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#spatiotemporal — Public Fediverse posts

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

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

    Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using #4EI satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”
    #ClimateChange #Heat #UrbanHeat #Heatwave #Climate #Warming #Infrastructure #London #Portsmouth #Resilience #OrdnanceSurvey #Geospatial #Data #extremeweather #extremeweather #publichealth #publicsafety #deaths #UK #GreatBritain #OS #HeatVulnerabilityIndex #vulnerability #spatialanalysis #mapping #model #modeling #analysis #spatiotemporal #climate #climatemodeling #spatial #mapping #remotesensing #earthobservation #solarradiation #natural #manmade #retainedheat #mitigation #planning #policy #infrastructure #concrete #asphalt #buildings #hardsurfaces #globalwarming #urbanheatislands #cities #urban
    @Ordnance Survey

    #4ei #climatechange #heat #urbanheat #heatwave #climate
  2. 💧🌏 Greg Cocks @GregCocks ·

    Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”

    @Ordnance Survey

    #4ei #climatechange #heat #urbanheat #heatwave #climate
  3. 💧🌏 Greg Cocks @GregCocks ·

    Improved Tidal Analysis Datum Calculator [NOAA, USA]
    --
    tidesandcurrents.noaa.gov/news <-- shared technical article
    --
    access.co-ops.nos.noaa.gov/dat <-- shared ‘Tidal Analysis Datum Calculator’ online tool
    --
    tidesandcurrents.noaa.gov/map/ <-- shared Interactive Map to Locate Control Stations
    --
    access.co-ops.nos.noaa.gov/dat <-- shared user guide
    --
    access.co-ops.nos.noaa.gov/dat <-- shared technical report
    --
    tidesandcurrents.noaa.gov/datu <-- shared details of the National Tidal Datum Epoch
    --
    H/T @NOAA National Ocean Service
    “NOAA has upgraded its tidal datum calculation tool with new integrated features! [links above] Did you know you can use this resource to perform your own tidal datum computations and understand local flood frequency?
    The Tidal Datum Analysis Calculator is available now for users to input their data to help make informed decisions on tidal flooding, coastal inundation, and other critical coastal issues.
    With the National Tidal Datum Epoch release rescheduled for 2029, this tool serves as a resource for stakeholders to analyze and understand water level data in their areas of interest by calculating preliminary datums relative to the new epoch…”

    @NOAA @NOAA National Ocean Service

    #tidal #datum #calculation #tool #noaa #tidalanalysisdatumcalculator
  4. 💧🌏 Greg Cocks @[email protected] ·

    Improved Tidal Analysis Datum Calculator [NOAA, USA]
    --
    tidesandcurrents.noaa.gov/news <-- shared technical article
    --
    access.co-ops.nos.noaa.gov/dat <-- shared ‘Tidal Analysis Datum Calculator’ online tool
    --
    tidesandcurrents.noaa.gov/map/ <-- shared Interactive Map to Locate Control Stations
    --
    access.co-ops.nos.noaa.gov/dat <-- shared user guide
    --
    access.co-ops.nos.noaa.gov/dat <-- shared technical report
    --
    tidesandcurrents.noaa.gov/datu <-- shared details of the National Tidal Datum Epoch
    --
    H/T @NOAA National Ocean Service
    “NOAA has upgraded its tidal datum calculation tool with new integrated features! [links above] Did you know you can use this resource to perform your own tidal datum computations and understand local flood frequency?
    The Tidal Datum Analysis Calculator is available now for users to input their data to help make informed decisions on tidal flooding, coastal inundation, and other critical coastal issues.
    With the National Tidal Datum Epoch release rescheduled for 2029, this tool serves as a resource for stakeholders to analyze and understand water level data in their areas of interest by calculating preliminary datums relative to the new epoch…”
    #tidal #datum #calculation #tool #NOAA #TidalAnalysisDatumCalculator #TADC #fedscience #fedservice #opendata #usecase #flood #flooding #coast #coastal #inundation #NationalTidalDatumEpoch #waterlevel #spatialanalysis #mapping #spatial #spatiotemporal #vertical #USA #MSL #MHW #MLW
    @NOAA @NOAA National Ocean Service

    #tidal #datum #calculation #tool #noaa #tidalanalysisdatumcalculator
  5. 💧🌏 Greg Cocks @GregCocks ·

    Detection And Mapping Of Daya Features In The Limestone Plateau Of Northwest Egypt
    --
    doi.org/10.1177/03091333261480 <-- shared paper
    --
    doi.org/10.1007/s41207-024-004 <-- shared paper
    --
    H/T @wael Galal | Geologist at Geology Department, Assiut University
    “[The author’s] study systematically investigates ‘DAYAS’ shallow, ephemeral wetlands scattered across the limestone plateau of Northwest Egypt. Previously undocumented in Egyptian territory, these saucer-shaped depressions play a vital role in regional hydrology and karst evolution.
    Through an integrated approach combining Sentinel-2 satellite imagery, multi-decadal Landsat time-series analysis (1984-2025), ALOS PALSAR DEMs, and extensive field validation, [they] identified and mapped 5,430 daya features covering 258.1 km² achieving 91% classification accuracy.
    [Their] research established a typological framework distinguishing bowl-shaped, small flat-bottom, and large flat-bottom dayas representing a continuum from active karst dissolution to relict, sediment-sealed systems. Critically, [they] documented a 32.63% net increase in daya surface area over 41 years, demonstrating that karst modification remains an active geomorphic process even under present-day hyper-arid conditions.
    This work provides government agencies and water resource planners with the first high-resolution spatial inventory of these features essential baseline data for sustainable groundwater management, water-harvesting infrastructure, and conservation prioritisation in this water-scarce region…”

