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#landsat — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #landsat, aggregated by home.social.

  1. 💧🌏 Greg Cocks @[email protected] ·

    On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
    --
    etdata.org/ <-- OpenET SSEBop platform implementation (water management)
    --
    usgs.gov/landsat-missions/land <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
    --
    H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
    “This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
    Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
    It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
    --
    “HIGHLIGHTS
    • ESPA platform allows access to on-demand, global, Landsat-based, ET products.
    • SSEBop model has been used to create ET data since 1982 through ESPA.
    • A quick estimation of field-scale crop consumptive water use can be achieved.
    • Numerous orders reflect worldwide extensive interest and utilization of the data.
    • Method, workflow, and performance of the actual ET data are presented in the study..."
    #EROSScienceProcessingArchitecture #climate #global #Evapotranspiration #ET #GIS #spatial #mapping #remotesensing #earthobservation #water #hydrology #opendata #Landsat #OpenET #SSEBop #WaterManagement #Agriculture #USGS #EROS #datadelivery #food #foodsecurity #energy #water #watersecurity #model #modeling #ESPA #farming #cropland #wateruse #waterresources #workflow
    @USGS @EROS

    #erosscienceprocessingarchitecture #climate #global #evapotranspiration #et #gis
  2. kīpuka @[email protected] ·

    Au mois d’août, l’Atacama (Chili) a connu des chutes de neige, un phénomène très inhabituel dans cette région parmi les plus arides du monde. Si le sommet du volcan Licancabur (ici à gauche) est parfois coiffé d’un chapeau blanc, il est fort rare que la neige descende jusqu’à son pied, comme sur cette image satellite capturée le 14 août dernier.
     Ce manteau blanc souligne les « pressure ridges » à la surface des coulées de lave visqueuse qui rayonnent depuis le sommet. Ces structures en arc de cercle se forment perpendiculairement à l’écoulement. Leur morphologie rappelle celle des ogives glaciaires, même si le processus de genèse est différent.

    #Chili #Atacama #volcan #neige #satellite #NASA #Landsat

    #chili #atacama #volcan #neige #satellite #nasa
  3. Hernán De Angelis @[email protected] ·

    Fresh article on #LandIS, the planned next generation #Landsat instrument, designed for working together with #Sentinel2 and continuing the Landsat legacy.

    sciencedirect.com/science/arti

    #remotesensing #earthscience #geology #geoscience #ecology #environment #GIS

    #landis #landsat #sentinel2 #remotesensing #earthscience #geology
  4. 💧🌏 Greg Cocks @[email protected] ·

    Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley
    --
    doi.org/10.3390/rs18142282 <-- shared paper
    --
    usgs.gov/publications/comparin <-- shared USGs publication page
    --
    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…”
    --
    “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

    #hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture
  5. EOMasters @[email protected] ·

    🚀 Thinking about building Landsat‑10 in your garage?
    Better hurry — NASA’s RFP is out and the clock’s ticking. Early delivery even gets you a bonus.
    Details: usgs.gov/landsat-missions/news
    #EarthObservation #Landsat

    #earthobservation #landsat
  6. 💧🌏 Greg Cocks @[email protected] ·

    Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”
    #GIS #spatial #mapping #satellite #remotesening #earthobservation #Landsat #thermalinfrared #TIR #surface #temperature #emissivity #thermal #infrared #analysisreadydata #Collection2 #Collection3 #opendata #atmosphericcorrection #global #climate #monitoring #waterresources #ecosystems #dynamics #urbanheat #spatialanalysis #spatiotemporal #naturalhazards #updates #EROS #USGS
    @USGS EROS | @USGS

    #gis #spatial #mapping #satellite #remotesening #earthobservation
  7. 💧🌏 Greg Cocks @[email protected] ·

    Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”
    #GIS #spatial #mapping #satellite #remotesening #earthobservation #Landsat #thermalinfrared #TIR #surface #temperature #emissivity #thermal #infrared #analysisreadydata #Collection2 #Collection3 #opendata #atmosphericcorrection #global #climate #monitoring #waterresources #ecosystems #dynamics #urbanheat #spatialanalysis #spatiotemporal #naturalhazards #updates #EROS #USGS
    @USGS EROS | @USGS

    #gis #spatial #mapping #satellite #remotesening #earthobservation
  8. 💧🌏 Greg Cocks @[email protected] ·

    Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”
    #GIS #spatial #mapping #satellite #remotesening #earthobservation #Landsat #thermalinfrared #TIR #surface #temperature #emissivity #thermal #infrared #analysisreadydata #Collection2 #Collection3 #opendata #atmosphericcorrection #global #climate #monitoring #waterresources #ecosystems #dynamics #urbanheat #spatialanalysis #spatiotemporal #naturalhazards #updates #EROS #USGS
    @USGS EROS | @USGS

    #gis #spatial #mapping #satellite #remotesening #earthobservation
  9. 💧🌏 Greg Cocks @[email protected] ·

    Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”
    #GIS #spatial #mapping #satellite #remotesening #earthobservation #Landsat #thermalinfrared #TIR #surface #temperature #emissivity #thermal #infrared #analysisreadydata #Collection2 #Collection3 #opendata #atmosphericcorrection #global #climate #monitoring #waterresources #ecosystems #dynamics #urbanheat #spatialanalysis #spatiotemporal #naturalhazards #updates #EROS #USGS
    @USGS EROS | @USGS

    #usgs #eros #updates #naturalhazards #spatiotemporal #spatialanalysis
  10. 💧🌏 Greg Cocks @GregCocks ·

    Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”

    @USGS EROS | @USGS

    #gis #spatial #mapping #satellite #remotesening #earthobservation
  11. Eugene @[email protected] ·

    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:
    datastory.org.ua/calgarys-summ

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

    #rstats #sentinel2 #opendata #yyc #calgary #gis
  12. Eugene @[email protected] ·

    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:
    datastory.org.ua/calgarys-summ

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

    #rstats #sentinel2 #opendata #yyc #calgary #gis
  13. Eugene @[email protected] ·

    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:
    datastory.org.ua/calgarys-summ

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

    #rstats #sentinel2 #opendata #yyc #calgary #gis
  14. Eugene @[email protected] ·

    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:
    datastory.org.ua/calgarys-summ

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

    #greennessofcalgary #urbanforestry #googleearthengine #rspatial #landsat #fossgis
  15. Eugene @[email protected] ·

    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:
    datastory.org.ua/calgarys-summ

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

    #rstats #sentinel2 #opendata #yyc #calgary #gis
  16. 💧🌏 Greg Cocks @[email protected] ·

    A Multi-Level, Multi-Scale Comparison Of Lidar- And LANDSAT-Based Habitat Selection Models Of Mexican Spotted Owls In A Post-Fire Landscape
    --
    doi.org/10.1016/j.ecoinf.2025. <-- shared paper
    --
    #GIS #spatial #mapping #Habitat #suitability #Megafire #Scaling #Habitatsuitability #selection #model #modeling #framework #Strixoccidentalis #Wildfire #bushfire #remotesening #LIDAR #elevation #ecosystem #LANDSAT #comparasion #mexican #spottedowl #avian #postfire #landscape #vegetation #ecosystem #damage #risk #hazard #earthobservation #scaling #scale #multifactor #homerange #microhabitat #ecology #recovery #spatialanalysis #spatiotemporal #modelperformance #reliability #accuracy #precision #forest #integration #conservation #landscape #fireaffected #spatialdisagreement

    #gis #spatial #mapping #habitat #suitability #megafire
  17. 💧🌏 Greg Cocks @[email protected] ·

    A Multi-Level, Multi-Scale Comparison Of Lidar- And LANDSAT-Based Habitat Selection Models Of Mexican Spotted Owls In A Post-Fire Landscape
    --
    doi.org/10.1016/j.ecoinf.2025. <-- shared paper
    --
    #GIS #spatial #mapping #Habitat #suitability #Megafire #Scaling #Habitatsuitability #selection #model #modeling #framework #Strixoccidentalis #Wildfire #bushfire #remotesening #LIDAR #elevation #ecosystem #LANDSAT #comparasion #mexican #spottedowl #avian #postfire #landscape #vegetation #ecosystem #damage #risk #hazard #earthobservation #scaling #scale #multifactor #homerange #microhabitat #ecology #recovery #spatialanalysis #spatiotemporal #modelperformance #reliability #accuracy #precision #forest #integration #conservation #landscape #fireaffected #spatialdisagreement

    #gis #spatial #mapping #habitat #suitability #megafire
  18. 💧🌏 Greg Cocks @[email protected] ·

