The installation of forest fire sensors in the Harz mountains, especially along the Brocken railway line, was once celebrated as a milestone in early fire detection technology. With the increasing risk of forest fires and the particular fire hazard posed by the steam-powered narrow-gauge railway, the concept seemed promising. But after almost two years of operation, the results are sobering: not a single fire has been detected by the sensors in time.
Great promises, sobering reality
The idea behind the sensors is simple and effective: by measuring gases such as carbon monoxide, the devices are designed to detect fires at an early stage. Supported by artificial intelligence (AI), this data can be evaluated and alarms triggered within milliseconds. However, in practice, this system has so far failed. According to the Ministry of Economic Affairs and Forestry of Saxony-Anhalt, the reason why the detectors did not work is currently being investigated. However, a definitive explanation is still pending.
Particularly problematic is the fact that the sensors have not been able to record any successes despite being placed in a targeted manner along the Brocken railway line. The region is known for its susceptibility to fire, especially near the tracks, where sparks from the steam locomotives are a common cause of fires. In 2024 alone, there were fires in several places in the Harz region, most recently in May and September – without the sensors sounding the alarm.
Criticism of the technology
The project has been heavily criticised. Kai-Uwe Lohse, chairman of the State Fire Brigade Association, spoke plainly: ‘Both the sensors and the satellite monitoring did not work.’ This honest assessment reveals the weaknesses of the measures taken so far. Particularly disappointing is the failure of the satellite monitoring, which has even been completely discontinued.
The failure raises questions about the reliability and limitations of the technologies used. Despite an annual operating budget of almost €29,000 for the sensors, the results are sobering. Even the introduction of modern technologies such as AI has not yet achieved any significant improvements.
Root cause analysis: What went wrong?
A central question remains as to why the sensors are not working. Possible causes could be technical defects, incorrect calibration or unsuitable placement of the devices. The data processing by the AI could also have weaknesses. The range of use of two to five kilometres per sensor may be sufficient in theory, but in the hilly and partly densely forested landscape of the Harz Mountains, obstacles could make it difficult to achieve seamless monitoring.
Furthermore, the question arises as to whether the sensors have been sufficiently tested under real-world conditions. Laboratory experiments and controlled tests cannot always replicate the complex influences of nature. Wind, humidity and other environmental factors could distort the results.
The future of forest fire sensors: optimisation or abandonment?
Despite the problems so far, the country plans to expand the project. This could be seen as a political signal to regain confidence in the technology. But before further funds are invested, fundamental improvements should be made. These include a transparent investigation of the previous mistakes and the optimisation of the sensors and the associated AI.
At the same time, alternative approaches should be considered. The integration of drones that regularly monitor the region or the improvement of classic monitoring methods by trained personnel could be useful additions.
Conclusion
The forest fire sensors in the Harz Mountains impressively demonstrate how difficult it can be to implement technological innovations in practice. Although the idea offers great potential, without reliable results the benefits are questionable. A critical examination of the weaknesses to date and an honest evaluation of the technology are essential to ensure the long-term protection of the national park and its surroundings. It remains to be seen whether the sensors can still fulfil their potential after optimisation – or whether the project will go down in history as an expensive failure.
