#remotesensing — Public Fediverse posts

🔥 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

🔥 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

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
--
https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
--
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…”
--
“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

Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
--
https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
--
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…”
--
“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

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

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

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 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
--
https://www.wri.org/insights/global-trends-forest-fires <-- shared technical article
--
http://alturl.com/efp6m <-- shared (focused) #GlobalNatureWatch web map
--
https://science.nasa.gov/earth/explore/wildfires-and-climate-change/ <-- shared NASA technical article, ‘Wildfires and Climate Change’
--
https://doi.org/10.3389/frsen.2022.825190 <-- shared paper
--
https://doi.org/10.1073/pnas.2505418122 <-- shared paper
--
https://doi.org/10.1088/1748-9326/add606 <-- shared paper
--
https://globalnaturewatch.org/dashboards/global/ <-- shared Global Nature Watch dashboard
--
https://youtu.be/-0-pv1Bqm-U?si=IHcZJNiVphosbeVt <-- shared overview video
--
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’
--
https://www.nytimes.com/2026/04/29/climate/wri-report-forest-loss.html <-- shared media article
--
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

The Latest Data Confirms - Forest Fires Are Getting Worse
--
https://www.wri.org/insights/global-trends-forest-fires <-- shared technical article
--
http://alturl.com/efp6m <-- shared (focused) #GlobalNatureWatch web map
--
https://science.nasa.gov/earth/explore/wildfires-and-climate-change/ <-- shared NASA technical article, ‘Wildfires and Climate Change’
--
https://doi.org/10.3389/frsen.2022.825190 <-- shared paper
--
https://doi.org/10.1073/pnas.2505418122 <-- shared paper
--
https://doi.org/10.1088/1748-9326/add606 <-- shared paper
--
https://globalnaturewatch.org/dashboards/global/ <-- shared Global Nature Watch dashboard
--
https://youtu.be/-0-pv1Bqm-U?si=IHcZJNiVphosbeVt <-- shared overview video
--
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’
--
https://www.nytimes.com/2026/04/29/climate/wri-report-forest-loss.html <-- shared media article
--
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

Ten years teaching GIS and the hardest leap is still software-clicks to spatial thinking — knowing what a raster IS, not which button makes it. Fellow educators: how do you teach that leap?
#GIS #Teaching #RemoteSensing #GISchat

Ten years teaching GIS and the hardest leap is still software-clicks to spatial thinking — knowing what a raster IS, not which button makes it. Fellow educators: how do you teach that leap?
#GIS #Teaching #RemoteSensing #GISchat

From Fragmentation To Integration - A Review Of Data–Model Integration In Land Subsidence Research
--
https://doi.org/10.1016/j.ancene.2026.100567 <-- shared paper
--
H/T @manonzero current drought conditions, increasing pressure on ecosystems, and ongoing climate adaptation challenges, land subsidence is receiving growing attention worldwide.
For anyone wanting to learn more about land subsidence, or get a refresher, [the authors] provide an overview of the processes involved, the ways it can be measured or estimated, the models used to simulate it, and how observations and models can be combined, [their] new [#openaccess] review paper [link above] may be of interest!
A central message of the paper is that understanding and managing land subsidence requires bringing these different sources of information together…”
--
“HIGHLIGHTS:
• Presents a comprehensive synthesis that unifies all major elements of integral land-subsidence research.
• Defines the methodological steps needed for full integration and positions them within the broader challenges posed by subsidence.
• Brings together and distills the key components of data, modelling, and integration into a coherent framework.
ABSTRACT: Land subsidence, the sinking of the Earth’s surface, is a multi-faceted hazard driven by both natural and anthropogenic factors, and poses significant risk to environments, ecosystems, and society. Despite decades of growing research output, substantial gaps persist between investigations on the diversity of causes, reflected in data and model insufficiency. These gaps hinder the understanding of the issue and impede the effectiveness of mitigation measures. Many studies have urged to include all identified subsidence processes acting in a single area in an integral framework, for which a complex analysis has not systematically been outlined before. Therefore, [they] focus here on bridging the gaps between various technical research disciplines involved. [They] stress the urgency for an integral approach that combines observations with subsurface information of all known subsidence processes in an area, and [they] appeal for utilizing them in physics-guided data integrations. Only then can all subsidence drivers be understood, and effective mitigation measures designed. [They] outline the elements for an integral approach in categorical tables and schematized figures, and [they] discuss the main opportunities and challenges in a stepwise workflow. Leveraging these opportunities naturally leads to more robust, scalable, and policy-relevant solutions and fosters a more sustainable future…”
#Land #subsidence #processes #Verticallandmotion #SLR #sealevel #sealevelrise #relativesealevelrise #modeling #data #model #InSAR #elevation #holistic #GIS #spatial #mapping #climate #drought #extremeweather #climatechange #climateadaption #ecosystems #measurement #monitoring #research #review #framework #overview #risk #hazard #anthropogenic #mitigation #engineering #water #hydrology #policy #planning #design #remotesensing #spatialanalysis #spatiotemporal

