PPG-Ciência do Solo, State University of São Paulo (UNESP), Jaboticabal, 15385-000, Brazil.
PPG-Bionorte, State University of Mato Grosso (UNEMAT), Sinop, 78550-000, Brazil.
Sci Rep. 2024 Aug 31;14(1):20277. doi: 10.1038/s41598-024-71430-2.
Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO₂) flux (FCO₂), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO₂ data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO₂ for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO₂ emission and the eucalypt species, providing a distinction of values between the species in the statistical data.
桉树物种在全球碳循环中扮演着重要的角色,特别是在减少温室效应和储存大气 CO₂方面。因此,评估森林地区土壤中释放的 CO₂量可以为环境监测提供重要信息。本研究旨在验证基于无人机的多光谱相机获取的土壤二氧化碳(CO₂)通量(FCO₂)、光谱波段和植被指数(VIs)与桉树物种之间的关系。从 Parrot Sequoia 传感器获取多光谱成像(绿光、红边和近红外)、衍生的植被指数以及 LI-COR 8100 分析仪的 FCO₂数据,并结合土壤湿度和温度数据进行了收集和相关分析。使用土壤校正因子的植被指数 ATSAVI(调整后的土壤调整 VI)、GSAVI(绿色土壤调整植被指数)和 SAVI(土壤调整植被指数)与 E. camaldulensis、E. saligna 和 E. urophylla 物种的 FCO₂表现出强烈的负相关。方差的多元分析表明(p<0.01),物种因素具有显著意义,这表明在同时考虑所有变量时,存在差异。本研究的结果表明,土壤 CO₂排放数据与桉树物种之间存在特定的相关性,为统计数据中的物种提供了有价值的区分。