权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
MR/T019832/1
负责人：
Emily Lines
金额：
$ 122万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT019832%2F1
关键词：
Next generation forest dynamics modelling

项目摘要

Ecosystems are threatened globally by climate change and biodiversity loss. Many countries plan to use forests for climate change mitigation, but many forest ecosystems are threatened by climate change itself. For example, climate-induced species' shifts may halve the value of European forests by 2100. How forests will absorb and store carbon in the future depends critically on individual species' responses to climate change, so predicting the impact of climate change on forests is a priority. Existing models are not suitable because they are constructed around small-scale plot data, so cannot predict the future of forests at national, continental or global scales.Accurate predictions of the future of European forests require large monitoring networks, optimal use of existing data, cutting-edge measurement techniques, and predictive, data-driven models. This fellowship will develop wholly novel approaches to measuring and modelling forest dynamics by integrating existing information with data from new, cutting-edge remote sensing technologies using techniques drawn from machine learning and artificial intelligence.Forest models that include diversity and ecological detail capture long-term succession dynamics and diversity shifts, and can predict changes in carbon storage, but are spatially limited because they require detailed ground data not widely available. National forest inventories contain information on diversity and demographic rates from spatially extensive plot networks. However, they are labour intensive to survey so are often only carried out once per decade, and contain only simple ground measurements of structure (such as trunk diameter and height). Earth Observation satellite data are available at large spatial and over long temporal scales, and provide information on forest function. However, these data are underused by ecologists due to challenges in interpretation, low spatial resolution, and mismatches between what is measurable from space (primarily canopy properties) and what is represented in models based on ground measurements (primarily of individual trunks).New technologies such as Terrestrial Laser Scanning and drone remote sensing can capture 3D information on individual trees and whole-forest canopies in unprecedented detail, offering a link between ground and Earth Observation data. They can measure tree and crown shape, leaf area and arrangement, and crown tessellation in canopies, which are known drivers of productivity and dynamics, opening up the possibility of new approaches to forest modelling. Additionally, new Earth Observation satellites such as the European Space Agency's Sentinels provide global data at high spatial resolution, creating new opportunities for monitoring.This Fellowship will create a new conceptual framework for modelling forest dynamics, parameterised and tested with forest data from across Europe. The model will link Terrestrial Laser Scanning and drone data to plot information on diversity and dynamics, and will predict forest responses to climate change robustly by additionally assimilating Earth Observation data. This will improve model spatial coverage as well as accuracy, with calibration and validation possible at monthly rather than decadal time-steps. New 3D measurements will give novel insights into how canopy structure influences dynamics. Machine learning and artificial intelligence approaches will be used to automate species detection from drone data, allowing ecological monitoring across large spatial areas.The Fellowship will create new knowledge of how European forests function and how they will respond to climate change, with a fully data-driven model that incorporates cutting-edge monitoring. The approach will enable robust and updatable predictions of climate change impacts on forest diversity and dynamics, with flexibility to incorporate future data streams, that could inform climate change mitigation policy across the continent.

全球生态系统受到气候变化和生物多样性丧失的威胁。许多国家计划利用森林减缓气候变化，但许多森林生态系统受到气候变化本身的威胁。例如，到2100年，气候引起的物种变化可能会使欧洲森林的价值减半。森林在未来如何吸收和储存碳在很大程度上取决于个别物种对气候变化的反应，因此预测气候变化对森林的影响是一个优先事项。现有模型是不合适的，因为它们是围绕小尺度样地数据构建的，因此无法预测国家、大陆或全球尺度上森林的未来。对欧洲森林未来的准确预测需要庞大的监测网络、对现有数据的最佳利用、尖端的测量技术以及预测性的数据驱动模型。该研究金将利用机器学习和人工智能技术，将现有信息与来自新的尖端遥感技术的数据相结合，开发测量和模拟森林动态的全新方法。包括多样性和生态细节的森林模型捕捉了长期演替动态和多样性变化，并可以预测碳储量的变化，但由于需要详细的地面数据而无法广泛获得，因此在空间上受到限制。国家森林清查载有来自空间广泛的样地网络的关于多样性和人口比率的信息。然而，这些测量是劳动密集型的，因此通常每十年只进行一次，并且只包含简单的结构地面测量（如树干直径和高度）。地球观测卫星数据具有大空间和长时间尺度，可提供关于森林功能的信息。然而，由于解释方面的挑战、低空间分辨率以及从空间测量的数据（主要是冠层属性）与基于地面测量的模型（主要是单个树干）之间的不匹配，生态学家没有充分利用这些数据。地面激光扫描和无人机遥感等新技术可以以前所未有的细节捕获单株树木和整个森林冠层的3D信息，从而在地面和地球观测数据之间建立联系。它们可以测量树木和树冠的形状、叶面积和排列，以及树冠的镶嵌，这些都是已知的生产力和动态驱动因素，为森林建模的新方法开辟了可能性。此外，新的地球观测卫星，如欧洲空间局的哨兵卫星，提供高空间分辨率的全球数据，为监测创造了新的机会。该研究金将创建一个新的概念框架，用于模拟森林动态，并使用欧洲各地的森林数据进行参数化和测试。该模型将把地面激光扫描和无人机数据联系起来，绘制多样性和动态信息，并通过额外吸收地球观测数据，可靠地预测森林对气候变化的响应。这将提高模型的空间覆盖范围和准确性，可以按月而不是按年进行校准和验证。新的3D测量将为冠层结构如何影响动力学提供新的见解。机器学习和人工智能方法将用于从无人机数据中自动检测物种，从而实现跨大空间区域的生态监测。该研究金将创造有关欧洲森林功能及其如何应对气候变化的新知识，采用完全由数据驱动的模型，结合尖端监测。该方法将使人们能够对气候变化对森林多样性和动态的影响作出可靠和可更新的预测，并灵活地纳入未来的数据流，从而为整个非洲大陆的减缓气候变化政策提供信息。