#remote-sensing — Public Fediverse posts

My MS thesis mapped dust sources on the Southern High Plains from a decade of MODIS imagery, 2001 to 2010, finding where the sky goes brown before the weather station reports it. Old data, still teaching me things.
#RemoteSensing #MODIS #Dust #GIS

🌍 East Africa Landcover Map 2025 🇹🇿🇰🇪🇺🇬🇷🇼🇧🇮 🗺️ From the Great Lakes, forests and savannas to Kilimanjaro—mapped using ESRI Landcover 2025 by @impactobservatory & @Esri and CGIAR-SRTM DEM by @divagis. #GIS #Cartography #EAC #Landcover #RemoteSensing #Geospatial

Based on satellite imagery from June to August 2025, my research revealed 58,673 distinct vegetative patches across Calgary, covering a total of 39,009 hectares. However, exactly 50% of the city's total green cover is concentrated in just 44 large megaclusters. The remaining half is dispersed across tens of thousands of tiny, highly fragmented patches.

UPD:
Previous steps of my research you can find here:
https://www.datastory.org.ua/

#GIS #RemoteSensing #EarthObservation #Calgary #SpatialDataScience #MachineLearning #Geoscience #OpenData #GreennessOfCalgary #UrbanResilience #fossgiss #RStats #Alberta #Canada

Are you interested in analyzing satellite data in R? My two courses from the OpenGeoHub summer school on flood detection and land surface temperature (LST) analysis are now available!

You can find learning materials for beginners in this repository: https://github.com/kadyb/ogh2026

#rstats #rspatial #gischat #remotesensing

EOMasters Toolbox 2.0 marks a big shift: one unified toolbox, all former Pro features now free. A new chapter begins — with more power for every SNAP user.
📖 ➡️ https://www.eomasters.org/post/there-can-only-be-one
#EarthObservation #remotesensing

Challenges In The Use Of Local Data For Regional Scale Mapping Of C And N Stocks In The Continuous Permafrost Zone At The Yukon Coastal Plain | Heatwave Risks To Tipping Point Of Permafrost
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https://doi.org/10.5194/soil-12-113-2026 <-- shared paper
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https://doi.org/10.1038/s41558-026-02603-2 <-- shared paper
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https://www.theguardian.com/environment/2026/aug/20/tipping-points-heatwaves-wildfires-permafrost-climate-crisis <-- shared media article
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https://www.cbc.ca/news/canada/north/permafrost-slumps-herschel-island-qikiqtaruk-yukon-9.7168780 <-- shared media article
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[putting together two different ‘sorts’/focuses of research/reporting, but…]
H/T @gustaf Hugelius | Professor at Stockholm University
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“Permafrost soils are particularly vulnerable to climate change. To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region. In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales. Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional maps of terrestrial carbon stocks based on spatial modelling has been conducted yet. This study combines available field data from the Canadian Yukon coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data. [The authors] developed models using the data for the entire region and separate models for the coastal mainland area and Qikiqtaruk Herschel Island. Each model was used to map SOC and N stocks for its respective area. [They] assessed the performance of the different random forest models by using crossvalidation. [They] further assessed model results using the Area of Applicability (AOA) method and the quantile regression forest approach, comparing the results and discussing their implications within the context of both methods. [They] explore[d] local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks..."
#permafrost #soils #geology #climatechange #temperature #thawing #melting #emissions #CO2 #methane #carbon #nitrogen #GIS #spatial #mapping #Qikiqtaruk #HerschelIsland #Yukon #Canada #soilorganiccarbon #SOC #arctic #cryosphere #BeaufortSea #coast #coastal #machinelearning #model #modeling #remotesensing #earthobservation #carbonstocks #island #mainland #spatialanalysis #scale

Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley
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https://doi.org/10.3390/rs18142282 <-- shared paper
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https://www.usgs.gov/publications/comparing-desis-hyperspectral-and-landsat-10-simulated-superspectral-data-crop-type <-- shared USGs publication page
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H/T @USGS
“How can we get better at classifying crops from space? 🛰️🌽
Some types of satellite imagery are better at distinguishing crops than others. A USGS study compared two approaches and found one produced more accurate crop maps.
Here's what the researchers found:
• Hyperspectral imagery achieved 86% accuracy, compared to 75% for simulated superspectral imagery.
• Using just 14 carefully selected hyperspectral bands produced nearly the same results as using all 60 DLR Earth Sensing Imaging Spectrometer (DESIS) bands, showing those wavelengths contain much of the information needed to distinguish crop types.
• Using superspectral imagery on the upcoming Landsat 10 mission will allow for routine tracking of crops and enhance finer crop mapping.
• The findings help scientists identify which wavelengths provide the most useful information for crop mapping and future remote sensing applications.
Better crop maps will help governments and scientists track global agriculture, monitor the current crop season, and study agricultural trends…”
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“HIGHLIGHTS:
• What are the main findings?
- The 14 DESIS hyperspectral narrowbands (10 nm) aligned with the Landsat 10 (formerly Landsat Next) spectral dataset produced similar accuracy results to the full 60-band DESIS hyperspectral dataset for classifying crop types. These 14 DESIS narrowbands resulted in higher accuracy than the 14 simulated Landsat 10 superspectral broadbands.
- When using DESIS narrowbands, Support Vector Machine (SVM) resulted in higher accuracy than Random Forest (RF).
• What are the implications of the main findings?
- A carefully selected set of 14 DESIS hyperspectral narrowbands (10 nm) can achieve classification accuracy comparable to those obtained using all 60 DESIS narrowbands across the 400–1000 nm range. These 14 strategically positioned narrowbands classified crop types with higher classification accuracy than the corresponding 14 Landsat 10 superspectral broadbands within the same spectral range.
- This study underscores the importance of multi-temporal imagery across the full crop-growing season for achieving more detailed and accurate crop type classifications. Such temporal coverage is more feasible with the planned Landsat 10 routine acquisition of broadband imagery than with task-based hyperspectral collections…”
#hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture #crops #croptype #classifaction #croplands #California #CentralValley #GIS #spatial #mapping #remotesensing #earthobservation #imagery #DESIS #Landsat #Landsat10 #satellite #spatialanalysis #spatiotemporal #global #AI #machinelearning #model #modeling #SupportVectorMachine #SVM #RandomForest #RF #GoogleEarthEngine
@USGS

For flash-drought early warning, which single indicator has served you best? VPD, soil moisture, ET anomaly, an index? My hunch is VPD, but I want to be argued with.
#Drought #RemoteSensing #Hydrology #ClimateChange

Have you already noticed that 𝗘𝗦𝗔 𝗦𝗡𝗔𝗣 𝟭𝟰 is out? The changelog sounds promising.
https://step.esa.int/main/public-roadmap-and-changelog/

#remotesensing #earthobservation

🔥 Mapping Calgary's Fuel Continuity: Where Can Wildfire Actually Spread?

In a baseline, unsuppressed fire scenario ("apocalyptic scenario"), surface fires can only sustain a continuous front where vegetation forms an unbroken canopy or grassland corridor.

Using my 2025 Calgary Land Cover Model (v6.0), I isolated all vegetated pixels and calculated the spatial continuity and total area of every single contiguous fuel patch across the city:
🌾 The Nose Hill Island (~1,100 ha): A massive grassland fuel bed right in the city's heart.
🌲 River Corridors (Fish Creek Provincial Park & Bow Valley, 500–1,500 ha): Linear fuel superclusters acting as natural conduits.
🏙️ Urban Built-up Fragmentation: Inside established communities, the continuous network of asphalt, concrete, and roofing fractures vegetation into micro-patches (< 1 ha).

