New space-based approaches to studying disturbance-recovery trends in wetlandscapes

以湿地为主的景观——或“湿地景观”——的特点是由各种连接的、高度动态的湿地组成的网络。它们提供与水资源、生物多样性和碳循环有关的重要生态系统服务。由于气候介导和人为（土地利用）变化，许多这些景观正面临严重威胁。湿地恢复力——在不断变化的变化机制中继续提供生态系统服务的能力——是一个需要理解生态和水文动力学的关键问题。然而，由于其高度的时间变异性和空间异质性，这些动态在湿地景观中往往难以量化。虽然卫星对地观测数据在湿地景观研究中发挥着越来越重要的作用，但大多数卫星传感器缺乏足够的空间分辨率或时间观测频率来充分捕捉其时空动态。然而，开放卫星数据和高性能计算能力的趋势正在改变景观动力学研究的方式。特别是，在土地覆盖监测领域出现了“虚拟星座”（VCs）的概念，作为解决空间和时间分辨率问题的潜在解决方案。风险投资涉及整合来自多个卫星传感器的数据，以解决单一监测目标。在开发这一概念的过程中，一个特别的挑战是融合来自不同传感器的数据。中分辨率光学和合成孔径雷达（SAR）传感器通常用于监测湿地水文和植被，但如何整合这两种传感器的信息，以产生时空一致的地表动态参数，如淹没、连通性或植被趋势，是一个关键的研究空白。我的研究项目是在vc和光学sar融合的概念基础上发展起来的，以解决湿地景观对气候和土地利用变化的弹性问题。因此，本项目旨在解决三个研究目标：(1)通过开发新的光学- sar合成方法，量化湿地景观中流域尺度的淹没动态；(2)利用精细分辨率影像时间序列和图论指标研究湿地连通性的时空变化趋势；(3)了解以泥炭为主的湿地对火灾干扰的水文和生态响应。研究地点将从北极、北方、温带和亚热带湿地景观中选择，代表气候和生态系统的梯度。将来自历史和现代光学传感器（如Landsat和Sentinel-1）的数据与来自Sentinel-1任务和RADARSAT星座任务（RCM）的SAR数据相结合的新方法将是实现这三个目标的基础。来自预期的NASA-ISRO长波长SAR任务的数据也将在可用时纳入。

Wetland-dominated landscapes - or "wetlandscapes" - are characterized by networks of variably connected, highly dynamic wetlands. They provide vital ecosystem services related to water resources, biodiversity and carbon cycling. Many of these landscapes are facing serious threats due to climate-mediated and anthropogenic (land use) changes. Wetlandscape resilience - their ability to continue providing ecosystem services in the presence of shifting change regimes - is a critical question requiring an understanding of ecological and hydrological dynamics. However, these dynamics are often difficult to quantify in wetlandscapes because of their high temporal variability and spatial heterogeneity. While satellite Earth observation data have been playing an increasingly important role in the study of wetlandscapes, most satellite sensors lack either the spatial resolution or temporal observation frequency to adequately capture their spatio-temporal dynamics. However, trends in open satellite data and high-performance computing capabilities are transforming the way in which landscape dynamics are studied. In particular, the concept of "Virtual Constellations" (VCs) has emerged in the field of land cover monitoring as a potential solution to the problem of spatial and temporal resolution. VCs involve the integration of data from multiple satellite sensors to address a single monitoring objective. A particular challenge in the development this concept is the fusion of data from disparate sensors. Moderate resolution optical and Synthetic Aperture Radar (SAR) sensors are commonly used to monitor wetland hydrology and vegetation, but how to integrate information from both types of sensors to generate spatially and temporally consistent estimates of dynamic land surface parameters like inundation, connectivity or vegetation trends represents a key research gap. My research program develops upon the concept of VCs and optical-SAR fusion to address the problem of resilience of wetlandscapes to climate and land use changes. Accordingly, this program seeks to address three research objectives: (1) Quantify basin-scale inundation dynamics in wetlandscapes through the development of novel optical-SAR compositing approaches; (2) Study spatio-temporal trends of wetland connectivity with fine resolution image time series and graph theory metrics; and (3) Develop an understanding of the hydrological and ecological responses to fire disturbances in peat-dominated wetlandscapes. Study sites will be selected from Arctic, boreal, temperate and sub-tropical wetlandscapes, representing a climate and ecosystem gradient. New methods that integrate data from historical and contemporary optical sensors like Landsat and Sentinel-1 with SAR data from the Sentinel-1 mission and the RADARSAT Constellation Mission (RCM) will be foundational to all three objectives. Data from the anticipated long-wavelength NASA-ISRO SAR mission will also be incorporated as they become available.