#spatiotemporal — Public Fediverse posts on home.social

💧🌏 Greg Cocks @[email protected] · 2026-09-01 · 02:30 UTC

Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
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https://doi.org/10.31223/X5MV3R <-- shared paper
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https://landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
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https://gee-community-catalog.org/projects/landcast/ <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
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H/T @andrew Zimmer || Geographer | Research Scientist @ Oak Ridge National Laboratory
“LSM-TS provides annual global estimates of ambient population from 1975–2024 at ~90 m resolution, creating a spatially and temporally consistent reconstruction of population change designed for longitudinal analysis. Reconstruction of historical populations are driven by current LandScan Mosaic building-level population distributions, scaled by built-surface change from #GHSL … and calibrated to annual national-level estimates from U.S. Census Bureau…”
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“LandScan is a globally recognized, R&D100-winning population data platform developed at Oak Ridge National Laboratory (ORNL). Its datasets provide human population distribution estimates down to 100 meter grid resolution. LandScan supports disaster response, humanitarian aid, environmental analysis, and urban planning by providing insights into where people live and how they move…”
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“Gridded population data support assessments of human exposure, settlement change, infrastructure demand, and access to services, yet global datasets combining annual coverage over multiple decades with fine spatial resolution remain limited. LandScan Mosaic Time Series provides 50 annual estimates of global ambient population distribution at 3 arc-second resolution from 1975 through 2024. The series is anchored to the 2024 LandScan Mosaic surface, produced through a building-level population modeling workflow. Historical surfaces are reconstructed using annualized changes in built surface and derived first-level administrative population trajectories. Every layer, including 2024, is normalized to administrative targets scaled to annual country totals from the U.S. Census Bureau International Database. The dataset is distributed as 50 single-band Cloud Optimized GeoTIFFs on an identical WGS84 grid, with values representing estimated persons per cell. Its common grid and methodology support fine-scale longitudinal analysis of ambient population distribution while documented validation and limitations guide appropriate reuse…”
#demographics #change #spatiotemporal #1975 #2024 #population #change #grid #global #LandScan #POPGRID #LSMTS #ambientpopulation #GHSL #OakRidge #ORNL #usecase #opendata #disasterresponse #humanitarianaid #environmentalanalysis #urbanplanning #humanexposure #settlementchange #city #rural #infrastructure #demand #accesstoservices #GIS #spatial #mapping #building #outline #model #modeling
@OAK Ridge National Laboratory | National Security Sciences at ORNL | @POPGRID Data Collaborative

#ghsl #demographics #change #spatiotemporal #population #grid

💧🌏 Greg Cocks @[email protected] · 2026-08-27 · 14:34 UTC

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
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https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
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H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
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“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

#gis #spatial #mapping #modis #trmm #riverdischarge

💧🌏 Greg Cocks @[email protected] · 2026-08-27 · 14:28 UTC

Historic Snowfall Map Shows States Bracing For Significant Snow Amid El Niño [North America]
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https://www.mensjournal.com/news/historic-snowfall-map-shows-states-bracing-for-significant-snow-amid-el-nino <-- shared technical media article
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https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/ensodisc.shtml <-- shared link to NOAA release ‘El Niño/Southern Oscillation (Enso) Diagnostic Discussion - issued By Climate Prediction Center/NCEP/NWS | 13 August 2026’
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https://www.cnn.com/2026/08/20/weather/super-el-nino-us-winter-forecast-climate <-- shared media article
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H/T @Mens Journal
“The National Oceanic and Atmospheric Association [NOAA] updated its prediction for the El Niño, which is strengthening and now has a greater than 90% chance of a very strong event during the Northern Hemisphere fall and winter 2026-27…
Additionally, the NOAA revealed that during the October-December 2026 season, there is now a 69% chance of a historic event that would exceed the strength of previous El Niño events dating back to 1950. Historical records from the NOAA only date back to 1950, but other reports suggest this could be one of the strongest El Niños in history.
This El Niño “is something that’s very unusual, if not a once in a lifetime type of (strength),” according to Nat Johnson, a meteorologist with the Geophysical Fluid Dynamics Laboratory at the National Oceanic and Atmospheric Administration.
El Niño is a natural climate cycle marked by warmer than average water temperatures along the equator in the Pacific Ocean. The warmer water triggers corresponding shifts in the atmosphere that have a domino-like influence on weather patterns around the globe – usually in ways that are largely predictable well in advance…”
#weather #climate #USA #forecast #fedscience #fedservice #NOAA #ElNiño #ElNino #snow #snowfall #climatecycle #water #hydrology #rainfall #precipitation #NorthAmerica #atmosphere #weatherpatterns #model #modeling #spatialanalysis #spatiotemporal #spatial #mapping #NorthernHemisphere #global
@NOAA | @nws