    #research #karstgeomorphology #remotesensing #gis #spatial #mapping
  6. 💧🌏 Greg Cocks @[email protected] ·

    Detection And Mapping Of Daya Features In The Limestone Plateau Of Northwest Egypt
    --
    doi.org/10.1177/03091333261480 <-- shared paper
    --
    doi.org/10.1007/s41207-024-004 <-- shared paper
    --
    H/T @wael Galal | Geologist at Geology Department, Assiut University
    “[The author’s] study systematically investigates ‘DAYAS’ shallow, ephemeral wetlands scattered across the limestone plateau of Northwest Egypt. Previously undocumented in Egyptian territory, these saucer-shaped depressions play a vital role in regional hydrology and karst evolution.
    Through an integrated approach combining Sentinel-2 satellite imagery, multi-decadal Landsat time-series analysis (1984-2025), ALOS PALSAR DEMs, and extensive field validation, [they] identified and mapped 5,430 daya features covering 258.1 km² achieving 91% classification accuracy.
    [Their] research established a typological framework distinguishing bowl-shaped, small flat-bottom, and large flat-bottom dayas representing a continuum from active karst dissolution to relict, sediment-sealed systems. Critically, [they] documented a 32.63% net increase in daya surface area over 41 years, demonstrating that karst modification remains an active geomorphic process even under present-day hyper-arid conditions.
    This work provides government agencies and water resource planners with the first high-resolution spatial inventory of these features essential baseline data for sustainable groundwater management, water-harvesting infrastructure, and conservation prioritisation in this water-scarce region…”
    #Research #KarstGeomorphology #RemoteSensing #GIS #spatial #mapping #spatialanalysis #spatiotemporal #WaterResources #Egypt #Dayas #Sustainability #PhysicalGeography #water #hydrology #hydrography #wetland #daya #geology #limestone #karst #sentinel #Landsat #hydrogeomorphology #geomorphology #geomorphometry #arid #hyperarid #planning #policy #sustainable #groundwater #conservation

    #research #karstgeomorphology #remotesensing #gis #spatial #mapping
  7. 💧🌏 Greg Cocks @[email protected] ·

    To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
    (Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
    --
    science.org/content/article/pr <-- shared technical article
    --
    doi.org/10.1029/2026GL122182 <-- shared paper
    --
    H/T @hannah Richter
    “Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
    Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
    --
    "... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”
    #GIS #spatial #mapping #spatialanalysis #spatiotemporal #Australia #WesternAustralia #WA #forests #vegetation #bush #jarrah #karri #drought #heat #extremedrought #extremeweather #climatechange #water #waterresources #dieoff #soil #weathering #erosion #moisture #nutrients #airborne #gammarays #GRS #granite #gneiss #bedrock #geology #potassium40 #potassium #K #remotesensing #earthobservation #groundwater #interstitial #subsurface #waterstorage #electricalresistivitytomography

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

    WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
    --
    deepmind.google/blog/weatherne <-- shared technical Google DeepMind blog post
    --
    doi.org/10.1038/s41586-026-109 <-- shared paper
    --
    deepmind.google/science/weathe <-- shared data
    --
    github.com/google-deepmind/wea <-- shared GitHub repository
    --
    H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
    [this post should not be considered an endorsement of a particular organisation or their approach]
    “[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
    Here is how WeatherNext is transforming cyclone forecasting:
    • Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
    • Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
    • Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
    • Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
    [The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
    Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
    #Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
    @Google | @WeatherNext

    #google #deepmind #googleresearch #ai #ensembles #functionalgenerativenetworks
  9. 💧🌏 Greg Cocks @[email protected] ·

    GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

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

    GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

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

    GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

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

    GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

    #fao #foodandagricultureorganizationfao #monitoring #earlywarning #humanimpacts #wateruse
  13. 💧🌏 Greg Cocks @GregCocks ·

    GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”

    @FAO - Food and Agriculture Organization

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

    Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
    #Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling

    #sowing #harvest #tillage #tillagedetection #cropland #croplandmanagement
  15. 💧🌏 Greg Cocks @[email protected] ·

    Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
    #Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling

    #sowing #harvest #tillage #tillagedetection #cropland #croplandmanagement
  16. 💧🌏 Greg Cocks @[email protected] ·

    Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
    #Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling

    #sowing #harvest #tillage #tillagedetection #cropland #croplandmanagement
  17. 💧🌏 Greg Cocks @[email protected] ·

    Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
    #Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling

    #modeling #model #region #europe #growingseason #environmentalstewardship
  18. 💧🌏 Greg Cocks @GregCocks ·

    Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”

    #sowing #harvest #tillage #tillagedetection #cropland #croplandmanagement
  19. 💧🌏 Greg Cocks @[email protected] ·

    On The Use Of Rainfall Time Series For Regional Landslide Prediction By Means Of Functional Regression
    --
    doi.org/10.1016/j.enggeo.2026. <-- shared paper
    --
    #rainfallinduced #landslide #Functionalregression #Japan #Earlywarningsystem #Spacetimeprediction #spatialanalysis #spatiotemporal #massmovement #engineeringgeology #geomorphology #hydrogeomorphology #geomorphometry #remotesensing #geostatistics #model #modeling #mechanics #rainfall #threshold #precipitation #water #hydrology #risk #hazard #hazardassessment #terrain #landscape #landform #landuse #geology #soil #lithology #functionalGeneralizedAdditiveModel #FGAM #prediction #casestudy #AI #LLM

    #rainfallinduced #landslide #functionalregression #japan #earlywarningsystem #spacetimeprediction
  20. 💧🌏 Greg Cocks @[email protected] ·

    On The Use Of Rainfall Time Series For Regional Landslide Prediction By Means Of Functional Regression
    --
    doi.org/10.1016/j.enggeo.2026. <-- shared paper
    --
    #rainfallinduced #landslide #Functionalregression #Japan #Earlywarningsystem #Spacetimeprediction #spatialanalysis #spatiotemporal #massmovement #engineeringgeology #geomorphology #hydrogeomorphology #geomorphometry #remotesensing #geostatistics #model #modeling #mechanics #rainfall #threshold #precipitation #water #hydrology #risk #hazard #hazardassessment #terrain #landscape #landform #landuse #geology #soil #lithology #functionalGeneralizedAdditiveModel #FGAM #prediction #casestudy #AI #LLM

    #rainfallinduced #landslide #functionalregression #japan #earlywarningsystem #spacetimeprediction
  21. 💧🌏 Greg Cocks @[email protected] ·

    Pan-European Assessment Of Coastal Flood Hazards
    --
    doi.org/10.5194/nhess-26-1859- <-- shared paper
    --
    #coast #coastal #flood #flooding #model #modeling #Europe #UK #naturalhazard #extremeweather #bathtub #dynamic #stormsurge #remotesensing #GIS #spatial #mapping #spatialanalysis #spatiotemporal #risk #hazard #assessment #water #hydrology #marine #hydrography #hydrograph #meansealevel #MSL #tides #waves #tidal #parameters #DEM #dynamicmodels #floodplain #literaturereview

    #coast #coastal #flood #flooding #model #modeling
  22. 💧🌏 Greg Cocks @[email protected] ·

    National-Scale Field Delineation In Mozambique Refines Our Understanding Of Cropland Distribution, Field Size, And Deforestation Actors.
    --
    doi.org/10.1088/1748-9326/ae5c <-- shared paper
    --
    philipperufin.github.io/blog/m <-- shared associated technical article from one of the paper’s authors
    --
    #foodsecurity #subsistence #smallholder #farms #farming #agriculture #crops #Mozambique #subSaharan #Africa #GIS #spatial #mapping #remotesensing #earthobservation #policy #planning #cropland #spatialanalysis #spatiotemporal #field #deforestation #countrywide #landuse #sustainability #delineation #satellite #SPOT #model #modeling #deeplearning #AI #machinelearning #survey #GeoAI #spatialdata

    #foodsecurity #subsistence #smallholder #farms #farming #agriculture
  23. 💧🌏 Greg Cocks @[email protected] ·

    National-Scale Field Delineation In Mozambique Refines Our Understanding Of Cropland Distribution, Field Size, And Deforestation Actors.
    --
    doi.org/10.1088/1748-9326/ae5c <-- shared paper
    --
    philipperufin.github.io/blog/m <-- shared associated technical article from one of the paper’s authors
    --
    #foodsecurity #subsistence #smallholder #farms #farming #agriculture #crops #Mozambique #subSaharan #Africa #GIS #spatial #mapping #remotesensing #earthobservation #policy #planning #cropland #spatialanalysis #spatiotemporal #field #deforestation #countrywide #landuse #sustainability #delineation #satellite #SPOT #model #modeling #deeplearning #AI #machinelearning #survey #GeoAI #spatialdata

    #foodsecurity #subsistence #smallholder #farms #farming #agriculture
  24. 💧🌏 Greg Cocks @[email protected] ·

