Isoaho Aleksi, Elo Merja, Marttila Hannu, Rana Parvez, Lensu Anssi, Räsänen Aleksi
Natural Resources Institute Finland (Luke), Paavo Havaksen tie 3, FI-90570 Oulu, Finland; Water, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu, FI-90014 Oulu, Finland.
Finnish Environment Institute, Survontie 9A, FI-40500 Jyväskylä, Finland; Department of Biological and Environmental Science, University of Jyvaskyla, FI-40014 Jyväskylä, Finland; School of Resource Wisdom, University of Jyvaskyla, FI-40014 Jyväskylä, Finland.
Sci Total Environ. 2024 Dec 20;957:177697. doi: 10.1016/j.scitotenv.2024.177697. Epub 2024 Nov 26.
Restoration can initiate a succession of plant communities towards those of pristine peatlands. Field inventory-based vegetation monitoring is labour-intensive and not feasible for every restored site. While remote sensing has been used to monitor hydrological changes in peatlands, it has been less used to monitor post-restoration changes in vegetation composition. We utilised vegetation inventories from Finnish peatland monitoring network containing 10-year before-after-control-impact monitoring data from 150 peatland sites, representing three peatland types (spruce mire forests, pine mire forests, open mires), and optical observations from Landsat 5-9 and Sentinel-2 satellites. We employed non-metric multidimensional scaling (NMDS) to produce floristic gradients, representing wetness and productivity, from the vegetation data. We constructed random forest regression models with NMDS dimensions, i.e. floristic gradients, as response variables and satellite imagery variables as the predictors. Our results show that the floristic gradients in different peatland types should be monitored with different satellite imagery variables. However, midsummer NIR and red band consistently explain variation in the gradients in all peatland types. Our results indicate that the gradients and the post-restoration changes in them can be modelled with reasonable accuracy in open mires and sparsely treed pine mire forests but not in densely treed spruce mire forests. We suggest that optical satellite imagery can serve as a proxy for assessing the post-restoration vegetation changes in peatlands with little or no trees.
恢复工作可以启动一系列植物群落向原始泥炭地的群落发展。基于实地清查的植被监测工作强度大，对每个恢复地点来说都不可行。虽然遥感技术已被用于监测泥炭地的水文变化，但较少用于监测恢复后植被组成的变化。我们利用了芬兰泥炭地监测网络的植被清查数据，其中包含来自150个泥炭地地点的10年前后对照影响监测数据，这些地点代表了三种泥炭地类型（云杉泥炭沼泽森林、松树泥炭沼泽森林、开阔泥炭地），以及来自陆地卫星5 - 9号和哨兵2号卫星的光学观测数据。我们采用非度量多维尺度分析（NMDS）从植被数据中生成代表湿度和生产力的植物区系梯度。我们构建了以NMDS维度（即植物区系梯度）作为响应变量、卫星图像变量作为预测变量的随机森林回归模型。我们的结果表明，不同泥炭地类型的植物区系梯度应由不同的卫星图像变量来监测。然而，仲夏近红外和红波段始终能解释所有泥炭地类型梯度的变化。我们的结果表明，在开阔泥炭地和树木稀疏的松树泥炭沼泽森林中，可以以合理的精度对梯度及其恢复后的变化进行建模，但在树木茂密的云杉泥炭沼泽森林中则不行。我们建议，光学卫星图像可以作为评估几乎没有树木或没有树木的泥炭地恢复后植被变化的替代方法。