#weather #climate #usa #forecast #fedscience #fedservice

💧🌏 Greg Cocks @[email protected] · 2026-08-18 · 03:21 UTC

Spatiotemporal Distribution, Climatic Factors, And Seasonal Precipitation Patterns Characterizing 66 Years Of Widespread Shallow Landslide Events In Piedmont (Northwestern Italy)
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https://doi.org/10.3389/feart.2026.1884300 <-- shared paper
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“Widespread shallow landslide events constitute one of the primary drivers of recurrent societal and economic losses in mountain regions. Although the influence of climate variability on landslide frequency and magnitude has been widely recognized, long-term, event-level analyses linking spatiotemporal landslide patterns to precipitation climatology at the regional scale remain scarce. This study presents a statistical analysis of 128 widespread shallow landslide events recorded in Piedmont, northwestern Italy, over the 66-year period 1960–2025, cross-referenced against the regional precipitation climatology. Events were characterized by season, primary physiographic unit, provincial coverage, and an ordinal magnitude index (scale 1–7) encoding combined spatial extent and estimated landslide count. Results indicate that no statistically significant monotonic trend in event frequency was detected over the study period (mean rate: 1.97 events yr⁻1); however, a moderate positive correlation was established between event magnitude and the number of provinces affected (R2 = 0.51, p < 0.00), validating the magnitude index as a proxy for spatial footprint. Mean event magnitude reached its highest value in the 2010s (3.71), while all five events of magnitude ≥ 6 occurred in autumn. The summer fraction of the catalogue increased markedly, from 6% in the 1960s to 31% in the 2000s and 25% in the 2020s, concurrent with stable or declining summer mean precipitation totals, consistent with Clausius–Clapeyron amplification of convective intensity under documented regional warming. Cross-analysis with regional records identifies three tiers of rainfall–landslide coupling: a stationary direct seasonal coupling; a non-stationary, strengthening intensity-mediated summer coupling; and a structural susceptibility-mediated spatial decoupling whereby the driest provinces generate the highest landslide occurrence frequencies due to the lower triggering thresholds characteristic of Tertiary Piedmont Basin sedimentary environments. A post-hoc assessment of the triggering thresholds used for regional shallow landslide early warning system demonstrates superior detection performance for high-magnitude autumn events (hit rate up to 89% for the 2000–2025 sub-period) and identifies sub-daily convective accumulation windows in summer as the primary domain requiring threshold recalibration…”
#massmovement #landslide #engineeringgeology #Italy #Piedmont #NorthernItaly #weather #rainfall #precipitation #climate #risk #hazard #corrleation #relationship #earlywarningsystems #damage #loss #community #infrastructure #mountain #spatiotemporal #mapping #spatialanalysis #statistics #geostatistics #climatology #regional #scale #weatherpatterns #physiography #geomorphology #water #hydrology #hydrogeomorphology #geology #soils

