Danaus genutia, commonly known as the tiger butterfly, is a visually appealing species in the Danaidae family. As it is not currently classified as endangered, it is excluded from key protected species lists at national and local levels, limiting focus on its population and habitat status, which may result in it being overlooked in local butterfly conservation initiatives. Yunnan, characterized by high butterfly diversity, presents an ideal region for studying habitat suitability for D. genutia, which may support the conservation of regional biodiversity. This study employs the MaxEnt ecological niche model, predictions regarding suitable habitat distribution, and trends for D. genutia and identifying primary environmental factors influencing their distribution. The results indicate that the niche model that includes interspecies relationships provides a distribution prediction closely aligned with the observed range of D. genutia. Under current climatic conditions, highly suitable habitats for both D. genutia and its host plant, Cynanchun annularium, are located predominantly in the Yuanjiang River Valley. Optimal conditions occur at average annual temperatures of 19.80–22 °C for D. genutia and 22–24 °C for C. annularium. The distribution range of C. annularium is a vital biological factor limiting D. genutia’s habitat. By 2040, projections under four future climate scenarios indicate a potential increase in the total area of suitable habitats for D. genutia, with a general trend of northward expansion.

Advancements in remote sensing technology have enabled the acquisition of high spatial and radiometric resolution imagery, offering abundant and reliable data sources for forest fire monitoring. In order to explore the ability of Sustainable Development Science Satellite 1 (SDGSAT-1) in wildfire monitoring, a systematic and comprehensive study was proposed on smoke detection during the wildfire early warning phase, fire point identification during the fire occurrence, and burned area delineation after the wildfire. The smoke detection effect of SDGSAT-1 was analyzed by machine learning and the discriminating potential of SDGSAT-1 burned area was discussed by Mid-Infrared Burn Index (MIRBI) and Normalized Burn Ratio 2 (NBR2). In addition, compared with Sentinel-2, the fixed-threshold method and the two-channel fixed-threshold plus contextual approach are further used to demonstrate the performance of SDGSAT-1 in fire point identification. The results show that the average accuracy of SDGSAT-1 fire burned area recognition is 90.21%, and a clear fire boundary can be obtained. The average smoke detection precision is 81.72%, while the fire point accuracy is 97.40%, and the minimum identified fire area is 0.0009 km², which implies SDGSAT-1 offers significant advantages in the early detection and identification of small-scale fires, which is significant in fire emergency and disposal. The performance of fire point detection is superior to that of Sentinel-2 and Landsat 8. SDGSAT-1 demonstrates great potential in monitoring the entire process of wildfire occurrence, development, and evolution. With its higher-resolution satellite imagery, it has become an important data source for monitoring in the field of remote sensing.