From Fragmentation To Integration - A Review Of Data–Model Integration In Land Subsidence Research
--
https://doi.org/10.1016/j.ancene.2026.100567 <-- shared paper
--
H/T @manonzero current drought conditions, increasing pressure on ecosystems, and ongoing climate adaptation challenges, land subsidence is receiving growing attention worldwide.
For anyone wanting to learn more about land subsidence, or get a refresher, [the authors] provide an overview of the processes involved, the ways it can be measured or estimated, the models used to simulate it, and how observations and models can be combined, [their] new [#openaccess] review paper [link above] may be of interest!
A central message of the paper is that understanding and managing land subsidence requires bringing these different sources of information together…”
--
“HIGHLIGHTS:
• Presents a comprehensive synthesis that unifies all major elements of integral land-subsidence research.
• Defines the methodological steps needed for full integration and positions them within the broader challenges posed by subsidence.
• Brings together and distills the key components of data, modelling, and integration into a coherent framework.
ABSTRACT: Land subsidence, the sinking of the Earth’s surface, is a multi-faceted hazard driven by both natural and anthropogenic factors, and poses significant risk to environments, ecosystems, and society. Despite decades of growing research output, substantial gaps persist between investigations on the diversity of causes, reflected in data and model insufficiency. These gaps hinder the understanding of the issue and impede the effectiveness of mitigation measures. Many studies have urged to include all identified subsidence processes acting in a single area in an integral framework, for which a complex analysis has not systematically been outlined before. Therefore, [they] focus here on bridging the gaps between various technical research disciplines involved. [They] stress the urgency for an integral approach that combines observations with subsurface information of all known subsidence processes in an area, and [they] appeal for utilizing them in physics-guided data integrations. Only then can all subsidence drivers be understood, and effective mitigation measures designed. [They] outline the elements for an integral approach in categorical tables and schematized figures, and [they] discuss the main opportunities and challenges in a stepwise workflow. Leveraging these opportunities naturally leads to more robust, scalable, and policy-relevant solutions and fosters a more sustainable future…”
#Land #subsidence #processes #Verticallandmotion #SLR #sealevel #sealevelrise #relativesealevelrise #modeling #data #model #InSAR #elevation #holistic #GIS #spatial #mapping #climate #drought #extremeweather #climatechange #climateadaption #ecosystems #measurement #monitoring #research #review #framework #overview #risk #hazard #anthropogenic #mitigation #engineering #water #hydrology #policy #planning #design #remotesensing #spatialanalysis #spatiotemporal

Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
--
https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
--
H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
--
https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
--
H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

A raster isn't an image — it's a grid of measurements with a coordinate system stapled on. Get that, and half of remote sensing stops being mysterious.
#RemoteSensing #GIS #Teaching #Geospatial

Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
--
https://doi.org/10.1371/journal.pwat.0000416 <-- shared paper
--
H/T @Guillaume Wright | Executive Editor, PLOS
“💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
[The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
#GIS #spatial #mapping #California #SantaClara #SantaClaraValley #custom #watermanagement #practices #waterresources #agriculture #remotesensing #spatialanalysis #machinelearning #earthobservation #AI #planning #wateruse #efficiency #water #hydrology #irrigation #conservation #adaptivewatermanagement #model #modeling #drought #extremeweather #hydroclimate #stress #crop #cropland #evapotranspiration #ET #NDVI #PRISM #precipitation #rainfall #watermanagementplan #groundwater

Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
--
https://doi.org/10.1371/journal.pwat.0000416 <-- shared paper
--
H/T @Guillaume Wright | Executive Editor, PLOS
“💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
[The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
#GIS #spatial #mapping #California #SantaClara #SantaClaraValley #custom #watermanagement #practices #waterresources #agriculture #remotesensing #spatialanalysis #machinelearning #earthobservation #AI #planning #wateruse #efficiency #water #hydrology #irrigation #conservation #adaptivewatermanagement #model #modeling #drought #extremeweather #hydroclimate #stress #crop #cropland #evapotranspiration #ET #NDVI #PRISM #precipitation #rainfall #watermanagementplan #groundwater

Notes from ML4EO 2026 🌍

A short blog post summarizing my talk and workshop from the ML4EO conference on spatial machine learning validation.

https://jakubnowosad.com/posts/2026-07-03-ml4eo/

#rspatial #EarthObservation #MachineLearning #RemoteSensing #GeoSpatial

Notes from ML4EO 2026 🌍

A short blog post summarizing my talk and workshop from the ML4EO conference on spatial machine learning validation.

https://jakubnowosad.com/posts/2026-07-03-ml4eo/

#rspatial #EarthObservation #MachineLearning #RemoteSensing #GeoSpatial

🌍 Saudi Arabia Landcover Map 2025 🇸🇦 🗺️
From vast deserts to the Asir Mountains — Saudi Arabia’s land cover mapped using ESRI Landcover 2025 by @impactobservatory &
@esri
and CGIAR-SRTM DEM by
@divagis
.
#GIS #Cartography #SaudiArabia #Landcover #Desert #Asir #RemoteSensing

🌍 Saudi Arabia Landcover Map 2025 🇸🇦 🗺️
From vast deserts to the Asir Mountains — Saudi Arabia’s land cover mapped using ESRI Landcover 2025 by @impactobservatory &
@esri
 and CGIAR-SRTM DEM by
@divagis
.
#GIS #Cartography #SaudiArabia #Landcover #Desert #Asir #RemoteSensing

NetCDF and Zarr are essential formats for modern geospatial, climate, and big data workflows, especially as cloud-native pipelines become the norm. Check out these three new R packages created by Patrick van Laake:

- ncdfCF (https://r-cf.github.io/ncdfCF/) - modern interface to NetCDF data.
- zarr (https://r-cf.github.io/zarr/) - native support for Zarr arrays.
- geozarr (https://github.com/R-CF/geozarr) - handling of geospatially referenced Zarr arrays.

#rstats #rspatial #remotesensing #gischat

NetCDF and Zarr are essential formats for modern geospatial, climate, and big data workflows, especially as cloud-native pipelines become the norm. Check out these three new R packages created by Patrick van Laake:

- ncdfCF (https://r-cf.github.io/ncdfCF/) - modern interface to NetCDF data.
- zarr (https://r-cf.github.io/zarr/) - native support for Zarr arrays.
- geozarr (https://github.com/R-CF/geozarr) - handling of geospatially referenced Zarr arrays.

#rstats #rspatial #remotesensing #gischat

📈 Imperviousness vs. Temperature: Calgary's Primary Thermodynamic Trend

Mapping 313 Calgary communities reveals a linear relationship between % impervious surface area (Random Forest model on Summer 2025 Sentinel-1/2 & Landsat 8/9) and Land Surface Temperature (LST).

📊 Key Data Points:
• Cool Pole (<28°C, 0–15% impervious): Glenmore Park, Fish Creek (water & mature canopy).
• Hot Pole (37–40°C, 85–95% impervious): Industrial (Franklin, Foothills) & residential (Marlborough, Rundle).
• Thermal Slope: Every +10% impervious cover adds +1.2°C to +1.5°C to LST.

🔬 Variance Drivers:
Material albedo, building geometry (shading), topography, and canopy vs. lawn structure.

💡 Takeaway:
Impervious cover is the dominant microclimate driver. Urban planning—road width, building footprint, and canopy retention—is direct thermodynamic engineering.