    National-Scale Field Delineation In Mozambique Refines Our Understanding Of Cropland Distribution, Field Size, And Deforestation Actors.
    --
    doi.org/10.1088/1748-9326/ae5c <-- shared paper
    --
    philipperufin.github.io/blog/m <-- shared associated technical article from one of the paper’s authors
    --
    #foodsecurity #subsistence #smallholder #farms #farming #agriculture #crops #Mozambique #subSaharan #Africa #GIS #spatial #mapping #remotesensing #earthobservation #policy #planning #cropland #spatialanalysis #spatiotemporal #field #deforestation #countrywide #landuse #sustainability #delineation #satellite #SPOT #model #modeling #deeplearning #AI #machinelearning #survey #GeoAI #spatialdata

    #foodsecurity #subsistence #smallholder #farms #farming #agriculture
  25. 💧🌏 Greg Cocks @[email protected] ·

    National-Scale Field Delineation In Mozambique Refines Our Understanding Of Cropland Distribution, Field Size, And Deforestation Actors.
    --
    doi.org/10.1088/1748-9326/ae5c <-- shared paper
    --
    philipperufin.github.io/blog/m <-- shared associated technical article from one of the paper’s authors
    --
    #foodsecurity #subsistence #smallholder #farms #farming #agriculture #crops #Mozambique #subSaharan #Africa #GIS #spatial #mapping #remotesensing #earthobservation #policy #planning #cropland #spatialanalysis #spatiotemporal #field #deforestation #countrywide #landuse #sustainability #delineation #satellite #SPOT #model #modeling #deeplearning #AI #machinelearning #survey #GeoAI #spatialdata

    #spatialdata #geoai #survey #machinelearning #ai #deeplearning
  26. 💧🌏 Greg Cocks @GregCocks ·

    National-Scale Field Delineation In Mozambique Refines Our Understanding Of Cropland Distribution, Field Size, And Deforestation Actors.
    --
    doi.org/10.1088/1748-9326/ae5c <-- shared paper
    --
    philipperufin.github.io/blog/m <-- shared associated technical article from one of the paper’s authors
    --

    #foodsecurity #subsistence #smallholder #farms #farming #agriculture
  27. 💧🌏 Greg Cocks @[email protected] ·

    Detecting Land Use And Land Cover Changes And Quantifying Soil Erosion And Sediment Export Using GIS And Remote Sensing In The GERD Catchment, Ethiopia
    --
    doi.org/10.1016/j.iswcr.2026.1 <-- shared paper
    --
    infonile.org/en/2023/05/battli <-- shared technical media article
    --
    doi.org/10.1007/978-3-031-6524 <-- shared technical book chapter
    --
    doi.org/10.3390/rs8121020 <-- shared 2016 paper
    --
    #landuse #landcover #RUSLE #model #soil #erosion #sedimentation #GERD #catchment #NorthAfrica #Ethiopia #AbbayBasin #sustainability #landmanagement #agriculture #farmland #foodsecurity #water #hydrogology #reservior #watermanagement #watersecurity #waterresources #catchment #GrandEthiopianRenaissanceDam #BlueNile #hydrography #impoundment #GIS #spatial #mapping #remotesensing #earthobservation #thematic #spatialanalysis #spatiotemporal #landsat #elevation #DEM #CHIRPS #rainfall #precipitation #LULC #AI #forest #grazing #grassland #field #crops #cropland #waterbodies #urban #biophysical #risk #hazard #mitigation #soilquality

    #landuse #landcover #rusle #model #soil #erosion
  28. 💧🌏 Greg Cocks @[email protected] ·

    Detecting Land Use And Land Cover Changes And Quantifying Soil Erosion And Sediment Export Using GIS And Remote Sensing In The GERD Catchment, Ethiopia
    --
    doi.org/10.1016/j.iswcr.2026.1 <-- shared paper
    --
    infonile.org/en/2023/05/battli <-- shared technical media article
    --
    doi.org/10.1007/978-3-031-6524 <-- shared technical book chapter
    --
    doi.org/10.3390/rs8121020 <-- shared 2016 paper
    --
    #landuse #landcover #RUSLE #model #soil #erosion #sedimentation #GERD #catchment #NorthAfrica #Ethiopia #AbbayBasin #sustainability #landmanagement #agriculture #farmland #foodsecurity #water #hydrogology #reservior #watermanagement #watersecurity #waterresources #catchment #GrandEthiopianRenaissanceDam #BlueNile #hydrography #impoundment #GIS #spatial #mapping #remotesensing #earthobservation #thematic #spatialanalysis #spatiotemporal #landsat #elevation #DEM #CHIRPS #rainfall #precipitation #LULC #AI #forest #grazing #grassland #field #crops #cropland #waterbodies #urban #biophysical #risk #hazard #mitigation #soilquality

    #landuse #landcover #rusle #model #soil #erosion
  29. 💧🌏 Greg Cocks @[email protected] ·

