#modis — Public Fediverse posts on home.social

💧🌏 Greg Cocks @[email protected] · 2026-04-22 · 05:02 UTC

Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
--
https://doi.org/10.1007/s12665-026-12848-x <-- shared paper
--
H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
“১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
--
#GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

#gis #spatial #mapping #remotesensing #bangladesh #earthobservation

💧🌏 Greg Cocks @[email protected] · 2026-04-22 · 05:02 UTC

Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
--
https://doi.org/10.1007/s12665-026-12848-x <-- shared paper
--
H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
“১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
--
#GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

#gis #spatial #mapping #remotesensing #bangladesh #earthobservation

💧🌏 Greg Cocks @[email protected] · 2026-04-22 · 05:02 UTC

Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
--
https://doi.org/10.1007/s12665-026-12848-x <-- shared paper
--
H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
“১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
--
#GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

#gis #spatial #mapping #remotesensing #bangladesh #earthobservation

💧🌏 Greg Cocks @[email protected] · 2026-04-22 · 05:02 UTC

Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
--
https://doi.org/10.1007/s12665-026-12848-x <-- shared paper
--
H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
“১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
--
#GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

#planning #policy #watersecurity #irrigation #geostatistics #pearsoncorrelation

💧🌏 Greg Cocks @GregCocks · 2026-04-22 · 05:02 UTC

Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
--
https://doi.org/10.1007/s12665-026-12848-x <-- shared paper
--
H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
“১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
--
#GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

#gis #spatial #mapping #remotesensing #bangladesh #earthobservation

Österreich @[email protected] · 2026-04-16 · 18:23 UTC

https://www.europesays.com/at/106592/ Auch Bollywood ruft – Bundeskanzler Stocker von Premier Modi empfangen #AT #Austria #Bundeskanzler #ChristianStocker #DroupadiMurmu #indische #indischen #KarlNehammer #Modis #NarendraModi #NeuDelhi #Österreich #österreichische #RepublicOfAustria #RepublikÖsterreich #SubrahmanyamJaishankar #Wien

#wien #subrahmanyamjaishankar #republikosterreich #republicofaustria #osterreichische #osterreich

Eesti @[email protected] · 2026-02-06 · 08:10 UTC

https://www.europesays.com/ee/102078/ FOTO ⟩ «Jääaeg on tagasi!» Vaata, kui lummav näeb Eesti praegu välja kosmosest #BreakingNews #BreakingNews #EE #Eesti #EestiKeel #Estonia #Estonian #FeaturedNews #FeaturedNews #Headlines #KairoKiitsak #läänemeri #LatestNews #LatestNews #modis #News #PeipsiJärv #PopulaarseimadLood #TopStories #TopStories #ÜldisedUudised #Uudised #väinameri #ViimasedUudised #Võrtsjärv

#vortsjarv #viimaseduudised #vainameri #uudised #uldiseduudised #topstories

💧🌏 Greg Cocks @[email protected] · 2026-01-29 · 04:17 UTC

Remote Sensing and GIS Assessment of Drought Dynamics in the Ukrina River Basin, Bosnia and Herzegovina
--
https://doi.org/10.3390/atmos17020124 <-- shared paper
--
#GIS #spatial #mapping #drought #risk #hazard #remotesensing #MODIS #satellite #earthobservation #spatialanalysis #spatiotemporal #monitoring #Ukrina #RiverBasin #Bosnia #Herzegovina #BosniaHerzegovina #water #waterresources #socioeconomic #risk #hazard #cost #economics #agriculture #foodsecurity #watersecurity #landcover #vegetation #croplands #fruit #crops #metrology #hydrology #droughtexposure #basins #riverbasins #watersheds #monitoring #mitigation #planning #droughtrisk

#gis #spatial #mapping #drought #risk #hazard

💧🌏 Greg Cocks @[email protected] · 2026-01-29 · 04:17 UTC

Remote Sensing and GIS Assessment of Drought Dynamics in the Ukrina River Basin, Bosnia and Herzegovina
--
https://doi.org/10.3390/atmos17020124 <-- shared paper
--
#GIS #spatial #mapping #drought #risk #hazard #remotesensing #MODIS #satellite #earthobservation #spatialanalysis #spatiotemporal #monitoring #Ukrina #RiverBasin #Bosnia #Herzegovina #BosniaHerzegovina #water #waterresources #socioeconomic #risk #hazard #cost #economics #agriculture #foodsecurity #watersecurity #landcover #vegetation #croplands #fruit #crops #metrology #hydrology #droughtexposure #basins #riverbasins #watersheds #monitoring #mitigation #planning #droughtrisk

