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

目前世界对地球森林中储存了多少碳的认识已被证明是“根本不准确的”，这将对应对气候变化产生严重影响。**这些问题具有现实世界的影响。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）**更快地扩大规模**。《巴黎协定》设定了2摄氏度的升温目标。目前，我们正处于升温3度的轨道上，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/中介机构和卖家）**需要准确、有效的数据，以确保无摩擦、增加交易，证明他们的净零索赔。然而，**目前的**测量技术依赖于**过时和有偏差的碳估计**，这些估计是从树木的直径和高度和不准确的采样中近似生物量和碳。机器学习（ML）模型提供了在全球范围内从当前和迫在眉睫的原始卫星地球观测（EO）数据中准确估计地面上生物量（AGB）的能力。然而，他们需要准确、校准良好的训练数据来训练机器学习模型，这是目前缺乏的，也是项目的重点。** *目前** **可用于训练从卫星EO数据推断AGB的模型的数据是“库存衍生AGB数据”。这是通过人工测量两个标准参数来收集的：树的直径和树的高度，然后使用一个异速生长模型来估计树木的体积和生物量，该模型将树木的测量值与体积联系起来，使用一个简单的线性模型。**该过程不能准确地量化生物量**，最近已被证明表现出高达50的系统偏差（**）%**) 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.**

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frictionless, increased trade, proving their net zero claims.** However, **current** measurement techniques rely on **outdated and biased carbon estimations** which approximate biomass, and hence carbon, from tree diameter and height and inaccurate sampling.Machine Learning (ML) models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.**