• 论文与研究报告 •

### 基于空间仿真的仙居县森林碳分布估算

1. 1. 浙江农林大学环境与资源学院 临安 311300;
2. 浙江农林大学 浙江省森林生态系统碳循环与固碳减排重点实验室 临安 311300;
3. Department of Geography and Environmental Resources, Southern Illinois University at Carbondale Carbondale 62901
• 收稿日期:2013-08-25 修回日期:2014-10-01 出版日期:2014-11-25 发布日期:2014-12-04
• 基金资助:

国家自然科学基金项目(30972360;41201563).

### Estimation of Forest Carbon Distribution for Xianju County Based on Spatial Simulation

Zhang Maozhen1,2, Wang Guangxing3, Ge Hongli1,2, Xu Lihua1,2

1. 1. School of Environmental & Resource Sciences, Zhejiang A&F University Lin'an 311300;
2. Zhejiang Provincial Key Laboratory of Forest Ecosystem Carbon Cycling and Sequestration, and Emission Reduction Zhejiang A & F University Lin'an 311300;
3. Department of Geography and Environmental Resources, Southern Illinois University Carbondale Carbondale 62901
• Received:2013-08-25 Revised:2014-10-01 Online:2014-11-25 Published:2014-12-04

Abstract:

In this study, forest carbon stock and its spatial distribution of Xianju County, Zhejiang, were simulated by using a sequential Gaussian co-simulation algorithm based on data, collected in a forest resource inventory from sample plots in 2008, and the Landsat Thematic MapperTM image. The obtained forest carbon map was assessed using three accuracy measures of overall estimate consistency (OEC), average coefficient of variation (ACV), and relative root mean square error (RRMSE). Moreover, temporary sample plots were selected surrounding the permanent sample plots and the data were collected. The permanent and temporary plot data sets were respectively combined with the TM image to scale up forest carbon stock from a spatial resolution of 30 m × 30 m to 1 km × 1 km using a sequential Gaussian block co-simulation algorithm. The up-scaled map from the temporary plot data were used to assess the accuracy of the corresponding map from the permanent plot data and to analyze their spatial representatives. The results showed that the 2008's forest carbon stock estimated for the county was 2 667 878 Mg. Most of the stock was found in the southern and northern mountainous areas and less amount of the stock in the central west to the east narrow areas that had lower elevation. The forest carbon density varied from 0 to 65.7 Mg·hm-2. Whether all the permanent plots or half of them were used, the population estimates of forest carbon fell into the confidence intervals of the plot data at a significant level of 5%. When the data from the permanent sample plots were compared with those from the temporary plots, the coefficient of correlation for carbon density was 0.95 and 0.85 at the spatial resolutions of 30 m × 30 m and 1 km × 1 km, respectively. This result implied that when forest carbon stock was scaled up from a spatial resolution of 30 m × 30 m to 1 km × 1 km, the obtained map using the permanent plots re-produced the spatial distribution of the forest carbon density very well. In addition, if a cost efficiency was defined as (1-RRMSE)/number of sample plots, using half of the sample plots showed a higher cost efficiency than using all the sample plots, implying this cost efficiency indicator can be used to determine an appropriate number of field plots in sampling design for generating spatial distribution of forest carbon stock.