#gis #spatial #mapping #drought #risk #hazard

💧🌏 Greg Cocks @[email protected] · 2026-01-29 · 04:17 UTC

Remote Sensing and GIS Assessment of Drought Dynamics in the Ukrina River Basin, Bosnia and Herzegovina
--
https://doi.org/10.3390/atmos17020124 <-- shared paper
--
#GIS #spatial #mapping #drought #risk #hazard #remotesensing #MODIS #satellite #earthobservation #spatialanalysis #spatiotemporal #monitoring #Ukrina #RiverBasin #Bosnia #Herzegovina #BosniaHerzegovina #water #waterresources #socioeconomic #risk #hazard #cost #economics #agriculture #foodsecurity #watersecurity #landcover #vegetation #croplands #fruit #crops #metrology #hydrology #droughtexposure #basins #riverbasins #watersheds #monitoring #mitigation #planning #droughtrisk

#gis #spatial #mapping #drought #risk #hazard

💧🌏 Greg Cocks @[email protected] · 2026-01-29 · 04:17 UTC

Remote Sensing and GIS Assessment of Drought Dynamics in the Ukrina River Basin, Bosnia and Herzegovina
--
https://doi.org/10.3390/atmos17020124 <-- shared paper
--
#GIS #spatial #mapping #drought #risk #hazard #remotesensing #MODIS #satellite #earthobservation #spatialanalysis #spatiotemporal #monitoring #Ukrina #RiverBasin #Bosnia #Herzegovina #BosniaHerzegovina #water #waterresources #socioeconomic #risk #hazard #cost #economics #agriculture #foodsecurity #watersecurity #landcover #vegetation #croplands #fruit #crops #metrology #hydrology #droughtexposure #basins #riverbasins #watersheds #monitoring #mitigation #planning #droughtrisk

#droughtrisk #planning #mitigation #watersheds #riverbasins #basins

💧🌏 Greg Cocks @GregCocks · 2026-01-29 · 04:17 UTC

Remote Sensing and GIS Assessment of Drought Dynamics in the Ukrina River Basin, Bosnia and Herzegovina
--
https://doi.org/10.3390/atmos17020124 <-- shared paper
--
#GIS #spatial #mapping #drought #risk #hazard #remotesensing #MODIS #satellite #earthobservation #spatialanalysis #spatiotemporal #monitoring #Ukrina #RiverBasin #Bosnia #Herzegovina #BosniaHerzegovina #water #waterresources #socioeconomic #risk #hazard #cost #economics #agriculture #foodsecurity #watersecurity #landcover #vegetation #croplands #fruit #crops #metrology #hydrology #droughtexposure #basins #riverbasins #watersheds #monitoring #mitigation #planning #droughtrisk

#gis #spatial #mapping #drought #risk #hazard

Ireland @[email protected] · 2026-01-27 · 22:36 UTC

https://www.europesays.com/ie/306890/ Cloud Cover Conundrum: New Study Helps Improve Cloud Predictions in Atmospheric Models #Aqua #AtmosphericRadiation #CALIOP #CALIPSO #Ceres #CERESFM1 #CERESFM2 #CERESFM3 #CERESFM4 #CERESFM6 #CloudReflectance #CloudSat #CloudSatCPR #Éire #Hydrometeors #IE #Ireland #MISR #MODIS #NOAA20 #Science #terra

#terra #science #noaa20 #modis #misr #ireland

💧🌏 Greg Cocks @[email protected] · 2025-12-13 · 05:31 UTC

Ensemble Deep Learning Towards High-Resolution Soil-Moisture Mapping For Enhanced Water Management In California's Central Valley
--
https://doi.org/10.1016/j.envsoft.2025.106824 <-- shared paper
--
#agriculture #crops #groundwater #pumping #monitoring #agronomic #irrigation #planning #scheduling #wateruse #sustainability #wateruse #watersecurity #deeplearning #framework #soilmoisture #downscaling #watermanagement #california #centralvalley #ensemblemodeling #model #modeling #SMAP #satellite #rootzone #remotesensing #spatialanalysis #spatiotemporal #AI #sensitivityanalysis #fieldobservation #instrumentation #fieldchecking #seasonal #crop #crops #production #foodsecurity #NVDI #NDWI #ET #evapotranspiration #MODIS #pluvial

#agriculture #crops #groundwater #pumping #monitoring #agronomic

💧🌏 Greg Cocks @[email protected] · 2025-12-13 · 05:31 UTC

Ensemble Deep Learning Towards High-Resolution Soil-Moisture Mapping For Enhanced Water Management In California's Central Valley
--
https://doi.org/10.1016/j.envsoft.2025.106824 <-- shared paper
--
#agriculture #crops #groundwater #pumping #monitoring #agronomic #irrigation #planning #scheduling #wateruse #sustainability #wateruse #watersecurity #deeplearning #framework #soilmoisture #downscaling #watermanagement #california #centralvalley #ensemblemodeling #model #modeling #SMAP #satellite #rootzone #remotesensing #spatialanalysis #spatiotemporal #AI #sensitivityanalysis #fieldobservation #instrumentation #fieldchecking #seasonal #crop #crops #production #foodsecurity #NVDI #NDWI #ET #evapotranspiration #MODIS #pluvial