#massmovement #landslide #engineeringgeology #italy #piedmont #northernitaly

💧🌏 Greg Cocks @[email protected] · 2026-08-18 · 03:01 UTC

Widespread Landslide Activity in an Extreme Wet Season and Implications for Regional Sediment Management, Eastern San Francisco Bay Area, California
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https://doi.org/10.1029/2026EA005227 <-- shared paper
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H/T @amy East, Ph.D., P.G. | Researcher integrating geoscience and climate-change preparedness
“[This paper (link above) is] a collaboration with [the H/T’s] colleagues from [the] USGS Landslide Hazards Program, who mapped over 8,900 landslides in the eastern San Francisco Bay Area during an extreme wet winter.
How much sediment does such an extreme winter produce, from landslides or in stream discharge? How does that compare with long-term sediment production and landscape denudation rates?
[They] f[o]nd that landslide sediment mobilization is comparable to long-term denudation rates, emphasizing the role of extreme events in long-term sediment production. However, one extreme wet year has a negligible effect toward counteracting ongoing problems of sediment deficit in San Francisco Bay: to keep pace with sea-level rise, extreme wet conditions would need to occur in 50 out of the next 75 years…”
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"PLAIN LANGUAGE SUMMARY: Watersheds will likely produce more sediment in a warmer future with more extreme rain, primarily through landslides in steep terrain. This study examines how an extremely wet season affected sediment production and transport in the eastern San Francisco Bay area, California. By mapping and measuring 8,928 landslides, [they] found that rare, extreme rain conditions are likely responsible for the vast majority of long-term hillslope erosion rates in this region. However, due to long residence times for sediment on hillslopes and in stream channels, a maximum of 1%–2% of that newly mobilized landslide material could have potentially contributed to sediment carried by streams into the Bay that year. Even extremely wet years cannot provide enough sediment for Bay wetlands and shorelines to keep pace with rising sea levels. To meet the demand for sediment in the Bay, such extreme rain and sediment production would need to occur in most years, which is not realistic. To restore wetlands and protect shorelines, managers likely will need to supplement the coastal system with repurposed dredged material…”
#massmovement #soil #water #hydrology #hydrography #geology #soils #geomorphometry #hydrogeomorphology #geomorphology #landslide #masswasting #climatechange #extremeweather #precipitation #rainfall #weather #climate #mapping #engineeringgeology #mapping #SanFrancisco #BayArea #USA #California #fedscience #fedservice #oublicgood #sediment #stream #discharge #extremewinter #sealevelrise #SLR #hillslope #erosion #sedimentation #tidal #wetlands #coast #coastline #shoreline #GIS #spatial #spatialanalysis #spatiotemporal #watershed
#USGS | #USGSLandslideHazardsProgram

#massmovement #soil #water #hydrology #hydrography #geology

💧🌏 Greg Cocks @[email protected] · 2026-08-07 · 04:46 UTC

Coastal Flooding At Predictable Hours
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https://doi.org/10.1038/s41467-026-75710-5 <-- shared paper
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H/T @bruna Alves | Nature Communications | Editor
“When do coastal floods happen? ⏱️🌊
A new study [link above] shows that, in many tide-dominated coastal regions, flooding tends to occur at specific and recurring times of day. Rather than being completely random, flood events often cluster around predictable hours driven by local tidal patterns.
By analysing coastal flood observations from the UK and the US, [the authors] show[ed] that the timing of flood events can be highly structured, with some locations experiencing floods disproportionately during certain parts of the day.
This adds a temporal dimension to coastal flood risk. We often focus on how frequently floods occur, how severe they are, and where they happen. This study highlights that when they occur may also matter, particularly as rising sea levels increase the frequency of coastal flooding.
The findings provide a useful perspective for coastal adaptation, risk communication, and emergency planning…”
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“Flooding is typically perceived as a sudden and unpredictable hazard. Here, [they] show that recurrent flooding can occur at highly predictable times in tidally dominated coastal systems. This predictability stems from phase-locking of tidal constituents and constituent pairs with the solar day, causing peak tides to recur at consistent local times set by regional tidal propagation. Using tide-gauge records from the United States and the United Kingdom, [they] quantif[ied] the intraday timing of coastal flood events and show strong clustering at specific hours, particularly where semidiurnal or mixed tides dominate. For example, floods in Boston cluster around noon and midnight, whereas in southern California they occur in the morning. Sites with stronger non-tidal variability show weaker clustering. This temporal predictability extends beyond nuisance flooding to larger consequential events involving inundation, road closures and infrastructural damage, highlighting opportunities for anticipatory risk communication, emergency planning and time-sensitive coastal adaptation…”
#coast #coastal #flood #flooding #spatialanalysis #spatiotemporal #time #statistics #geostatistics #tide #tidal #timing #temporal #floodrisk #risk #hazard #sealevel #sealevelrise #climatechange #emergency #planning #tideguage #UK #USA #innundation #infrastructure #transportation #riskcommunication

