森林土壤有机质预测模型阈值优化——以广东省为例  

Threshold optimization for forest soil organic matter prediction model:a case study of Guangdong Province of southern China

作　　者：詹思颖 刘鹏举[1] 李海奎[1] 张聪 刘琪 Zhan Siying;Liu Pengju;Li Haikui;Zhang Cong;Liu Qi(Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Key Laboratory of Forest Management and Growth Modelling,National Forestry and Grassland Administration,Beijing 100091,China)

机构地区：[1]中国林业科学研究院资源信息研究所,国家林业和草原局森林经营与生长模拟重点实验室,北京100091

出　　处：《北京林业大学学报》2025年第2期32-39,共8页Journal of Beijing Forestry University

基　　金：国家重点研发计划(2021YFD220040401)。

摘　　要：【目的】探究优化的地理相似性模型在省级尺度上森林土壤有机质预测研究中的适用性。【方法】以广东省土壤肥力监测系统的1 175个样点数据为基础,选择土壤因子、林分因子、气候因子作为环境协变量,改进个体样点代表性的数字土壤制图(iPSM)方法。选用决定系数、平均绝对误差和均方根误差3个指标评价该改进方法在省级尺度森林土壤有机质预测研究中的适用性,并与随机森林预测模型的结果进行比较。【结果】(1)优化后的iPSM方法的预测精度优于随机森林预测模型,模型的决定系数可达0.741 9;(2)土壤因子中土壤全氮含量和速效钾含量是广东省森林土壤有机质预测模型中影响最大的环境协变量。【结论】优化后的iPSM方法在广东省森林土壤有机质预测研究中相较于iPSM方法精度有一定提高,在省级尺度上有较好的适用性。[Objective]This paper takes Guangdong Province of southern China as an example,and explores the applicability of optimized geographical similarity model in the prediction of forest soil organic matter at provincial scale.[Method]Based on the data of 1 175 sample points from soil fertility testing system in Guangdong Province,we selected soil factors,stand factors and climate factors as environmental covariates to improve the“individual point representativeness digital soil mapping (iPSM)”.This article evaluates the applicability of the model in predicting forest soil organic matter at the provincial level using three indicators:coefficient of determination (R~2),mean absolute error (MAE),and root mean square error (RMSE),and compares its predictive performance with that of random forest prediction model[Result](1) The prediction accuracy of optimized iPSM was better than that of random forest prediction model,and the coefficient of determination of model can reach 0.741 9.(2) The total nitrogen content and available potassium content in soil factors were the most influential environmental covariates in prediction model of forest soil organic matter in Guangdong Province.[Conclusion]Compared with iPSM method the optimized iPSM has improved the accuracy of forest soil organic matter prediction in Guangdong Province.The model has good applicability at the provincial level and can provide a new method for predicting soil organic matter at the provincial level.

关 键 词：土壤有机质 数字土壤制图 环境变量 地理相似性 模型优化 

分 类 号：S714.5[农业科学—林学]