#agriculture #crops #groundwater #pumping #monitoring #agronomic

💧🌏 Greg Cocks @[email protected] · 2025-12-13 · 05:31 UTC

Ensemble Deep Learning Towards High-Resolution Soil-Moisture Mapping For Enhanced Water Management In California's Central Valley
--
https://doi.org/10.1016/j.envsoft.2025.106824 <-- shared paper
--
#agriculture #crops #groundwater #pumping #monitoring #agronomic #irrigation #planning #scheduling #wateruse #sustainability #wateruse #watersecurity #deeplearning #framework #soilmoisture #downscaling #watermanagement #california #centralvalley #ensemblemodeling #model #modeling #SMAP #satellite #rootzone #remotesensing #spatialanalysis #spatiotemporal #AI #sensitivityanalysis #fieldobservation #instrumentation #fieldchecking #seasonal #crop #crops #production #foodsecurity #NVDI #NDWI #ET #evapotranspiration #MODIS #pluvial

#agriculture #crops #groundwater #pumping #monitoring #agronomic

💧🌏 Greg Cocks @[email protected] · 2025-12-13 · 05:31 UTC

Ensemble Deep Learning Towards High-Resolution Soil-Moisture Mapping For Enhanced Water Management In California's Central Valley
--
https://doi.org/10.1016/j.envsoft.2025.106824 <-- shared paper
--
#agriculture #crops #groundwater #pumping #monitoring #agronomic #irrigation #planning #scheduling #wateruse #sustainability #wateruse #watersecurity #deeplearning #framework #soilmoisture #downscaling #watermanagement #california #centralvalley #ensemblemodeling #model #modeling #SMAP #satellite #rootzone #remotesensing #spatialanalysis #spatiotemporal #AI #sensitivityanalysis #fieldobservation #instrumentation #fieldchecking #seasonal #crop #crops #production #foodsecurity #NVDI #NDWI #ET #evapotranspiration #MODIS #pluvial

#pluvial #modis #evapotranspiration #et #ndwi #nvdi

💧🌏 Greg Cocks @GregCocks · 2025-12-13 · 05:31 UTC

Ensemble Deep Learning Towards High-Resolution Soil-Moisture Mapping For Enhanced Water Management In California's Central Valley
--
https://doi.org/10.1016/j.envsoft.2025.106824 <-- shared paper
--
#agriculture #crops #groundwater #pumping #monitoring #agronomic #irrigation #planning #scheduling #wateruse #sustainability #wateruse #watersecurity #deeplearning #framework #soilmoisture #downscaling #watermanagement #california #centralvalley #ensemblemodeling #model #modeling #SMAP #satellite #rootzone #remotesensing #spatialanalysis #spatiotemporal #AI #sensitivityanalysis #fieldobservation #instrumentation #fieldchecking #seasonal #crop #crops #production #foodsecurity #NVDI #NDWI #ET #evapotranspiration #MODIS #pluvial

#agriculture #crops #groundwater #pumping #monitoring #agronomic

Eugene @[email protected] · 2025-11-19 · 18:16 UTC

Exploring large-scale landscape structures with a simple Principal Component Analysis (PCA) applied to a MODIS composite.

The PCA reveals striking contrasts in surface materials, vegetation patterns, and terrain transitions across western Canada — from the Rocky Mountains into the prairies and the Canadian Shield.
Even with moderate-resolution MODIS data, spectral variability produces a surprisingly rich view of regional geography and geomorphology.

#RemoteSensing #EarthObservation #Geology #PCA #MODIS #OpenData #GIS #QGIS #Geomorphology #Geoscience #RStats #Lithology #GeoDataArt #GeoSpectralArt #BorealForest #Taiga #Canada #Alberta

#remotesensing #earthobservation #geology #pca #modis #opendata

Eugene @[email protected] · 2025-11-13 · 16:18 UTC

🛰️ Today I’m sharing one of my favourite large-scale remote sensing experiments:
a Principal Component Analysis (PCA) of MODIS composite data for the three central provinces of Canada (Alberta, Saskatchewan, Manitoba).

On this map:
- PC1 emphasizes broad ecological zones and vegetation productivity
- PC2 highlights soil and surface moisture differences
- PC3 captures subtle spectral variations — often linked to geology, wetlands, disturbance patterns, or local microclimates

Even though it looks abstract, PCA is a kind of “spectral fingerprint” of the land. It summarises thousands of square kilometres into a single visual structure that shows how the Canadian Prairies and Boreal regions differ and transition into one another.

