In this case study, we’ll show how you can identify changes in industrial activity with a few simple analyses applied to Hydrosat data.

Introduction

Tata Steel IJmuiden is one of Europe's largest integrated steelworks and a major employer and exporter for the Netherlands, producing several million tonnes of steel a year at its site on the North Sea coast. In April 2026, the plant's DSP unit was shut down after air quality measurements showed chromium-6 emissions from one of its chimneys exceeding permissible limits, a serious environmental and public health concern. Source: GMK Center.

Thermal satellite imagery offers a way to independently verify whether the DSP has genuinely gone offline, without relying solely on self-reported compliance data.

Loading the L1B LIRI Collection

We'll start with a single Level-1B LIRI scene, VZ02_L1b_20260302_132735, taken before the DSP shutdown. Before diving into any analysis, we'll take a quick look at the images and their metadata.

from pathlib import Path
import matplotlib.pyplot as plt
import rioxarray
import xarray as xr

# Define the scene ID and data directory for the imagery
DATA_DIR = Path("data")
SCENE_ID = "VZ02_L1b_20260302_132735"

# Read LWIR bands
lwir1 = rioxarray.open_rasterio(DATA_DIR / SCENE_ID / "LWIR1.tiff", masked=True).squeeze("band", drop=True)
lwir2 = rioxarray.open_rasterio(DATA_DIR / SCENE_ID / "LWIR2.tiff", masked=True).squeeze("band", drop=True)

# Build an RGB preview from the individual RED/GREEN/BLUE bands
red = rioxarray.open_rasterio(DATA_DIR / SCENE_ID / "RED.tiff", masked=True).squeeze("band", drop=True)
green = rioxarray.open_rasterio(DATA_DIR / SCENE_ID / "GREEN.tiff", masked=True).squeeze("band", drop=True)
blue = rioxarray.open_rasterio(DATA_DIR / SCENE_ID / "BLUE.tiff", masked=True).squeeze("band", drop=True)
preview = xr.concat([red, green, blue], dim="band").assign_coords(band=["red", "green", "blue"])

Visualizing the LWIR Bands and Preview

rioxarray already masked out nodata pixels for us on load. L1B LIRI pixel values are scaled brightness temperature, so multiplying by the 0.01 scaling factor converts them to Kelvin, which we then convert to Fahrenheit for readability. We'll also show the full-resolution RGB preview alongside the two thermal bands for context.

Here we can see the broader context of the area, including the North Sea coastline and the harbor canal running through IJmuiden. Let's focus in on the steelworks for our next steps.

The full scene extends well beyond the steelworks. We'll crop LWIR1 to a region of interest (ROI) around the plant, loaded from a local file, so the same box can be reused across scenes with different projections or pixel grids.

Here, several distinct hotspots stand out clearly against the surrounding land: individual stacks and process units across the steelworks, each running well above ambient. reveals that the DSP is located in the middle-left part of the cropped region. Since this image was taken before the DSP shutdown, its thermal signature is visible (the two hotspots near the shoreline). Let's take a look at how the overall thermal picture changes after the DSP was taken offline.

We have two LWIR1 acquisitions bracketing the shutdown: 20260302 (before, March 2026) and 20260619 (after, June 2026), both from the VZ02 sensor. We'll plot them side by side with a shared color scale for direct comparison.

When sharing a color scale, the March scene shows a handful of sharp, distinct hotspots against a cool background, while the June scene is warm across almost the entire site. This is a product of summer solar heating, making it hard to tell whether the DSP (or anything else) is active. Normalizing each frame relative to its own background can help separate genuine activity from ambient seasonal heating.

Different acquisitions can have different baseline brightness temperatures due to atmospheric and seasonal conditions unrelated to plant activity. Simply subtracting each frame's median still leaves frames with a lot of background texture (e.g. a hot summer day) looking uniformly "hot," since the deviations are on a different scale from frame to frame.