#Calgary #YYC #GIS #RemoteSensing #RStats #DataScience #UrbanHeat #CityPlanning #Microclimate #GreennessOfCalgary

📈 Imperviousness vs. Temperature: Calgary's Primary Thermodynamic Trend

Mapping 313 Calgary communities reveals a linear relationship between % impervious surface area (Random Forest model on Summer 2025 Sentinel-1/2 & Landsat 8/9) and Land Surface Temperature (LST).

📊 Key Data Points:
• Cool Pole (<28°C, 0–15% impervious): Glenmore Park, Fish Creek (water & mature canopy).
• Hot Pole (37–40°C, 85–95% impervious): Industrial (Franklin, Foothills) & residential (Marlborough, Rundle).
• Thermal Slope: Every +10% impervious cover adds +1.2°C to +1.5°C to LST.

🔬 Variance Drivers:
Material albedo, building geometry (shading), topography, and canopy vs. lawn structure.

💡 Takeaway:
Impervious cover is the dominant microclimate driver. Urban planning—road width, building footprint, and canopy retention—is direct thermodynamic engineering.

#Calgary #YYC #GIS #RemoteSensing #RStats #DataScience #UrbanHeat #CityPlanning #Microclimate #GreennessOfCalgary

The Gendox AI Agent is adapting the SeaScope platform for wildfire monitoring.

These Copernicus Sentinel-2 images show the Porto Germeno wildfire on 31 July 2026 at approximately 09:30 UTC.

We hope everyone is safe. Strength and courage to all those working on the ground.

#Gendox #SeaScope #EarthObservation #WildfireMonitoring #WildfireDetection #Copernicus #Sentinel2 #RemoteSensing #ArtificialIntelligence #PortoGermeno #Greece

The Gendox AI Agent is adapting the SeaScope platform for wildfire monitoring.

These Copernicus Sentinel-2 images show the Porto Germeno wildfire on 31 July 2026 at approximately 09:30 UTC.

We hope everyone is safe. Strength and courage to all those working on the ground.

#Gendox #SeaScope #EarthObservation #WildfireMonitoring #WildfireDetection #Copernicus #Sentinel2 #RemoteSensing #ArtificialIntelligence #PortoGermeno #Greece

Asian Association on Remote Sensing (Remote sensing 🛰️)

Asian Association on Remote Sensing is a non-governmental organization established in 1981 to promote remote sensing in the Asia-Pacific region; it currently has members from 29 countries.

https://en.wikipedia.org/wiki/Asian_Association_on_Remote_Sensing

#AsianAssociationOnRemoteSensing #Geodesy #RemoteSensing

From orbit, the Earth stops being a map and becomes a living thing. 🌍 Clouds move like breath, cities glow like stories, rivers write their names into the land. This is what a geographer sees from above — not borders, but patterns. Satellites gather the signals; remote sensing turns light into meaning; GeoAI learns the planet’s rhythm. 🛰️

#RemoteSensing #GeoAI #Geography #Satellites #EarthObservation

From orbit, the Earth stops being a map and becomes a living thing. 🌍 Clouds move like breath, cities glow like stories, rivers write their names into the land. This is what a geographer sees from above — not borders, but patterns. Satellites gather the signals; remote sensing turns light into meaning; GeoAI learns the planet’s rhythm. 🛰️

