不同采样密度下县域森林碳储量仿真估计

Simulation of regional forest carbon storage under different sampling densities

采用浙江省台州市仙居县森林资源二类调查样地实测地上部分碳储量数据,结合Landsat TM影像数据,利用序列高斯协同仿真(SGCS)算法、序列高斯块协同仿真(SGBCS)算法,以4种地面采样密度(SD)SD_1=0.012%、SD_2=0.010%、SD_3=0.007%、SD_4=0.005%,估计全县森林碳储量及其空间分布,分析不同地面采样密度对区域森林碳储量及其分布格局估计精度的影响。结果表明:1)不同采样密度下SGCS和SGBCS估计的森林碳储量分布趋势相似,SGCS估计在采样密度为SD_2时可以满足精度要求,且均值与实测最相符;SGBCS估计受采样密度影响较小,在四种采样密度下均可满足精度要求。2)SGCS、SGBCS估计的不确定性随着采样密度的降低均呈现出整体升高的趋势,增长速率在SD_2采样密度时最低,相对SD_1分别升高1.08%、-1.71%;当SGBCS算法的采样密度由SD_2变为SD_3时,样地数的减少对不确定性影响最大,但对区域空间变异格局估计没有实质性影响。3)将采样密度控制在SD_2(0.010%)水平,利用SGCS和SGBCS算法均能得到准确可靠的森林碳储量及其分布信息,同时能节省至少20%左右的森林调查工作量。

Forest inventory data still represent the most direct, accurate and reliable source of information over a long period. Given the substantial labor, material and finances required, we need a reasonable sampling density （SD） to reduce the workload of field investigation. SD is a key issue on the accuracy and cost of estimation. A minimum number of sample plots given certain accuracy requirements is the most economical solution for spatial estimation of forest carbon. At present, most research on forest carbon storage is only valid for a particular SD. Studies of estimation accuracy at different sampling densities are rare. Generally, the greater the SD, the smaller the error. However, blindly increasing SD is not desirable and does not continuously reduce total error. This study is based on Forest Management Inventory-measured aboveground carbon storage data of Xianju County for Taizhou （Zhejiang Province）, and Landsat Thematic Mapper imagery of 30 m×30 m resolution. Using sequential Gaussian co-simulation （SGCS） and sequential Gaussian block co-simulation （SGBCS） algorithms, a geostatistical image-based conditional simulation technique was used to map and analyze uncertainty of natural resources and environmental systems during recent years. We explored the effect of SD on forest carbon and its spatial distribution estimates, uncertainties, and spatial variability at four SD levels, i.e., SD1=100%, SD2=80%, SD3=60%, and SD4=40% of total plots. Because the international forest carbon market needs various scales of spatial distribution, we designed two scale levels：1） the impact of different SDs on the spatial distribution of carbon estimation at regional scale, using the SGCS algorithm with spatial resolution 30 m×30 m; and 2） the impact of different SDs on upscaling for regional forest carbon estimation, using the SGBCS algorithm with spatial resolution 900 m×900 m. This study is an attempt to reduce the investigation workload and provides a reference for implementation of a forest resource inventory. The results show the following. 1） Under different SDs, SGCS and SGBCS had the same distribution trends in estimation of forest carbon density. SGCS estimation was able to meet accuracy requirements when for SD2, carbon density was 0-67.485 Mg/hm^2 with mean 15.425 Mg/hm^2, consistent with the measurement. SGBCS carbon density estimation was less influenced by SD, all SDs could meet the accuracy requirements, and a smaller SD had no substantial impact on upscaling. 2） Uncertainty of the SGCS and SGBCS estimation had overall rising trends, and the increase rate was smallest for SD2. For SD1, uncertainty of SGCS and SGBCS estimation increased by 1.08% and decreased by -1.71%, respectively. Uncertainty of carbon density estimation by SGBCS was less influenced by SD. When SD was changed from SD2 to SD3, it reduced the plot number, resulting in the greatest impact on uncertainty of SGBCS estimation. SDhad less contribution to estimation of the spatial variability. 3） Estimation of forest carbon storage and its distribution with the SGCS/SGCBS algorithms could reduce the requirement of SD appropriately. Not only were we able to obtain reliable estimation information, but we could also reduce the workload of the forest survey by at least 20% for SD at the SD2 level （about 0.010% of total regional area）.