Instead, we use a robust z-score, which normalizes both the center and the spread of each frame using statistics that resist being skewed by a small number of extreme hot pixels:

where MAD(x) = median(|x - median(x)|) is the median absolute deviation. Subtracting the median (instead of the mean) keeps the center from being pulled up by a handful of very hot pixels, and dividing by the (rescaled) MAD instead of the standard deviation keeps the scale from being inflated by those same outliers. The 1.4826 constant rescales MAD so it's comparable to a standard deviation for normally-distributed data, making the resulting z-score readable the same way as an ordinary one (e.g. +3 ≈ "3 robust standard deviations above typical").

After normalization, the difference between the images becomes clear: by June, Tata Steel has shut off the DSP (as per the article), and the Warmband facility (the hot-rolled strip steel mill) also appears to be offline. With a few simple image processing steps, we have gone from raw imagery to actionable compliance insights.

Beyond a visual inspection, extracting pixel values can provide further insight into the thermal activity of a scene. A profile (a cross-sectional view that shows the change in pixel values along a linear path) through the hottest point tells us two distinct things at once:

1. Whether a given unit is active or idle (does the profile spike well above the surrounding background, or does it stay flat and close to ambient?)

2. How active it is (values along the profile indicate the intensity of the thermal signal, not just its presence)

We'll use the 20260302 acquisition, taken before the DSP shutdown, since it's where the plant's activity registers most clearly.

This profile is (taken through the Warmband) shows a clear signal of activity. By extracting similar profiles through our images, or by taking pixel statistics for an AOI polygon, we can further learn about the thermal characteristics of locations and derive insights from thermal imagery.

Industrial facilities contain many processes that generate, transfer, or release heat. While these processes may be difficult to distinguish in visible imagery alone, thermal imagery provides an effective way to observe activity across a site and over time.

By comparing thermal observations temporally, we can identify changes in heat signatures associated with operational units, flaring, water discharge, and other industrial processes.

Seeing industrial activity in thermal data

The images below show Hydrosat longwave infrared observations of the Mina Al-Ahmadi and Mina Abdullah refinery sites in Kuwait.

At first glance, visible imagery provides useful context about the layout of the facilities. Thermal imagery adds another layer of information by showing differences in emitted heat across the site.

Areas with stronger thermal signals stand out from their surroundings, making it possible to see where heat-producing processes are occurring within a large and complex industrial facility.

Looking at change over time

A single thermal image provides a snapshot. Because industrial processes are so dynamic, repeated observations make it possible to compare activity from one date to another.

Hydrosat collected multiple observations of the refinery sites between September 2025 and January 2026, including both daytime and nighttime imagery.

Across the series, some thermal features remain relatively consistent while others appear, disappear, or change in intensity. These differences provide a simple way to identify areas where facility activity levels are changing.

Looking more closely at individual parts of the site reveals several examples of changing thermal activity.

Flaring is clearly visible in some observations and absent in others. Other parts of the facility shift between higher and lower levels of thermal activity, as indicated by temperature. Comparing the same locations across dates makes these changes easier to distinguish from the surrounding background.

Thermal imagery can also reveal features beyond the main processing areas.

In this example, a warm industrial discharge is visible in the January 14 nighttime observation. A nearby tailings basin also shows a thermal signal on some dates but not others.

These examples illustrate an important advantage of thermal monitoring: rather than relying only on how a facility looks with visible imagery, we can observe how its heat signature physically changes over time to understand aspects that are on, off, or operating at different thresholds.

Industrial sites can be large, complex, and geographically dispersed. Satellite thermal imagery provides a consistent way to observe heat-producing activity across these sites without requiring sensors to be installed at each facility.

Repeated thermal observations can help analysts:

• Monitor variations in facility activity

• Identify persistent or intermittent heat sources

• Observe flaring and other high-temperature events

• Monitor discharge and thermal patterns around infrastructure

Large-area coverage, spectral diversity, frequent revisit, and nighttime imaging make it possible to monitor industrial activity across broad areas and compare changing thermal conditions consistently over time.

Thermal data provides a key physical signal that identifies when, where and to what degree industrial changes happen.