In recent years, the frequency and severity of large-scale forest fires have increased globally, threatening forest ecosystems, human lives, and property while potentially triggering cascading ecological and social crises. Despite significant advancements in remote sensing-based forest fire monitoring, early warning systems, and fire risk zoning, post-fire thermal damage assessment remains insufficiently addressed. This study introduces an innovative approach combining multispectral imaging with a dual annealing constrained optimization algorithm to enable dynamic monitoring of fire temperature distribution. Based on this method, we develop a dynamic thermal damage assessment model to quantify thermal impacts during forest fires. The proposed model provides valuable insights for defining thermal damage zones, optimizing evacuation strategies, and supporting firefighting operations, ultimately enhancing emergency response and forest fire management efficiency.

To address the challenges of data processing caused by uncertain emissivity in multispectral radiation thermometry, this paper proposes a temperature retrieval method based on the Chaotic Artificial Hummingbird Algorithm (CAHA). Without relying on an assumed emissivity model, the method can automatically identify the emissivity distribution and selects the optimal output through multiple iterations to enhance accuracy. Simulations and offline tests conducted on rocket nozzles demonstrate that CAHA maintains high accuracy both in noise-free conditions and under 5% noise, with a single execution time of approximately 0.15 s. Furthermore, the method is validated through experiments on blackbody sources and candle flames: the relative error in retrieved temperature for blackbody sources remains below 0.93%, while the retrieved outer flame temperature of candle flames shows a relative error of 0.66% compared with thermocouple measurements. Combining high precision with rapid computation, this method is suitable for practical applications in radiation thermometry.