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• W4313467543 abstract "Riparian forests are complex ecosystems shaped by their connectivity to a river system, which produces a mosaic of ages and species. Because of increasing anthropic pressure from factors such as damming or climate change, they are often endangered and suffer from a drop in groundwater accessibility and increased water stress. By combining hyperspectral, LiDAR, and forestry datasets along a 20 km corridor of the Ain River, this paper assesses the ability of remote sensing to characterize and monitor such environments. These datasets are used to investigate changes in site conditions and forest characteristics, such as height and canopy water content, along a gradient of ecosystem ages and for reaches under distinct geomorphic conditions (shifting, sediment-starved, incised). The data show that, over time, forest patches aggrade, and the forest grows and becomes more post-pioneer. However, forest patches that are located in the incised reach aggrade more and appear to be less developed in height, more stressed, and feature species compositions reflecting dryer conditions, in comparison with better-connected patches of the same age. Random forest analysis was applied to predict the indicators of forest connectivity with remotely sensed LIDAR and hyperspectral data, in order to identify the spatial trends at the reach scale and compare them with the geomorphic segmentation of the river. The random forest classifications achieved an accuracy between 80% and 90% and resulted in spatial trends that highlighted the differences in hydrological connectivity between differing geomorphic conditions. Overall, remote sensing appears to be a good tool for characterizing the impact of channel incisions and adjustments on riparian forest conditions by identifying the locations of dryer forest patches. In addition, good accuracy was achieved when attempting to classify these forest patches, even when using hyperspectral data alone, which suggests that satellite data could become a powerful tool for monitoring the health of riparian forests, in the context of increasing anthropic pressures." @default.
• W4313467543 created "2023-01-06" @default.
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• W4313467543 date "2022-12-21" @default.
• W4313467543 modified "2023-10-17" @default.
• W4313467543 title "Combining Hyperspectral, LiDAR, and Forestry Data to Characterize Riparian Forests along Age and Hydrological Gradients" @default.
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• W4313467543 doi "https://doi.org/10.3390/rs15010017" @default.
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