#RemoteSensing #MODIS #Geospatial #EarthObservation #Rstats #DataVisualization #PCA #SatelliteData #Canada #Alberta #Saskatchewan #Manitoba #EnvironmentalData #GeoDataArt #GeoSpectralArt

#remotesensing #modis #geospatial #earthobservation #rstats #datavisualization

Terviral @[email protected] · 2025-10-20 · 17:37 UTC

Profil dan Kontroversi Adila Vania dengan Video Viralnya

#Terviral - #Adila #Vania adalah #model dan #kreator #konten #media #sosial asal #Bandung yang memilih jalur #modelling #lingerie dan gaya hidup #modis. Dengan akun #Instagram #adillavania69 dan #TikTok #adilavania3, ia telah membangun pengikut dan ekspos yang signifikan. Baca: Viral! Its Anggi Teriak Tengah Malam Bilang “Jangan Nakal” 1. Biodata Singkat

https://terviral.id/profil-dan-kontroversi-adila-vania-dengan-video-viralnya/

#terviral #adila #vania #model #kreator #konten

Terviral @[email protected] · 2025-10-20 · 17:37 UTC

Profil dan Kontroversi Adila Vania dengan Video Viralnya

#Terviral - #Adila #Vania adalah #model dan #kreator #konten #media #sosial asal #Bandung yang memilih jalur #modelling #lingerie dan gaya hidup #modis. Dengan akun #Instagram #adillavania69 dan #TikTok #adilavania3, ia telah membangun pengikut dan ekspos yang signifikan. Baca: Viral! Its Anggi Teriak Tengah Malam Bilang “Jangan Nakal” 1. Biodata Singkat

https://terviral.id/profil-dan-kontroversi-adila-vania-dengan-video-viralnya/

#adilavania3 #tiktok #adillavania69 #instagram #modis #lingerie

💧🌏 Greg Cocks @[email protected] · 2025-06-06 · 00:03 UTC

Darkening Of The Global Ocean
--
https://doi.org/10.1111/gcb.70227 <-- shared paper
--
[a little outside of ‘my usual’, but fascinating (and alarming, frankly…)]
#GIS #spatial #mapping #marine #ocean #photic #photiczone #ecology #change #sunlight #moonlight #spatialanalysis #spatiotemporal #darkening #habitat #biogeochemical #penetration #MODIS #remotesensing #earthobservation #satellite #light #coast #deepsea #openocean #nutrient #sediment #currents #circulation #productivity #fisheries #surface #ecology #ecosystems #impacts #humanimpacts

#gis #spatial #mapping #marine #ocean #photic

💧🌏 Greg Cocks @[email protected] · 2025-06-06 · 00:03 UTC

Darkening Of The Global Ocean
--
https://doi.org/10.1111/gcb.70227 <-- shared paper
--
[a little outside of ‘my usual’, but fascinating (and alarming, frankly…)]
#GIS #spatial #mapping #marine #ocean #photic #photiczone #ecology #change #sunlight #moonlight #spatialanalysis #spatiotemporal #darkening #habitat #biogeochemical #penetration #MODIS #remotesensing #earthobservation #satellite #light #coast #deepsea #openocean #nutrient #sediment #currents #circulation #productivity #fisheries #surface #ecology #ecosystems #impacts #humanimpacts

#gis #spatial #mapping #marine #ocean #photic

💧🌏 Greg Cocks @[email protected] · 2025-06-06 · 00:03 UTC

Darkening Of The Global Ocean
--
https://doi.org/10.1111/gcb.70227 <-- shared paper
--
[a little outside of ‘my usual’, but fascinating (and alarming, frankly…)]
#GIS #spatial #mapping #marine #ocean #photic #photiczone #ecology #change #sunlight #moonlight #spatialanalysis #spatiotemporal #darkening #habitat #biogeochemical #penetration #MODIS #remotesensing #earthobservation #satellite #light #coast #deepsea #openocean #nutrient #sediment #currents #circulation #productivity #fisheries #surface #ecology #ecosystems #impacts #humanimpacts

#gis #spatial #mapping #marine #ocean #photic

💧🌏 Greg Cocks @[email protected] · 2025-06-06 · 00:03 UTC

Darkening Of The Global Ocean
--
https://doi.org/10.1111/gcb.70227 <-- shared paper
--
[a little outside of ‘my usual’, but fascinating (and alarming, frankly…)]
#GIS #spatial #mapping #marine #ocean #photic #photiczone #ecology #change #sunlight #moonlight #spatialanalysis #spatiotemporal #darkening #habitat #biogeochemical #penetration #MODIS #remotesensing #earthobservation #satellite #light #coast #deepsea #openocean #nutrient #sediment #currents #circulation #productivity #fisheries #surface #ecology #ecosystems #impacts #humanimpacts

#humanimpacts #impacts #ecosystems #surface #fisheries #productivity

💧🌏 Greg Cocks @GregCocks · 2025-06-06 · 00:03 UTC

Darkening Of The Global Ocean
--
https://doi.org/10.1111/gcb.70227 <-- shared paper
--
[a little outside of ‘my usual’, but fascinating (and alarming, frankly…)]
#GIS #spatial #mapping #marine #ocean #photic #photiczone #ecology #change #sunlight #moonlight #spatialanalysis #spatiotemporal #darkening #habitat #biogeochemical #penetration #MODIS #remotesensing #earthobservation #satellite #light #coast #deepsea #openocean #nutrient #sediment #currents #circulation #productivity #fisheries #surface #ecology #ecosystems #impacts #humanimpacts

