Lmst

Zonal Statistics in Python with Earth Engine and Colab (New guide)

Inside:
• Template for processing multiple rasters at once
• Unified reducer (mean/median/std/min/max in one pass)
• Convert to GeoDataFrame
• Export to GPKG/CSV + upload to Google Drive
• Visual maps (relief, NDVI, soil temperature)

🎁 Bonus: ready-to-use Colab notebook at the end.

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#Geospatial #GIS #EarthEngine #Python #Geemap #ZonalStatistics #NDVI #DataScience

Zonal statistics in Google Earth Engine in a single pass: how to combine terrain and NDVI into a single stack, calculate mean/median/standard deviation/min/max for thousands of polygons, and immediately visualize and export the results. Assembling the package + ready-made script.

Watch the full tutorial here:
https://medium.com/@anton.biatov/how-to-combine-topography-and-vegetation-a-practical-guide-to-zonal-statistics-in-google-earth-d8167615937b

#GIS #EarthEngine #RemoteSensing #NDVI #DataScience #geospatial #EarthObservation #gischat

🛰️ Using satellite vegetation data (eVIIRS NDVI) to analyze North Korea's 2025 crop conditions:
📊 Key findings:

Vegetation index dropped slightly: 0.834 → 0.813 (-0.021)
Minimal change suggests harvest similar to 2024 or slightly worse
Data from NOAA's JPSS satellite via USGS EarthExplorer

⚠️ Limitation: Basic analysis using only vegetation imagery. Comprehensive assessment would need soil, weather, fertilizer data, etc.
#NorthKorea #Agriculture #Satellite #NDVI

https://www.dailynk.com/english/satellite-data-shows-north-korea-2025-crop-conditions-slightly-worse-2024/

Liancheng Zhang et al. explore the impact of climate change and human activities on the #ForestCoverageArea and normalized difference vegetation index ( #NDVI ) of the Altai Mountains.

#ForestPolicy | #SnowmeltWater | #PartialLeastSquaresMethod

https://doi.org/10.1093/jpe/rtaf001

Dynamics of annual forest cover in the Altai Mountains from 1992 to 2020 (a: Altai Mountains; b: evergreen needle-leaved forest; c: deciduous needle-leaved forest; d: mixed forest; e: deciduous broad-leaved forest; f: low mountain belt; g: middle mountain belt; h: subalpine belt; i: alpine belt).

Do any #RStats users have a suggestion for R packages to access Sentinel-2 data, specifically NDVI now that {sen2r} is no longer maintained? I thought I saw something earlier this week but now I can't find it again. 🙏 #Sentinel2 #NDVI #DataAcess

【EDITOR'S CHOICE】
Are regional #Precipitation_productivityRelationships robust to decadal-scale dry period?

#Grassland | #NetPrimaryProductivity | #Drought | #ClimateChange | #NDVI

https://doi.org/10.1093/jpe/rtac008

Annual PPT and aridity index (the ratio of PPT to PET) for the grassland region of Inner Mongolia in northern China for the period 1982–2015.

Weeds Detection In A Citrus Orchard Using Multispectral UAV Data And Machine Learning Algorithms - A Case Study From Souss-Massa Basin, Morocco
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https://doi.org/10.1016/j.rsase.2025.101553 <-- shared paper
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#GIS #spatial #mapping #seedsdetection #citrusorchard #unmannedaerialvehicle #UAV #multispectralsensors #machinelearning #AI #modeling #model #deeplearning #agriculture #orchard #SoussMassa #Morocco ##citrus #groundcontrol #pointcloud #DSM #imagery #NDVI #NDWI #MLC #data #production #food #management #water #hydrology #fertiliser

IMAP Testing and Integration at NASA's Marshall Space Flight Center

#Agriculture #Crop/PlantYields #Heliosphere #Imap #Iowa #LandSurface/agricultureIndicators #NDVI #PlantCharacteristics

⏩ 1 new picture and 2 new videos from NASA (SVS) https://commons.wikimedia.org/wiki/Special:ListFiles?limit=13&user=OptimusPrimeBot&ilshowall=1&offset=20250412125831

2020_Iowa_Derecho_(SVS31341_-_Iowa_derecho_landsat_sentinel_ndvi_2048x1152).png

2020_Iowa_Derecho_(SVS31341_-_Iowa_derecho_rgb_l8_20200811_2048x1152).png

The Harmonized Landsat Sentinel-2 Project Releases Vegetation Indices
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https://www.earthdata.nasa.gov/news/harmonized-landsat-sentinel-2-project-releases-vegetation-indices <-- shared technical article
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https://hls.gsfc.nasa.gov/ <-- shared NASA Harmonized Landsat Sentinel-2 Project page
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https://youtu.be/63ljR84c85M?si=3Q0PDOuFgKG8nIrA <-- shared NASA overview video
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#GIS #spatial #mapping #fedscience #OPERA #remotesensing #earthobservation #satellite #opendata #global #vegetation #indices #HLS #Landsat #Sentinel #HLSL30VI #HLSSS30VI #greenness #moisturecontent #burned #change #OLI #MultiSpectral #NDVI
@nasa | @ESA