#GIS #RemoteSensing #EarthObservation #Calgary #SpatialDataScience #LandCover #MachineLearning #GreennessOfCalgary #Rstats #UrbanTrees #TreeCanopy #UrbanHealth #Wildfire

Earth Engine tip: use the Sentinel-2 SCL band to mask clouds and shadows before you composite. Garbage pixels in, garbage index out.
#EarthEngine #RemoteSensing #Sentinel2 #Teaching

Mapping Snow On Northern Winter Roads - A Dual-Frequency Polarimetric Radar Approach For Snow Characterization Over Land, Lake And Sea Ice
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https://doi.org/10.5194/tc-20-4367-2026 <-- shared paper
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H/T @Monojit Saha | Geospatial Analysis | Remote Sensing | Satellite Altimetry | Cryosphere
“Winter roads are essential transportation links for many remote northern communities, but their safety and reliability depend strongly on snow conditions and ice growth. In this study [link above], [the authors] evaluated a fully polarimetric, dual-frequency Ku- and Ka-band radar approach for retrieving snow depth across landfast sea ice, lake ice, and tundra.
Using field measurements near Churchill, Manitoba, and Resolute Bay, Nunavut [Canada], [they] found that the approach produced snow-depth retrieval bias and error within 3 cm over landfast ice, with encouraging Ku-band performance over frozen ground as well. [They] also developed an interface-detection approach for lake ice that can retrieve both snow depth and ice thickness - a promising direction for characterizing conditions relevant to winter-road planning and safety…”
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“Winter roads are lifelines for remote northern communities. Built over land, lakes, rivers, and sea ice, these travel routes are increasingly vulnerable to warming temperatures and variable precipitation. To ensure safety and adapt to these changes, operators require high-resolution monitoring of snow depth across these diverse surfaces, as natural snow accumulation dictates ice growth rates, route viability and road stability. This study extends our polarimetric radar method, previously demonstrated on pack ice, to landfast sea ice, tundra, and frozen lakes and assesses how well we can retrieve snow depth over these surfaces. Results indicate consistency with earlier sea ice analyses, maintaining a mean snow depth retrieval bias and error within 3 cm over the landfast ice. Promising performance is also found over frozen ground using Ku-band (mean biases less than 6 cm). To address the specific challenge of lake ice, which includes strong returns from the ice/water interface, we present a new interface-detection technique that simultaneously retrieves snow depth and ice thickness. While current validation focuses on undisturbed snow, this approach could provide a path forward for characterizing the cryospheric environment in a way that can directly support the optimization of winter roads…”
#Cryosphere #RemoteSensing #Snow #SeaIce #LakeIce #WinterRoads #characterisation #ArcticResearch #EarthObservation #PolarScience #maintainence #ploughing #winter #roads #transportation #northern #communities #mines #FirstNation #canada #remotesensing #polarimetric #radar #snowdepth #ice #landfastice #iceroad #tundra #Churchill #Manitoba #ResoluteBay #Nunavut #monitoring #planning #safety #trucking #freight

🔥 Calgary Urban Heat: How the Solid-to-Tree Ratio drives an 8°C gap

My Summer 2025 ML satellite analysis reveals a direct relationship between a community's built-to-canopy footprint and surface temperature (LST):

🌳 Cooling Refuges (~29.5–31°C): Roxboro, Rideau Park, Discovery Ridge, Eau Claire (mature canopies + river valleys).
🔥 Northeast Heat Dome (~37–38°C): Marlborough, Rundle, Temple (dense low-rise footprint, minimal mature canopy).
🏗️ New Suburbs: Seton, Redstone, Rangeview (canopy lag: fully built out, but young saplings need years to mature).

🌲 Urban trees are not decorative landscaping — they are critical municipal climate infrastructure.