#gis #spatial #mapping #marine #ocean #photic

💧🌏 Greg Cocks @[email protected] · 2025-05-30 · 19:20 UTC

Understanding Wildfire Behaviour Across Alaska Using Remote Sensing Of Fire Intensity
--
https://www.usgs.gov/programs/ecosystems-land-change-science-program/news/understanding-wildfire-behavior-across-alaska <-- shared article
--
https://doi.org/10.1071/WF24062 <-- shared paper
--
https://www.sciencebase.gov/catalog/item/66c3aef6d34e03388287afd0 <-- shared USGS open data release - MODIS Fire Radiative Power Data Compiled From Active Fires In Alaska, 2003-2022
--
https://akfireinfo.com/ <-- shared Alaska Wildland Fire Information page
--
#GIS #spatial #mapping #WildfireScience #Alaska #RemoteSensing #satellite #WildfireAwarenessMonth #WildlandFireScience #fedscience #fedservice #wildfire #bushfire #blackspruce #boreal #fireintensity #fireradiativepower #FRP #MODIS #postfire #shrubland #tundra #whitespruce #peat #ecosystems #risk #hazard #management #planning #recovery #severity

#gis #spatial #mapping #wildfirescience #alaska #remotesensing

Tino Eberl @[email protected] · 2025-05-07 · 12:14 UTC

Mithilfe von #Satellitendaten erkennt ein neues Verfahren das winzige #Zooplankton Calanus finmarchicus, das für Nordatlantische #Glattwale lebenswichtig ist.

Die Methode hilft, Fressgebiete im Golf von Maine vorherzusagen. Das könnte Kollisionen mit Schiffen und Verwicklungen in Fischereigeräte reduzieren. Die Daten stammen von NASA-Instrumenten wie #MODIS und künftig #PACE.

https://dx.doi.org/10.3389/fmars.2025.1507638

#Weltnaturschutz #Meeresforschung #Klimawandel #NASA #Ozeanbeobachtung #Wale #Artenschutz

#satellitendaten #zooplankton #glattwale #modis #pace #weltnaturschutz

Tino Eberl @[email protected] · 2025-05-07 · 12:14 UTC

Mithilfe von #Satellitendaten erkennt ein neues Verfahren das winzige #Zooplankton Calanus finmarchicus, das für Nordatlantische #Glattwale lebenswichtig ist.

Die Methode hilft, Fressgebiete im Golf von Maine vorherzusagen. Das könnte Kollisionen mit Schiffen und Verwicklungen in Fischereigeräte reduzieren. Die Daten stammen von NASA-Instrumenten wie #MODIS und künftig #PACE.

https://dx.doi.org/10.3389/fmars.2025.1507638

#Weltnaturschutz #Meeresforschung #Klimawandel #NASA #Ozeanbeobachtung #Wale #Artenschutz

#satellitendaten #zooplankton #glattwale #modis #pace #weltnaturschutz

Tino Eberl @[email protected] · 2025-05-07 · 12:14 UTC

Mithilfe von #Satellitendaten erkennt ein neues Verfahren das winzige #Zooplankton Calanus finmarchicus, das für Nordatlantische #Glattwale lebenswichtig ist.

Die Methode hilft, Fressgebiete im Golf von Maine vorherzusagen. Das könnte Kollisionen mit Schiffen und Verwicklungen in Fischereigeräte reduzieren. Die Daten stammen von NASA-Instrumenten wie #MODIS und künftig #PACE.

https://dx.doi.org/10.3389/fmars.2025.1507638

#Weltnaturschutz #Meeresforschung #Klimawandel #NASA #Ozeanbeobachtung #Wale #Artenschutz

#satellitendaten #zooplankton #glattwale #modis #pace #weltnaturschutz

Tino Eberl @[email protected] · 2025-05-07 · 12:14 UTC

Mithilfe von #Satellitendaten erkennt ein neues Verfahren das winzige #Zooplankton Calanus finmarchicus, das für Nordatlantische #Glattwale lebenswichtig ist.

Die Methode hilft, Fressgebiete im Golf von Maine vorherzusagen. Das könnte Kollisionen mit Schiffen und Verwicklungen in Fischereigeräte reduzieren. Die Daten stammen von NASA-Instrumenten wie #MODIS und künftig #PACE.

https://dx.doi.org/10.3389/fmars.2025.1507638

#Weltnaturschutz #Meeresforschung #Klimawandel #NASA #Ozeanbeobachtung #Wale #Artenschutz

#artenschutz #wale #ozeanbeobachtung #nasa #klimawandel #meeresforschung

Tino Eberl @[email protected] · 2025-05-07 · 12:14 UTC

Mithilfe von #Satellitendaten erkennt ein neues Verfahren das winzige #Zooplankton Calanus finmarchicus, das für Nordatlantische #Glattwale lebenswichtig ist.

