Using airborne LiDAR and enhanced-geolocated GEDI metrics to map structural traits over a Mediterranean forest

IF 5.2 Q1 ENVIRONMENTAL SCIENCES Science of Remote Sensing Pub Date : 2025-06-01 Epub Date: 2025-01-12 DOI:10.1016/j.srs.2025.100195
Aaron Cardenas-Martinez , Adrian Pascual , Emilia Guisado-Pintado , Victor Rodriguez-Galiano
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Abstract

The estimation of three-dimensional (3D) vegetation metrics from space-borne LiDAR allows to capture spatio-temporal trends in forest ecosystems. Structural traits from the NASA Global Ecosystem Dynamics Investigation (GEDI) are vital to support forest monitoring, restoration and biodiversity protection. The Mediterranean Basin is home of relict forest species facing the consequences of intensified climate change effects and whose habitats have been progressively shrinking over time. We used two sources of 3D-structural metrics, LiDAR point clouds and full-waveform space-borne LiDAR from GEDI to estimate forest structure in a protected area of Southern Spain, home of relict species in jeopardy due to recent extreme water-stress conditions. We locally calibrated GEDI spaceborne measurements using discrete point clouds collected by Airborne Laser Scanner (ALS) to adjust the geolocation of GEDI waveform metrics and to predict GEDI structural traits such as canopy height, foliage height diversity or leaf area index. Our results showed significant improvements in the retrieval of ecological indicators when using data collocation between ALS point clouds and comparable GEDI metrics. The best results for canopy height retrieval after collocation yielded an RMSE of 2.6 m, when limited to forest-classified areas and flat terrain, compared to an RMSE of 3.4 m without collocation. Trends for foliage height diversity (FHD; RMSE = 2.1) and leaf area index (LAI; RMSE = 1.6 m2/m2) were less consistent than those for canopy height but confirmed the enhancement derived from collocation. The wall-to-wall mapping of GEDI traits framed over ALS surveys is currently available to monitor Mediterranean sparse mountain forests with sufficiency. Our results showed that combining different LiDAR platforms is particularly important for mapping areas where access to in-situ data is limited and especially in regions with abrupt changes in vegetation cover, such as Mediterranean mountainous forests.

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利用机载激光雷达和增强的地理定位GEDI指标来绘制地中海森林的结构特征
利用星载激光雷达估算三维(3D)植被指标,可以捕捉森林生态系统的时空趋势。NASA全球生态系统动力学调查(GEDI)的结构特征对支持森林监测、恢复和生物多样性保护至关重要。地中海盆地是濒临灭绝的森林物种的家园,这些物种面临着日益加剧的气候变化影响的后果,它们的栖息地随着时间的推移逐渐缩小。我们使用了两个3d结构指标来源,即来自GEDI的激光雷达点云和全波形星载激光雷达,来估计西班牙南部一个保护区的森林结构,该保护区是由于最近极端缺水条件而处于危险中的濒危物种的家园。利用机载激光扫描仪(机载激光扫描仪)收集的离散点云对GEDI星载测量数据进行局部校准,调整GEDI波形指标的地理位置,并预测GEDI的结构特征,如冠层高度、叶高多样性或叶面积指数。我们的研究结果表明,当使用ALS点云和可比GEDI指标之间的数据搭配时,生态指标的检索显着改善。当限定在森林分类区和平坦地形时,配置后的冠层高度反演的RMSE为2.6 m,而未配置的RMSE为3.4 m。叶片高度多样性趋势(FHD);RMSE = 2.1)和叶面积指数(LAI;RMSE = 1.6 m2/m2)的一致性不如冠层高度的RMSE,但证实了植被配置的增强作用。目前,通过ALS调查构建的GEDI特征的全面映射可充分监测地中海稀疏山林。我们的研究结果表明,结合不同的激光雷达平台对于绘制获取原位数据有限的区域尤其重要,特别是在植被覆盖突变的地区,如地中海山区森林。
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