Accurate Above Ground Biomass Estimation using novel hierarchical datasets to train Machine Learning Models

世界目前对地球森林中储存了多少碳的理解已经被证明是**根本不准确的**，对**应对气候变化的斗争产生了**严重影响。**这些问题具有现实的世界影响。世界需要以自然为基础的解决方案项目(如重新造林、森林保护等)**更快地扩大规模**。《巴黎协定》设定了升温2摄氏度的情景。我们目前正处于3摄氏度变暖的轨道上，这将带来所有灾难性的后果。碳市场的所有参与者(项目开发商、经纪人/中介和卖家)**需要准确、有效的数据，以确保无摩擦、增加贸易，证明他们的净零主张。**然而，**当前**测量技术依赖于**过时和有偏见的碳估计**，这接近于生物量，因此碳，机器学习(ML)模型提供了从当前和即将获得的全球范围内卫星对地观测(EO)原始数据准确估计地上生物量(AGB)的能力。**然而，他们需要准确的、校准良好的训练数据**用来训练ML模型，**这是目前还没有的，也是项目的重点。**从卫星EO数据推断AGB的**当前可用的数据是“库存导出的AGB数据”。这是通过手动测量两个标准参数来收集的：树木直径和树高，然后使用异速生长模型估计树木体积，从而估计生物量，该模型将这些树木测量结果与材积联系起来，并使用简单的线性模型。**这一过程不能准确地量化生物量**，最近被证明在量化大树的碳方面存在系统偏差(**高达50%**)，大树是森林中碳储存的主要来源。同样，使用空间或机载激光雷达和即将到来的欧空局生物质合成孔径雷达(SAR)任务的最新方法，提供了极好的AGB推断准确性的潜力。但也受到地面测量**低精度**的限制。这是一个令人兴奋的150万GB项目，由Sylvera与其合作伙伴**伦敦大学学院和NASA喷气推进实验室**共同领导，以推动最先进的地球观测技术。该团队将捕捉世界森林中储存的碳的准确数据，目的是彻底改变全球碳市场，使它们能够**扩大规模并支持数十亿美元的森林恢复和种植**。

The world's current understanding of how much carbon is stored in the Earth's forests has been shown to be **fundamentally inaccurate**, with **serious implications for the fight against climate change.** These issues have real world impact. The world needs Nature Based Solutions projects (i.e. reforestation, forestry protection etc.) **to scale more quickly**. The Paris Agreement set a 2 degree warming scenario. We are currently on track for a 3 degree warming scenario, with all the catastrophic consequences this entails.All the various players in the Carbon Market (project developers, brokers/intermediaries and sellers) **need accurate, validated data to ensure frictionless, increased trade, proving their net zero claims.** However, **current** measurement techniques rely on **outdated and biased carbon estimations** which approximate biomass, and hence carbon, from tree diameter and height and inaccurate sampling.Machine Learning (ML) models offer the capability to accurately estimate Above Ground Biomass (AGB) from the current and imminently available raw Satellite Earth Observation (EO) data at a global scale. **However, they need accurate, well-calibrated training data** with which to train ML models with, **which is currently absent and is the focus of the project.**The **currently** available data to train models that infer AGB from satellite EO data is "inventory derived AGB data". This is gathered by manually measuring two standard parameters: tree diameter and tree height, and then estimating the tree volume and hence biomass using an allometric model which relates those tree measurements to volume, with a simple linear model. **This process does not accurately quantify biomass** and has recently been demonstrated to exhibit systematic bias (**up to 50%**) for quantifying carbon in large trees \[7\], which dominate the stores of carbon in forests.Likewise, very recent approaches using Space or Airborne LIDAR and the forthcoming ESA BIOMASS Synthetic Aperture Radar (SAR) mission, offer potential for excellent AGB inference accuracy \[6\], but are also constrained by the **low accuracy of ground measurements**.This is an exciting £1.5 million project led by Sylvera in conjunction with its partners **University College London and the NASA Jet Propulsion Lab** to push forward the state-of-the-art in Earth Observation technology. The team will capture accurate data on the carbon stored in the world's forests, with the aim of revolutionising global carbon markets, allowing them to **scale and support billions of dollars of forest restoration and planting.**