Investigating Grassland-Wetland Ecosystems and the Impacts of Environmental Change using Remote Sensing Big Data

利用遥感大数据研究草原湿地生态系统及环境变化的影响

• 批准号: RGPIN-2022-03679
• 负责人: Lu, Bing
• 金额: $ 2.19万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754808
• 关键词: Investigating; Grassland; Wetland; Ecosystems; Impacts

Grasslands and wetlands are valuable ecosystems that have essential ecological and economic functions. Over the past few decades, environmental change induced by climate change and human activities has greatly affected grasslands and wetlands from the global to the local level. Many studies have investigated grasslands and wetlands for understanding ecosystem loss and degradation. However, few have investigated grasslands and wetlands cohesively. They often coexist in a landscape, and grasslands are natural buffers for wetlands. Transitions and conversions between grasslands and wetlands are also sensitive to climate change. Such grassland-wetland ecosystems are critical habitats for many plant and animal species and provide essential ecosystem services for human well-being. Environmental disturbances (e.g., droughts, invasion of non-native species, and urban development) can considerably influence such mixed ecosystems and cause degradation or irreversible loss of ecosystem services, which have not been well investigated in previous studies. Remote sensing is a powerful tool for monitoring ecosystems, especially for grasslands-wetlands that have substantial spatio-temporal variations. Remote sensing big data, which include multi-type images (e.g., optical, LiDAR, Radar, and thermal) acquired by different platforms (e.g., satellites, airplanes, and drones), are capable of capturing ecosystem features from different perspectives and at different scales, and thus providing valuable data and insights for analyzing ecosystem loss and degradation. Such big data have become more widely available in recent years, offering unprecedented opportunities for ecosystem monitoring. However, utilizations of these big data, such as the fusion of multi-type and multi-platform images, remains challenging. The long-term goal of this research is to investigate the loss and degradation of grasslands-wetlands under the environmental change using remote sensing big data and to understand underlying ecological mechanisms for supporting ecosystem management. While working with collaborators from different sectors, I will pursue the following short-term objectives over the next five years: 1) investigating ecological features and processes in grasslands-wetlands using remote sensing big data and advanced analytical models; 2) monitoring the loss and degradation of selected grassland-wetland ecosystems using a long time series of images; and 3) evaluating the impacts of environmental disturbances (e.g., droughts, wildfires, and human activities) on grasslands-wetlands and assessing ecosystem resilience. It is expected that this research will improve our understanding of ecological processes and underlying mechanisms in grasslands-wetlands together with the impacts of environmental change. The exploration of remote sensing big data and the development of advanced analytical methods will also promote the adoption of this big data technology in ecosystem monitoring.

草地和湿地是具有重要生态和经济功能的宝贵生态系统。在过去几十年中，气候变化和人类活动引起的环境变化极大地影响了从全球到地方一级的草原和湿地。许多研究调查了草原和湿地，以了解生态系统的损失和退化。然而，很少有人对草原和湿地进行过研究。它们通常在一个景观中共存，草地是湿地的天然缓冲区。草原和湿地之间的过渡和转换也对气候变化敏感。这种草地-湿地生态系统是许多动植物物种的重要生境，为人类福祉提供了必不可少的生态系统服务。环境干扰（例如，干旱、非本地物种的入侵和城市发展）可对这种混合生态系统产生重大影响，并造成生态系统服务的退化或不可逆转的损失，而这在以往的研究中尚未得到充分的调查。遥感是监测生态系统的一个有力工具，特别是对时空变化很大的草地-湿地。遥感大数据，其中包括多类型的图像（例如，光学、LiDAR、雷达和热）由不同平台获取（例如，卫星、飞机和无人机等能够从不同角度和不同尺度捕捉生态系统特征，从而为分析生态系统损失和退化提供有价值的数据和见解。近年来，这些大数据变得越来越广泛，为生态系统监测提供了前所未有的机会。然而，这些大数据的利用，例如多类型和多平台图像的融合，仍然具有挑战性。本研究的长期目标是利用遥感大数据调查环境变化下草地-湿地的损失和退化，并了解支持生态系统管理的潜在生态机制。在与来自不同部门的合作者合作的同时，我将在未来五年内实现以下短期目标：1）利用遥感大数据和先进的分析模型研究草地-湿地的生态特征和过程; 2）使用长时间序列图像监测选定的草地-湿地生态系统的损失和退化;以及3）评估环境干扰的影响（例如，干旱、野火和人类活动）对草地-湿地的影响，并评估生态系统的复原力。希望本研究能增进我们对草地-湿地生态过程及其机制的了解，以及环境变迁的影响。对遥感大数据的探索和先进分析方法的发展，也将推动这种大数据技术在生态系统监测中的采用。