基于局域模型的凉水国家自然保护区土壤全氮空间分布

以凉水国家自然保护区激光雷达数据为基础，建立数字高程模型，提取基本地形属性如坡度、坡向和复合地形属性湿度指数和相对径流强度指数等，在成土因素学说基础上，对全氮含量(TN)进行地理加权回归建模(GWR)，同时运用反距离加权(IDW)、普通克里格(OK)和泛克里格(UK)对TN进行空间插值.结果表明： 对于研究区TN的预测，GWR模型预测精度(77.4%)高于其他3种空间插值方法，IDW预测精度(69.4%)高于OK(63.5%)和UK(60.6%)的预测精度.利用GWR模型预测研究区域土壤TN平均达到4.82 g·kg^-1；在高海拔、地形湿度大以及相对径流强的区域，土壤TN相对较高.对预测结果进行探讨发现，不同坡位、坡向的土壤TN也存在一定差异.因此，基于地形属性的局域模型是土壤属性空间分布预测的更为有效的方法.

Spatial distribution of soil total nitrogen in Liangshui National Nature Reserve based on local model.

Based on LiDAR data of Liangshui National Nature Reserve, digital elevation model (DEM) was constructed and both primary terrain attributes (slope, aspect, profile curvature, etc.) and secondary terrain attributes (wetness index, sediment transport index, relative stream power index, etc.) were extracted. According to the theory of soil formation, geographically weighted regression (GWR) was applied to predict soil total nitrogen (TN) of the area, and the predicted results were compared with those of three traditional interpolation methods including inverse distance weighting (IDW), ordinary Kriging (OK) and universal Kriging (UK). Results showed that the prediction accuracy of GWR (77.4%) was higher than that of other three interpolation methods and the accuracy of IDW (69.4%) was higher than that of OK (63.5%) and UK (60.6%). The ave rage of TN predicted by GWR reached 4.82 g·kg^-1 in the study area and TN tended to be higher in the region with higher elevation, bigger wetness index and stronger relative stream power index than in other areas. Further, TN also varied partly with various aspects and slopes. Thus, local model using terrain attributes as independent variables was effective in predicting soil attribute distribution.