#RemoteSensing #GeoAI #Geography #Satellites #EarthObservation

Anatomy Of A Seafloor Spreading Event Captured By In Situ Seismogeodesy
--
https://doi.org/10.1038/s41586-026-10785-0 <-- shared paper
--
https://www.smithsonianmag.com/smart-news/in-a-first-scientists-witness-the-seafloor-spread-in-real-time-giving-them-a-rare-glimpse-at-a-mysterious-geologic-process-180989123/ <-- shared technical media article
--
H/T @Seabed 2030
“🔍 For the first time, scientists have observed seafloor spreading in real time.
Seafloor spreading is the process by which new oceanic crust is formed at mid-ocean ridges - a geological process that has shaped entire ocean basins over millions of years.
During a research expedition in the Indian Ocean, scientists had just deployed a suite of instruments when a series of earthquakes triggered a seafloor spreading event, allowing them to observe the process as it unfolded.
The findings offer rare new insights into how new oceanic crust forms and how the seafloor continues to evolve…”
--
“Earth’s outermost layer - the crust - is constantly renewing itself. It’s broken into giant chunks called tectonic plates that pull apart, push against or slide past one another, creating grand geologic features.
Underwater mountain ranges, or mid-ocean ridges, for instance, generally take shape where two tectonic plates are moving away from each other. Magma can then bubble up in between, solidifying and turning into new oceanic crust as part of a process called seafloor spreading. Although the phenomenon has created entire ocean basins, it remains quite mysterious because it happens so deep in the water.
Now, for the first time, scientists have observed this dynamic activity happening in real time. They describe their findings - and their stroke of luck - in a study [link above], shedding light on a mechanism that made roughly two-thirds of Earth’s crust…”
--
#Seabed2030 #OceanMapping #Hydrospatial #remotesensing #seafloor #seafloorspreading #oceanic #crust #geology #structuralgeology #IndianOcean #earthquake #midoceanridge #instrumentation #marine #seabed #hydrography #model #modeling #mapping #GIS #spatial #tectonicplates #magma #fortuitous #survey #seismogeodetic #monitoring #submarine #rifting #observation #volcanism #seismicity #dyke #fault #faulting #midoceanridge #MOR

Anatomy Of A Seafloor Spreading Event Captured By In Situ Seismogeodesy
--
https://doi.org/10.1038/s41586-026-10785-0 <-- shared paper
--
https://www.smithsonianmag.com/smart-news/in-a-first-scientists-witness-the-seafloor-spread-in-real-time-giving-them-a-rare-glimpse-at-a-mysterious-geologic-process-180989123/ <-- shared technical media article
--
H/T @Seabed 2030
“🔍 For the first time, scientists have observed seafloor spreading in real time.
Seafloor spreading is the process by which new oceanic crust is formed at mid-ocean ridges - a geological process that has shaped entire ocean basins over millions of years.
During a research expedition in the Indian Ocean, scientists had just deployed a suite of instruments when a series of earthquakes triggered a seafloor spreading event, allowing them to observe the process as it unfolded.
The findings offer rare new insights into how new oceanic crust forms and how the seafloor continues to evolve…”
--
“Earth’s outermost layer - the crust - is constantly renewing itself. It’s broken into giant chunks called tectonic plates that pull apart, push against or slide past one another, creating grand geologic features.
Underwater mountain ranges, or mid-ocean ridges, for instance, generally take shape where two tectonic plates are moving away from each other. Magma can then bubble up in between, solidifying and turning into new oceanic crust as part of a process called seafloor spreading. Although the phenomenon has created entire ocean basins, it remains quite mysterious because it happens so deep in the water.
Now, for the first time, scientists have observed this dynamic activity happening in real time. They describe their findings - and their stroke of luck - in a study [link above], shedding light on a mechanism that made roughly two-thirds of Earth’s crust…”
--
#Seabed2030 #OceanMapping #Hydrospatial #remotesensing #seafloor #seafloorspreading #oceanic #crust #geology #structuralgeology #IndianOcean #earthquake #midoceanridge #instrumentation #marine #seabed #hydrography #model #modeling #mapping #GIS #spatial #tectonicplates #magma #fortuitous #survey #seismogeodetic #monitoring #submarine #rifting #observation #volcanism #seismicity #dyke #fault #faulting #midoceanridge #MOR

Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.

🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).

📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).

💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.

🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/

#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2

Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.

🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).

📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).

💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.

🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/

#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2

Fran Meissner and I are organizing a special session on "Critical remote sensing and the urban" at the "Joint Urban Remote Sensing Event" in #Amsterdam. Join us! #CfP : https://www.jurse2027.org/special-sessions/SS01/ #JURSE2027 #RemoteSensing #CriticalRemoteSensing #PhilCity

