Exploring the potential of SAR and terrestrial and airborne LiDAR in predicting forest floor spectral properties in temperate and boreal forests

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-11-03 DOI:10.1016/j.rse.2024.114486
Audrey Mercier , Mari Myllymäki , Aarne Hovi , Daniel Schraik , Miina Rautiainen
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Abstract

Forest floor vegetation plays a crucial role in ecosystem processes of temperate and boreal forests. Remote sensing offers a valuable tool to characterize the forest floor through reflectance spectra. While passive optical airborne and satellite data have been used to map spectral properties of forest understory, these sensors are limited by cloud cover, especially in high latitudes. To date, LiDAR and SAR have not been explored for this application even though their data are less dependent on illumination conditions and provide information on tree canopy structure and tree distribution which is connected to forest floor properties. We investigated active remote sensing techniques to establish links between forest structure and spectral properties of forest floor across European temperate, hemiboreal and boreal forest ecosystems. First, in the exploratory part, the research question was : Which forest structure metrics are connected to the spectral properties of the forest floor? Next, our predictive part focused on: What is the potential of (1) terrestrial laser scanning (TLS) data, (2) airborne laser scanning data, (3) satellite-borne SAR data, and (4) these data sources combined to predict forest floor spectral properties? Our results revealed that nine forest structure metrics were potentially associated with forest floor reflectance. We identified TLS-derived clumping index and SAR-derived VV backscatter coefficient and VH/VV ratio as significantly connected to forest floor reflectance in certain Sentinel-2 spectral bands. Overall, the active remote sensors achieved the best predictions for forest floor reflectance in red-edge, near-infrared and shortwave infrared regions. Using data from all three sensors together to predict the forest floor spectra yielded better results than using any of the sensors alone. When data from a single sensor were used, the highest prediction accuracies for forest floor reflectance in the red-edge and near-infrared regions were achieved with SAR data, and in the shortwave infrared region with either SAR or TLS data. In the future, the accuracy of predicting forest floor characteristics in temperate and boreal forests could benefit from a synergy of passive and active technologies.
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探索合成孔径雷达、陆地和机载激光雷达在预测温带和北方森林林地光谱特性方面的潜力
林地植被在温带和北方森林的生态系统过程中发挥着至关重要的作用。遥感技术为通过反射光谱来描述林下植被提供了宝贵的工具。虽然被动光学机载和卫星数据已被用于绘制林下植被的光谱特性图,但这些传感器受到云层覆盖的限制,尤其是在高纬度地区。迄今为止,尽管激光雷达和合成孔径雷达的数据对光照条件的依赖性较低,并能提供与林下特性相关的树冠结构和树木分布信息,但它们尚未被应用于这一领域。我们研究了主动遥感技术,以建立欧洲温带、半寒带和寒带森林生态系统中森林结构与林地光谱特性之间的联系。首先,在探索部分,研究问题是:哪些森林结构指标与林地的光谱特性相关?其次,我们的预测部分侧重于(1) 地面激光扫描数据 (TLS)、(2) 机载激光扫描数据、(3) 星载合成孔径雷达数据以及 (4) 这些数据源结合起来预测林地光谱特性的潜力有多大?我们的研究结果表明,有九种森林结构指标可能与林地反射率有关。我们发现,在哨兵-2 的某些光谱波段中,TLS 导出的团聚指数和 SAR 导出的 VV 后向散射系数和 VH/VV 比值与林地反射率有显著关联。总体而言,主动遥感器在红边、近红外和短波红外区域对林地反射率的预测效果最佳。同时使用三个传感器的数据来预测林地光谱,比单独使用任何一个传感器的结果都要好。使用单一传感器的数据时,红边和近红外区域的林地反射率预测精度最高的是合成孔径雷达数据,短波红外区域的林地反射率预测精度最高的是合成孔径雷达或 TLS 数据。未来,温带和北方森林的林地特征预测精度将受益于被动和主动技术的协同作用。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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