摘要
为有效遏制并扭转全球生物多样性快速丧失的严峻趋势,《生物多样性公约》第十五次缔约方大会制定并通过了《昆明-蒙特利尔全球生物多样性框架》(简称《昆蒙框架》)这一最新行动纲领,确定了4项长期目标及23项行动目标。目前,有效跟踪盘点《昆蒙框架》目标进展是国际关注的热点,但仍面临全球进展不明朗、监测不及时、评价不全面等多重挑战,亟需突破标准不一、指标众多、数据不足等技术难题。本文旨在探索基于遥感监测的《昆蒙框架》执行进展快速评估路径,通过遥感地基数据融合、定量定性评估结合,满足多尺度《昆蒙框架》目标快速盘点需求。本文首先指出,现有监测框架在有效评估《昆蒙框架》目标进展方面存在较大不确定性,因此有必要研发一套执行性更强、空间分辨率更高、更新频率更快的指标集、指数计算方法和高质量数据集,以确保《昆蒙框架》目标得到及时、有效的跟进与盘点。其次,本文深入分析了遥感技术在生物多样性监测中的应用,评估了其在《昆蒙框架》目标进展评估中的可行性。在此基础上,进一步提出了构建数据-知识-计算一体化时空智能服务框架,以支持生态系统制图、生物多样性制图以及遥感生物多样性核心变量(RS-EBVs)研发。最后,本文建议采用基于RS-EBVs的定量评估方法,结合基于缔约方国家生物多样性战略和行动计划和国家报告的定性评估,并借助人工智能技术开发《昆蒙框架》实时进展监测智能体,实现《昆蒙框架》进展的多尺度快速评估。这一系列技术手段旨在为《昆蒙框架》进展盘点提供切实可行的支持,为各国制定和实施生物多样性保护政策提供科学依据。
Background:The Earth is approaching a critical tipping point of irreversible biodiversity loss.As the latest global action plan for biodiversity conservation,the Kunming-Montreal Global Biodiversity Framework(KMGBF)sets out 4 long-term goals and 23 action targets.Tracking and assessing progress toward the KMGBF has become a global concern.However,challenges such as unclear progress,untimely monitoring,and incomplete evaluations remain prominent,highlighting the urgent need to address technical barriers like a large number of evaluation indicators,inconsistent assessment standards,and weak data foundations.Aims:This study aims to explore rapid assessment methods for evaluating the implementation progress of the KMGBF using remote sensing monitoring.By integrating remote sensing-based and ground-based data,as well as combining quantitative and qualitative evaluations,this approach seeks to meet the multi-scale needs of quickly tracking the progress of the KMGBF.Problems&Prospects:This paper first points out that the existing monitoring frameworks exhibit significant uncertainties in effectively assessing the progress of the KMGBF.Therefore,it is necessary to develop a more operationally robust set of indicators,indicator calculation methods,and high-quality datasets with higher spatial resolution and more frequent updates to ensure the timely and effective tracking and assessment of the KMGBF.Second,this paper provides an in-depth analysis of the application of remote sensing technology in biodiversity monitoring and evaluates its feasibility in assessing the progress of the KMGBF.Based on this analysis,a spatial intelligence service framework integrating data,knowledge,and computation is proposed to support ecosystem mapping,biodiversity mapping,and the development of remote sensing-based essential biodiversity variables(RS-EBVs).Finally,this paper advocates for a quantitative assessment approach based on RS-EBVs,complemented by a qualitative assessment derived from National Biodiversity Strategies and Action Plans(NBSAPs)and National Reports(NRs).Additionally,it suggests leveraging artificial intelligence to develop an intelligent real-time monitoring system for the KMGBF,enabling rapid multi-scale progress assessments.These technological approaches aim to provide practical and feasible support for tracking the progress of the KMGBF and offer scientific evidence for countries to formulate and implement biodiversity conservation policies.
作者
武慧
俞乐
杜贞容
赵强
戚文超
曹越
王金洲
申小莉
孙尧
马克平
Hui Wu;Le Yu;Zhenrong Du;Qiang Zhao;Wenchao Qi;Yue Cao;Jinzhou Wang;Xiaoli Shen;Yao Sun;Keping Ma(Department of Earth System Science,Tsinghua University,Beijing 100084,China;Ministry of Education Ecological Field Station for East Asian Migratory Birds,Beijing 100084,China;School of Information and Communication Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China;Department of Landscape Architecture,School of Architecture,Tsinghua University,Beijing 100084,China;Chinese Research Academy of Environmental Sciences,Beijing 100012,China;Institute of Botany,Chinese Academy of Sciences,Beijing 100093,China;Beijing Zhipu Huazhang Technology Co.,Ltd.,Beijing 100089,China;College of Forestry,Northeast Forestry University,Harbin 150040,China)
出处
《生物多样性》
北大核心
2025年第3期107-118,共12页
Biodiversity Science
基金
科技部重点研发计划(2024YFF1307600,2022YFE0209400)
国家自然科学基金(42401314,52408075)
中国博士后科学基金(2023M741885)
清华大学自主科研计划(20223080017)。
关键词
生物多样性
遥感监测
昆明-蒙特利尔全球生物多样性框架
人工智能
biodiversity
remote sensing monitoring
Kunming-Montreal Global Biodiversity Framework
artificial intelligence