    Detecting Land Use And Land Cover Changes And Quantifying Soil Erosion And Sediment Export Using GIS And Remote Sensing In The GERD Catchment, Ethiopia
    --
    doi.org/10.1016/j.iswcr.2026.1 <-- shared paper
    --
    infonile.org/en/2023/05/battli <-- shared technical media article
    --
    doi.org/10.1007/978-3-031-6524 <-- shared technical book chapter
    --
    doi.org/10.3390/rs8121020 <-- shared 2016 paper
    --
    #landuse #landcover #RUSLE #model #soil #erosion #sedimentation #GERD #catchment #NorthAfrica #Ethiopia #AbbayBasin #sustainability #landmanagement #agriculture #farmland #foodsecurity #water #hydrogology #reservior #watermanagement #watersecurity #waterresources #catchment #GrandEthiopianRenaissanceDam #BlueNile #hydrography #impoundment #GIS #spatial #mapping #remotesensing #earthobservation #thematic #spatialanalysis #spatiotemporal #landsat #elevation #DEM #CHIRPS #rainfall #precipitation #LULC #AI #forest #grazing #grassland #field #crops #cropland #waterbodies #urban #biophysical #risk #hazard #mitigation #soilquality

    #landuse #landcover #rusle #model #soil #erosion
  30. 💧🌏 Greg Cocks @[email protected] ·

    Detecting Land Use And Land Cover Changes And Quantifying Soil Erosion And Sediment Export Using GIS And Remote Sensing In The GERD Catchment, Ethiopia
    --
    doi.org/10.1016/j.iswcr.2026.1 <-- shared paper
    --
    infonile.org/en/2023/05/battli <-- shared technical media article
    --
    doi.org/10.1007/978-3-031-6524 <-- shared technical book chapter
    --
    doi.org/10.3390/rs8121020 <-- shared 2016 paper
    --
    #landuse #landcover #RUSLE #model #soil #erosion #sedimentation #GERD #catchment #NorthAfrica #Ethiopia #AbbayBasin #sustainability #landmanagement #agriculture #farmland #foodsecurity #water #hydrogology #reservior #watermanagement #watersecurity #waterresources #catchment #GrandEthiopianRenaissanceDam #BlueNile #hydrography #impoundment #GIS #spatial #mapping #remotesensing #earthobservation #thematic #spatialanalysis #spatiotemporal #landsat #elevation #DEM #CHIRPS #rainfall #precipitation #LULC #AI #forest #grazing #grassland #field #crops #cropland #waterbodies #urban #biophysical #risk #hazard #mitigation #soilquality

    #soilquality #mitigation #hazard #risk #biophysical #urban
  31. 💧🌏 Greg Cocks @GregCocks ·

    Detecting Land Use And Land Cover Changes And Quantifying Soil Erosion And Sediment Export Using GIS And Remote Sensing In The GERD Catchment, Ethiopia
    --
    doi.org/10.1016/j.iswcr.2026.1 <-- shared paper
    --
    infonile.org/en/2023/05/battli <-- shared technical media article
    --
    doi.org/10.1007/978-3-031-6524 <-- shared technical book chapter
    --
    doi.org/10.3390/rs8121020 <-- shared 2016 paper
    --

    #landuse #landcover #rusle #model #soil #erosion
  32. 💧🌏 Greg Cocks @[email protected] ·

    Influence Of Modeling Assumptions On Pedestrian Evacuation Success For Non-Eruptive Lahar Hazards At Mount Rainier, Washington
    --
    doi.org/10.1016/j.ijdrr.2026.1 <-- shared paper
    --
    sciencebase.gov/catalog/item/6 <-- shared, related open data source
    --
    [I still remember working on and being fascinated by lahars being an engineering geologist in Washington State (and from my time studying in New Zealand), although (of course) not to this level of detail/focus]
    #volcano #lahar #evacuation #exposure #model #modeling #engineeringeology #risk #hazard #naturalhazard #MountRainer #Washington #USA #spatialanalysis #spatiotemporal #emergencymanagement #GIS #spatial #mapping #publicsafety #hazardzone #vulcanism #downstream #debrisflow #massmovement #monitoring #detection #geostatistics #demographics #atrisk #fedscience #publicgood #fedservice #opendata
    @USGS

    #volcano #lahar #evacuation #exposure #model #modeling
  33. 💧🌏 Greg Cocks @[email protected] ·

    🌟 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 [Google Research’s] 𝐆𝐫𝐨𝐮𝐧𝐝𝐬𝐨𝐮𝐫𝐜𝐞 - 𝐀𝐧 𝐨𝐩𝐞𝐧 𝐬𝐨𝐮𝐫𝐜𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐨𝐟 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜 𝐟𝐥𝐨𝐨𝐝 𝐞𝐯𝐞𝐧𝐭𝐬 𝐟𝐫𝐨𝐦 𝐧𝐞𝐰𝐬 𝐚𝐫𝐭𝐢𝐜𝐥𝐞𝐬.
    --
    doi.org/10.31223/X5RR2K / eartharxiv.org/repository/view <-- shared paper
    --
    zenodo.org/records/18647054 <-- shared link to associated dataset
    --
    sites.research.google/gr/flood <-- shared link to Google Research flood forecasting effort entry page
    --
    #GoogleResearch #Google #Gemini #AI #ClimateTech #MachineLearning #DataScience #FloodForecasting #Sustainability #TechForGood #aggregation #curated #newsarticles #news #media #article #harvesting #reports #reporting #global #world #historic #naturalhazards #naturaldisaster #floods #flooding #flashflood #water #hydrology #extremeweather #climatechange #GDACS #Groundsource #GIS #spatial #mapping #spatialanalysis #spatiotemporal #geographic #openaccess #openscience #opendata #floodevents #LLM #gemini #largelanguagemodel #deeplearning #AI #precision #metrics #historicresource #model #modeling #forecasting #opensource

