Remote Sensing and Mapping of Fine Woody Carbon With Satellite Imagery and Super Learner

Riyaaz Uddien Shaik;Mohamad Alipour;Eric Rowell;Adam Watts;Christopher Woodall;Ertugrul Taciroglu
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Abstract

Deadwood is a critical component of forest ecosystems, storing nutrients for plants and serving as a carbon store and emission source. Climate change influences forest ecosystem dynamics with the potential for deadwood to emit carbon more rapidly due to accelerated decay and increased wildfires and increased inputs via mass forest mortality and disturbance events. To objectively inform our understanding of wildfires and associated carbon emissions, this study estimates the carbon content of dead fine woody debris (FWD) using multimodal data, such as Landsat-8 multispectral imagery, Sentinel-1 (C-band) and PALSAR (L-band) synthetic aperture radar (SAR) imagery, and terrain features to estimate the FWD of less than 0.25 in (1 h), 0.25–1 in (10 h), and 1–3 in (100 h). This data fusion provides spectral information to assess vegetation health that correlates with deadwood, as well as penetrability from SAR, resulting in structural information and biomass sensitivity. An ensemble machine learning (ML) model was trained using measurements from the Forest Inventory and Analysis (FIA) Database. A feature importance analysis was also performed to investigate the importance of input features to the model’s performance. A super learner regression (SLR) model composed of 9 base learners, including an ElasticNet model as meta-learner, was proposed and achieved the $R^{2}$ values of 0.75, 0.72, and 0.62 to estimate 1-, 10-, and 100-h FWD, respectively. The validated model was then used to estimate deadwood carbon in the 2021 Dixie Fire region of California, demonstrating the effectiveness of our approach, emphasizing the value of multimodal data for real-time FWD carbon stock estimation.
基于卫星图像和超级学习器的精细木质碳遥感与制图
枯木是森林生态系统的重要组成部分,为植物储存养分,并作为碳储存和排放源。气候变化影响森林生态系统动态,由于腐烂加速、野火增加以及大规模森林死亡和干扰事件增加的投入,枯木可能更快地排放碳。为了客观地了解野火及其相关的碳排放,本研究利用多模态数据(如Landsat-8多光谱图像、Sentinel-1 (c波段)和PALSAR (l波段)合成孔径雷达(SAR)图像)和地形特征估算死细木屑(FWD)的碳含量,估计FWD小于0.25 in (1 h)、0.25 -1 in (10 h)。这种数据融合提供了光谱信息,用于评估与枯木相关的植被健康状况,以及SAR的穿透性,从而获得结构信息和生物量敏感性。使用森林清查和分析(FIA)数据库的测量数据训练集成机器学习(ML)模型。还进行了特征重要性分析,以调查输入特征对模型性能的重要性。提出了一个由9个基本学习器组成的超级学习器回归(SLR)模型,其中包括一个ElasticNet模型作为元学习器,并分别获得了0.75、0.72和0.62的$R^{2}$值来估计1、10和100-h的FWD。然后将验证模型用于估算2021年加州Dixie Fire地区的枯木碳,证明了我们方法的有效性,强调了多模式数据对实时FWD碳储量估算的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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