#coast #coastal #flood #flooding #spatialanalysis #spatiotemporal

💧🌏 Greg Cocks @[email protected] · 2026-08-06 · 04:43 UTC

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

#land #subsidence #processes #verticallandmotion #slr #sealevel

💧🌏 Greg Cocks @[email protected] · 2026-08-05 · 05:02 UTC

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

#gis #spatial #mapping #googleearthengine #machinelearning #remotesensing

💧🌏 Greg Cocks @[email protected] · 2026-08-04 · 04:50 UTC

Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
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https://openscholar.uga.edu/record/26998/files/Zhang_uga_0077E_16145.pdf <-- shared technical publication / dissertation
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https://www.aag.org/award-grant/william-l-garrison-award-for-best-dissertation-in-computational-geography/ <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
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[again, way outside any expertise I might have, but fascinating spatial analysis use case…]
H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
“[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”
#explainable #causal #AI #model #modeling #PublicHealth #GIS #spatial #mapping #spatialanalysis #spatiotemporal #AAG2026 #AAG #Award #geostatistics #Georgia #health #risk #hazard #cardiacarrest #cardiovacscular #usecase #metrics #midocine #urbanplanning #resilience #survival #OutofHospital #PhD #Dissertation #CardiacArrest #AutomatedExternalDefibrillator #SpatialOptimization #GeographicallyExplainableArtificialIntelligence #GeoAI #GeoXAI #SpatiallyAwareCausalInference #OverlayedSpatioTemporalOptimization #healthcare #medical #intervention #GIS #spatial #mappingt #spatialanalysis #spatiotemporal #heart #heartattack #AED #survival #survivaloutcomes #machinelearning #AI #publichealth #healthgeographers #counterfactual #explainable #deeplearning #model #modeling

#explainable #causal #ai #model #modeling #publichealth

💧🌏 Greg Cocks @[email protected] · 2026-07-21 · 19:38 UTC

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
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https://doi.org/10.1029/2026EF008578 <-- shared paper
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https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
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https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
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https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
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H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

#arctic #circumpolar #permafrost #model #modeling #spatialanalysis

💧🌏 Greg Cocks @[email protected] · 2026-07-15 · 02:49 UTC

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
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https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
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"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

#deforestation #probability #machinelearning #algorithms #ai #gazipur

💧🌏 Greg Cocks @[email protected] · 2026-06-10 · 03:39 UTC

Decoupling Of Surface Water Storage From Precipitation In Global Drylands Due To Anthropogenic Activity
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https://doi.org/10.1038/s44221-024-00367-7 <-- shared paper
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“The availability of surface water in global drylands is essential for both human society and ecosystems. However, the long-term drivers of change in surface water storage, particularly those related to anthropogenic activities, remain unclear. Here [they] use[d] multi-mission remote sensing data to construct monthly time series of water storage changes from 1985 to 2020 for 105,400 lakes and reservoirs in global drylands. An increase of 2.20 km³ per year in surface water storage is found primarily due to the construction of new reservoirs. For lakes and old reservoirs (constructed before 1983), conversely, the trend in storage is minor when aggregated globally, but they dominate surface water storage trends in 91% of individual global dryland basins. Further analysis reveals that long-term storage changes in these water bodies are primarily linked to anthropogenic factors - including human-induced warming and water-management practices - rather than to precipitation changes, as previously thought. These findings reveal a decoupling of surface water storage from precipitation in global drylands, raising concerns about societal and ecosystem sustainability…”
#water #hydrology #hydrography #waterstorage #waterresources #surfacewater #global #drylands #precipitation #rainfall #watersecurity #ecosystems #habitat #publichealth #anthropogenic #GIS #spatial #mapping #remotesensing #earthobservation #spatiotemporal #spatialanalysis #monitoring #geostatistics #engineering #reservoirs #infrastructure #lakes #waterbodies #globalwarming #climatechange #sustainability #planning #baseline

