[Spatial distribution of forest carbon storage in Maoershan region, Northeast China based on geographically weighted regression kriging model.]

Forest carbon storage has important impacts on climate change. The previous models do not take into consideration of the inherent spatial correlation structure of residual and non-stationary of forest carbon storage which limits the prediction accuracy. Based on ETM+ remotely sensed imagery and 193 fixed plots of Maoershan Experimental Forest Farm of Northeast Forestry University, we established the geographically weighted regression kriging (GWRK) model between forest carbon storage and extracted factors from remotely sensed imagery and topographic factors. The prediction accuracy of GWRK, ordinary least square (OLS) model and geographically weighted regression (GWR) were compared. The results showed that the mean absolute error (MAE) and root mean square error (RMSE) of GWRK were lower than those of OLS and GWR models, while the mean error (ME) of GWRK model was lower than that of GWR model and was close to that of OLS model. The prediction accuracy of GWRK model was 83.2%, which was 6% and 10% higher than that of OLS model (73.7%) and GWR model (77.3%). Therefore, the GWRK model was more effective in estimating forest carbon storage than the others. The mean value of forest carbon storage predicted by GWRK model was 70.31 t·hm森林碳储量对于全球气候变化具有重要影响,以往的模型估算未考虑到模型残差的空间相关性和碳储量数据的非平稳性,影响模型的预测精度.本研究基于东北林业大学帽儿山实验林场的ETM+遥感影像数据和193块固定样地,利用地理加权克里格回归(GWRK)建立森林碳储量与遥感和地形因子的回归模型,同时对比最小二乘模型(OLS)、地理加权回归模型(GWR)的预测精度.结果表明: 对于帽儿山地区的森林碳储量估算,GWRK的平均绝对误差(MAE)、均方根误差(RMSE)低于OLS模型和GWR模型,GWRK模型的平均误差(ME)低于GWR模型,与OLS模型相近.GWRK模型的预测精度为83.2%,较OLS模型(73.7%)和GWR模型(77.3%)分别提高6%和10%,拟合精度明显提高,说明GWRK模型是森林碳储量估算的有效方法.利用GWRK模型预测的研究区森林碳储量平均值为70.31 t·hm