Department of Biology, Algoma University, Sault Ste. Marie, ON P6A2G4, Canada.
Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China.
Sensors (Basel). 2023 Apr 19;23(8):4101. doi: 10.3390/s23084101.
Ecosystem conditions at the regional level are critical factors for environmental management, public awareness, and land use decision making. Regional ecosystem conditions may be examined from the perspectives of ecosystem health, vulnerability, and security, as well as other conceptual frameworks. Vigor, organization, and resilience (VOR) and pressure-stress-response (PSR) are two commonly adopted conceptual models for indicator selection and organization. The analytical hierarchy process (AHP) is primarily used to determine model weights and indicator combinations. Although there have been many successful efforts in assessing regional ecosystems, they remain affected by a lack of spatially explicit data, weak integration of natural and human dimensions, and uncertain data quality and analyses. In the future, regional ecosystem condition assessments may be advanced by incorporating recent improvements in spatial big data and machine learning to create more operative indicators based on Earth observations and social metrics. The collaboration between ecologists, remote sensing scientists, data analysts, and scientists in other relevant disciplines is critical for the success of future assessments.
区域水平的生态系统状况是环境管理、公众意识和土地利用决策的关键因素。可以从生态系统健康、脆弱性和安全性以及其他概念框架的角度来考察区域生态系统状况。活力、组织和弹性(VOR)以及压力-应激反应(PSR)是两个常用的选择和组织指标的概念模型。层次分析法(AHP)主要用于确定模型权重和指标组合。尽管在评估区域生态系统方面已经取得了许多成功的努力,但它们仍然受到缺乏空间明确数据、自然和人文维度的整合薄弱以及数据质量和分析不确定的影响。未来,通过将空间大数据和机器学习的最新进展结合起来,根据地球观测和社会指标创建更具操作性的指标,区域生态系统状况评估可能会得到推进。生态学家、遥感科学家、数据分析人员以及其他相关学科的科学家之间的合作对于未来评估的成功至关重要。