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

草原和湿地是具有重要生态和经济功能的珍贵生态系统。近几十年来，由气候变化和人类活动引起的环境变化，从全球到局部都对草地和湿地产生了巨大影响。许多研究对草原和湿地进行了调查，以了解生态系统的丧失和退化。然而，很少有人将草原和湿地结合起来进行研究。它们经常在景观中共存，草原是湿地的天然缓冲带。草原和湿地之间的过渡和转换对气候变化也很敏感。这种草地湿地生态系统是许多动植物物种的重要栖息地，并为人类福祉提供必要的生态系统服务。环境干扰（如干旱、非本地物种入侵和城市发展）可以对这种混合生态系统产生相当大的影响，并导致生态系统服务功能的退化或不可逆转的丧失，这在以前的研究中没有得到很好的调查。遥感是监测生态系统的有力工具，特别是对具有显著时空变化的草地湿地。遥感大数据包括不同平台（如卫星、飞机和无人机）获取的多类型图像（如光学、激光雷达、雷达和热成像），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的丧失和退化提供有价值的数据和见解。近年来，这种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，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研究环境变化下草原湿地的丧失和退化，了解潜在的生态机制，为生态系统管理提供支持。在未来五年内，我将与来自不同领域的合作者合作，追求以下短期目标：1)利用遥感大数据和先进的分析模型研究草原湿地的生态特征和过程；2)利用长时间序列影像监测选定草原湿地生态系统的丧失与退化；3)评估环境干扰（如干旱、野火和人类活动）对草原湿地的影响，并评估生态系统的恢复能力。本研究将有助于我们进一步认识草地湿地的生态过程和潜在机制，以及环境变化的影响。对遥感大数据的探索和先进分析方法的发展，也将推动这一大数据技术在生态系统监测中的应用。

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.