ Land use and zoning 🗺️ Geospatial intelligence 🗺️ Environmental management … And so much more! The data collected by our satellites will be able to provide accurate topographical and land cover information, identify hazard-prone areas, assist in risk mitigation and monitor and manage natural resources, such as forests and water bodies. Explore all the  transformative possibilities of space-based LiDAR here: https://nuview.space #WeAreNuview #EarthObservation #SpaceTechnology #LiDAR #NationalMapping

Today's Sentinel-2/LiDAR perspective... The Aspy Fault, Cape Breton Highlands, Nova Scotia. To accentuate the Aspy Fault, I shrouded the lower elevations in low lying fog and adjusted the shadow cast which makes the steep hillsides stand out. The Sentinel-2 image was taken on April 25th, 2021, which is what this part of Cape Breton would look like now. The Aspy Fault is a faultline dating back to the Ordovician geological period when it formed on the edges of Gondwanaland in the microcontinent called Avalonia. Avalonia ultimately broke up, with remnants now lying in parts of North America and western Europe. The fault itself is a major feature of the scenery of the Cape Breton Highlands, extending 40 kilometres from the centre of the highlands to Aspy Bay, and it is possibly the same fault line which appears as the Great Glen Fault in the Scottish Highlands. Created using #Aerialod, Sentinel-2 and Nova Scotia provincial LiDAR data resampled to a 5 metre pixel.

LiDAR technology is being explored for its potential in earthquake prediction by mapping seismic hazards more accurately. A recent study in northern Yellowstone Park showed that LiDAR can detect geological faults in hard-to-reach areas, providing vital data that could improve earthquake forecasting and contingency planning. ACI Corporation offers expert consulting on choosing the best LiDAR tools for specific projects. For more details, check out the full blog https://lnkd.in/eqr76R4A #LiDAR #Geospatial #Surveying #Mapping #3DModeling #3DMapping #RemoteSensing #AerialSurvey #Topography #GIS #DataVisualization #PointCloud #LandSurveying #Teledyne #PhaseOne #Geomatics #CivilEngineering #SpatialData #EnvironmentalMonitoring #InfrastructureInspection

Based on a permanent TLS station (measuring forest every hour, data resemble drone laser scanning (in geometry) since TLS is placed 30 m above ground level): - Hyper-temporal LiDAR time series captures phenological characteristics of tree species. - Tree-wise phenological dynamics are species-specific and support species classification. - Late autumn and optionally late spring LiDAR data support best species classification (single wavelength system). https://lnkd.in/dDGw9F_C

We're excited to introduce our new series: Ask Maya about Nature! 📢 This series will explore the complexities of our natural world through the lens of geospatial technology. Join us as we explore questions where geospatial analysis and Nature-based Solutions meet. 🤝 Stay tuned for insights from answering questions such as: 🔍 Why is it essential to conduct a 10-year land cover check? 🌳 What is the NDVI and how is it relevant to Nature-based Solutions? 🛰️ What are PDSI, Landsat, Sentinel, LiDAR, and PALSAR? – We'll break it down for you! 💚 And much more! Follow our page to stay up to date! 👀 #AskMaya #YourGeospatialAnalyst #GIS #GenAI #GeospatialProcessAutomation #NaturebasedSolutions #NaturalCapital

If you use a global #DEM (e.g. SRTM) as #topography data for #flood modelling, you may be interested in our recent paper. We assess five contemporary options (FABDEM, Copernicus DEM, AW3D30, NASADEM & SRTM) using a very large reference dataset (65 airborne LiDAR surveys) representing flood-prone environments globally. TL;DR - the Copernicus DEM (GLO-30 in the figure) is generally a big improvement over older DEMs (SRTM, AW3D30) except under forests (hard for X-band radar to penetrate), and FABDEM achieves further improvements using #ML (random forest models) to predict corrections for forests & built-up areas. More nuance & details in the (open-access) paper linked below. Thanks to everyone on here who helped by suggesting reference data sources, to my co-authors at RMIT University, Simon Jones & Karin Reinke, and to Natural Hazards Research Australia for their scholarship support! Published in the International Journal of Digital Earth: https://lnkd.in/g7aU63_j