    A Multi-Level, Multi-Scale Comparison Of Lidar- And LANDSAT-Based Habitat Selection Models Of Mexican Spotted Owls In A Post-Fire Landscape
    --
    doi.org/10.1016/j.ecoinf.2025. <-- shared paper
    --
    #GIS #spatial #mapping #Habitat #suitability #Megafire #Scaling #Habitatsuitability #selection #model #modeling #framework #Strixoccidentalis #Wildfire #bushfire #remotesening #LIDAR #elevation #ecosystem #LANDSAT #comparasion #mexican #spottedowl #avian #postfire #landscape #vegetation #ecosystem #damage #risk #hazard #earthobservation #scaling #scale #multifactor #homerange #microhabitat #ecology #recovery #spatialanalysis #spatiotemporal #modelperformance #reliability #accuracy #precision #forest #integration #conservation #landscape #fireaffected #spatialdisagreement

    #gis #spatial #mapping #habitat #suitability #megafire
  19. 💧🌏 Greg Cocks @[email protected] ·

    A Multi-Level, Multi-Scale Comparison Of Lidar- And LANDSAT-Based Habitat Selection Models Of Mexican Spotted Owls In A Post-Fire Landscape
    --
    doi.org/10.1016/j.ecoinf.2025. <-- shared paper
    --
    #GIS #spatial #mapping #Habitat #suitability #Megafire #Scaling #Habitatsuitability #selection #model #modeling #framework #Strixoccidentalis #Wildfire #bushfire #remotesening #LIDAR #elevation #ecosystem #LANDSAT #comparasion #mexican #spottedowl #avian #postfire #landscape #vegetation #ecosystem #damage #risk #hazard #earthobservation #scaling #scale #multifactor #homerange #microhabitat #ecology #recovery #spatialanalysis #spatiotemporal #modelperformance #reliability #accuracy #precision #forest #integration #conservation #landscape #fireaffected #spatialdisagreement

    #spatialdisagreement #fireaffected #conservation #integration #forest #precision
  20. 💧🌏 Greg Cocks @GregCocks ·

    A Multi-Level, Multi-Scale Comparison Of Lidar- And LANDSAT-Based Habitat Selection Models Of Mexican Spotted Owls In A Post-Fire Landscape
    --
    doi.org/10.1016/j.ecoinf.2025. <-- shared paper
    --

    #gis #spatial #mapping #habitat #suitability #megafire
  21. 💧🌏 Greg Cocks @[email protected] ·

    [USGS] Landsat At Work – Preparing Residents When Hurricanes Threaten
    --
    Landsat-Based National Land Cover Datasets (NCLD) Helps Predict Wind Risk To Homes And Other Structures
    --
    usgs.gov/centers/eros/news/lan <-- shared technical article
    --
    #GIS #spatial #mapping #remotesensing #landsat #satellite #fedopendata #publicgood #fedscience #risk #hazard #model #modeling #actuary #insurance #rates #appraisal #hurricane #naturaldisaster #infrastructure #damage #cost #economics #loss #NLCD #landcover #landuse #evulation #projection #Florida #residential #EROS #USGS #coast #coastline #computermodels #wind #surge #flood #flooding #USGS #NOAA #NASA #EROS #HurricaneLossProjectionMethodology

    #gis #spatial #mapping #remotesensing #landsat #satellite
  22. OptimusPrimeBot @[email protected] ·

    X2.7 and more flares from Active Region 14087 - May 14, 2025

    #EUVImaging #ExtremeUltravioletImaging #Landsat #Multiplesolarflares #PointSpreadFunction(PSF) #SolarActiveRegions #SolarActivity #SolarCycle25

    ⏩ 2 new pictures and 2 new videos from NASA (SVS) commons.wikimedia.org/wiki/Spe

    #euvimaging #extremeultravioletimaging #landsat #multiplesolarflares #pointspreadfunction #solaractiveregions
  23. 💧🌏 Greg Cocks @[email protected] ·

    Multispectral Remote Sensing Expression of Lineaments and Alteration Minerals in Part of Crystalline Rock Units of Southwestern Nigeria - Implication on Gold Prospecting
    --
    doi.org/10.1016/j.oreoa.2025.1 <-- shared paper
    --
    #GIS #spatial #mapping #ASTER #Gold #Hydrothermal #HydrothermalAlteration #Lineament #Landsat #ETM #EnhancedThematicMapper #phyllic #Ilesha #schist #belt #geology #mining #Nigeria #naturalresources #mineral #mineralresources #golddeposits #aerogeophysical #remotesensing #geophysical #spatialanalysis #geomorphology #geomorphometry #alteration #propylitic #argillic #mineralogy #XRD #structuralgeology #lithology #PrincipalComponentAnalysis #PCA #prospecting #fracture #joints #faulting #Multispectral #Lineaments #Crystalline