Oceanic Evasion Fuels Arctic Summertime Rebound Of Atmospheric Mercury And Drives Transport To Arctic Terrestrial Ecosystems
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https://doi.org/10.1038/s41467-025-56300-3 <-- shared paper
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#GIS #spatial #mapping #mercury #Hg #contamination #summerrebound #arctic #summertime #atmospheric #transport #contaminate #terrestrial #ecosystems #Hg0 #model #modeling #framework #spatialanalysis #spatiotemporal #concentrations #seaice #atmosphere #river #riverine #GDAS #GEOS #GEOSChem #MERRA2 #GTMM #global #NDVI #remotesensing #cycling #marine #ocean #water #hydrology #seawater #icemelt #anthropogenic #coast #coastal #tundra #permafrost #climatechange #evasion #vegetation #uptake #mitigation

photo - It used to be that researchers didn't study what happens in the Arctic during the polar night because it was difficult to work there. But over the past decade, researchers have ventured into this environment and have come up with surprising findings. Norway's research-dedicated icebreaker, Kronprins Håkon, launched in 2017, is an important tool for this research. Photo: Robin Hjertenes
https://norwegianscitechnews.com/2022/08/arctic-mercury-levels-drop-during-the-depths-of-the-winter/

schematic - representation of the comprehensive coupled model framework depicting Hg transport across air–land–ice–sea interfaces

maps and charts - Spatial pattern of Hg0 in the Arctic atmosphere and ocean during summer

schematic - integrated modeling of Arctic mercury cycling across air, land, sea ice, and ocean

Striking Satellite Images Reveal Antarctica Is 10 Times Greener Than 35 Years Ago
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https://www.sciencealert.com/striking-satellite-images-reveal-antarctica-is-10-times-greener-than-35-years-ago <-- shared technical article
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https://doi.org/10.1038/s41561-024-01564-5 <-- shared paper
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#GIS #spatial #mapping #remotesensing #landsat #change #Antarctic #AntarcticPeninsula #spatialanalysis #spatiotemporal #climatechange #greening #vegetation #change #warming #satellite #browning #cryosphere #anthropogenic #warming #moss #terrestrial #ecosystems #GoogleEarthEngine #NDVI #TCG #icefree #model #modeling

chart - change in the areal extent of likely vegetation cover

maps - spatial and temporal complexity in AP greening trend over the past 35 years

map & chart - Spatial complexity in the recent localized greening (and browning) trend for the southern section of King George Island and Ardley Island

photos - Moss-dominated ecosystems across the AP [Antarctic Peninsula]

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
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https://doi.org/10.1016/j.jag.2022.102716 <-- shared 2022 paper
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“HIGHLIGHTS:
• [They] evaluated the impacts of topographic correction on forest mapping in the mountains.
• The enhanced C-correction and the physical model reduced topographic effects.
• The corrected Landsat imagery time series resulted in higher accuracy.
• Terrain information improved classification but not as much as topographic correction.
• [They] recommend using topographic correction for forest cover mapping..."
#GIS #spatial #AtmosphericCorrection #IlluminationCondition #LandCover #ModelComparison #TimeSeries #TopographicCorrection #remotesensing #comparasion #topographic #correction #NDVI #forest #vegetation #model #modeling #spatialanalyis #accuracy #forestcover #Russia #Georgia #CaucasusMountains #spatiotemporal #landsat #elevation #DEM

maps / images - Landsat summer (panel A) and autumn (Panel B) images (RGB: 743) for the study area. Subset region marked in black frame. Panels C-F show uncorrected (panel C), corrected summer image using the enhanced C-correction (panel D) or the physical model (panel E), and the corresponding illumination condition (F). Panels G-J show the same as panel C-F, but for the autumn image. The QA layers generated from FORCE were not applied here.

map - Forest cover classification agreement among the 18 sets of input variables. Pixels in red color were classified by all sets of input variables as coniferous, in green color as broadleaf forest, and in blue color as mixed forest. Black color indicates that no forest was predicted by any set of input variables. Two subsets A and B which are marked in white frames were zoomed in for a detailed map comparison in [another figure]

