## 1 Introduction  
Forest fires, as global ecological disasters, are increasingly influenced by climate change in terms of both frequency and intensity. These events not only result in a significant reduction in forest biomass carbon storage--leading to an estimated annual loss of approximately 350 million tons in carbon sequestration--but also trigger cascading effects such as biodiversity loss, accelerated soil erosion, and substantial increases in greenhouse gas emissions [1, 2, 3]. These impacts pose serious threats to ecosystems, the atmosphere, and human health [4, 5, 6]. Significant progress has been achieved in forest fire monitoring. [PERSON] et al. [7] employed satellite-based spatiotemporal data and time-series classification to construct a high-precision forest fire hotspot dataset, reaching 95.12% accuracy in real-world monitoring. [PERSON] et al. [8] developed a threshold-weighted fusion (TWF) method that integrates sub-pixel smoke detection with infrared thermal data, enhancing fire detection accuracy from 83.96% (infrared-only) to 93.97%. For early detection, [PERSON] et al. [9] introducedFFireNet, a deep learning-based model that effectively differentiates fire from non-fire images in smart city environments. [PERSON] et al. [10] designed a fire warning system combining BPNNire and IoT technologies, improving detection robustness by fusing multi-modal features (flame, smoke, color, and shape). To overcome occlusion and adaptability limitations in traditional methods, [PERSON] et al. [11] proposed the Forest Fire Detection and Segmentation Model (FFDSM), leveraging UAV-based infrared imaging for high-precision detection across diverse wildfire scenarios. Predictive models for forest fire occurrence are essential tools for fire prevention and suppression. [PERSON] et al. [12] applied multiple machine learning algorithms, including multilayer perceptrons (MLP), logistic regression, and support vector machines (SVM), for fire occurrence prediction. [PERSON] et al. [13] employed ensemble learning techniques to forecast burn area and the likelihood of large-scale fires, improving the efficiency of global forest fire management. Fuel characteristics, as key factors in fire danger rating systems, must be analyzed to understand fire behavior. [PERSON] et al. [14] examined regional forest characteristics and predicted fire intensity and type to facilitate early intervention strategies. Although fire is inherently a natural phenomenon and cannot be entirely controlled, delineating forest fire risk zones has been shown to significantly reduce fire occurrence [15]. [PERSON] et al. [16] conducted risk zoning in Wuyi Mountain National Park using a combination of Random Forest (RF) and Fuzzy Analytic Network Process (FANP), achieving a model accuracy of 88.5%. [PERSON] et al. [17] applied RF, SVM, and Gradient Boosted Decision Trees (GBDT) to predict forest fire probabilities in Hunan Province, selecting RF to construct a regional fire risk map. [PERSON] et al. [18] integrated ignition effects and topographic influences into an optimized fire risk model using digital terrain slope factors, improving the accuracy of early warning areas compared to single-source models. Although existing technology has made remarkable progress and can effectively identify forest fire areas, providing early warnings for fires and risk area division, there is a lack of specific assessment methods for the thermal damage effect caused by forest fires. In this study, we propose a thermal damage assessment method that integrates multispectral imaging with a dual simulated annealing optimization algorithm. Compared with the traditional thermal imaging camera method, this method can effectively eliminate the influence of emissivity and improve the accuracy of temperature measurements. The approach first utilizes multispectral imagery to derive the spatial distribution of fire temperatures, followed by the development of a dynamic thermal damage evaluation model.