...
首页> 外文期刊>Biogeosciences Discussions >High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment
【24h】

High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

机译:利用机器学习的多年冻土地形土壤有机碳的高分辨率数字测绘 - 以北极泥炭环境为例

获取原文
           

摘要

Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1?m (1×1?m). A high-resolution (1?m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150?cm) is estimated to be 8.3?±?8.0?kg?C?m?2 and the SOC stored in the top meter (0–100?cm) to be 7.7?±?6.2?kg?C?m?2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000?m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions ???30?m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000?years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.
机译:储存在北部泥炭地和受影响的土壤中的土壤有机碳(SoC)是全球碳循环中的关键部件。本文量化了瑞典北部北极山区的亚天北极山泥土环境中的SoC股。对SOC量化评估了四种机器学习技术:多个线性回归,人工神经网络,支持向量机和随机林。随机森林模型表现最佳,并且用于以1Ωm(1×1Ωm)的空间分辨率以几个深度增量预测SOC。为本研究产生的高分辨率(1?M)覆盖分类是最相关的预测变量。景观平均值SOC存储(0-150?cm)估计为8.3?±8.0?kg?c?m?2和存储在顶部仪表(0-100?cm)中的SoC为7.7? 6.2?kg?c?m?2。预测性建模突出了湿地区域的相对重要性,特别是泥炭Platea为景观的SoC储存。还在2,10,30,100,250和1000?m的降低的空间分辨率下预测总SOC,并在陆地覆盖阶级细节中显示出显着下降,并在决议中低估SoC的趋势??? 30?m。这与许多小型湿地的出现相关联,形成在粗略分辨率以省略的SOC存储的局部热点的局部热点。与陆地覆盖和永久冻土分布相关的SOC存储中的急剧过渡是最具挑战性的方法论方面。然而,在本研究中,在局部,区域和循环阶段,限制强大的SOC绘图努力的主要因素是来自整个环境空间的土壤Pedon数据的稀缺。对于Abisko地区,过去的SoC和Permafrost动态表明大多数SoC勉强2000年?岁月,非常动态。未来的研究需要探讨永久冻土降解的几何反应和跨国后景观景观的所有景观隔间的SOC的命运。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号