#water #hydrology #hydrography #waterstorage #waterresources #surfacewater

💧🌏 Greg Cocks @[email protected] · 2026-06-09 · 15:13 UTC

On The Use Of Rainfall Time Series For Regional Landslide Prediction By Means Of Functional Regression
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https://doi.org/10.1016/j.enggeo.2026.108860 <-- shared paper
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#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

💧🌏 Greg Cocks @[email protected] · 2026-05-12 · 04:28 UTC

Half Of European Municipalities Have Fewer Inhabitants Than 60 Years Ago
--
https://correctiv.org/aktuelles/2026/04/21/die-haelfte-der-europaeischen-gemeinden-hat-weniger-einwohner-als-vor-60-jahren/ <-- shared technical / spatiotemporal article
--
https://data.jrc.ec.europa.eu/dataset/37fcacbf-12e2-4b31-b1af-83117a74b2c7 <-- shared European Joint Research Centre (JRC) ARDECO Local Population Time-Series – 1961-2024
--
#GIS #spatial #mapping #emptyspain #demographics #population #agingpopulation #declining #birthrate #immigration #emigration #shrinking #interactive #webmap #spatialanalysis #spatiotemporal #Europe #geostatistics #logistics #rural #urban #city #transportation #cost #economics #services #infrastructure #workers #qualityoflife #planning
@JointResearchCentre #JRC @europeancommission @correctiv_org

#gis #spatial #mapping #emptyspain #demographics #population

💧🌏 Greg Cocks @[email protected] · 2026-04-21 · 02:38 UTC

Influence Of Modeling Assumptions On Pedestrian Evacuation Success For Non-Eruptive Lahar Hazards At Mount Rainier, Washington
--
https://doi.org/10.1016/j.ijdrr.2026.106132 <-- shared paper
--
https://www.sciencebase.gov/catalog/item/697ba3e5b66b0197c3043d2f <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-03-31 · 03:50 UTC

Assessment of Shoreline Change in Southeast Ireland Using Geospatial Techniques
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https://doi.org/10.3390/su18073280 <-- shared paper
--
"... KEY INSIGHTS:
• Coastlines are highly dynamic — 57% accretion vs 42% erosion
• Strong contrasts between east-facing (Irish Sea) and south-facing (Atlantic) coasts
• Identification of critical erosion hotspots (e.g., Tramore) and accretion zones in embayments
• Coastal change is driven by a combination of wave climate, sediment availability, geology, and human activity
--
#GIS #spatial #mapping #Ireland #coast #coastal #dynamics #erosion #accretion #shoreline #change #digitalshoreline #spatialanalysis #spatiotemporal #remotesensing #earthobservation #SoutheastIreland #embayments #wave #climate #stormsurge #geology #humanimpacts #coastalmanagement #risk #hazard #mitigation #sealevel #RSL #risingsealevels #climatechanage #adaption #extremeweather #stormintensity #planning #monitoring #sustainable #Landsat #satellite #regional

#gis #spatial #mapping #ireland #coast #coastal

💧🌏 Greg Cocks @[email protected] · 2026-03-19 · 04:59 UTC

A Review Of Current Best Practices And Future Directions In Assimilating GRACE/-FO Terrestrial Water Storage Data Into Numerical Models
--
https://doi.org/10.5194/hess-30-985-2026 <-- shared technical article/review 📖 🔗
--
https://grace.jpl.nasa.gov/ <-- @nasa @JPL home page, Gravity Recovery and Climate Experiment (GRACE)
--
#GRACE #GRACEFO #DataAssimilation #EarthObservation #Hydrology #WaterResources #ClimateScience #RemoteSensing #CRC1502 #GIS #spatial #mapping #remotesensing #satellite #earthobservation #water #hydrology #model #modeling #landsurface #hydrogeomorphology #geomorphology #geomorphometry #waterresources #waterstorage #monitoring #groundwater #trends #spatialanalysis #spatiotemporal #droughts #floods #extremeweather #literature #review #summary #watercycle #geostatistics #humanimpacts #irrigation #watermanagement #anthropogenic #pumping #extraction #AI #ML #machinelearning

