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#landsat8

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Public
💧🌏 Greg Cocks

Identification Of Geothermal Anomalies From Landsat Derived Land Surface Temperature, Mount Meager Volcanic Complex, British Columbia, Canada
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doi.org/10.1016/j.rse.2025.114 <-- shared paper
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“Highlights:
• A novel method for detecting geothermal components from solar energy dominated LST.
• Using LST time series to eliminate temporal variant solar energy input.
• Uncertainty in anomaly identification quantified by probability measure.
• Capable of revealing LST anomalies caused by geothermal, anthropogenic and surface processes..."
#GIS #spatial #mapping #britishcolumbia #BC #solar #geothermal #remotesensing #earthobservation #LST #spatialanalysis #spatiotemporal #naturalresources #volcanic #geology #geostatistics #landsurfacetemperature #satellite #geothermalheatflux #GHF #energybalance #calculation #model #MountMeager #Landsat #landsat8 #hotspring #landslide #massmovement #engineeringgeology #spring #seep #anthropogenic #HEP #hydropower #monitoring #risk #hazard

photo - sampling a fumerole, Job Glacier, Mount Meager, British Columbia, Canada
Inverted resistivity model from AMT data showing the subsurface rock resistivity structure in South Mount Meager (figure modified from Hormozzade Ghalati et al., 2022). a) resistivity slice at 500 m elevation (asl) showing two distinct high conductive anomalies and locations of the cross sections. Cross sections b) and c) in W-E direction, and d) and e) in N-S direction. Anomaly C1 is spatially in accordance with the occurrence of hot-warm spring swarms along the margin of the volcanic complex. Anomaly C2 appears in a circular shape, coincident spatially with the core area of the known deep geothermal prospect.
a) Map showing the location of Mount Meager Volcanic Complex (MMVC) and its relationship with the Garibaldi Volcanic Belt in western Canada (modified from Stewart, 2002). BR (Bridge River), CC (Mt. Cayley), EV (Elaho Valley), MG (Mt. Garibaldi), MC (Mountain Creek), WP (Watts Point). The red rectangular marks the study area. b, a Landsat-8 colour image (2017-07-18) showing details of the study area by a summer view of the MMVC with glacier (blue) at high altitude and vegetation cover (green) along the river valley. The red circle indicates locations of geothermal exploration wells, and orange polygons, the known hot springs.
Enlarged GHF anomaly maps and Google images explaining some of the possible anthropogenic and natural causes of high LST anomalies: a) GHF component of East Meager Anomaly; b) Google Earth image showing hydro-power station related facilities, and open quarries for local construction and road maintenance; c) GHF component of North Meager; d) Google Earth image showing a possible landslide site coincident with the anomalies.
Public
💧🌏 Greg Cocks

New Report Ranks USGS Assets High Among Earth Observations Systems Surveyed
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usgs.gov/news/featured-story/n <-- link to article
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usgeo.gov/working-groups.html <-- USGEO Working Group reports and summaries
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usgeo.gov/uploads/EOA%20Agricu <-- 2023 Earth Observations Assessment Report - Agriculture and Forestry
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#GIS #spatial #mapping #remotesensing #earthobservation #landsat #summary #overview #agriculture #forestry #global #farming #ranching #hydrology #water #watersheds #waterresources #watersecurity #naturalresources #landmanagers #landcover #landuse #USGEO #NLIP #society #satellite #metrics #landsat8 #landsat9 #economic #economicbenefits #production #sustainability #resourcemanagement
@USGS @USGEO @nasa @NOAA

graphic - Landsat impact metrics
graphic - overview of the 2023 Earth observation assessment focused on agriculture and forestry, including metrics
aerial photo - Landsat was found to be one of the most impactful Earth observation inputs surveyed in the EOA. Here is a Landsat image captured above an agricultural development, Canterbury, New Zealand
images / models / 3d representations - Elevation data was also found to be among the most impactful for agriculture and forestry. By processing lidar point clouds (left) data to bare earth digital elevation model (right), the vegetation is stripped away to reveal parts of landscapes that are masked by forested canopies or other features. For example, in dense forests, landslides--especially old landslides--might be invisible on aerial images and hard to detect from the ground but can be seen with the aid high resolution elevation data.
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