Discover and read the best of Twitter Threads about #MODIS
Most recents (4)
Happy New Year🎆 You can now create sea surface temperature timelapse with a colorbar using the #streamlit web app in <60 seconds 👇
App: streamlit.gishub.org
GitHub: github.com/giswqs/streaml…
#EarthEngine #geemap #eochat #gischat #oceancolor #dataviz #geospatial
App: streamlit.gishub.org
GitHub: github.com/giswqs/streaml…
#EarthEngine #geemap #eochat #gischat #oceancolor #dataviz #geospatial
Here is a demo using the @streamlit web app for creating ocean color timelapse with #MODIS Aqua data @chris_harrod
Keep in mind that you can create timelapse by selecting a yearly, quarterly, weekly, or daily frequency
🔥 THREAD: global fire patterns 🔥
Recent drought-related #wildfires have underscored how #climate change can increase fire risks regionally.
But various factors (e.g. human ignitions/suppression, vegetation growth) control fire, making regional fire patterns horribly complex!
Recent drought-related #wildfires have underscored how #climate change can increase fire risks regionally.
But various factors (e.g. human ignitions/suppression, vegetation growth) control fire, making regional fire patterns horribly complex!
It's tough to distill all these complexities into general statements, so here's a thread to showcase what 🛰 observations can reveal about 🔥 trends at various regional scales.
I invite you to get lost in the maps that follow and appreciate the complexity in all its glory! 🤘
I invite you to get lost in the maps that follow and appreciate the complexity in all its glory! 🤘
First up, a basic question: Where do most fires happen?
👇 Plot shows mean annual burned area during 2001-2020, summed within regions and mapped thematically.
(Note: log colour scale)
👇 Plot shows mean annual burned area during 2001-2020, summed within regions and mapped thematically.
(Note: log colour scale)
Massive new #QGIS plugin: #GEE Timeseries Explorer offers instant access to #EarthEngine image collections! Fetch #Landsat, #Sentinel2, or #MODIS #timeseries for any location and visualize images: geetimeseriesexplorer.readthedocs.io 🧵
Choose from our predefined collections, for instance merged & #cloud masked #Landsat TM, ETM+, OLI surface reflectance, cloud masked Sentinel2AB L2A, or just define your awesome custom collection in the built-in #Python editor.
Extract time series profiles for #training / #validation by clicking on map, or by navigating through vector point file. Download raw time series data for #sample-based workflows efficiently with parallel download.
Farmers in NW India are about to begin harvesting rice crop. #Aqua #MODIS NDVI for Sep historically largest this year--indicating a whooping crop prod>12 m tons. Given traditional crop residue burning practice, it means an intense fire season with ~16000 incidences in Oct & Nov
@NASAEarth @NASAGoddard @moesgoi @rajeevan61 @jayashreenandi @VishnuNDTV @parthaabosu @airqualityindia @m_parrington @robertdfield @UrbanEmissions @iainmarlow @USRAedu @SmokeyBot @urbansciencesIN @avoiland @vinoj_iisc @jmccarty_geo
@NASAEarth @NASAGoddard @moesgoi @rajeevan61 @jayashreenandi @VishnuNDTV @parthaabosu @airqualityindia @m_parrington @robertdfield @UrbanEmissions @iainmarlow @USRAedu @SmokeyBot @urbansciencesIN @avoiland @vinoj_iisc @jmccarty_geo This is an initial estimate based on 16-day Sep data from #Aqua #MODIS. I will revise it with monthly observations once they are available (likely in 2nd week of Oct), however not expecting major change in results.
