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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.
Based on the LIDAR data of Liangshui National Nature Reserve, a digital elevation model was established to extract the basic landform attributes such as slope, aspect and composite terrain wetness index and relative runoff intensity index. Based on the theory of soil forming factor, (TN) were calculated by using the method of Geo-Weighted Regression Modeling (GWR), and TN was interpolated by inverse distance weighted (IDW), ordinary kriging (OK) and uk- TN prediction accuracy of the GWR model is higher than that of the other three spatial interpolation methods (77.4%), IDW prediction accuracy (69.4%) is higher than that of OK (63.5%) and UK (60.6% TN of the soil in the area reached 4.82 g · kg -1 on average, and the soil TN was relatively high in the areas of high altitude, high relative humidity and high relative runoff.The results of the prediction showed that soil with different slope positions and slopes TN, there are some differences.Therefore, the local model based on topographic attributes is a more effective method to predict the spatial distribution of soil attributes.