Differentiating Cheatgrass And Medusahead Phenological Characteristics In Western United States Rangelands [remote sensing]
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https://doi.org/10.3390/rs16224258 <-- shared paper
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#GIS #spatial #mapping #vegetation #grassland #cheatgrass #medusahead #BRTE #TACA8 #phenology #growingseason #rangeland #EAG #invasivespecies #management #landmanagers #habitat #cost #loss #economicimpact #ecosystem #deterioration #impacts #USWest #spatialanalysis #spatiotemporal #model #modeling #treemodel #landsat #sentinel #HLS #satellite #remotesensing #vegetationindex #NDVI #research #testcase #corrleation #metrics #controlmeasures #herbicide #cattle #grazing #mitigation #landcover #NLDI

photos - cheatgrass various growth stages

graphic / chart - Phenology metrics indicated on a 52-week normalized difference vegetation index (NDVI) time-series curve.

map - Study area boundaries of Snake River Plain and Northern Basin and Range ecoregions within the exotic annual grass (EAG) study area of western U.S. rangelands. The masked-out areas (white/hollow) within the ecoregion are elevations greater than 2350 m or areas not classified as shrub or grassland by the 2019 National Land Cover Database.

work flow graphic - Phenology method flowchart. This flowchart is the overview of capturing the phenology training data for cheatgrass (BRTE [44]) and medusahead (TACA8)
(a) The methods for extracting high probability BRTE and TACA8 pixels
(b) the decision tree analysis development using exotic annual grass (EAG) training data from Benedict et al.
(c) developing the training data
(d) developing the phenology model.

Vegetation #NDVI and Urban Heat Islands #UHI of #Txorierri obtained from a #Landsat9 image from July. Heat accumulation in industrial areas and airport is evident but what happens in #AthleticClub training fields? #RemoteSensing #rstats #GIS #Larrabetzu #Lezama #Zamudio #Derio #Loiu #Sondika

Video tutorial
Download #sentinel2 data and calculate the #NDVI in #Python using Remotior Sensus

https://www.youtube.com/watch?v=f_WJzEUHcaI

For instructions read
https://fromgistors.blogspot.com/2024/11/sentinel-2-ndvi-remotior-sensus.html

Ich weiß nicht ob das experimentell genug ist für den #FotoVorschlag heute. Die Bilder habe ich mit einem #raspberrypi mit Touch und der #pinoir Kamera gemacht (darf kein infrared-blocking filter haben). Ich hab ein Skript geschrieben das mein Pi immer das 'custom low effort Digicam Script' bootet und via Touch auslöst. Vor der Kamera war ein Blaufilter. Ist eine low budget Technik um die Pflanzengesundheit zu checken (#NDVI). Irgendwann will ich das nochmal machen

#infrared #citizenscience

Das sowjetische Ehrenmal im Berliner Treptower Park. Durch den Blaufilter und dem fehlenden infrared-blocking filter der Pi-Noir Kamera wird alles grün knallig weiß.

Das sowjetische Ehrenmal im Berliner Treptower Park. Wenn der Blaufilter nicht gerade vor der Pi-Noir Kamera war verschiebt sich das grün in Richtung pink. Gerade die Gänseblümchen ploppen dadurch erst recht hervor.

I have to share my colleague's first #PhD paper. It is a cool one!

Comparison of #greenery across 86 European cities and their active travel environments.

Residential areas are much more green than travel environments in European cities. Seasonality is often overlooked, but crucial for accurate assessments of #urban greenery.

Klein, et al. (2024) Temporal variation in travel greenery across 86 cities in Europe.

https://doi.org/10.1016/j.ufug.2024.128566

#accessibility #mobility #europe #ERC #GreenTravel #NDVI

Figure showing differences in the greenery of residential areas and travel environments using NDVI. The cities shown as maps are Helsinki, London, and Las Palmas.

Antarctica Is Turning Green At An Alarming Rate, Satellite Images Show
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https://www.cnn.com/2024/10/04/climate/antarctica-greening-vegetation-satellite-images/index.html <-- shared media article
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https://doi.org/10.1038/s41561-024-01564-5 <-- shared paper
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#GIS #spatial #mapping #remotesensing #landsat #Antarctica #anthropogenic #climatechange #greening #AntarcticPeninsula #vegetation #plant #NDVI #landscape #spatialanalysis #spatiotemporal #ice #snow #moss #landarea #temperature #extremes #extremeweather #soil #invasivespecies #native #wildlife

satellite Image - Robert Island on the Antarctic Peninsula, showing vegetation

maps - Spatial and temporal complexity in Antarctic Peninsula greening trend over the past 35 years

photos - Moss-dominated ecosystems across the Antarctic Peninsula

SNAP-Tutorials #12 - Introductory Remote Sensing
In this series of videos Shaun Levick explores #Sentinel-2 data, calculates #NDVI and classifies data using different classification methodologies, like unsupervised, supervised and supervised Random Forest.

Tutorial: https://youtube.com/playlist?list=PLf6lu3bePWHCOUjTDZRNx5N07otDM8iUq&si=7dmZa9MbMdTlR1ts
Shaun Levick: https://www.linkedin.com/in/shaun-levick-05534615/

#tutorial #esa_snap #eo #remotesensing