#grace #gracefo #dataassimilation #earthobservation #hydrology #waterresources

💧🌏 Greg Cocks @[email protected] · 2026-03-18 · 14:40 UTC

What Shapes Earthquake Risk In Bangladesh? Geospatial Insights From Physical And Social Factors Using A Spatial Meta-Regression Approach
--
https://doi.org/10.1007/s41748-026-01089-4 <-- shared paper
--
#EarthquakeRisk #GIS #Bangladesh #DisasterManagement #SEAsia #GIS #spatial #mapping #risk #hazard #earthquake #naturalhazard #urban #rural #infrastructure #publicsafety #model #modeling #spatialanalysis #spatiotemporal #geomorphology #geology #tectonics #social #cultural #demographics #fault #faulting #factoranalysis #seismichazard #model #modeling #socioeconomics #geophysics #remotesensing #geostatistics #landuse #framework #magnitude #engineering #buildingcode #mitigation #zoning #development #growth #vulnerability

#earthquakerisk #gis #bangladesh #disastermanagement #seasia #spatial

💧🌏 Greg Cocks @[email protected] · 2026-03-17 · 05:02 UTC

🌟 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 [Google Research’s] 𝐆𝐫𝐨𝐮𝐧𝐝𝐬𝐨𝐮𝐫𝐜𝐞 - 𝐀𝐧 𝐨𝐩𝐞𝐧 𝐬𝐨𝐮𝐫𝐜𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐨𝐟 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜 𝐟𝐥𝐨𝐨𝐝 𝐞𝐯𝐞𝐧𝐭𝐬 𝐟𝐫𝐨𝐦 𝐧𝐞𝐰𝐬 𝐚𝐫𝐭𝐢𝐜𝐥𝐞𝐬.
--
https://doi.org/10.31223/X5RR2K / https://eartharxiv.org/repository/view/12083/ <-- shared paper
--
https://zenodo.org/records/18647054 <-- shared link to associated dataset
--
https://sites.research.google/gr/floodforecasting/ <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-03-14 · 01:32 UTC

Populus Tremuloides [Aka Aspen] As A Natural Fire Barrier In Canada’s Boreal Forest Under A Changing Climate
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https://doi.org/10.1016/j.foreco.2026.123671 <-- shared paper 🔗
--
#GIS #spatial #mapping #flammable #barrier #extremeweather #fireweather #fireactivity #wildfire #bushfire #fire #firebreak #FireKnowledge #FireEcology #AspenTrees #Canada #spatialanalysis #spatiotemporal #forest #risk #hazard #damage #climatechange #warming #tree #mixed #diversity #species #perimeter #ecozones #ecology #geostatistics #forest #forestcover #burn #severity #stand #burnarea #management #aspen #populustremuloides #Fireactivity #Burnseverity #Fireweather #Deciduous #Phenology
#NRCANScience #NRCAN #NaturalResourcesCanada

#gis #spatial #mapping #flammable #barrier #extremeweather

💧🌏 Greg Cocks @[email protected] · 2026-03-10 · 03:20 UTC

Drivers Of Forest Disturbance In Southeast Asia [incl. spatial analysis]
--
https://doi.org/10.1016/j.jag.2026.105220 <-- shared paper 🔗
--
https://github.com/shijuanchen/SEA_forest_dis <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-03-03 · 03:12 UTC

Detection And Spatial Modelling Of Trends In UK Rainfall Frequency
--
https://doi.org/10.1080/02626667.2026.2622458 <-- shared paper
--
#GIS #spatial #mapping #extremeweather #rainfall #pluvial #frequency #trends #nonstationarity #extremevalue #model #modeling #monitoring #UnitedKingdom #UK #Britian #England #Scotland #Wales #precipitation #spatialanalysis #spatiotemporal #drainage #monitoring #planning #resilience #naturalhazards #flooding #flood #water #hydrology # #sewerage #sewage #wastewater #waterways #pollution #spills #releases #stormwater #erosion #massmovement #landslides #engineeringgeology #risk #hazard #climatic #covariates #raingauges #maximums #duration #winter #NorthernEngland #NorthAtlanticOscillation #NAO #index #metrics #annualmaximum #AMAX #seasonalmaximum #SMAX #peaksoverthreshold #POT #statistics #geostatistics #MetOffice #weather #climate

