Change and Stability of Canada's Forested and Aquatic Landscapes: Big-data Remote Sensing on Multiple Platforms

加拿大森林和水生景观的变化与稳定：多平台大数据遥感

• 批准号: RGPIN-2021-03253
• 负责人: Cardille, Jeffrey
• 金额: $ 3.13万
• 依托单位: McGill 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=761297
• 关键词: Change; Stability; Canada; Forested; Aquatic

Recent developments in satellite remote sensing show that we have entered a new era. Where satellite information once quite scarce and expensive, vast archives have been opened for free public access. Where processing power and disk space once could throttle a plan for a large-area study, new tools with enormous capacity have emerged to put supercomputers at anyone's fingertips. Remote sensing's advances with this new paradigm have been rapid and impressive, producing global assessments of a wide range of phenomena: global tidal flat maps, high-resolution population estimates, times series maps of forest loss and gain-- to name only a few. Despite this enormous progress, considerable obstacles constrain our use of the vast potential of the world's earth-resources satellites. Combining data streams together from multiple platforms is quite complex: diverse sensors offer imagery with different spatial resolutions, have different numbers and specifications of spectral bands, arrive at irregular times, are often partially covered by clouds, and were designed for a wide range of purposes. It is exciting to conceive of these sensors as a single Earth-observing machine constantly grabbing views from different parts of the electromagnetic spectrum-but it is difficult to handle more than a few dimensions of this signal at a time.  There are two goals of this research. First, we will use our unique algorithms to blend information from a wide array of sensors, allowing us to view change and stability more rapidly and reliably. Second, we will integrate new machine-learning techniques to interpret older low-information imagery, allowing us to see how Canada looked near the beginning of the satellite era. Together these goals will more fully explore the immense trove of imagery available across Earth's surface, from the early 1970s until the very latest fires of this coming summer, on land and in water. Over the next five years my research team of pre-undergraduates, undergraduates, Master's students, PhD students, and postdocs will pursue four medium-term objectives: (1) Combine multiple data streams for weekly mapping of harvest, fire borders and other change across Canada; (2) track wildfire growth in near-real-time by fusing fire growth models with information streaming from multiple sensors; (3) create new estimations of the chemical properties of more than 100,000 lakes of Canada; and (4) implement new big-data approaches to old imagery to map Canada's land use / land cover in the 1970s. Built on solid prototypes created in my lab and fuelled by new data and powerful analysis platforms, this research is likely to provide concrete benefits to both researchers and non-scientists alike. We will better inform Canadians about the wildfires that increasingly capture the public's attention; we will peer into Canada's great aquatic resource; and we will develop approaches that we foresee will be of broad interest to the remote sensing community.

卫星遥感的最新发展表明，我们已经进入了一个新时代。在卫星信息一度相当稀缺和昂贵的地方，大量档案已开放供公众免费查阅。处理能力和磁盘空间曾经可以限制大面积研究的计划，而具有巨大容量的新工具已经出现，可以将超级计算机置于任何人的指尖。遥感在这一新模式方面的进展是迅速和令人印象深刻的，产生了对各种现象的全球评估：全球潮滩图、高分辨率人口估计、森林损失和增加的时间序列图-仅举几例。 尽管取得了这一巨大进展，但仍有相当多的障碍限制我们利用世界地球资源卫星的巨大潜力。将来自多个平台的数据流组合在一起是相当复杂的：不同的传感器提供具有不同空间分辨率的图像，具有不同数量和规格的光谱带，到达时间不规则，通常部分被云覆盖，并且被设计用于各种目的。把这些传感器想象成一个单一的地球观测机器是令人兴奋的，它不断地从电磁频谱的不同部分获取视图，但很难同时处理这种信号的几个维度。首先，我们将使用我们独特的算法来混合来自各种传感器的信息，使我们能够更快速，更可靠地查看变化和稳定性。其次，我们将整合新的机器学习技术来解释旧的低信息图像，让我们看到加拿大在卫星时代开始时的样子。这些目标将更充分地探索地球表面从20世纪70年代初到今年夏天最新火灾的巨大图像宝藏，在陆地和水中。 在接下来的五年里，我的研究团队的预本科生，本科生，硕士生，博士生和博士后将追求四个中期目标：（1）结合联合收割机多个数据流，每周绘制收获，火灾边界和加拿大各地的其他变化;（2）通过融合火灾增长模型与来自多个传感器的信息流，近实时跟踪野火增长;（3）对加拿大10万多个湖泊的化学性质进行新的估计;（4）对旧图像实施新的大数据方法，以绘制20世纪70年代加拿大的土地利用/土地覆盖图。 这项研究建立在我实验室创建的坚实原型基础上，并由新数据和强大的分析平台推动，很可能为研究人员和非科学家提供具体的好处。我们将更好地让加拿大人了解日益引起公众注意的野火;我们将研究加拿大丰富的水产资源;我们将开发我们预计将引起遥感界广泛兴趣的方法。