Next generation forest dynamics modelling using remote sensing data

使用遥感数据的下一代森林动力学建模

• 批准号: MR/Y033981/1
• 负责人: Emily Lines
• 金额: $ 75.74万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY033981%2F1
• 关键词: Next; generation; forest; dynamics; modelling

Globally, human-induced climate change and biodiversity loss threaten ecosystem function and the services the biosphere provides for humans. Forests are carbon-dense ecosystems and are home to the majority of terrestrial biodiversity, so are crucial tools to mitigate adverse impacts. Indeed, many countries, including many in Europe, have ambitious policies to restore and replant forests to restore carbon and habitats. However, forests are themselves threatened by climate change and biodiversity loss, so understanding and predicting their future in the face of global change is a priority. In order to understand how forests are changing, and how they will change in the future, we need large monitoring networks collecting data, to embrace new measurement techniques, to fuse data from multiple sources, and to create robust, data-driven, predictive models. Traditional forest data is severely limited in both its spatiotemporal coverage and what it can measure, and whilst existing ecological models are tailored to such data, these focus on the small scale and cannot predict the future of forests at large enough scales to help understand the impacts of climate change. New approaches are needed.This fellowship will use cutting-edge remote sensing data and modern data science techniques to generate new understanding of current and future forest functioning. Active and passive remote sensors, including terrestrial and drone laser scanning and structure from motion photogrammetry, are able to capture the full three-dimensional structure of a forest to sub-cm scale within three-dimensional point clouds. This fellowship will collect and collate such data from tens of thousands of trees across hundreds of forest plots in Europe, creating a massive new dataset of tree and forest structure. Such data are extremely complex to analyse, and the project will use specially developed and tailored deep learning techniques to extract ecological information from noisy point clouds. Some plots that have already been measured will be re-measured, to capture three dimensional tree growth and forest structural change.The fellowship will analyse these data to determine how trees and forests are structured across Europe, and how their three-dimensional structure affects and is affected by their productivity, carbon storage, and the diversity of both the trees and other species living in forests. New insights into how biodiversity is related to three-dimensional structure will bring help develop approaches to co-monitoring biodiversity and biomass, crucial for demonstrating the value of ecosystems towards tackling both climate change and biodiversity loss. Using newly developed software, the fellowship will scale local, single-measurement plot-scale information to continental scale and continuous monitoring by fusing ground and Earth Observation (satellite) data. Using deep learning to link the structural and diversity information from hundreds of thousands of plot locations across Europe with the spectral properties measured by satellite sensors, the fellowship will bring new understanding on how forests are structured and how they are changing across Europe. Finally, using findings from all parts of the fellowship, a new modelling framework which can predict ecological change on the ground at local scale but which can ingest satellite data will be developed. This data-driven approach will enable robust and updatable predictions of climate change impacts on forest diversity and dynamics across Europe. It will be constructed to be flexible to incorporate future data streams, so informing inform climate change mitigation policy across the continent.

在全球范围内，人类引起的气候变化和生物多样性损失威胁着生态系统的功能以及生物圈为人类提供的服务。森林是碳密集的生态系统，是大多数陆地生物多样性的所在地，因此减轻不利影响的关键工具也是如此。实际上，许多国家，包括欧洲许多国家，都有雄心勃勃的政策来恢复和重新种植森林，以恢复碳和栖息地。但是，森林本身受到气候变化和生物多样性损失的威胁，因此，在面对全球变化的情况下，理解和预测其未来是当务之急。为了了解森林的变化以及将来的变化方式，我们需要大型监视网络收集数据，采用新的测量技术，融合来自多个来源的数据，并创建强大的，数据驱动的预测模型。传统的森林数据在其时空覆盖范围及其可以测量的范围都受到严重限制，尽管现有的生态模型是针对此类数据量身定制的，但这些模型的侧重于小规模，无法预测足够大的森林的未来，以帮助了解气候变化的影响。需要新的方法。该奖学金将使用尖端的遥感数据和现代数据科学技术来对当前和未来的森林功能产生新的了解。主动和被动的遥控传感器，包括运动摄影测量法的陆地和无人机激光扫描和结构，能够在三维点云中捕获森林的整个三维结构到亚基量表。该奖学金将从欧洲数百种森林地块中成千上万的树木中收集和整理这些数据，从而创建一个大量的树木和森林结构数据集。这样的数据非常复杂，可以使用专门开发和量身定制的深度学习技术来从嘈杂的点云中提取生态信息。一些已经测量的图将被重新测量，以捕获三维树木的生长和森林结构变化。研究金将分析这些数据，以确定欧洲整个欧洲的树木和森林的结构，以及它们的三维结构如何影响和如何影响其生产力，碳存储以及居住在森林中的树木和其他种类的多样性。关于生物多样性与三维结构如何相关的新见解将为共同监督生物多样性和生物量提供帮助，这对于证明生态系统在应对气候变化和生物多样性损失方面的价值至关重要。使用新开发的软件，奖学金将通过融合地面和地球观测（卫星）数据来扩展本地单程图量表信息到大陆尺度和连续监视。利用深度学习将欧洲数十万个情节位置的结构和多样性信息与卫星传感器衡量的光谱特性联系起来，研究金将对森林的结构如何结构以及它们在整个欧洲的变化进行新的了解。最后，使用奖学金的所有部分的发现，这是一个新的建模框架，可以预测当地规模的生态变化，但可以开发摄入卫星数据。这种数据驱动的方法将使气候变化对整个欧洲的森林多样性和动态的影响有强大的更新预测。它将被构建为灵活地纳入未来的数据流，因此请告知整个非洲大陆的气候变化缓解政策。