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

研究湿地景观扰动恢复趋势的新天基方法

• 批准号: RGPIN-2020-05743
• 负责人: DeVries, Benjamin
• 金额: $ 1.82万
• 依托单位: University of Guelph
• 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=754779
• 关键词: New; space; based; approaches; studying

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.

湿地主导的景观-或"湿地景观"-的特点是相互连接，高度动态的湿地网络。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。许多这些景观正面临着严重的威胁，由于气候介导的和人为（土地利用）的变化。湿地景观的复原力-它们在不断变化的制度下继续提供生态系统服务的能力-是一个关键问题，需要了解生态和水文动态。然而，这些动态往往是难以量化的湿地景观，因为它们的高时间变异性和空间异质性。虽然卫星地球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要么缺乏空间分辨率，要么缺乏时间观测频率，无法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态研究的方式。特别是在土地覆盖监测领域出现了"虚拟星座"的概念，作为空间和时间分辨率问题的一个潜在解决办法。风险投资涉及综合来自多个卫星传感器的数据，以实现单一的监测目标。在开发这个概念的过程中，一个特别的挑战是来自不同传感器的数据的融合。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，但如何整合来自这两种类型传感器的信息，以生成淹没、连通性或植被等动态地表参数的空间和时间一致的估计趋势代表了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，以解决湿地对气候和土地利用变化的适应能力问题。因此，该计划旨在解决三个研究目标：（1）量化流域规模的淹没动态湿地景观通过开发新的光学合成孔径雷达合成方法;（2）研究湿地连接与精细分辨率图像时间序列和图论指标的时空趋势;（3）发展的水文和生态响应泥炭为主的湿地火灾干扰的理解。研究地点将从代表气候和生态系统梯度的北极、寒带、温带和亚热带湿地景观中选择。将Landsat和Sentinel-1等历史和当代光学传感器的数据与Sentinel-1使命和雷达卫星星座使命（RCM）的合成孔径雷达数据相结合的新方法将是所有三个目标的基础。从预期的美国航天局-印度空间研究组织长波长合成孔径雷达使命获得的数据也将被纳入。