GEDICorrect: A Scalable Python Tool for Orbit-, Beam-, and Footprint-Level GEDI Geolocation Correction
Leonel Corado, Sérgio Godinho, Carlos Alberto Silva, Juan Guerra-Hernández, Francesco Valérioa, Teresa Gonçalves, Pedro Salgueiro
准确的地理定位对于在足迹尺度应用中可靠使用GEDI LiDAR数据至关重要,这些应用包括地上生物量建模、数据融合和生态系统监测。然而,由系统偏差和随机ISS引起的抖动所产生的残余地理定位误差会显著影响衍生植被和地形指标的准确性。本研究的主要目标是开发和评估一个灵活、计算效率高的框架(GEDICorrect),该框架能够在轨道、波束和足迹级别对GEDI数据进行地理定位校正。该框架集成了现有的GEDI模拟器模块(gediRat和gediMetrics),并通过灵活的校正逻辑、多种相似性指标、自适应足迹聚类和优化的I/O处理扩展了其功能。使用Kullback–Leibler散度作为波形相似性指标,GEDICorrect将冠层高度(RH95)的准确性从R^2 = 0.61(未校正)提高到轨道级别校正后的0.74,并在足迹级别校正后达到R^2 = 0.78,将RMSE从2.62米(rRMSE = 43.13%)降低到轨道级别的2.12米(rRMSE = 34.97%)和足迹级别的2.01米(rRMSE = 33.05%)。地形高程准确性也有所改善,相对于未校正数据RMSE降低了0.34米,与GEDI模拟器基线相比降低了0.37米。在计算效率方面,GEDICorrect在单进程模式下比GEDI模拟器实现了约2.4倍的加速(将运行时间从约84小时减少到约35小时),并能高效扩展到24个核心,在约4.3小时内完成相同任务——总体改进约19.5倍。GEDICorrect提供了一个稳健且可扩展的解决方案,用于提高GEDI地理定位准确性,同时保持与标准GEDI数据产品的完全兼容性。
Accurate geolocation is essential for the reliable use of GEDI LiDAR data in footprint-scale applications such as aboveground biomass modeling, data fusion, and ecosystem monitoring. However, residual geolocation errors arising from both systematic biases and random ISS-induced jitter can significantly affect the accuracy of derived vegetation and terrain metrics. The main goal of this study is to develop and evaluate a flexible, computationally efficient framework (GEDICorrect) that enables geo...