    #googleresearch #google #gemini #ai #climatetech #machinelearning
  34. 💧🌏 Greg Cocks @[email protected] ·

    Drivers Of Forest Disturbance In Southeast Asia [incl. spatial analysis]
    --
    doi.org/10.1016/j.jag.2026.105 <-- shared paper 🔗
    --
    github.com/shijuanchen/SEA_for <-- shared GitHub ‘data repository 🔗
    --
    #GIS #spatial #mapping #spatialanalysis #spatiotemporal #forest #vegetation #Forestdisturbance #Forestdegradation #Deforestation #remotesensing #SoutheastAsia #Asia #tropicalforests #timeseriesanalysis #geostatistics #disturbance #clearing #planting #agriculture #earthobservation #imagery #CCDC #changedetection #landcover #landuse #change #plantation #cultivation #slashandburn #planning #mitigation #catalogue #conservation #management #landmanagement #machinelearning #imageanalysis #AI

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

    Mapping What Matters - How the USGS delivers actionable geospatial information to address Federal priorities and support CBP Goals
    --
    gis.chesapeakebay.net/gsat/pri <-- shared technical article
    --
    chesapeakebay.net/files/docume <-- shared Revised Chesapeake Bay Watershed Agreement
    --
    baltimoremagazine.com/section/ <-- shared technical media article – “The Wonder Of The Bay”
    --
    #GIS #spatial #mapping #USGS #fedscience #fedservice #publicgood #GeospatialScienceandApplicationsTeam #GSAT #ChesapeakeBay #USA #partnership #spatialanalysis #spatiotemporal #watershed #water #hydrology #conservation #restoration #planning #polict #management #StrategicPlan #usecase
    #USGS

    #gis #spatial #mapping #usgs #fedscience #fedservice
  36. 💧🌏 Greg Cocks @[email protected] ·

    Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    “HIGHLIGHTS:
    • ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    • District-level RF/XGBoost model training with expert-weighted validation.
    • Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    • RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    • NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

    #forestdisturbance #forest #disturbance #remotesensing #landtrendr #workflow
  37. 💧🌏 Greg Cocks @[email protected] ·

    Provisional Land Use Data From Water Year 2024 Is Now Available On [CA]DWR Atlas, CNRA Open Data, And SGMA Data Viewer For Public Use
    --
    gis.water.ca.gov/app/CADWRLand <-- shared web-based CDWR datasets / map
    --
    “The collected data is used by various federal, state, and local agencies, academic researchers and private consultants , and can help estimate the amount of water available for agriculture. Using this information, farmers can adapt and make decisions to better manage scarce water supplies more effectively.”
    #GIS #spatial #mapping #California #download #opendata #water #hydrology #datause #datasharing #download #landuse #spatialanalysis #spatiotemporal #wateryear #DWRAtlas #statewide #CNRA #SGMA #publicdata #publicgood #usecase #crops #croplands #cropmapping #counties #countysurvey #CADWR #DWR #groundwater #irrigation #wateruse #watermanagement #federal #state #local #webmapping #agriculture #watersecurity #foodsecurity #watersupply
    #CaliforniaDepartmentOfWaterResources

    #gis #spatial #mapping #california #download #opendata
  38. 💧🌏 Greg Cocks @[email protected] ·

    Map Reveals How America's Forests Have Changed Over Time
    --
    newsweek.com/america-forests-c <-- shared 2025 media article
    --
    doi.org/10.5194/essd-15-1005-2 <-- shared 2023 paper
    --
    doi.org/10.5281/zenodo.7055086 <-- shared study datasets
    --
    #GIS #spatial #mapping #remotesensing #spatialanalysis #spatiotemporal #copernicus #CONUS #USA #forest #model #modeling #landuse #landcover #change #forestland #agriculture #urban #development #cities #industry #vegetation #America #humanimpacts #settlement #decline #cropland #pasture #grassland #grazing #reforestation #deforestation #urbanization #conservation #ecosystems #logging #mining #railroad #infrastructure #forestcover #NLDI #farmland #colonial #LULC #census #climate #hydrology #biogeochemical

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

    Map Reveals How America's Forests Have Changed Over Time
    --
    newsweek.com/america-forests-c <-- shared 2025 media article
    --
    doi.org/10.5194/essd-15-1005-2 <-- shared 2023 paper
    --
    doi.org/10.5281/zenodo.7055086 <-- shared study datasets
    --
    #GIS #spatial #mapping #remotesensing #spatialanalysis #spatiotemporal #copernicus #CONUS #USA #forest #model #modeling #landuse #landcover #change #forestland #agriculture #urban #development #cities #industry #vegetation #America #humanimpacts #settlement #decline #cropland #pasture #grassland #grazing #reforestation #deforestation #urbanization #conservation #ecosystems #logging #mining #railroad #infrastructure #forestcover #NLDI #farmland #colonial #LULC #census #climate #hydrology #biogeochemical

