Jeong Gwanyong, Choi Kwanghun, Spohn Marie, Park Soo Jin, Huwe Bernd, Ließ Mareike
Department of Geosciences/ Soil Physics Division, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany.
Biogeographical Modelling, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany.
PLoS One. 2017 Aug 24;12(8):e0183205. doi: 10.1371/journal.pone.0183205. eCollection 2017.
Nitrogen (N) and phosphorus (P) in topsoils are critical for plant nutrition. Relatively little is known about the spatial patterns of N and P in the organic layer of mountainous landscapes. Therefore, the spatial distributions of N and P in both the organic layer and the A horizon were analyzed using a light detection and ranging (LiDAR) digital elevation model and vegetation metrics. The objective of the study was to analyze the effect of vegetation and topography on the spatial patterns of N and P in a small watershed covered by forest in South Korea. Soil samples were collected using the conditioned latin hypercube method. LiDAR vegetation metrics, the normalized difference vegetation index (NDVI), and terrain parameters were derived as predictors. Spatial explicit predictions of N/P ratios were obtained using a random forest with uncertainty analysis. We tested different strategies of model validation (repeated 2-fold to 20-fold and leave-one-out cross validation). Repeated 10-fold cross validation was selected for model validation due to the comparatively high accuracy and low variance of prediction. Surface curvature was the best predictor of P contents in the organic layer and in the A horizon, while LiDAR vegetation metrics and NDVI were important predictors of N in the organic layer. N/P ratios increased with surface curvature and were higher on the convex upper slope than on the concave lower slope. This was due to P enrichment of the soil on the lower slope and a more even spatial distribution of N. Our digital soil maps showed that the topsoils on the upper slopes contained relatively little P. These findings are critical for understanding N and P dynamics in mountainous ecosystems.
表层土壤中的氮（N）和磷（P）对植物营养至关重要。对于山区景观有机层中N和P的空间格局，人们了解得相对较少。因此，利用激光探测与测距（LiDAR）数字高程模型和植被指标分析了有机层和A层中N和P的空间分布。本研究的目的是分析植被和地形对韩国一个森林覆盖的小流域中N和P空间格局的影响。采用条件拉丁超立方抽样法采集土壤样本。将LiDAR植被指标、归一化植被指数（NDVI）和地形参数作为预测因子。利用随机森林和不确定性分析获得了N/P比的空间明确预测结果。我们测试了不同的模型验证策略（重复2折到20折交叉验证和留一法交叉验证）。由于预测的准确率较高且方差较低，因此选择重复10折交叉验证进行模型验证。地表曲率是有机层和A层中P含量的最佳预测因子，而LiDAR植被指标和NDVI是有机层中N的重要预测因子。N/P比随地表曲率增加，在上凸上坡比在下凹下坡更高。这是由于下坡土壤中P的富集以及N更均匀的空间分布。我们的数字土壤图显示上坡的表层土壤含P相对较少。这些发现对于理解山区生态系统中的N和P动态至关重要。