Die Methode hilft, Fressgebiete im Golf von Maine vorherzusagen. Das könnte Kollisionen mit Schiffen und Verwicklungen in Fischereigeräte reduzieren. Die Daten stammen von NASA-Instrumenten wie #MODIS und künftig #PACE.

https://dx.doi.org/10.3389/fmars.2025.1507638

#Weltnaturschutz #Meeresforschung #Klimawandel #NASA #Ozeanbeobachtung #Wale #Artenschutz

#satellitendaten #zooplankton #glattwale #modis #pace #weltnaturschutz

💧🌏 Greg Cocks @[email protected] · 2025-05-06 · 22:12 UTC

Assessment Of Snow Cover Dynamics And The Effects Of Environmental Drivers In High Mountain Ecosystems
--
https://doi.org/10.1016/j.eiar.2025.107969 <-- shared paper
--
#GIS #spatial #mapping #remotesensing #earthobservation #snow #ice #snowcover #dynamics #climatechange #mountains #ecosystems #spatialanalysis #spatiotemporal #MODIS #model #modeling #extremeweather #water #hydrology #climate #zones #trendanalysis #linearregression #RandomForest #cryosphere

#gis #spatial #mapping #remotesensing #earthobservation #snow

anlomedad @[email protected] · 2025-04-21 · 08:07 UTC

"Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events" by Yuchao Yan et al 2025.
Fascinating and educational. All the more for us in Germany and Finland, and likely other Europeans, whose forests morphed from CO2 sink to source. The study ends with 2020 data tho, Europe with 2018.
Only non-fire mortality events were analyzed.
I learned how recovery after a drought-driven forest mortality event depends on🌡️💧during recovery; not so much the event severity.
https://www.nature.com/articles/s41477-025-01948-4
Free e-pdf provided by one of the authors:
https://rdcu.be/eigV4

Don't know about you but to me, a paper is particularly "good" if I'm left with a host of new pressing questions. "Why did they..? Was it maybe ..? What if it had been...?"

For a recovery phase, they differentiate between recovery of the canopy greening and recovery of water content in the canopy. Both are based on satellite obs only. And if a satellite image suggests greening is recovered to pre-mortality level, it might not actually be re-greening from recovered old or new young trees but could be merely dense shrubbery. The Greening parameter is often used to glean carbon stock. Shrubs have less biomass=less carbon than trees.
The water content in the canopy then somehow helps to clarify the actual recovery state. How? 🤷‍♀️

Water content in canopy always takes far longer to recover than re-greening.
Longer = years and years longer.
Always = in the 1980s as well. Which I take as: that's the normal baseline behaviour for a given biome, a given latitude zone, a given climate zone, a given elevation, a given human intervention etc.

Supplementary Fig. 5. c and d show numbers for North America and Tropics https://static-content.springer.com/esm/art%3A10.1038%2Fs41477-025-01948-4/MediaObjects/41477_2025_1948_MOESM1_ESM.pdf .
Recovery Time in years for water in canopy in North America
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 18, average 9.

in the Tropics:
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 11, average 7.

Europe is missing an extra whiskers plot. Maybe they saved this for their next paper. But European events are included up to 2018, if I got it right.

With all the factors to be considered, and bias in numbers of events in any given factor, making recovery comparable across regions, across biomes, across climate zones, a global average doesn't seem very useful.
However, here are the global numbers from Figure 1d for
Recovery time RT for water in canopy. In the 1980s RT was between 2 and 15, average 8, median 6 .
In the 1990s, RT was 2 - 22, average 8, median 6.
In the 2000s, RT was 2 - 20, average 9, median 9 years.

Am curious wrt the missing potential cause for the greatly reduced RecoveryTime in the 2010s in Fig.1d. Is that an artefact of the shortened observation time for these 10 most recent mortality yrs?
And Greening recovered astonishingly quickly in the 2010s. is it the high CO2 fertilisation or a regional bias from the events in this period?

#climate #ecosystem #drought #forest #satellite #MODIS #ForestMortality

#climate #ecosystem #drought #forest #satellite #modis

anlomedad @[email protected] · 2025-04-21 · 08:07 UTC

"Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events" by Yuchao Yan et al 2025.
Fascinating and educational. All the more for us in Germany and Finland, and likely other Europeans, whose forests morphed from CO2 sink to source. The study ends with 2020 data tho, Europe with 2018.
Only non-fire mortality events were analyzed.
I learned how recovery after a drought-driven forest mortality event depends on🌡️💧during recovery; not so much the event severity.
https://www.nature.com/articles/s41477-025-01948-4
Free e-pdf provided by one of the authors:
https://rdcu.be/eigV4

Don't know about you but to me, a paper is particularly "good" if I'm left with a host of new pressing questions. "Why did they..? Was it maybe ..? What if it had been...?"

For a recovery phase, they differentiate between recovery of the canopy greening and recovery of water content in the canopy. Both are based on satellite obs only. And if a satellite image suggests greening is recovered to pre-mortality level, it might not actually be re-greening from recovered old or new young trees but could be merely dense shrubbery. The Greening parameter is often used to glean carbon stock. Shrubs have less biomass=less carbon than trees.
The water content in the canopy then somehow helps to clarify the actual recovery state. How? 🤷‍♀️

Water content in canopy always takes far longer to recover than re-greening.
Longer = years and years longer.
Always = in the 1980s as well. Which I take as: that's the normal baseline behaviour for a given biome, a given latitude zone, a given climate zone, a given elevation, a given human intervention etc.

