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

卫星遥感的最新发展表明，我们已经进入了一个新时代。在卫星信息曾经相当稀缺和昂贵的地方，大量的档案已经开放，供公众免费获取。处理能力和磁盘空间曾经可以扼杀大规模研究的计划，但现在出现了具有巨大容量的新工具，可以将超级计算机置于任何人的指尖。遥感在这一新模式下取得了迅速和令人印象深刻的进展，产生了对广泛现象的全球评估：全球潮滩地图、高分辨率人口估计、森林损益的时间序列地图--仅举几例。尽管取得了巨大进展，但仍有相当大的障碍限制了我们对世界地球资源卫星巨大潜力的利用。将来自多个平台的数据流组合在一起是相当复杂的：不同的传感器提供不同空间分辨率的图像，具有不同数量和规格的光谱波段，到达时间不规则，通常部分被云层覆盖，并且设计用于广泛的目的。将这些传感器设想为一台不断从电磁频谱的不同部分获取视角的单一地球观测机器是令人兴奋的，但一次处理这些信号的几个维度是困难的。首先，我们将使用我们独特的算法来融合来自各种传感器的信息，使我们能够更快、更可靠地查看变化和稳定性。其次，我们将集成新的机器学习技术来解释较旧的低信息图像，使我们能够看到加拿大在卫星时代开始时的样子。这些目标一起，将更全面地探索从20世纪70年代初到今年夏天最新的陆地和水中大火，整个地球表面可用的巨大图像宝库。在接下来的五年里，我的研究团队由预科本科生、本科生、硕士生、博士生和博士后组成，他们将追求四个中期目标：(1)结合多个数据流，每周绘制加拿大各地的收获、火灾边界和其他变化地图；(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100,000多个湖泊的化学性质做出新的估计；以及(4)对旧图像实施新的大数据方法，以绘制加拿大20世纪70年代的土地利用/土地覆盖图。这项研究建立在我的实验室创造的坚实的原型之上，并以新的数据和强大的分析平台为动力，很可能为研究人员和非科学家提供具体的好处。我们将更好地向加拿大人通报日益引起公众注意的野火；我们将深入了解加拿大丰富的水生资源；我们将开发我们预计将引起遥感社区广泛兴趣的方法。

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.