Heatwave Reveals Lost Archaeological Sites Across Wales [UK]
--
https://www.bbc.com/news/articles/cn8n99241y3o <-- shared technical media article
--
https://www.dailypost.co.uk/news/north-wales-news/lost-historical-places-found-north-34364058 <-- shared media article
--
https://archaeology.org/issues/november-december-2015/letters-from/letter-from-wales-iron-age-hillforts/ <-- shared technical article
--
[the 95% reduction in rain is ugly of course, but glad they are surveying as it were]
“Lost [sic] archaeological sites have emerged in grasslands across Wales after a prolonged spell of hot and dry weather.
Wales has seen about 5% of the average rainfall for July, after a series of back-to-back heatwaves and a drier than usual April to June. A drought has also been triggered in parts of Wales.
Experts said these dry conditions have brought to light a range of hidden archaeological remains, including Roman buildings and practice trenches from World War One.
Many are only visible from the air as distinctive marks in crops and grass, with some not being seen for "two or three decades", according to the Royal Commission on the Ancient and Historical Monuments of Wales (RCAHMW).
The crop marks are made by vegetation drawing on better nutrients and water supplies trapped in long-gone fortification ditches - leading to green growth that stands out…”
#Wales #UK #drought #heatwave #RCAHMW #ArchaeologicalSites #archaeology #grasslands #ancientsites #Roman #WW1 #fortification #remotesensing #aerialimagery #IronAge #history #monuments
@Royal Commission on the Ancient and Historical Monuments of Wales

Heatwave Reveals Lost Archaeological Sites Across Wales [UK]
--
https://www.bbc.com/news/articles/cn8n99241y3o <-- shared technical media article
--
https://www.dailypost.co.uk/news/north-wales-news/lost-historical-places-found-north-34364058 <-- shared media article
--
https://archaeology.org/issues/november-december-2015/letters-from/letter-from-wales-iron-age-hillforts/ <-- shared technical article
--
[the 95% reduction in rain is ugly of course, but glad they are surveying as it were]
“Lost [sic] archaeological sites have emerged in grasslands across Wales after a prolonged spell of hot and dry weather.
Wales has seen about 5% of the average rainfall for July, after a series of back-to-back heatwaves and a drier than usual April to June. A drought has also been triggered in parts of Wales.
Experts said these dry conditions have brought to light a range of hidden archaeological remains, including Roman buildings and practice trenches from World War One.
Many are only visible from the air as distinctive marks in crops and grass, with some not being seen for "two or three decades", according to the Royal Commission on the Ancient and Historical Monuments of Wales (RCAHMW).
The crop marks are made by vegetation drawing on better nutrients and water supplies trapped in long-gone fortification ditches - leading to green growth that stands out…”
#Wales #UK #drought #heatwave #RCAHMW #ArchaeologicalSites #archaeology #grasslands #ancientsites #Roman #WW1 #fortification #remotesensing #aerialimagery #IronAge #history #monuments
@Royal Commission on the Ancient and Historical Monuments of Wales

Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
--
https://doi.org/10.1016/j.jhydrol.2026.135999 <-- shared paper
--
https://youtu.be/VHzYLvSYR7k?si=5oGGPeH6T0dFusfY <-- recent overview video created about the research
--
https://doi.org/10.1016/j.wroa.2025.100396 <-- share (earlier) paper
--
H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
“… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
[The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”
#publichealth #risk #hazard #watersecurity #Floodrisk #Humanhealthrisk #Urbanflooding #Hydrodynamicmodelling #waterquality #model #modeling #SupportVectorRegression #flood #flooding #urban #city #sewage #pathogens #contaminant #disease #streets #community #quantification #remotesensing #GIS #spatial #mapping #earthobservation #spatialanalysis #water #hydrology #climatechange #extremeweather #spatiotemporal #AI #machineleraning #fateandtransport #hydrodynamic #microbial #rainfall #drainage #streamflow #topography #hydrogeomorphology #Delhi #India #floodplain

📉 Comparing the Solid-to-Tree Ratio with the Land Surface Temperature (LST) data obtained in the previous phase of the study allows for a visual assessment of the relationship between surface sealing and summer surface heating across Calgary’s residential communities.
🔥 The plot reveals a strong pattern for the vast majority of communities: a sharp increase in temperature occurs within the ratio range of 0 to 5. The Downtown Commercial Core stands out as a distinct outlier, where low LST values are driven by deep geometric shading from high-rise buildings. Additionally, neighborhoods such as Manchester, Seton, Redstone, Beltline, and Rangeview, among a few others, slightly diverge from the main trend.
📊 Full methodology and additional charts via the link:👇
https://www.datastory.org.ua/calgarys-microclimatic-anatomy-what-lies-beneath-the-citys-green-appearance/

#Calgary #OpenData #UrbanHeat #DataScience #ClimateResilience #YYC #Geoscience #CityPlanning #RemoteSensing #RStats #MachineLearning #GreennessOfCalgary

The Red River of the North floods differently — it flows north into ice, across a lakebed-flat valley. I've been studying its floods and, lately, how spring cryo-pulses move PFAS through the system.

