Live thermal detections from NASA FIRMS — refreshed every time this story runs.
The finding
In the most recent 24-hour window, NASA’s VIIRS instruments logged 32 active-fire detections across Northern California, USA, emitting a combined 136 MW of fire radiative power. Dyaan reads these straight from the public FIRMS feed and keeps only the detections inside the area.
Fire radiative power is a live measure of how much energy the fires are releasing right now — the higher it climbs, the more intense the burning. Because this story re-runs on a schedule, the figure above is as fresh as the last satellite pass over the area.
What a detection does and does not mean
A detection is a pixel hot enough to stand out from its surroundings. That is a strong signal, but it is not a headcount of wildfires: a single large fire can light up many pixels, and industrial heat or agricultural burning can light up a few. The value of the feed is its speed and its coverage — it sees everywhere, every day — not its ability to name a fire.
How to read a live fire feed
A thermal feed trades certainty for speed. It cannot confirm a wildfire the way a firefighter on the ground can, but it sees the entire region every day, in daylight and dark, and it never looks away. That makes it superb for one job in particular: noticing that something has changed. A jump in detections or a surge in radiative power is a prompt to look closer, not a verdict on its own.
Read the two numbers together. Detections count how many hot pixels the satellite saw; fire radiative power measures how fiercely they are burning. A handful of intense detections and a scatter of weak ones tell very different stories, and only the pair distinguishes a serious fire front from routine background heat.
Why a live fire feed matters
When a fire is moving, hours matter, and a feed that refreshes with every satellite pass buys some of those hours back. It will not replace ground truth, but it flags the places that deserve a closer look before the smoke is visible from a road.
Across a season, the same feed becomes a map of where and when fire tends to arrive — the kind of pattern that informs where to pre-position people and what to watch as conditions dry out.
Reading this alongside the other layers
A fire feed gains meaning from context. Overlay it on a land-cover read to separate forest fire from crop burning, and check a vegetation layer to see how dry the fuel was going in. The detection says where the heat is; the other layers say what is burning and why it caught.
How Dyaan measured this
This article was not written from a press release or a secondary summary. It was generated directly from a live read of the underlying satellite archive, performed at publication time by the same engine that powers the Dyaan workspace. There is no human in the loop inventing a number to fit a narrative — the narrative is assembled around numbers the machine measured.
The data source is NASA FIRMS VIIRS 375 m active fire, last 24 h (NRT, C2). Dyaan does not download the whole archive; it issues ranged HTTP requests that pull only the bytes covering this bounding box, decodes the relevant raster tiles in memory, and counts the pixels that fall inside the area. No API key is used, and nothing is cached and then re-presented as fresh: a read either succeeds against the live archive or the article records that it could not.
Because the input is a fixed, published dataset and the area is a fixed bounding box, this analysis is reproducible. Anyone can open the same area in the Dyaan workspace, run the same analysis, and get the same figures — the definition of a measurement rather than an opinion. That reproducibility is the whole point: it is what separates an intelligence product you can audit from a chart you have to trust.
What this analysis is — and what it is not
A thermal detection is an anomaly hot enough to register from orbit, not a confirmed wildfire: gas flares, furnaces and agricultural burning all show up. Near-real-time detections are provisional and may be revised by NASA, and cloud can hide a fire from the satellite entirely.
Dyaan’s design rule is to state that footprint plainly rather than paper over it. A number here is only ever as good as the sensor and the method behind it, and both are named. Where the data cannot answer a question, the honest answer is that it cannot — and the tool will say so rather than fill the gap with something that merely looks authoritative.
Reproduce this yourself in about a minute
Every figure above is checkable, and checking it is the fastest way to trust it:
- Open the Dyaan workspace and sign in (the free tier is enough for this).
- Search for Northern California, USA, or draw a rectangle over the same area on the map.
- Choose the Wildfire analysis from the panel and press Run.
- Read the headline numbers, open the table for the full breakdown, and export a CSV or PDF if you want to keep it.
You are not limited to the places Dyaan writes about. The same engine runs over any lake, city, forest or coastline on Earth — so if there is a place you actually care about, that is the one to point it at.
A short glossary
- Detection — a single pixel flagged as anomalously hot by the satellite in one overpass.
- Fire radiative power (FRP) — the rate of energy a fire is emitting, in megawatts — a live proxy for intensity.
- VIIRS — the 375 m thermal instrument aboard the Suomi-NPP and NOAA-20 satellites.
Why this is written by a machine — and why that helps
This article was assembled automatically, and that is a feature rather than an apology. A person writing to a deadline is tempted to round a number toward the story they already have in mind; an engine has no story to protect. It reads the archive, counts what is there, and lays the figures out in the same structure every time, for every place, whether the result is dramatic or dull. The consistency is the point: you can compare this week’s reading with last month’s, or this city with the next, and know the only thing that changed is the ground, not the method or the mood of the author.
It also means the work scales in a way human analysis never could. There are millions of lakes, forests and floodplains on Earth and a handful of people who study any given one. An automated pipeline that reads public data honestly can, in principle, keep an eye on all of them — and surface the ones that are changing fastest to the people who would otherwise never hear about them until it was too late. That is the ambition behind these field notes: not to replace the expert, but to make sure the expert, the official and the ordinary resident all get to see the same measured record, at the same time, for free.
Every safeguard that makes the tool trustworthy is applied here too. The numbers are counted, never estimated; the limits are stated, never hidden; and if a read fails, the failure is recorded rather than papered over with a plausible-looking figure. An automated writer is only as honest as the rules it follows, and these are the rules.
The bottom line
This is an original Dyaan analysis. The words, the framing and the pictures are Dyaan’s own; the only thing borrowed is the raw public dataset it reads — NASA FIRMS VIIRS 375 m active fire, last 24 h (NRT, C2) — which is an input, not an article. Nothing here is copied from another publication, and every number can be reproduced in the tool.
If a place matters to you — a lake near your home, a forest you grew up beside, a valley that floods every year — you do not have to wait for Dyaan to write about it. Point the tool at it yourself and read the record for that exact ground. Open the workspace → · How every number is measured →