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

    Map Reveals How America's Forests Have Changed Over Time
    --
    newsweek.com/america-forests-c <-- shared 2025 media article
    --
    doi.org/10.5194/essd-15-1005-2 <-- shared 2023 paper
    --
    doi.org/10.5281/zenodo.7055086 <-- shared study datasets
    --
    #GIS #spatial #mapping #remotesensing #spatialanalysis #spatiotemporal #copernicus #CONUS #USA #forest #model #modeling #landuse #landcover #change #forestland #agriculture #urban #development #cities #industry #vegetation #America #humanimpacts #settlement #decline #cropland #pasture #grassland #grazing #reforestation #deforestation #urbanization #conservation #ecosystems #logging #mining #railroad #infrastructure #forestcover #NLDI #farmland #colonial #LULC #census #climate #hydrology #biogeochemical

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

    Map Reveals How America's Forests Have Changed Over Time
    --
    newsweek.com/america-forests-c <-- shared 2025 media article
    --
    doi.org/10.5194/essd-15-1005-2 <-- shared 2023 paper
    --
    doi.org/10.5281/zenodo.7055086 <-- shared study datasets
    --
    #GIS #spatial #mapping #remotesensing #spatialanalysis #spatiotemporal #copernicus #CONUS #USA #forest #model #modeling #landuse #landcover #change #forestland #agriculture #urban #development #cities #industry #vegetation #America #humanimpacts #settlement #decline #cropland #pasture #grassland #grazing #reforestation #deforestation #urbanization #conservation #ecosystems #logging #mining #railroad #infrastructure #forestcover #NLDI #farmland #colonial #LULC #census #climate #hydrology #biogeochemical

    #biogeochemical #hydrology #climate #census #lulc #colonial
  42. 💧🌏 Greg Cocks @GregCocks ·

    Map Reveals How America's Forests Have Changed Over Time
    --
    newsweek.com/america-forests-c <-- shared 2025 media article
    --
    doi.org/10.5194/essd-15-1005-2 <-- shared 2023 paper
    --
    doi.org/10.5281/zenodo.7055086 <-- shared study datasets
    --

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

    New Research Aims To Better Predict And Understand Cascading Land Surface Hazards
    --
    phys.org/news/2025-06-aims-cas <-- shared technical article
    --
    doi.org/10.1126/science.adp955 <-- shared paper
    --
    #interconnected #geology #engineeringgeology #landcover #landsurface #GIS #spatial #mapping #landcover #hydrology #water #geomorphology # geomiorphmetry #risk #hazard #extremeweather #climatechange #cascading #cascadinghazards #debrisflow #wildfire #massmovement #landslides #flood #flooding #mudflow #susceptibility #chainreaction #spatialanalysis #spatiotemporal #prediction #management #planning #mitigation #multifactor #sediment #erosion #fluvial #earthquake #fault #faulting #hazardassessment #riskassessment #volcano #vulcanism #sealevelchange #cryosphere #ice #snow #GLOF #humanimpacts #urban #infrastructure #cost #economics #loss #damage #earthobservation #remotesensing #LiDAR #UAV

    #interconnected #geology #engineeringgeology #landcover #landsurface #gis
  44. 💧🌏 Greg Cocks @[email protected] ·

    New Research Aims To Better Predict And Understand Cascading Land Surface Hazards
    --
    phys.org/news/2025-06-aims-cas <-- shared technical article
    --
    doi.org/10.1126/science.adp955 <-- shared paper
    --
    #interconnected #geology #engineeringgeology #landcover #landsurface #GIS #spatial #mapping #landcover #hydrology #water #geomorphology # geomiorphmetry #risk #hazard #extremeweather #climatechange #cascading #cascadinghazards #debrisflow #wildfire #massmovement #landslides #flood #flooding #mudflow #susceptibility #chainreaction #spatialanalysis #spatiotemporal #prediction #management #planning #mitigation #multifactor #sediment #erosion #fluvial #earthquake #fault #faulting #hazardassessment #riskassessment #volcano #vulcanism #sealevelchange #cryosphere #ice #snow #GLOF #humanimpacts #urban #infrastructure #cost #economics #loss #damage #earthobservation #remotesensing #LiDAR #UAV

    #interconnected #geology #engineeringgeology #landcover #landsurface #gis
  45. 💧🌏 Greg Cocks @[email protected] ·

