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

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，因为它们是碳汇：可持续的森林管理和自然资源木材的利用有助于减少二氧化碳。由于森林从空气中吸收二氧化碳，它们有助于减少大气中的温室气体。木材储存二氧化碳，通过用木材代替其他材料，这种效果得到增强。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，并确保饮用水安全。这个项目是一个令人兴奋的机会，可以参与统计、数据和森林科学方面的跨学科研究，改进森林碳制图以监测森林特性，并帮助减轻气候变化的影响。它将使您掌握遥感，统计建模和机器学习以及数据科学技能方面的重要技能，并有机会了解森林科学。您将与森林管理者合作，在苏格兰收集实地数据，并与您的案例合作伙伴（森林研究）一起实习3个月。我们将首先集中于欧洲温带森林，探讨以下问题：森林碳和林分密度制图方法的回顾与验证。模型验证：使用实地数据作为地面真实值，验证模型对森林变量的预测效果。统计设计：什么是野外数据和遥感数据的最佳组合，即森林碳制图资源的光学分配。这一最优是否取决于所测绘的森林类型（即均匀年龄的单一林地和混合物种/年龄的林地）？模型开发和验证：改进用于制图的统计模型；最先进的方法使用货架模型（多元回归，k近邻，支持向量回归，随机森林算法）。开发的模型可能涉及到在点上获得的数据（实地图树级数据）和面分辨率（来自卫星和激光雷达数据的像素）的融合。可能的途径是合并不同数据源的机器学习方法（参见Baez-Villanueva等人，2020）或使用贝叶斯分层框架实现的广义空间融合模型（参见Wang等人，2017，Cameletti等人，2019）。使用现场数据作为地面真实值的模型验证。如何解决信号饱和问题。整合激光雷达和现场绘图数据是否有助于解决这一问题？这些方法适用于其他类型的森林（温带或热带）的效果如何？需要使用的工具和数据包括：1。软件：R， INLA, Python, Stan2。数据集：基于CASE合作伙伴的野外地块和开放获取的飞机衍生激光雷达数据，来自管理的单一年龄林分（主要是锡特卡云杉树）和混合原生林地的苏格兰水平数据。o西班牙里奥哈地区和整个丹麦的森林清查和激光雷达数据集，来自其国家森林清查，包括Joshi使用的约1700个地块的数十万棵树木测量数据（2017年）。o利用Tim Baker的专业知识，从热带地区的未管理森林中获取开放的森林地块。在所有情况下，我们还将使用来自ALOS-2卫星的l波段合成孔径雷达（SAR）的相应数据，来自Sentinel-1卫星的c波段SAR数据，以及来自Sentinel-2卫星的光学数据。

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.