Prescribed burns reduce giant sequoia mortality by 77% during extreme wildfire events, according to a study from the University of California, Davis, published in Nature Communications. Researchers analyzed 26,400 trees across 19 groves in Sequoia and Kings Canyon National Parks. The study focused on areas hit by the Castle Fire (2020) and the KNP Complex Fire (2021). By integrating airborne lidar data with PlanetScope satellite imagery, the team mapped the volume of surface fuels-dry brush and undergrowth-prior to the blazes and correlated this with tree death tolls. The data shows that sequoias in zones treated with prescribed fire over the previous decade were four times more likely to survive than those in untreated areas. This intervention saved approximately 1,900 trees. Predictive modeling suggests that if these treatments had been applied to all groves, an additional 3,900 sequoias would have survived.

SAN FRANCISCO – Planet’s second Tanager hyperspectral satellite and 18 SuperDoves have arrived at Vandenberg Space Force Base for launch no earlier than October on the SpaceX Transporter-18 rideshare. The SuperDoves contribute to Planet’s daily Earth imagery. Paired with artificial intelligence, PlanetScope detects change in broad areas, which is “core to unlocking new markets,” Planet CEO Will Marshall said in a…

Planet Labs (NYSE: PL) reported that its Tanager-2 hyperspectral satellite and 18 SuperDove satellites from Flock 4J have arrived at Vandenberg Space Force Base in California for launch on the upcoming SpaceX Transporter-18 rideshare mission.According to Planet, Tanager-2 is designed to double the company’s hyperspectral capacity and halve revisit times for detecting methane super-emitters and other targets as part of its project with Carbon Mapper. The mission will augment Planet’s PlanetScope daily global monitoring, support expanding hyperspectral data offerings, and enable potential applications in climate, environmental, agricultural and infrastructure monitoring.

An interactive map of ~1.8 million individual tree crowns in New York City, classified by genus from PlanetScope satellite imagery and airborne lidar.

Glacioloog toont met satellietbeelden hoe ramp in Nepal ontstond. Bekijk de video op VRT NWS: nieuws.

2026年ネパール洪水の発生源と思われる箇所を，PlanetScope + Landsat衛星画像をもとに特定しました。 氷河が広範囲で崩壊し，土壌が剥き出しになっています。ここから洪水が発生したものと思われます。 (28.2877, 85.52723)

Planet Labs को स्याटेलाइट तस्बिरको विश्लेषणका आधारमा गरिएको प्रारम्भिक अध्ययनबाट नेपाल-चीन रसुवागढी नाकाबाट करिब २० किलोमिटर उत्तरपूर्व नेपाल-चीन सिमानामा गएको हिम-चट्टान पहिरोका कारण ल्हेन्दे खोलामा ...

Un “prima” e un “dopo” ormai misurato sempre più a distanza ravvicinata. Una serie di immagini satellitari di PlanetScope in collaborazione con il Politecnico di Torino mostrano il Piave e le campagne...

I am classifying a PlanetScope Imagery into 3 classes (water, non-water and mixed) using NDWI. Natural breaks (Jenks)​​ worked the best for me when I tried different data classification methods in ArcGIS Pro. Now, I need to automate this process using Python. I used 'jenkspy' and it took forever to classify even a single image. When I only use sample size of 100k pixels to find the class intervals, it is faster but the classification is messed up. I need high accuracy because the classification feeds into lake boundary extraction, and I’m working with time-series data, so long processing time per image isn’t feasible. Are there faster or more robust approaches for computing Jenks breaks (or suitable alternatives) for large rasters in Python?

I've download a few PlanetScope scenes from Celaya, MX. Taken alone they have a good contrast Then I've merged them with gdalbuildvrt, which lowered the contrast: I've thought, that I could revert this again by stretching the contrast after this guide. gdalinfo -mm test.vrt gave me: Band 1 Block=128x128 Type=UInt16, ColorInterp=Red Computed Min/Max=664.000,48804.000 Band 2 Block=128x128 Type=UInt16, ColorInterp=Green Computed Min/Max=3688.000,30697.000 Band 3 Block=128x128 Type=UInt16, ColorInterp=Blue Computed Min/Max=19.000,38786.000 Band 4 Block=128x128 Type=UInt16, ColorInterp=Undefined Computed Min/Max=2521.000,39791.000 So I've stretched the contrast with GDAL gdal_translate test.vrt out.tif -scale 664 48804 0 65535 -exponent 0.5 -co COMPRESS=DEFLATE -co PHOTOMETRIC=RGB but the result has even less contrast: What went wrong?

I am trying to order PlanetScope data from Planet Explorer and have it delivered directly as an asset to my image collection in Google Earth Engine, but it is unclear how to do this. I have read and implemented everything the documentation and watched the one youtube video on this subject, yet I still cannot figure out how to send the order (of my selected scenes) to my account. This is my first time working with Planet imagery, so perhaps this task is more straightforward than what I am experiencing.