    Small Waterbodies Of Large Conservation Concern - Towards An Integrated Approach To More Accurately Measuring Surface Water Dynamics
    --
    doi.org/10.1016/j.ecolind.2025 <-- shared paper
    --
    “HIGHLIGHTS
    • Hydrologic models were used to assess remotely sensed surface water estimates.
    • The accuracy of satellite surface water estimates diminished for waterbodies < 2 ha.
    • Vegetation reduces accuracy of surface water detection, especially during wet years..."
    #GIS #spatial #mapping #waterbodies #Conservation #planning #surfacewater #Hydrologicmodeling #remotesensing #earthobservation #water #hydrology #hydrographic #model #modeling #spatialanalysis #spatiotemporal #waterbody #small #conservation #inventory #monitoring #accuracy #aquatic #vegetation #climate #NorthAmerica #kettle #landforms #landcover #glaciation #prairie #pothole

    #gis #spatial #mapping #waterbodies #conservation #planning
  46. 💧🌏 Greg Cocks @[email protected] ·

    A New Tool To Characterise The Socio-Environmental Dimensions Of Urban Rivers: Urban River Socio-Environmental Index
    --
    doi.org/10.1016/j.landurbplan. <-- shared paper
    --
    #GIS #spatial #mapping #EnvironmentalEvaluation #SocioEcosystem #SocialConnectivity #UrbanRiver #Indicators #RiverRestoration #spatialanalysis #spatiotemporal #water #hydrology #urban #stream #ecosystem #cities #residents #community #nature #humanimpacts #artifical #engineered #URBS #watershed #drainage #management #restoration #geomorphology #hydrogeomorphology #ecology #urbansprawl #interdisciplinary #biophysical #social #community #France #Paris #MorbrasRiver #macroinvertebrates #riparian #vegetation #reaches #typology #landscape #landuse #landcover #variety #blandscaping

    #gis #spatial #mapping #environmentalevaluation #socioecosystem #socialconnectivity
  47. 💧🌏 Greg Cocks @[email protected] ·

    Observations Reveal Changing Coastal Storm Extremes Around The United States
    --
    doi.org/10.1038/s41558-025-023 <-- shared paper
    --
    #GIS #spatial #mapping #extremeweather #coast #coastal #spatialanalysis #spatiotemporal #model #modeling #communities #publicsafety #climatechange #stormsurge #USA #flood #flooding #risk #hazard #damage #infrastructure #cost #economics #mitigation #insurance #sealevel #SLR #sealevelrise #Bayesian #hierarchical #framework #tideguage #tide #tidal #water #hydrology #hydrography #extremes #intensity #monitoring

    #gis #spatial #mapping #extremeweather #coast #coastal
  48. 💧🌏 Greg Cocks @[email protected] ·

    Assessment Of Western Oregon Debris-Flow Hazards In Burned And Unburned Environments
    --
    doi.org/10.1002/esp.70045 <-- shared paper
    --
    [with more and more catastrophic wildfires, the engineering geology awareness and possible mapping of post-fire debris flow risk is important]
    #GIS #spatial #mapping #wildfire #fire #oregon #risk #hazard #geology #geomorphology #risk #hazard #landslide #massmovement #landslide #postfire #soil #spatialanalysis #spatiotemporal #engineeringgeology #extremeweather #naturaldisaster #debrisflow #mechasisms #property #infrastrure #publicsafety #lossoflife #model #modeling #calibration #water #hydrology #inventory #hazardassessment #geomorphometry

    #gis #spatial #mapping #wildfire #fire #oregon
  49. 💧🌏 Greg Cocks @[email protected] ·

    Assessment Of Western Oregon Debris-Flow Hazards In Burned And Unburned Environments
    --
    doi.org/10.1002/esp.70045 <-- shared paper
    --
    [with more and more catastrophic wildfires, the engineering geology awareness and possible mapping of post-fire debris flow risk is important]
    #GIS #spatial #mapping #wildfire #fire #oregon #risk #hazard #geology #geomorphology #risk #hazard #landslide #massmovement #landslide #postfire #soil #spatialanalysis #spatiotemporal #engineeringgeology #extremeweather #naturaldisaster #debrisflow #mechasisms #property #infrastrure #publicsafety #lossoflife #model #modeling #calibration #water #hydrology #inventory #hazardassessment #geomorphometry

    #gis #spatial #mapping #wildfire #fire #oregon
  50. 💧🌏 Greg Cocks @[email protected] ·

    Kinematic Evolution Of A Large Paraglacial Landslide In The Barry Arm Fjord Of Alaska
    --
    doi.org/10.1029/2023JF007119 <-- shared paper
    --
    doi.org/10.5066/P9EUCGJQ <-- shared technical article
    --
    sciencebase.gov/catalogMaps/ma <-- shared web mapping service
    --
    #GIS #spatial #mapping #massmovement #landslide #engineeringgeology #remotesensing #sitespecific #LiDAR #iFSAR #paraglacial #kinematics #mapping #monitoring #spatialanalysis #spatiotemporal #fedscience #risk #hazard #glacier #retreat #climatechange #Alaska #BarryArm #fjord #hazardassessment #tsunami #wave #BarryGlacier #change #sensors #fieldwork #surveillance #melting #permafrost #Whittier #geophysics #geology #Alaska #geomorphometry #deglaciating
    @USGS

    #gis #spatial #mapping #massmovement #landslide #engineeringgeology