Abstract:

Background: Biodiversity conservation remains one of the most urgent priorities on the global environmental agenda. Effectively halting and reversing the ongoing loss of biodiversity constitutes a fundamental challenge for contemporary ecological governance. In order to fulfill the objectives of global sustainable development, it is essential to establish a robust and responsive global biodiversity early warning system capable of real-time monitoring and dynamic assessment of biodiversity trends. The Kunming-Montreal Global Biodiversity Framework (KMGBF), adopted at the fifteenth meeting of the Conference of the Parties to the Convention on Biological Diversity (COP15), outlines a strategic roadmap for the future of global biodiversity governance. 

Aims: Based on the objectives articulated by the KMGBF, this study investigates the current status, principal challenges, emerging opportunities, and strategic pathways for advancing the development of a global biodiversity early warning system. Through enhanced international policy coordination and collaborative efforts, the envisioned system aspires to promote the scientific and intelligent governance of global biodiversity. It is expected to provide a rigorous scientific foundation for the formulation and execution of biodiversity conservation policies across nations, thereby making a substantive contribution to global biodiversity protection and the broader pursuit of sustainable development. 

Problems & Prospects: This study begins by underscoring the pivotal role of the KMGBF in guiding the development of a global biodiversity early warning system. Despite notable advancements in recent years, significant challenges persist, including incomplete data coverage, reliance on outdated technologies, and insufficient international cooperation. Building on the vision articulated in the KMGBF, this paper systematically outlines the foundational architecture and key components of a global biodiversity early warning system, and proposes a suite of global indicators to facilitate its implementation. Furthermore, this paper advocates the integration of multi-source data, including satellite remote sensing and ground-based camera observations, along with advanced technologies such as cloud computing and artificial intelligence. This integrated approach aims to establish a scalable and resilient global biodiversity early warning system that can support multidimensional, multilevel, and multiscale biodiversity assessments.

Key words: biodiversity, Kunming-Montreal Global Biodiversity Framework, remote sensing technique, monitoring and warning