用夜间灯光数据估飓风损失，预处理方法影响关键结果
Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters
- 对比多种灯光数据预处理方法，提升数据质量
- VNP46A2数据经质量掩膜与插补后，与经济损失相关性显著
- 适合关注灾害评估与遥感数据应用的研究者
随着气候变化加剧，飓风造成的社会经济影响日益严重，表现为经济损失增加和人口迁移上升。以往研究利用夜间灯光（NTL）数据预测飓风对经济损失的影响，但未系统分析不同预处理技术组合对结果的影响。本文在邮政编码层面，针对两组数据集（VSC-NTL 和 VNP46A2），探索了值阈值化、建筑掩膜和质量过滤与插补等多种预处理方法。实验评估了去噪后的夜间灯光数据与佛罗里达州四级及以上飓风经济损失之间的相关性，结果显示，对 VNP46A2 数据采用质量掩膜与插补技术后，其与经济损失数据的相关性显著提高。
原文摘要 · Abstract (English)
Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilized nighttime light data to predict the impact of hurricanes on economic losses. However, prior work did not provide a thorough analysis of the impact of combining different techniques for pre-processing nighttime light (NTL) data. Addressing this gap, our research explores a variety of NTL pre-processing techniques, including value thresholding, built masking, and quality filtering and imputation, applied to two distinct datasets, VSC-NTL and VNP46A2, at the zip code level. Experiments evaluate the correlation of the denoised NTL data with economic damages of Category 4-5 hurricanes in Florida. They reveal that the quality masking and imputation technique applied to VNP46A2 show a substantial correlation with economic damage data.
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