Supplementary Fig. 5. c and d show numbers for North America and Tropics https://static-content.springer.com/esm/art%3A10.1038%2Fs41477-025-01948-4/MediaObjects/41477_2025_1948_MOESM1_ESM.pdf .
Recovery Time in years for water in canopy in North America
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 18, average 9.

in the Tropics:
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 11, average 7.

Europe is missing an extra whiskers plot. Maybe they saved this for their next paper. But European events are included up to 2018, if I got it right.

With all the factors to be considered, and bias in numbers of events in any given factor, making recovery comparable across regions, across biomes, across climate zones, a global average doesn't seem very useful.
However, here are the global numbers from Figure 1d for
Recovery time RT for water in canopy. In the 1980s RT was between 2 and 15, average 8, median 6 .
In the 1990s, RT was 2 - 22, average 8, median 6.
In the 2000s, RT was 2 - 20, average 9, median 9 years.

Am curious wrt the missing potential cause for the greatly reduced RecoveryTime in the 2010s in Fig.1d. Is that an artefact of the shortened observation time for these 10 most recent mortality yrs?
And Greening recovered astonishingly quickly in the 2010s. is it the high CO2 fertilisation or a regional bias from the events in this period?

#climate #ecosystem #drought #forest #satellite #MODIS #ForestMortality

#climate #ecosystem #drought #forest #satellite #modis

anlomedad @[email protected] · 2025-04-21 · 08:07 UTC

"Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events" by Yuchao Yan et al 2025.
Fascinating and educational. All the more for us in Germany and Finland, and likely other Europeans, whose forests morphed from CO2 sink to source. The study ends with 2020 data tho, Europe with 2018.
Only non-fire mortality events were analyzed.
I learned how recovery after a drought-driven forest mortality event depends on🌡️💧during recovery; not so much the event severity.
https://www.nature.com/articles/s41477-025-01948-4
Free e-pdf provided by one of the authors:
https://rdcu.be/eigV4

Don't know about you but to me, a paper is particularly "good" if I'm left with a host of new pressing questions. "Why did they..? Was it maybe ..? What if it had been...?"

For a recovery phase, they differentiate between recovery of the canopy greening and recovery of water content in the canopy. Both are based on satellite obs only. And if a satellite image suggests greening is recovered to pre-mortality level, it might not actually be re-greening from recovered old or new young trees but could be merely dense shrubbery. The Greening parameter is often used to glean carbon stock. Shrubs have less biomass=less carbon than trees.
The water content in the canopy then somehow helps to clarify the actual recovery state. How? 🤷‍♀️

Water content in canopy always takes far longer to recover than re-greening.
Longer = years and years longer.
Always = in the 1980s as well. Which I take as: that's the normal baseline behaviour for a given biome, a given latitude zone, a given climate zone, a given elevation, a given human intervention etc.

Supplementary Fig. 5. c and d show numbers for North America and Tropics https://static-content.springer.com/esm/art%3A10.1038%2Fs41477-025-01948-4/MediaObjects/41477_2025_1948_MOESM1_ESM.pdf .
Recovery Time in years for water in canopy in North America
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 18, average 9.

in the Tropics:
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 11, average 7.

Europe is missing an extra whiskers plot. Maybe they saved this for their next paper. But European events are included up to 2018, if I got it right.

With all the factors to be considered, and bias in numbers of events in any given factor, making recovery comparable across regions, across biomes, across climate zones, a global average doesn't seem very useful.
However, here are the global numbers from Figure 1d for
Recovery time RT for water in canopy. In the 1980s RT was between 2 and 15, average 8, median 6 .
In the 1990s, RT was 2 - 22, average 8, median 6.
In the 2000s, RT was 2 - 20, average 9, median 9 years.

Am curious wrt the missing potential cause for the greatly reduced RecoveryTime in the 2010s in Fig.1d. Is that an artefact of the shortened observation time for these 10 most recent mortality yrs?
And Greening recovered astonishingly quickly in the 2010s. is it the high CO2 fertilisation or a regional bias from the events in this period?

#climate #ecosystem #drought #forest #satellite #MODIS #ForestMortality

#climate #ecosystem #drought #forest #satellite #modis

anlomedad @[email protected] · 2025-04-21 · 08:07 UTC

"Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events" by Yuchao Yan et al 2025.
Fascinating and educational. All the more for us in Germany and Finland, and likely other Europeans, whose forests morphed from CO2 sink to source. The study ends with 2020 data tho, Europe with 2018.
Only non-fire mortality events were analyzed.
I learned how recovery after a drought-driven forest mortality event depends on🌡️💧during recovery; not so much the event severity.
https://www.nature.com/articles/s41477-025-01948-4
Free e-pdf provided by one of the authors:
https://rdcu.be/eigV4

Don't know about you but to me, a paper is particularly "good" if I'm left with a host of new pressing questions. "Why did they..? Was it maybe ..? What if it had been...?"