#GIS #RemoteSensing #YYC #EarthObservation #Calgary #SpatialDataScience #LandCover #MachineLearning #Geoscience #GreennessOfCalgary #Rstats #UrbanTrees #TreeCanopy #UrbanHealth

Rivers are never still. 🌊 They carve, flood, and rebuild the land grain by grain, and from orbit, satellites watch it happen over years. What looks permanent on a map is really a slow-motion river of change. 🛰️

#RemoteSensing #Hydrology #Rivers #Geography #Satellites #EarthObservation

Long March 12 launches despite recent 7A failure, Long March 2C launches satellite for UAE

https://fed.brid.gy/r/https://spacenews.com/long-march-12-launches-despite-recent-7a-failure-long-march-2c-launches-satellite-for-uae/

North Dakota's surface water, fire, and vegetation are coupled — change one and the others answer. I built an Earth Engine explorer over 2000–2024 to watch it happen.
#EarthEngine #RemoteSensing #Fire #Hydrology

🔥 The Green Illusion: Why Zoning-Level Analysis Changes the Picture

📊 Gross community statistics can be misleading. When measuring an entire neighborhood, large municipal parks and green buffers inflate the overall 'greenness' score.

❗ When we filter the data down exclusively to actual Residential Land Use Districts in established communities, the picture changes significant:
🔹 Unvegetated/Impervious surfaces (Solid) explode to ~80% (79.22%) of the total zoned area.
🔹 Vegetative classes collapse: Functional lawns and canopy cover are compressed into narrow margins (Lawn: 2.15%, Forest: 0.59%), with community-level sparsed trees (Park) accounting for only 17.87%.

☝️ Moving from macro-boundaries to parcel-level zoning exposes the true physical density of Calgary’s residential footprint.

#GIS #RemoteSensing #EarthObservation #Calgary #SpatialDataScience #LandCover #MachineLearning #Geoscience #GreennessOfCalgary #Rstats #YYC #DataScience #Urban #Sentinel

How #ArcGISPro multiscale surface tools reveal the hidden #seafloor structures that affect #ocean circulation https://tinyurl.com/msxnmvzn

#water #marine #oceanography #imagery #RemoteSensing #GIS #esri #arcgis #mapping @esri @gisandscience @esritraining @urisa

Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
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https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
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https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
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H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
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“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal

The Congo Basin is under cloud so often that optical EO barely works there, and I keep landing on SAR. For persistently cloudy regions, what's your actual workflow — radar, fusion, patience?
#RemoteSensing #SAR #EarthObservation #GeoAI

Listing of geospatial MCP servers: Sparkgeo has mapped and categorised 79 #MCP servers for geospatial work, spanning #geocoding, #routing, #remoteSensing, #STAC catalogs, and desktop GIS integrations. The open, community-maintained list aims to save developers from reinventing...
https://spatialists.ch/posts/2026/08/12-listing-of-geospatial-mcp-servers/ #GIS #GISchat #geospatial #SwissGIS

Honored to share that my spatial analytics research has been featured as an official Success Story on The City of Calgary’s Open Data Portal.

The feature highlights how open municipal datasets can be combined with remote sensing data to evaluate urban heat islands, land cover composition, and microclimate dynamics across Calgary neighbourhoods.

Key aspects of the project:
🔹 Processing multi-spectral satellite imagery to model surface temperature (LST) and vegetation dynamics.
🔹 Translating complex spatial data into accessible, decision-ready insights for climate resilience and urban planning.
🔹 Demonstrating the practical impact of open data in civic-focused research.

A sincere thank you to The City of Calgary Open Data team for recognizing this work and highlighting the value of turning raw open data into actionable insights.

Read the full story here: https://www.calgary.ca/research/open-data/articles/where-data-meets-sustainability.html

#GIS #SpatialData #RemoteSensing #OpenData #Calgary #ClimateResilience #DataScience #UrbanPlanning #GreennessOfCalgary

I've mentioned in the past that among other things, we rely on the #Meteosat geostationary satellites to monitor #MtEtna. Even though the spatial resolution is low (the sub-satellite pixel has a nominal resolution of 3km), these satellites provide information at a high temporal rate: MSG (Meteosat Second Generation) provides full-disk images are available every 15 minutes, and a RSS (Rapid Scan Service) that only covers Europe, with data available every 5 minutes. The RSS satellite is also located at 9° of longitude east, so there is less distortion on the Etna (~15° longitude).

