MCA: Combining Drone Imagery and Deep Learning to Map Fine-Scale Heterogeneity in Arctic Vegetation

MCA：结合无人机图像和深度学习来绘制北极植被的精细尺度异质性地图

• 批准号: 2321530
• 负责人: Michael Loranty
• 金额: $ 23.23万
• 依托单位: Colgate University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321530&HistoricalAwards=false
• 关键词: MCA; Combining; Drone; Imagery; Deep

The Arctic is warming nearly four times faster than the rest of the planet. As a result of this warming, plants in Arctic lands are changing. In some places, plants are taking advantage of warmer conditions and growing larger or spreading across the landscape. For example, shrubs may be increasing in height and growing in areas that used to contain only grasses, or grasses may be growing taller. Arctic warming is also resulting in more wildfires, which remove plants and create different conditions for new plants to grow. The investigators will use images from satellites to tell where plant growth is increasing and where fires have occurred. However, these satellite images do not have the resolution to tell us what types of plant changes are occurring. In this project, the investigators will analyze detailed images from drones using new computer science tools to help us understand how plants are changing, and to help interpret changes we see in less detailed satellite images. Using an archive of drone images from across the Arctic, the investigators will map patches of shrubs and grasses to understand how they influence estimates of plant growth made from satellites. The investigator will use a different image archive to see how plant growth after wildfires is related to the amount of plants present before the fire. The results will provide insights into how changes in Arctic plants influence the global climate. This research will also help establish new methods for using drone data and computer science tools to map plant processes in fine detail that can be used in other regions. The project will allow a college professor to develop new research skills and new teaching materials and provide research opportunities for undergraduate college students. Results from the project will be shared through scientific journal articles, and data and class materials will be shared publicly on the internet.As the Arctic warms nearly four times faster than the rest of the planet, a wide range of ecosystem changes are occurring. Besides general increases in plant productivity, changes in the geographic distribution of different vegetation types have also been observed. For example, in tundra ecosystems shrubs may be expanding into grass-dominated areas, while elsewhere grass productivity may be increasing without expansion of shrubs. Larger and more intense wildfires with warming may be changing patterns of vegetation recovery post-fire. Satellite images are a useful tool for observing and monitoring these types of changes. However, the resolution of these images is not high enough to identify the specific types of vegetation change that are occurring, which is important because the nature of these changes will determine their impacts on global climate. In this project, the investigators will combine high resolution drone images with cutting-edge machine learning tools to map variation in Arctic vegetation at the level of individual plants. Using an archive of drone images from across the Arctic, the investigators will map the distribution of shrubs and grasses to understand their respective changes, as well as how they affect satellite estimates of ecosystem productivity. Using a second archive, we will map live and dead trees and shrubs in areas affected by wildfire to determine how vegetation regrowth after fire varies in northeastern Siberia. These maps will help increase understanding of the processes driving Arctic vegetation change, and aid interpretation of changes inferred from satellite images. The results will aid our understanding of Arctic vegetation feedbacks to global climate. The combination of drone imagery and artificial intelligence tools will develop and refine methods for studying fine scale vegetation dynamics that can be employed in other regions and ecosystems. The project will enable a mid-career college professor to develop new analytical expertise and skills, and new teaching materials to incorporate in undergraduate classes focused on geographic analysis. Results from the project will be published in peer-reviewed journals, and the resulting data sets and teaching materials will be published in freely accessible online repositories.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

北极变暖的速度是地球其他地区的四倍。由于气候变暖，北极地区的植物正在发生变化。在一些地方，植物正在利用温暖的条件，生长得更大或遍布整个景观。例如，灌木可能会增加高度，并在过去只包含草的地区生长，或者草可能会长得更高。北极变暖也导致更多的野火，这将使植物消失，并为新植物的生长创造不同的条件。调查人员将利用卫星图像来判断哪里的植物生长正在增加，哪里发生了火灾。然而，这些卫星图像没有分辨率告诉我们正在发生什么类型的植物变化。在这个项目中，研究人员将使用新的计算机科学工具分析来自无人机的详细图像，以帮助我们了解植物是如何变化的，并帮助解释我们在不太详细的卫星图像中看到的变化。利用来自北极各地的无人机图像档案，研究人员将绘制灌木和草地的斑块，以了解它们如何影响卫星对植物生长的估计。研究人员将使用不同的图像档案，以了解野火后的植物生长与火灾前植物数量的关系。研究结果将为了解北极植物的变化如何影响全球气候提供见解。这项研究还将有助于建立新的方法，使用无人机数据和计算机科学工具来详细绘制可用于其他地区的植物过程。该项目将允许大学教授开发新的研究技能和新的教材，并为本科生提供研究机会。该项目的成果将通过科学期刊文章共享，数据和课堂材料将在互联网上公开共享。随着北极变暖的速度比地球其他地区快近四倍，广泛的生态系统变化正在发生。除了植物生产力普遍提高外，还观察到不同植被类型的地理分布发生了变化。例如，在苔原生态系统中，灌木可能会扩展到以草为主的地区，而在其他地方，草的生产力可能会增加，而灌木却没有扩展。随着气候变暖，更大、更强烈的野火可能正在改变火灾后植被恢复的模式。卫星图像是观测和监测这类变化的有用工具。然而，这些图像的分辨率不够高，无法识别正在发生的植被变化的具体类型，这一点很重要，因为这些变化的性质将决定其对全球气候的影响。在这个项目中，研究人员将把联合收割机高分辨率的无人机图像与尖端的机器学习工具结合起来，在单个植物的水平上绘制北极植被的变化。利用来自北极各地的无人机图像档案，研究人员将绘制灌木和草的分布图，以了解它们各自的变化，以及它们如何影响卫星对生态系统生产力的估计。使用第二个档案，我们将绘制受野火影响地区的活树和死树，以确定西伯利亚东北部火灾后植被再生的变化。这些地图将有助于加深对北极植被变化驱动过程的理解，并有助于解释从卫星图像推断的变化。这些结果将有助于我们了解北极植被对全球气候的反馈。无人机图像和人工智能工具的结合将开发和完善研究精细尺度植被动态的方法，这些方法可用于其他地区和生态系统。该项目将使一个职业生涯中期的大学教授开发新的分析专业知识和技能，并将新的教学材料纳入以地理分析为重点的本科课程。该项目的结果将发表在同行评审的期刊上，由此产生的数据集和教学材料将在免费访问的在线存储库中发布。该项目由地球科学理事会共同资助，以支持人工智能/ML在地球科学中的进步。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.