For a recovery phase, they differentiate between recovery of the canopy greening and recovery of water content in the canopy. Both are based on satellite obs only. And if a satellite image suggests greening is recovered to pre-mortality level, it might not actually be re-greening from recovered old or new young trees but could be merely dense shrubbery. The Greening parameter is often used to glean carbon stock. Shrubs have less biomass=less carbon than trees.
The water content in the canopy then somehow helps to clarify the actual recovery state. How? 🤷‍♀️

Water content in canopy always takes far longer to recover than re-greening.
Longer = years and years longer.
Always = in the 1980s as well. Which I take as: that's the normal baseline behaviour for a given biome, a given latitude zone, a given climate zone, a given elevation, a given human intervention etc.

Supplementary Fig. 5. c and d show numbers for North America and Tropics https://static-content.springer.com/esm/art%3A10.1038%2Fs41477-025-01948-4/MediaObjects/41477_2025_1948_MOESM1_ESM.pdf .
Recovery Time in years for water in canopy in North America
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 18, average 9.

in the Tropics:
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 11, average 7.

Europe is missing an extra whiskers plot. Maybe they saved this for their next paper. But European events are included up to 2018, if I got it right.

With all the factors to be considered, and bias in numbers of events in any given factor, making recovery comparable across regions, across biomes, across climate zones, a global average doesn't seem very useful.
However, here are the global numbers from Figure 1d for
Recovery time RT for water in canopy. In the 1980s RT was between 2 and 15, average 8, median 6 .
In the 1990s, RT was 2 - 22, average 8, median 6.
In the 2000s, RT was 2 - 20, average 9, median 9 years.

Am curious wrt the missing potential cause for the greatly reduced RecoveryTime in the 2010s in Fig.1d. Is that an artefact of the shortened observation time for these 10 most recent mortality yrs?
And Greening recovered astonishingly quickly in the 2010s. is it the high CO2 fertilisation or a regional bias from the events in this period?

#climate #ecosystem #drought #forest #satellite #MODIS #ForestMortality

#forestmortality #modis #satellite #forest #drought #ecosystem

anlomedad @[email protected] · 2025-04-21 · 08:07 UTC

"Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events" by Yuchao Yan et al 2025.
Fascinating and educational. All the more for us in Germany and Finland, and likely other Europeans, whose forests morphed from CO2 sink to source. The study ends with 2020 data tho, Europe with 2018.
Only non-fire mortality events were analyzed.
I learned how recovery after a drought-driven forest mortality event depends on🌡️💧during recovery; not so much the event severity.
https://www.nature.com/articles/s41477-025-01948-4
Free e-pdf provided by one of the authors:
https://rdcu.be/eigV4

Don't know about you but to me, a paper is particularly "good" if I'm left with a host of new pressing questions. "Why did they..? Was it maybe ..? What if it had been...?"

For a recovery phase, they differentiate between recovery of the canopy greening and recovery of water content in the canopy. Both are based on satellite obs only. And if a satellite image suggests greening is recovered to pre-mortality level, it might not actually be re-greening from recovered old or new young trees but could be merely dense shrubbery. The Greening parameter is often used to glean carbon stock. Shrubs have less biomass=less carbon than trees.
The water content in the canopy then somehow helps to clarify the actual recovery state. How? 🤷‍♀️

Water content in canopy always takes far longer to recover than re-greening.
Longer = years and years longer.
Always = in the 1980s as well. Which I take as: that's the normal baseline behaviour for a given biome, a given latitude zone, a given climate zone, a given elevation, a given human intervention etc.

Supplementary Fig. 5. c and d show numbers for North America and Tropics https://static-content.springer.com/esm/art%3A10.1038%2Fs41477-025-01948-4/MediaObjects/41477_2025_1948_MOESM1_ESM.pdf .
Recovery Time in years for water in canopy in North America
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 18, average 9.

in the Tropics:
in the 1990s took 2 - 12, average 6.
in the 2000s took 2 - 11, average 7.

Europe is missing an extra whiskers plot. Maybe they saved this for their next paper. But European events are included up to 2018, if I got it right.

With all the factors to be considered, and bias in numbers of events in any given factor, making recovery comparable across regions, across biomes, across climate zones, a global average doesn't seem very useful.
However, here are the global numbers from Figure 1d for
Recovery time RT for water in canopy. In the 1980s RT was between 2 and 15, average 8, median 6 .
In the 1990s, RT was 2 - 22, average 8, median 6.
In the 2000s, RT was 2 - 20, average 9, median 9 years.

Am curious wrt the missing potential cause for the greatly reduced RecoveryTime in the 2010s in Fig.1d. Is that an artefact of the shortened observation time for these 10 most recent mortality yrs?
And Greening recovered astonishingly quickly in the 2010s. is it the high CO2 fertilisation or a regional bias from the events in this period?

#climate #ecosystem #drought #forest #satellite #MODIS #ForestMortality