High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany

Open access publication under review.

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Abstract:

Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.

We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomassmodels. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.

Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validationwith estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation ofmodel prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.

Data:

This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.

Disclaimer:Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.

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摘要：
林火燃料是野火行为模拟与风险评估的核心要素，但精准量化难度极大。近年来野火发生频率攀升，尤其是在中欧这类传统上不易发生火灾的区域，使得对大规模遥感燃料信息的需求日益增长。本研究开发了一套仅依托开放遥感数据，在大尺度范围（德国全境）生成高空间分辨率冠层燃料分布图的方法。

我们提出了两步式研究方案：首先借助NASA的GEDI（全球生态系统动力学调查，Global Ecosystem Dynamics Investigation）仪器的测量数据，在足迹尺度上估算冠层燃料变量，随后再生成高分辨率栅格地图。本研究未依赖野外实测数据，而是通过分割机载激光雷达（LiDAR）点云，并利用异速冠层生物量模型处理单木指标，生成了GEDI足迹尺度下的冠层（基）高度（Canopy (Base) Height, CH、CBH）、盖度（Cover, CC）、体积密度（Bulk Density, CBD）以及燃料载量（Fuel Load, CFL）估算值。为估算足迹尺度的冠层燃料，我们构建并调优了随机森林（Random Forest）模型，并通过k折最近邻距离匹配法进行交叉验证。我们将超过160万个GEDI足迹的估算结果与生物物理栅格协变量相结合，借助通用克里金法（Universal Kriging）生成了分辨率为20米的全国范围燃料分布图。

针对来自同一数据集的验证数据，足迹尺度估算结果的拟合优度（均方根误差RMSE/决定系数R²）表现优异，而栅格地图估算结果则表现中等。基于国家森林清查数据的估算值开展的验证结果显示，二者仅存在低度至中度的一致性。与盖度或生物量相关的变量（CBD、CFL）相比，高度相关变量（CH、CBH）的估算精度更高。误差分析显示，模型估算结果与验证数据均存在一定偏差，同时模型估算标准误差存在低估情况。造成该问题的潜在因素包括异速方程带来的简化假设，以及输入数据的时空不匹配问题。本研究提出的工作流程有望为野火问题日益凸显且燃料与野外实测信息匮乏或缺失的区域提供支持。

数据：
本数据集仓库包含建模数据、R语言模型对象以及预测得到的燃料分布图。所有TIFF文件均包含6个波段：(1) 最终通用克里金结果；(2) 线性模型估算值；(3) 残差克里金估算值；(4) 克里金方差；(5) 线性模型估算标准误差；(6) 通用克里金估算标准误差。

免责声明：本仓库中的地图基于GEDI测量得到的冠层燃料估算值生成，其覆盖范围仅限南北纬51.6°之间的区域。超出该范围的地图估算结果可视为模型对未知生物物理域的外推。波段6的误差图可辅助您合理使用本数据集的冠层燃料分布图。