The main downside of RSS is that the service is not continuous: every 26 days, the satellite goes into “Full Earth Scan” mode for two days: this is needed to keep the mechanisms in good working order, and the data from these scans is NOT made available, which means that every 26 days there's a 2-day hole in the RSS data.

And obviously this happens during an ongoing #eruption. Murphy's law strikes again!

#remoteSensing #Etna

NDVI = (NIR − Red) / (NIR + Red). Healthy leaves reflect near-infrared hard and drink red light, so the ratio climbs. One line of math, a planet's worth of vegetation.
#RemoteSensing #NDVI #Teaching #Sentinel2

The OpenGeoHub Summer School in Istanbul starts in just one week! The topic of this year’s edition is "Data Science for Earth Observation", covering three programming languages: Julia, R, and Python. The program includes many interesting lectures and hands-on workshops. We will also do plenty of sightseeing. See you there!

#rspatial #gischat #remotesensing

The Latest Data Confirms - Forest Fires Are Getting Worse
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https://www.wri.org/insights/global-trends-forest-fires <-- shared technical article
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http://alturl.com/efp6m <-- shared (focused) #GlobalNatureWatch web map
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https://science.nasa.gov/earth/explore/wildfires-and-climate-change/ <-- shared NASA technical article, ‘Wildfires and Climate Change’
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https://doi.org/10.3389/frsen.2022.825190 <-- shared paper
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https://doi.org/10.1073/pnas.2505418122 <-- shared paper
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https://doi.org/10.1088/1748-9326/add606 <-- shared paper
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https://globalnaturewatch.org/dashboards/global/ <-- shared Global Nature Watch dashboard
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https://youtu.be/-0-pv1Bqm-U?si=IHcZJNiVphosbeVt <-- shared overview video
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https://grist.org/wildfires/the-us-has-lost-a-quarter-of-its-forest-cover-to-fire-since-2001/ <-- shared technical article, ‘Fire is responsible for a quarter of US forest loss since 2021’
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https://www.nytimes.com/2026/04/29/climate/wri-report-forest-loss.html <-- shared media article
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H/T @ World Resources Institute
[‘topical’ - Europe, North America, indeed globally, more & more…]
“New data shows that forest fires are getting worse, burning more than twice as much tree cover today as they did 20 years ago, largely due to climate change…
The latest data [2nd link above] confirms [that] forest fires are becoming more widespread and destructive around the globe. Updated data from researchers [3rd link above] shows that between 2001 and 2025 forest fires now burn over twice as much tree cover each year as they did two decades ago, and more than three times as much in the tropics.
This increased fire activity has been starkly visible in recent years. Record-setting blazes are becoming the norm, with four of the five worst years for global forest fires occurring since 2021. As fires worsen - including in historically low-risk areas, like rainforests - they are becoming an increasingly prevalent driver of global forest loss…”
#GlobalForestWatch #GlobalNatureWatch #deforestation #fire #wildfire #forest #vegetation #climatechange #risk #hazard #loss #ecosystems #GIS #spatial #mapping #remotesensing #earthobservation #spatialanalysis #spatiotemporal #global #worldwide #forestfire #damage #destruction #fireactivity #forestLOSS
@WRI | @Global Nature Watch

How does physical vegetation compare to administrative park maps?

In a new article by LiveWire Calgary, I shared technical insights from my 10-meter machine learning land cover classification model.

🛰️ Satellites map physical ground reality, not property boundaries. Multispectral land cover data reveals continuous fine fuel pathways (grass, brush, and canopy) extending across unmanaged ravines and private lots right up to residential property lines at Calgary's Wildland-Urban Interface.

🔗 Read the full article: https://livewirecalgary.com/2026/08/06/calgary-wildfire-campaign-high-risk-areas-map-gaps/

#GIS #RemoteSensing #EarthObservation #Calgary #SpatialDataScience #LandCover #WUI #MachineLearning #Geoscience #GreennessOfCalgary #LiveWireCalgary