#geostatistics — Public Fediverse posts on home.social

💧🌏 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] · 2026-08-18 · 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] · 2026-08-04 · 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] · 2026-06-17 · 05:25 UTC

Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
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https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
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https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared (earlier) paper
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H/T @abhinav Alangadan
“Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
[The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
[Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
#permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

#kinnaur #permafrost #distribution #gis #spatial #mapping

💧🌏 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
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https://doi.org/10.1016/j.ijdrr.2026.106132 <-- shared paper
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https://www.sciencebase.gov/catalog/item/697ba3e5b66b0197c3043d2f <-- shared, related open data source
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[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] · 2026-04-07 · 03:43 UTC

Scale Dependence In Remotely Sensed Biodiversity: Leveraging Continental-Scale Imaging Spectroscopy From The National Ecological Observatory Network [spatial analysis]
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https://doi.org/10.1002/rse2.70068 <-- shared paper
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https://doi.org/10.1038/s41559-022-01702-5 <-- shared paper
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#spectraldiversity #spectroscopy #spatialscale #US #NEON #NationalEcologicalObservatoryNetwork #species #ecology #humanimpacts #remotesensing #biodiversity #earthobservation #GIS #spatial #mapping #scale #scale #diversity #metrics #ecosystems #spectral #richness #scaledependency #principalcompoenent #divergence #spatialanalysis #raster #topography #climate #geomorphology #regression #geostatistics #vegetation #plant #area #region #largescale #continent #forest #tree

#spectraldiversity #spectroscopy #spatialscale #us #neon #nationalecologicalobservatorynetwork

💧🌏 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
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https://doi.org/10.1007/s41748-026-01089-4 <-- shared paper
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#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] · 2026-03-10 · 03:20 UTC

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

Planetary Ecologist @[email protected] · 2025-11-15 · 07:14 UTC

Point counting (geology) (Geology ⛰️)

In geology, point counting is a method to determine the proportion of an area that is covered by some objects of interest. In most cases the area is a thin section or a polished slab. The objects of interest vary between subdisciplines and can for example be quartz or feldspar grains in sedimentology, any type...

https://en.wikipedia.org/wiki/Point_counting_(geology)

#PointCounting #Geology #Petrology #Geostatistics #IgneousPetrology #GeologicalTechniques