Analysing forest aboveground carbon dynamics in the Amazonia forests using dense time-series of satellite data and artificial intelligence

概述：亚马逊森林拥有最大的森林碳库，但这一估计仍然难以量化(Espiro-Santo等人)。2014年)。用来估计热带森林，特别是巴西亚马逊地区地上生物量的空间变化的遥感方法通常很少，而且其空间分布往往有限。目前的生物量地图(图1)之间存在显著差异，导致在参考这些具体的生物量地图时，根据土地利用变化计算的碳排放量具有很高的不确定性。因此，我们迫切需要一种新的方法来估计森林生物量及其变化，而不是严重依赖林地数据。最近在云和高性能计算方面的进展与先进的人工智能(AI)算法和对新卫星任务的重大投资是平行的。机器学习(Le Cun et al.2015)以前已被应用于高光谱图像分类(Hu等人。2015年)，从哨兵1号合成孔径雷达图像绘制Corine土地覆盖图(Balzter等人。利用合成孔径雷达图像或光学图像绘制森林生物量图(Rodriguez-Veiga等人，2016年)。机器学习能够自动检测卫星图像中的森林变化。在卫星图像中寻找空间和时间模式，而不是历史上侧重于光谱信息的模式，可以确定不同类型的森林动态(干扰和演替)。人工智能还可用于从空间准确估计森林调查数据中难以测量的森林生物物理参数(Rodriguez-Veiga等人，2017年)(图1)。这项跨学科的研究旨在探索利用机器学习来量化巴西几个森林地点密集的时间序列卫星数据中地上生物量碳的变化。多光谱光学和合成孔径雷达(SAR)传感器的时间序列堆栈将输入到人工智能中。人工智能将根据从现场森林调查中收集的测量数据和对非常高分辨率图像的视觉解译进行训练。研究问题：1.亚马逊森林地上生物量的碳储量和通量是什么？2.如何准确地训练人工智能，以基于卫星时间序列信息量化亚马逊森林的动态？3.如何准确地估计相关的地上生物量损失或增加？图1：全球和泛热带地上生物量地图(Rodriguez-Veiga等人，2017年)。

Overview: Amazon forests hold the largest pools of forest carbon, but this estimate remains poorly quantified (Espirito-Santo et al. 2014). The remote sensing methods adopted to estimate the spatial variation of above-ground biomass in tropical forests, notably the Brazilian Amazon, are usually scarce and often limited in their spatial distribution. There are notable differences among current biomass maps (Figure 1), leading to high uncertainties in the carbon emissions calculated from land-use changes when referring to these specific biomass maps. Therefore, we urge a new approach to estimate forest biomass and its changes, without heavy reliance on forest plot data.Recent advances in cloud and high-performance computing are paralleled with advanced artificial intelligence (AI) algorithms and significant investment in new satellite missions. Machine learning (Le Cun et al. 2015) have previously been applied to hyperspectral image classification (Hu et al. 2015), CORINE land cover mapping from Sentinel-1 SAR images (Balzter et al. 2015) and forest biomass mapping using a combination or SAR and optical images (Rodriguez-Veiga et al, 2016). Machine learning enables automatic detection of forest changes of satellite images. The paradigm of looking for spatial and temporal patterns instead of the historic focus on spectral information in satellite imagery allows the identification of the different types of forest dynamics (disturbance and succession). AI can also be used to accurately estimate from space forest biophysical parameters that are difficult to measure in forest inventory data (Rodriguez-Veiga et al, 2017) (Figure 1). This interdisciplinary studentship aims to explore the use of machine learning to quantify changes of aboveground biomass carbon in dense time-series satellite data of several Brazilian forest sites. Time-series stacks of multispectral optical and synthetic Aperture Radar (SAR) sensors will be input into the AI. The AI will be trained based on measurements collected from in-situ forest inventories and visual interpretation of very high resolution images.Research questions:1. What are the carbon stocks and fluxes of aboveground biomass of the Amazon forests?2. How accurately can an AI be trained to quantify forest dynamics of the Amazon based on satellite time-series information? 3. How accurately can the associated aboveground biomass loss or gain be estimated? Figure 1: Global and pantropical maps of aboveground biomass (Rodriguez-Veiga, et al., 2017).