#gis #spatial #mapping #extremeweather #rainfall #pluvial

💧🌏 Greg Cocks @[email protected] · 2026-03-02 · 02:50 UTC

Ice-Patch Collapse And Early-Warning Implications From A Himalayan Flash Flood - Emerging Cryo-Hydrological Hazards Under Deglaciation
--
https://doi.org/10.1038/s44304-026-00191-x
--
https://youtu.be/zZXER7DocfY?si=lYOE_a6YwigP3-mB
--
#GIS #spatial #mapping #glacier #glacial #cryosphere #risk #hazard #flood #flooding #flashflood #earthobservation #cryohydrology #ice #snow #water #hydrogy #Himalaya #Himalayan #extremeweather #monitoring #risk #hazard #forecasting #DharaliFlashFlood #CryosphereScience #Deglaciation #EarlyWarning #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #GlacierResearch #OpenScience #highmountain #mountain #slope #steepness #erosion #fluvial #nivation #Uttarakhand #India #SrikantaGlacier #DEM #elevation #satellite #chronology #reconstruction #ablation #debris #sedimentation #debrisflow #meltwater #debris #surge #massmovement #engineeringgeology #cryospherichazard #terrain #spatialanalysis #spatiotemporal #earlywarning #naturalhazard #channel #topography #geography #hydrogeomorphology #geomorphology #Dharali

#gis #spatial #mapping #glacier #glacial #cryosphere

💧🌏 Greg Cocks @[email protected] · 2026-02-24 · 03:53 UTC

Challenges In Comparing National Forest Statistics - Canada As A Case Study
--
📑 https://pubs.cif-ifc.org/doi/abs/10.5558/tfc2026-006 <-- shared paper
--
H/T Michael Wulder |Senior Research Scientist at Natural Resources Canada
“Canada is a forest nation. Its forests are spatially extensive, ecologically diverse, and of global importance. While national level forest statistics are used for international reporting and comparison, they often fail to reflect the distinctive structure and stewardship context of Canada’s forest estate, which includes millions of hectares of remote, unmanaged forests alongside actively managed zones governed by provincial and territorial stewardship frameworks...”
--
#forest #Canada #national #geostatistics #forestestate #remote #unmanaged #contrast #managed #harvesting #naturalresources #resource #spatialanalysis #spatiotemporal #GIS #spatial #mapping #assessment #value #metrics #stewardship #management #monitoring #statistics #health #foresthealth #science
#NRCAN #NRCANScience #NaturalResourcesCanada

#forest #canada #national #geostatistics #forestestate #remote

💧🌏 Greg Cocks @[email protected] · 2026-02-14 · 17:58 UTC

Adaptive Thresholding (AT) For Snowmelt Detection With Calibrated Enhanced-Resolution Brightness Temperatures (CETB) - Timing And Regional Patterns For Case Study Of Alaska
--
https://doi.org/10.1016/j.srs.2026.100390 <-- shared paper
--
“HIGHLIGHTS:
• Data-driven Adaptive Thresholding (AT) for snowmelt detection using PMW CETB data.
• AT improves melt detection accuracy to ≤ 1.0 day MAE over legacy methods in Alaska.
• AT captures spatial and regional melt variability linked to snow class and terrain.
• AT is sensor-agnostic, scalable across frequencies and polarizations for broad use..."
#snowmelt #winter #snow #spatialanalysis #GIS #spatial #mapping #spatiotemporal #water #waterresources #hydrology #spatialdata #AdaptiveThresholding #Passivemicrowave #remotesensing #EnhancedResolutionData #Snowmeltdetection #DiurnalAmplitudeVariation #US #USA #Alaska #geostatistics #melt #melting #monitoring #regional