Chen Ke-Yi, Zhang Hui-Ru, Zhang Bo, He You-Jun
Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, China.
Research Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China.
Ying Yong Sheng Tai Xue Bao. 2021 Apr;32(4):1175-1183. doi: 10.13287/j.1001-9332.202104.002.
To accurately assess carbon storage and its spatial distribution in natural secondary forest at the regional scale, we constructed seven expansion models by modifying the geographically weighted regression (GWR) in aspects of spatial dimension, parameter heterogeneity and residual spatial autocorrelation, based on data collected from 165 bureau level permanent plots in Langxi Forest Farm of Wangqing Forestry Bureau in Jilin Province. Stand factor, topography factor, and soil factor were selected as the influencing factors. The expansion models included geographically and altitudinal weighted regression (GAWR), semiparametric geographically weighted regression (SGWR), semiparametric geographically and altitudinal weighted regression (SGAWR), geographically weighted regression Kriging (GWRK), geographically and altitudinal weighted regression Kriging (GAWRK), semiparametric geographically weighted regression Kriging (SGWRK), and semiparametric geographically and altitudinal weighted regression Kriging (SGAWRK). Coefficient of determination (), mean square error (MSE) and Akaike's Information Criterion (AIC) were used to evaluate the fitness of these models. Finally, the spatial distribution diagram of forest carbon storage was drawn with the fitting results of the optimal regression model, and the distribution pattern of forest carbon storage in the research area was analyzed. The stand factor and topographic factor had strong influence on carbon storage of natural secondary forests, among which the average diameter at breast height (DBH) of stands was the dominant variable. There was positive correlation between stand factor and topographic factor. SGWR and SGAWR model could reduce the spatial autocorrelation of the GWR model residual. The geographically regression expansion model could improve the fitting effect of GWR model. Among them, the SGWRK model had the highest and the lowest MSE and AIC. The method with altitude as the spatial weight did not effectively improve the fitting effect of the model. The total forest carbon storage of Langxi Forest Farm was 205×10 t, and the carbon density ranged from 8.56 to 145.74 t·hm, with a mean value of 57.98 t·hm. Overall, the distribution pattern of carbon storage was high in the northwest and low in the southeast, while high in the edge and low in the interior. By improving the parameter heterogeneity and residual spatial autocorrelation in the GWR model, we can accurately assess the spatial relationship between forest carbon storage and relevant variables in the study area, and improve the estimation accuracy of the forest carbon storage and its spatial distribution at the regional scale.
为了在区域尺度上准确评估天然次生林的碳储量及其空间分布，我们基于吉林省汪清林业局朗溪林场165个局级固定样地收集的数据，在空间维度、参数异质性和残差空间自相关性方面对地理加权回归（GWR）进行修改，构建了7个扩展模型。选取林分因子、地形因子和土壤因子作为影响因子。扩展模型包括地理和海拔加权回归（GAWR）、半参数地理加权回归（SGWR）、半参数地理和海拔加权回归（SGAWR）、地理加权回归克里金法（GWRK）、地理和海拔加权回归克里金法（GAWRK）、半参数地理加权回归克里金法（SGWRK）和半参数地理和海拔加权回归克里金法（SGAWRK）。利用决定系数（）、均方误差（MSE）和赤池信息准则（AIC）来评估这些模型的拟合优度。最后，用最优回归模型的拟合结果绘制森林碳储量空间分布图，并分析研究区域森林碳储量的分布格局。林分因子和地形因子对天然次生林碳储量有较强影响，其中林分平均胸径（DBH）是主导变量。林分因子和地形因子之间存在正相关关系。SGWR和SGAWR模型可以降低GWR模型残差的空间自相关性。地理回归扩展模型可以提高GWR模型的拟合效果。其中，SGWRK模型的最高，MSE和AIC最低。以海拔为空间权重的方法没有有效提高模型的拟合效果。朗溪林场森林碳储量总量为205×10 t，碳密度在8.56至145.74 t·hm之间，平均值为57.98 t·hm。总体而言，碳储量分布格局为西北高东南低，边缘高内部低。通过改善GWR模型中的参数异质性和残差空间自相关性，能够准确评估研究区域森林碳储量与相关变量之间的空间关系，提高区域尺度上森林碳储量及其空间分布的估计精度。