Water quality is where I started: my PhD fused hyperspectral remote sensing with in-situ data to estimate nutrient loads in the Shenandoah. Rivers keep handing me the best problems.

#Hydrology #PFAS #WaterQuality #RemoteSensing #Flooding

Which Calgary neighborhoods are best built to withstand summer heatwaves? 🌳☀️

To measure structural climate resilience across the city, I conducted a spatial analysis of 193 established residential communities, calculating the Solid-to-Tree Ratio—comparing bare artificial surfaces (asphalt, concrete, rooftops) directly against total tree canopy area.

Here are the Top 10 most shade-rich and climate-resilient communities in Calgary:
🟢 Queens Park Village — 0.3 (Just 0.3 ha of hard surface for every 1 ha of canopy!)
🟢 Discovery Ridge — 0.5
🟢 Roxboro — 0.5
🟢 Wildwood — 0.6
🟢 Rideau Park — 0.7
🟢 Medicine Hill — 0.8
🟢 Upper Mount Royal — 0.8
🟢 Crestmont — 0.9
🟢 Elbow Park — 0.9
🟢 Shaganappi — 0.9

👇 The full interactive dataset and study are here:
https://www.datastory.org.ua/calgarys-microclimatic-anatomy-what-lies-beneath-the-citys-green-appearance/

#UrbanAnalytics #GeospatialData #RemoteSensing #GIS #UrbanForestry #CityPlanning #Calgary #DataScience #Microclimate #MachineLearning #GreennessOfCalgary #RStats #FOSSGIS

MT @giswqs
GeoLibre v2.3.0 is here!

Try it out

- Launch GeoLibre Web: https://web.geolibre.app
- GitHub: https://github.com/opengeos/GeoLibre
- Documentation: https://geolibre.app
- Release notes: https://github.com/opengeos/GeoLibre/releases/tag/v2.3.0

#GIS #Geospatial #OpenSource #RemoteSensing #MapLibre #GeoLibre

We mapped urban heat island intensification in Minneapolis–St. Paul and Chicago with 32 years of Landsat (1984–2016). The pattern is stubborn: land-cover change writes itself into surface temperature, neighborhood by neighborhood, and it compounds.

Paper in Geocarto International: https://doi.org/10.1080/10106049.2019.1655802

#UrbanHeatIsland #Landsat #RemoteSensing #ClimateChange #Cities

#Earth #Observation with #RemoteSensing Foundation Models in #ArcGIS https://tinyurl.com/2vp8z5uh

#imagery #data #analysis #AI #embeddings #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esritraining @urisa

Global Performance of #RemoteSensing Based and Reanalysis-Driven Models to Estimate Open Water Evaporation
--
https://doi.org/10.1029/2025WR042363
--
“ABSTRACT: Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, [they] analyze[d] the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. [They] compare[d] three remote sensing-based models, one reanalysis-driven model and one ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. [Their] results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm/day, respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large-scale water resources management.
PLAIN LANGUAGE SUMMARY: Water loss through evaporation in lakes and reservoirs directly affects water availability, which highlights the need to monitor these losses. However, measuring evaporation in situ is challenging and expensive. An alternative is to estimate evaporation using remote-sensing models and compare these estimates with in-situ data to verify their accuracy. Here, [they] evaluated four models and their ensemble (the models' mean value) using measurements from 27 lakes and reservoirs worldwide. [They] found that the ensemble presented higher accuracy and consistency than any individual model because it benefits from the strengths of each model. This approach can guide future improvements in estimating open-water evaporation, which is essential for large-scale water-resource management…”
#global #mapping #earthobservation #GIS #spatial #spatialanalysis #spatiotemporal #model #modeling #water #hydrology #surfacewater #waterbody #lake #reservoir #evaporation #evapotranspiration #watercycle #weather #meteorology #usecase #waterresources #watermanagement #waterloss #regional #estimate #policy #planning #instrumentation #comparasion