Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden.
Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden.
Ambio. 2020 Feb;49(2):475-486. doi: 10.1007/s13280-019-01196-9. Epub 2019 May 9.
Comparisons between field data and available maps show that 64% of wet areas in the boreal landscape are missing on current maps. Primarily forested wetlands and wet soils near streams and lakes are missing, making them difficult to manage. One solution is to model missing wet areas from high-resolution digital elevation models, using indices such as topographical wetness index and depth to water. However, when working across large areas with gradients in topography, soils and climate, it is not possible to find one method or one threshold that works everywhere. By using soil moisture data from the National Forest Inventory of Sweden as a training dataset, we show that it is possible to combine information from several indices and thresholds, using machine learners, thereby improving the mapping of wet soils (kappa = 0.65). The new maps can be used to better plan roads and generate riparian buffer zones near surface waters.
实地数据与现有地图的对比表明，当前地图上缺少 64%的北方景观湿地。主要是森林湿地和溪流、湖泊附近的湿地土壤缺失，这使得它们难以管理。一种解决方案是使用高分辨率数字高程模型，从地形湿度指数和水深等指数中对缺失的湿地进行建模。然而，在具有地形、土壤和气候梯度的大面积区域工作时，不可能找到一种适用于所有地方的方法或阈值。我们利用瑞典国家森林清查的土壤湿度数据作为训练数据集，结果表明，使用机器学习可以结合多个指数和阈值的信息，从而提高土壤湿度图的绘制（kappa 值为 0.65）。新地图可用于更好地规划道路，并在地表水附近生成河岸缓冲区。