    #gis #spatial #mapping #aster #gold #hydrothermal
  24. 💧🌏 Greg Cocks @[email protected] ·

    Himalayan Snow Lines On The Rise
    --
    earthobservatory.nasa.gov/imag <-- shared NASA technical article
    --
    icimod.org/press-release/snow- <-- shared technical article on Himalaya snow persistence changes
    --
    #GIS #spatial #mapping #Nepal #India #Himalayas #Himalayan #mountains #climatechange #snowpersistence #remotesensing #earthobservation #glacier #glacial #melting #MountEverest #water #hydrology #cryology #snow #ice #waterresources #watersecurity #waterresources #fire #risk #hazard #OLI2 #Landsat #meteorology #weather #annual #precipitation #monsoon #sublimation #temperature #wind #humidity #extremeweather #wildfire #humanimpacts #snowline #newnormal #agriculture #foodsecurity #drought

    #gis #spatial #mapping #nepal #india #himalayas
  25. 💧🌏 Greg Cocks @[email protected] ·

    Greater Los Angeles Wildfires - January 2025 – Before (20250106) & During (20250114) Landsat Image Swipe Comparo
    --
    usgs.gov/media/before-after/gr <-- shared link to USGS images of LA area, with swipe comparo
    --
    #GIS #spatial #mapping #spatiotemporal #spatialanalysis #LosAngeles #LA #LosAngelesfires #risk #hazard #loss #damage #cost #economics #model #publicsafety #remotesening #Landsat #landsat8 #landsat9 #opendata #imagery #satellite #OLI #OLI2 #January2025 #wildfire #fire #bushfire #infrared #nearinfrared #burnscars #vegetation #contrast
    @USGS

    #gis #spatial #mapping #spatiotemporal #spatialanalysis #losangeles
  26. 💧🌏 Greg Cocks @[email protected] ·

    Greater Los Angeles Wildfires - January 2025 – Before (20250106) & During (20250114) Landsat Image Swipe Comparo
    --
    usgs.gov/media/before-after/gr <-- shared link to USGS images of LA area, with swipe comparo
    --
    #GIS #spatial #mapping #spatiotemporal #spatialanalysis #LosAngeles #LA #LosAngelesfires #risk #hazard #loss #damage #cost #economics #model #publicsafety #remotesening #Landsat #landsat8 #landsat9 #opendata #imagery #satellite #OLI #OLI2 #January2025 #wildfire #fire #bushfire #infrared #nearinfrared #burnscars #vegetation #contrast
    @USGS

    #gis #spatial #mapping #spatiotemporal #spatialanalysis #losangeles
  27. 💧🌏 Greg Cocks @[email protected] ·

    Greater Los Angeles Wildfires - January 2025 – Before (20250106) & During (20250114) Landsat Image Swipe Comparo
    --
    usgs.gov/media/before-after/gr <-- shared link to USGS images of LA area, with swipe comparo
    --
    #GIS #spatial #mapping #spatiotemporal #spatialanalysis #LosAngeles #LA #LosAngelesfires #risk #hazard #loss #damage #cost #economics #model #publicsafety #remotesening #Landsat #landsat8 #landsat9 #opendata #imagery #satellite #OLI #OLI2 #January2025 #wildfire #fire #bushfire #infrared #nearinfrared #burnscars #vegetation #contrast
    @USGS

    #gis #spatial #mapping #spatiotemporal #spatialanalysis #losangeles
  28. 💧🌏 Greg Cocks @[email protected] ·

    Greater Los Angeles Wildfires - January 2025 – Before (20250106) & During (20250114) Landsat Image Swipe Comparo
    --
    usgs.gov/media/before-after/gr <-- shared link to USGS images of LA area, with swipe comparo
    --
    #GIS #spatial #mapping #spatiotemporal #spatialanalysis #LosAngeles #LA #LosAngelesfires #risk #hazard #loss #damage #cost #economics #model #publicsafety #remotesening #Landsat #landsat8 #landsat9 #opendata #imagery #satellite #OLI #OLI2 #January2025 #wildfire #fire #bushfire #infrared #nearinfrared #burnscars #vegetation #contrast
    @USGS

    #contrast #vegetation #burnscars #nearinfrared #infrared #bushfire
  29. 💧🌏 Greg Cocks @GregCocks ·

