Using satellite data to improve mapping of stem density and forest carbon for sustainable forest management

利用卫星数据改进茎密度和森林碳的绘图，以实现可持续森林管理

• 批准号: 2438460
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2438460
• 关键词: Using; satellite; data; improve; mapping

Forests are important due to the many ecosystem services they provide. The most important one being climate protection because they act as a carbon sink: Sustainable forest management and use of the natural resource timber helps in reducing carbon dioxide. As forests remove carbon dioxide from the air they contribute to reducing green house gas in the atmosphere. Timber stores carbon dioxide and by replacing other materials with timber this effect is enhanced. In addition forests provide a source of livelihood, recreation, wildlife habitat, keep the climate in balance, filter pollutants and fine particles, help maintain the hydrological balance, protect from soil erosion and avalanches and secure drinking water.This project is an exciting opportunity to get involved in interdisciplinary research in statistics, data and forest science, to improve mapping of forest carbon for monitoring forest properties, and to help with mitigation of climate change effects. It will equip you with important skills in remote sensing, statistical modelling and machine learning, as well as data science skills, with hands on opportunities to learn about forest science. You will collect field data in Scotland in partnership with forest managers, and spend 3 months on a placement within your CASE Partner (Forest Research).Concentrating initially on European temperate forests we will explore the following questions:1. Review and validation of current state of the art methods for mapping forest carbon and stand density. Model validation: Validation of how well the models predict forest variables, using the field data as ground truth.2. Statistical Design: What is the optimal combination of field and remotely sensed data, i.e. optical allocation of resources for mapping forest carbon. Does this optimum depend on the forest type being mapped (i.e. even-age monoculture stands and mixed species/age woodlands).3. Model development and validation: Improving statistical models for mapping; state of the art method use of the shelf models (multiple regression, k-nearest neighbours, support vector regression, random forest algorithms) . The developed model will likely involve fusion of data obtained at point (field plot tree level data) and areal resolutions (pixels from satellite and lidar data). Possible avenues for this are machine learning methods for merging different data sources (see e.g. Baez-Villanueva et al, 2020) or generalized spatial fusion models implemented using a Baysian hierarchical framework (see e.g. Wang et al, 2017, Cameletti et al, 2019). Model validation using the field data as ground truth.4. How to solve the signal saturation problem. Does incorporating LiDAR and field plot data help with this?5. How well do the methods translate to other types of forest (temperate or tropical)?The tools and data to be used include:1. Software: R, INLA, Python, Stan2. Datasets:o Scotland level data from a combination of managed single-age stands (mostly of Sitka Spruce trees) and mixed native woodlands, based on field plots from the CASE partner and open access aircraft-derived LiDAR data.o Forest inventory and LiDAR datasets for the Rioja region of Spain and the whole of Denmark, from their National Forest Inventories, including hundreds of thousands of tree measurements from about 1700 plots used in Joshi (2017).o Using the expertise of Tim Baker, open access forest plots from unmanaged forests across the tropicso In all cases we also will be using the corresponding data from L-band synthetic aperture radar (SAR) from the ALOS-2 satellite, C-band SAR data from the Sentinel-1 satellite, and optical data from the Sentinel-2 satellite.

森林之所以重要，是因为它们提供了许多生态系统服务。最重要的是气候保护，因为它们作为碳汇：可持续森林管理和自然资源木材的使用有助于减少二氧化碳。由于森林从空气中去除二氧化碳，它们有助于减少大气中的绿色气体。木材储存二氧化碳，并通过用木材取代其他材料来增强这种效果。此外，森林还提供生计、娱乐和野生动物栖息地，保持气候平衡，过滤污染物和细颗粒，帮助维持水文平衡，防止土壤侵蚀和雪崩，保障饮用水安全。该项目是一个令人兴奋的机会，可以参与统计、数据和森林科学的跨学科研究，改善森林碳测绘，以监测森林财产，并帮助减轻气候变化的影响。它将为您提供遥感，统计建模和机器学习以及数据科学技能的重要技能，并有机会亲自了解森林科学。您将与森林管理人员合作收集苏格兰的实地数据，并在您的CASE合作伙伴（森林研究）中实习3个月。最初集中在欧洲温带森林，我们将探讨以下问题：1.审查和验证目前最先进的森林碳和林分密度绘图方法。模型验证：使用实地数据作为基本事实，验证模型预测森林变量的效果.统计设计：实地数据和遥感数据的最佳组合是什么，即用于森林碳测绘的资源的最佳分配。这一最佳值是否取决于所绘制的森林类型（即同龄的单作林和混合种/龄林地）。模型开发和验证：改进制图统计模型;使用大陆架模型的最新方法（多元回归、k-最近邻、支持向量回归、随机森林算法）。所开发的模型将可能涉及在点（实地图树级数据）和区域分辨率（来自卫星和激光雷达数据的像素）获得的数据的融合。可能的途径是用于合并不同数据源的机器学习方法（例如Baez-Villanueva et al，2020）或使用贝叶斯分层框架实现的广义空间融合模型（例如Wang et al，2017，Cameletti et al，2019）。以实测数据为基础的模型验证.如何解决信号饱和问题。结合LiDAR和现场绘图数据是否有助于实现这一点？5.这些方法在多大程度上适用于其他类型的森林（温带或热带）？使用的工具和数据包括：1。软件：R，INLA，Python，Stan 2.数据集：苏格兰水平的数据来自管理的单一年龄林分的组合o西班牙Rioja地区和整个丹麦的森林清单和LiDAR数据集，来自其国家森林清单，包括Joshi（2017）使用的约1700个地块的数十万树木测量。o利用Tim Baker的专业知识，开放访问热带未管理森林的森林地块。o在所有情况下，我们还将使用ALOS-2卫星的L波段合成孔径雷达（SAR）的相应数据，Sentinel-1卫星的C波段SAR数据，和哨兵2号卫星的光学数据