S. Hassan Alavi’s Post

🌟Geospatial Insights: DSM, DTM, CHM🌟 ✅DSM (Digital Surface Model): Captures Earth's top surface, including buildings and vegetation. ✅DTM (Digital Terrain Model): Represents the bare Earth, excluding surface features like buildings. ✅CHM (Canopy Height Model): Derived by subtracting DTM from DSM, isolating vegetation/building heights. Image: researchgate.net hashtag #Geospatial #RemoteSensing #GIS #EarthObservation 🛰️🌍

Geospatial Insights: DSM, DTM, CHM 💡DSM (Digital Surface Model): Captures Earth's top surface, including buildings and vegetation. 💡DTM (Digital Terrain Model): Represents the bare Earth, excluding surface features like buildings. 💡CHM (Canopy Height Model): Derived by subtracting DTM from DSM, isolating vegetation/building heights. Image: researchgate.net #Geospatial #RemoteSensing #GIS #EarthObservation 🛰️🌍

The power of time-integrated spectral indices! Many people struggle to differentiate subtle features in natural landscapes using automated #remotesensing analytics. Classifying and digitizing things like vegetation types (#species), small disturbances (#cutlines), or ephemeral land cover types like #wetlands. Please start leveraging the power of time-integrated satellite image analysis. Combining the temporal signatures in an image time series with the spectral and spatial information unlocks so much more capability for detecting and mapping not only land surface structural properties, but also functional characteristics like carbon fluxes, water dynamics, growth, stress and mortality events. Below we compare the visibility of cutline disturbances in the Canadian #boreal forest in a single date NDVI map vs a 6 month daily summed NDVI map (time-integrated NDVI). See how much more these disturbance features pop! This index also represents the vegetation productivity and growth rates (think #CO2 flux, forest recovery, ecosystem services regen, etc.) #reclamation #timeseries #satellite #vegetationmanagement #regeneration #automation #scalability #costefficiency #worksmarternotharder #gis #geoint

Making topography from HydroATLAS, SRTM and Natural Earth

🔺 What does Level of Detail (LOD) mean in 3D city models, and why is it important? 🌍 This quick guide answers these questions, explains The Open Geospatial Consortium (OGC) CityGML standards, and helps you choose the right LOD for your needs. Check it out to learn more! 👏 Many thanks to Hürcan Atay & SEDAT BAKICI for their valuable contributions! Read more👇 https://lnkd.in/gxtQcQPP #geospatial #geomatics #3dcitymodelling #levelofdetail #3dCadastre

🗺️ A bit behind on #30DayMapChallenge, but still going strong! For Day 26, I'm showing how map projections can alter and distort how spatial data is interpreted. Map projections try to portray the surface of the earth or a portion of the earth on a flat piece of paper or computer screen. In a lay man’s term, map projections tries to transform the earth from its spherical shape (3D) to a planar shape (2D) so that maps can be made on flat layers (e.g. maps). The data shown here is the WWF Marine Ecoregions of the World dataset - https://lnkd.in/dThmSHxd Originally projected with the World Geodetic System 1984, you can see how the data can be warped and distorted when projected onto other coordinate reference systems. #gis #mapping

" Exploring the world from above! Just calculated the Land Surface Temperature of Arbamich town using a Landsat image on Google Earth Engine (GEE)! It's incredible to see the Earth's surface temperature captured from space. From urban heat islands to natural landscapes, the data reveals fascinating insights into our town's dynamics. Let's continue to marvel at the wonders of remote sensing and our ever-evolving understanding of Earth's systems. #RemoteSensing #Landsat #EarthObservation #GEE #ScienceIsBeautiful" var LST = BT.expression( //LST = (BT / (1 + (0.00115 * BT / 1.4388) * Log(Ep))) '(BT / (1 + (0.00115 * BT / 1.4388) * log(Ep))) - 273.15', { 'BT': BT, 'Ep': E.select('Em') }).rename('LST'); Map.addLayer(LST, {palette:['blue', 'yellow', 'red'], min: 18, max: 50}, 'Land Surface Temperature'); print('LST:', LST); for full #GEE code, check my YouTube @geospatialnalysis https://lnkd.in/eAf8wgnb Like Comment Share

#Heat_Risk_Index (HRI) of Athens, Greece, is one of the most recent and long-term heat islands. With the help of Arc GIS #Living_Atlas , we use a Multispectral Landsat imagery layer for calculating max-min temperature data 0f 2021, one of 3 key data sets, which will be used for HRI. ArcGIS Living Atlas, add the European Space Agency (ESA) World_Cover 2021 Land Cover layer data for counting trees and determining the percentage lacking. Finally, the avg. population data from Multispectral imagery (L-5) and combining all 3 data for better visualization of HRI. Each polygon contains important data sets, further stakeholders can get ideas for decision-making, and adaptation plans for a better world. Find out the details results here- in 1st comment. #ESRI #ESA #Multispectral #GIS_for_climate_action #HRI #living_atlas

🌟DSM vs nDSM: 🌟 💡1-Digital Surface Model (DSM): A DSM represents the earth's surface and includes all objects on it (such as buildings, trees, and other structures) as well as the terrain itself. It includes both natural and human-made features. 💡2-Normalized Digital Surface Model (NDSM): NDSM is a derivative of a DSM where the heights of all objects (such as buildings and trees) are normalized with respect to the ground level. Essentially, it removes the terrain information and leaves only the heights of the objects above the ground level. 🔍In summary, while DSM includes the elevation of both the terrain and the objects on it, NDSM only includes the height of the objects above the ground level, removing the terrain information. image source: up42.com #GIS #Mapping #SpatialAnalysis #Geospatial #DataScience #RemoteSensing

🕵♂️ Identifying the precise location of an object is both a fascinating and intricate task. Every piece of information matters, and even the smallest detail can serve as a clue in helping us reach our objectives—whether it's a road sign, a traffic signal, terrain features, road markings, signboards, power lines, and more. Maps play a crucial role in GEOINT (Geospatial Intelligence) analysis. The following website contains a great collection of information and instruments you can use to practice and do your own research: https://lnkd.in/dNPxH75B