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丧失威胁着生态系统的功能和生物圈为人类提供的服务。森林是碳密度高的生态系统，是大多数陆地生物多样性的家园，也是减轻不利影响的重要工具。事实上，包括许多欧洲国家在内的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。然而，森林本身也受到气候变化和生物多样性丧失的威胁，因此，在全球变化的情况下了解和预测森林的未来是一个优先事项。为了了解森林正在如何变化以及未来将如何变化，我们需要收集数据的大型监测网络，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和可测量范围方面都受到严重限制，虽然现有的生态模型是针对这些数据量身定制的，但这些模型侧重于小规模，无法在足够大的尺度上预测森林的未来，以帮助了解气候变化的影响。该研究金将利用最先进的遥感数据和现代数据科学技术，对当前和未来的森林功能产生新的认识。主动和被动遥感器，包括地面和无人驾驶飞机激光扫描和动态摄影测量结构，能够在三维点云中捕捉到厘米以下尺度的森林完整三维结构。该研究金将收集和整理来自欧洲数百个森林地块的数万棵树木的数据，创建一个关于树木和森林结构的大规模新数据集。这些数据分析起来极其复杂，该项目将使用专门开发和定制的深度学习技术从嘈杂的点云中提取生态信息。一些已经测量过的地块将被重新测量，以捕捉三维树木生长和森林结构变化，该奖学金将分析这些数据，以确定欧洲树木和森林的结构，以及它们的三维结构如何影响它们的生产力，碳储存以及树木和森林中其他物种的多样性。对生物多样性如何与三维结构相关的新见解将有助于制定共同监测生物多样性和生物量的方法，这对于展示生态系统在应对气候变化和生物多样性丧失方面的价值至关重要。该研究金方案将利用新开发的软件，通过融合地面和地球观测（卫星）数据，将当地的单次测量地块尺度信息扩大到大陆尺度和连续监测。利用深度学习将欧洲数十万个地块的结构和多样性信息与卫星传感器测量的光谱特性联系起来，该奖学金将为欧洲森林的结构和变化带来新的认识。最后，将利用研究金各部分的研究结果，开发一个新的建模框架，该框架可以预测当地规模的地面生态变化，但也可以吸收卫星数据。这种数据驱动的方法将能够对气候变化对整个欧洲森林多样性和动态的影响进行可靠和可更新的预测。它将灵活地构建，以纳入未来的数据流，从而为整个非洲大陆的气候变化减缓政策提供信息。