Objectives In response to the existing issues in the forestry and grassland monitoring field, such as the low utilization of monitoring information from perception devices and the lack of fusion analysis of monitoring results from multiple types of devices, it is proposed to construct a forest and grassland ecological perception system based on multi-sensor integration technology.

Methods Multi-sensor integration referred to integrating various sensor technologies into a comprehensive perception system, utilizing the multifunctionality and data fusion capabilities of individual devices. The aim was to achieve comprehensive, precise, and real-time monitoring and perception of the forest and grassland ecosystem. This system integrates integrated monitoring methods from sky, air, ground, and human perspectives, relying on key technologies such as multi-source IoT information access, multi-source data integration, and fusion, AI service algorithm warehouse, and big data decision support. It leveraged the multifunctionality of individual devices, supplemented by AI intelligence support, to integrate and fuse multi-source data, utilize AI algorithms to enhance data processing efficiency and accuracy, deeply mine and analyze the underlying patterns in the data, and provide comprehensive monitoring and assessment for forestry and grassland resource management.

Results The ecological perception system based on multi-sensor integration technology should demonstrate significant advantages in forest fire prevention, biodiversity monitoring, pest identification and early warning, and other forestry and grassland operations, enhancing the efficiency and accuracy of forestry and grassland resource management.

Conclusions This system provided a theoretical basis and technical support for the intelligent monitoring of forestry and grassland resources, offering new ideas for ecological environmental protection and sustainable development.