Salko Sini-Selina, Hovi Aarne, Burdun Iuliia, Juola Jussi, Karlqvist Susanna, Rautiainen Miina
Aalto University School of Engineering Espoo Finland.
Ecol Evol. 2025 Aug 8;15(8):e71941. doi: 10.1002/ece3.71941. eCollection 2025 Aug.
Boreal peatlands, which act as significant sinks and storage of global soil organic carbon, are increasingly threatened by the changing climate conditions as well as land use changes. Despite the importance of these ecosystems, their vegetation and ecological features remain poorly mapped compared to other terrestrial ecosystems. Hyperspectral satellite imaging shows promise for detailed vegetation mapping and biodiversity monitoring of boreal peatlands. However, its effective application requires a fundamental understanding of the spectral properties of the vegetation communities of boreal peatlands. To address this, we combined newly available, open-source data consisting of close-range sensed spectral libraries of boreal peatland vegetation communities and single species. Our aim was to examine the extent to which close-range spectral data can be used to predict species-specific fractional cover in minerotrophic and ombrotrophic peatland habitats using hyperspectral and multispectral data, and to assess the connection between spectral signatures and α-diversity of the vegetation communities. Our findings show that hyperspectral data can be used to predict the fractional cover of certain plant species with moderate accuracy ( = 0.58). When comparing data types, hyperspectral data typically produced slightly better model fits for species with larger sample sizes, appearing to be superior to multispectral data. However, in certain cases, such as in the prediction of litter cover in ombrotrophic peatland habitats, multispectral data yielded marginally better results ( = 0.4-0.45). Furthermore, using hyperspectral data, we observed that the prediction of α-diversity of the ombrotrophic habitats was moderately better ( = 0.44) than that of the minerotrophic habitats ( = 0.22). These results enhance our understanding of the spectral properties of the complex, multilayered vegetation communities and thus aid in the mapping of these vital ecosystems.
北方泥炭地是全球土壤有机碳的重要汇和储存地，正日益受到气候变化和土地利用变化的威胁。尽管这些生态系统很重要，但与其他陆地生态系统相比，它们的植被和生态特征的测绘情况仍然很差。高光谱卫星成像在北方泥炭地的详细植被测绘和生物多样性监测方面显示出前景。然而，其有效应用需要对北方泥炭地植被群落的光谱特性有基本的了解。为了解决这个问题，我们结合了新获得的开源数据，这些数据包括北方泥炭地植被群落和单一物种的近程遥感光谱库。我们的目的是研究近程光谱数据在多大程度上可用于利用高光谱和多光谱数据预测矿质营养型和雨养营养型泥炭地生境中特定物种的盖度分数，并评估光谱特征与植被群落α多样性之间的联系。我们的研究结果表明，高光谱数据可用于以中等精度（ = 0.58）预测某些植物物种的盖度分数。在比较数据类型时，高光谱数据通常对样本量较大的物种产生稍好的模型拟合，似乎优于多光谱数据。然而，在某些情况下，例如在预测雨养营养型泥炭地生境中的凋落物盖度时，多光谱数据产生的结果略好（ = 0.4 - 0.45）。此外，使用高光谱数据，我们观察到雨养营养型生境的α多样性预测（ = 0.44）比矿质营养型生境（ = 0.22）略好。这些结果增强了我们对复杂的多层植被群落光谱特性的理解，从而有助于绘制这些重要生态系统的地图。
