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准确地评估森林净第一性生产力(NPP)对于评估全球收支有着十分重要的作用。本文充分利用森林资源清样调查资料,并动态地评估森林生产力,以油松林为例建立了反映生物因素(蓄积量V和林龄A)和气候因素(年实际蒸散E)综合影响的中国油松林生物气候生产力(NPPa)模型。基于所建模型和第四次我国油松林资源的清样调查资料(1989-1993年),估算了中国油松林的净第一性生产力,并借助于地理信息系统软件给出了中国油松林的分布格局。结果表明:我国油松林的平均净第一性生产力为7.82thm-2a-1,其变化幅度为3.32~11.87thm-2a-1。中国油松林净第一性生产力有明显的区域差异,表现为南高北低的分布趋势。山西和陕西为中国油松林的集中分布区,生产力水平处于中等,约为7.4thm-2a-1;油松林集中分布区的南部(四川、湖北、河南等省),生产力水平较高,均大于7.7thm-2a-1;而在油松林集中分布区的北部和西部(内蒙古、宁夏等省),生产力水平较低,NPP均低于5thm-2a-1。该研究为利用森林资源清样调查资料评估森林NPP的动态及研究其对气候变化的响应提供一个有效思路。图3表2参46。
Accurate assessment of net forest NPPs plays a very important role in assessing global payments. This paper makes full use of the sampling data of forest resources sampling and dynamically evaluates the forest productivity. Taking the Pinus tabulaeformis plantation as an example, this paper established a comprehensive model of Chinese pine woods (Pinus tabulaeformis), which reflects the combined effects of biological factors (volume V and forest age A) and climatic factors Bioclimatic productivity (NPPa) model. Based on the established model and the fourth sample survey of Chinese pine forest resources (1989-1993), the net primary productivity of Chinese pine forest was estimated and the distribution of Chinese pine forest was given with the aid of geographic information system software pattern. The results showed that the average net primary productivity of Pinus tabulaeformis forest in China was 7.82thm-2a-1, with a variation range of 3.32 ~ 11.87thm-2a-1. The net primary productivity of Chinese pine stands has obvious regional differences, showing the distribution trend of South High North. Shanxi and Shaanxi are the concentrated distribution areas of Pinus tabulaeformis forest in China. The productivity level is moderate, about 7.4thm-2a-1. The southern part of Pinus tabulaeformis forest distribution area (Sichuan, Hubei and Henan provinces) has higher productivity, 7.7thm-2a-1. However, in the north and west of the concentrated distribution area of Pinus tabulaeformis, Inner Mongolia and Ningxia provinces, the productivity was lower and NPP was lower than that of No.5m-2a-1. The study provides an effective way to evaluate the forest NPP dynamics and investigate its response to climate change by using the sampling data of forest resources sampling. Figure 3 Table 2 Reference 46.