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被证明是 ** 根本不准确的 **，对应对气候变化具有 ** 严重影响。这些问题具有真实的世界影响。世界需要基于自然的解决方案项目（即重新造林，森林保护等）** 更快地扩展 **。《巴黎协定》设定了2摄氏度的升温情景。我们目前正处于升温3摄氏度的情况下，这将带来所有灾难性的后果。碳市场中的所有参与者（项目开发商，经纪人/中介和卖家）** 都需要准确，有效的数据，以确保无摩擦，增加贸易，证明他们的净零索赔。然而，** 当前 ** 的测量技术依赖于 ** 过时和有偏见的碳估计 **，这些估计近似于生物量，因此根据树木的直径和高度以及不准确的采样来估计碳。机器学习（ML）模型提供了根据当前和即将可用的原始卫星地球观测（EO）数据在全球范围内准确估计地面生物量（AGB）的能力。** 然而，他们需要准确、校准良好的训练数据 ** 来训练ML模型，** 这是目前缺乏的，也是项目的重点。训练从卫星EO数据推断AGB的模型的 ** 当前 ** 可用数据是“库存衍生AGB数据”。这是通过手动测量两个标准参数收集的：树径和树高，然后使用异速生长模型估计树木体积和生物量，该模型将这些树木测量值与体积联系起来，并采用简单的线性模型。** 这一过程不能准确地量化生物量 **，而且最近已被证明存在系统偏差。（** 高达50%**）用于量化大树中的碳[7]，这些大树主导着森林中的碳储存。同样，最近使用空间或机载激光雷达和即将到来的欧空局生物质量合成孔径雷达（SAR）使命的方法，提供了优秀的AGB推理精度的潜力\[6\]，但也受到 ** 地面测量精度低 ** 的限制。这是一个令人兴奋的150万英镑的项目，由西尔维拉领导，与合作伙伴 ** 伦敦大学学院伦敦和美国宇航局喷气推进实验室 ** 合作推动地球观测技术的发展。该团队将捕捉世界森林中储存的碳的准确数据，旨在彻底改变全球碳市场，使其能够 ** 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frictionless, increased trade, proving their net zero claims.** However, **current** measurement techniques rely on **outdated and biased carbon estimations** which approximate biomass, and hence carbon, from tree diameter and height and inaccurate sampling.Machine Learning (ML) models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.**