    Greater Los Angeles Wildfires - January 2025 – Before (20250106) & During (20250114) Landsat Image Swipe Comparo
    --
    usgs.gov/media/before-after/gr <-- shared link to USGS images of LA area, with swipe comparo
    --

    @USGS

    #gis #spatial #mapping #spatiotemporal #spatialanalysis #losangeles
  30. 💧🌏 Greg Cocks @[email protected] ·

    Differentiating Cheatgrass And Medusahead Phenological Characteristics In Western United States Rangelands [remote sensing]
    --
    doi.org/10.3390/rs16224258 <-- shared paper
    --
    #GIS #spatial #mapping #vegetation #grassland #cheatgrass #medusahead #BRTE #TACA8 #phenology #growingseason #rangeland #EAG #invasivespecies #management #landmanagers #habitat #cost #loss #economicimpact #ecosystem #deterioration #impacts #USWest #spatialanalysis #spatiotemporal #model #modeling #treemodel #landsat #sentinel #HLS #satellite #remotesensing #vegetationindex #NDVI #research #testcase #corrleation #metrics #controlmeasures #herbicide #cattle #grazing #mitigation #landcover #NLDI

    #gis #spatial #mapping #cheatgrass #medusahead #brte
  31. 💧🌏 Greg Cocks @[email protected] ·

    Global Mangrove Watch [remote sensing]
    --
    globalmangrovewatch.org/ <-- shared Global Mangrove Watch webmap/data page
    --
    mangrovealliance.org/ <-- shared Global Mangrove Alliance
    --
    doi.org/10.3390/rs14153657 <-- shared paper
    --
    doi.org/10.1016/j.landusepol.2 <-- shared paper
    --
    #GIS #remotesensing #landsat #mapping #spatial #RSGISLIB #mangrove #change #extent #GlobalMangroveWatch #GMW #Lband #SAR #changedetection #satellite #imagery #map #global #spatialanalysis #spatiotemporal #protection #restoration #policymakers #opendata #model #modeling #algorithm #ecosystem #coast #coastal #fisheries #habitat #deforestation #environment #SAR #radar #JAXA #GlobalMangroveAlliance #conservation #risk #hazard #erosion #climatechange #planning #extremeweather

    #gis #remotesensing #landsat #mapping #spatial #rsgislib
  32. 💧🌏 Greg Cocks @[email protected] ·

    New National Heat Index [Maps & Data] Uses USGS [EROS] Data
    --
    usgs.gov/centers/eros/news/new <-- link to technical article
    --
    ephtracking.cdc.gov/Applicatio <-- link to CDC Heat & Health Tracker, by ZIP code
    --
    [new national heat index product for the US, relying on USGS EROS impervious cover layer from the National Land Cover Database (NLCD)]
    #GIS #spatial #mapping #NLCD #HeatAndHealthIndex #model #USA #index #publichealth #heat #heat #heatstress #heatexhaustion #heatstroke #climatechange #extremeweather #spatialanalysis #urban #urbanareas #heatislands #corrleation #heatindex #heatcycles #cities #concrete #buildings #vegetation #environment #impervious #impervioussurface #demographics #vulnerable #remotesensing #landsat #ZIPCode #builtenvironments #manmade #development
    @USGS @EROS @CDC

    #heat #gis #spatial #mapping #nlcd #heatandhealthindex
  33. 💧🌏 Greg Cocks @[email protected] ·

    A New, Rigorous Assessment Of Remote Sensing Tool's Accuracy For Supporting Satellite-Based Water Management [NASA's OpenET]
    --
    phys.org/news/2024-01-rigorous <-- shared technical article
    --
    doi.org/10.1038/s44221-023-001 <-- shared paper
    --
    nasa.gov/image-article/openet- <-- NASA OpenET satellite-based water data home page
    --
    youtu.be/uAapAAInDpQ?si=njJfqv <-- NASA OpenET overview video
    --
    #GIS #spatial #mapping #water #waterresources #hydrology #watersecurity #OpenET #remotesensing #model #modeling #satellite #watermanagement #statistics #geostatistics #opendata #arid #sustainable #landsat #evapotranspiration #ET #corrleation #watermanagement #soil #plant #crops #cropland #agriculture #farming #wheat #corn #soy #rice #waterconservation #watersustainability #watersupply #NASA
    @nasa

    #gis #spatial #mapping #water #waterresources #hydrology