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基于地统计学的广东省和广西省森林生物量和NPP空间格局分析

刘双,阮宏华**   

  1. (南京林业大学森林资源与环境学院, 南京 210037)
  • 出版日期:2013-09-10 发布日期:2013-09-10

Spatial pattern analysis of forest biomass and NPP in Guangdong and Guangxi provinces of South China based on geostatistics.

LIU Shuang, RUAN Hong-hua**   

  1. (College of Forest Resources and Environment Nanjing Forestry University, Nanjing 210037, China)
  • Online:2013-09-10 Published:2013-09-10

摘要:

区域尺度森林生物量和森林净初级生产力(NPP)的估测是目前生态学领域重要的科学问题之一。使用不同的方法获得的区域尺度森林生物量和NPP估算值存在较大差异。本文以广东和广西为例,通过搜集文献中两广地区的167个样地实测数据,运用林龄与生物量及NPP之间的关系方程,计算2010年的样地生物量和NPP;应用ArcGIS地统计分析和插值方法,研究区域森林生物量和NPP分布格局,并探讨两者及森林各组成部分生物量的空间分异特征。不同森林类型的生物量和NPP水平有较大差异,森林生物量格局表现为北高南低,NPP分布是中部较低,最高值在热带雨林分布的西部和南部。本研究为区域尺度森林生物量和NPP的研究提供了一种新的方法和途径。
 

关键词: 酶液, 高效降解菌系, 快速高通量筛选, 石油烃降解

Abstract: At present, the estimation of forest biomass and net primary productivity (NPP) on regional scale is one of the important issues in ecology. The estimates on forest biomass and NPP on regional scale by using different methods vary greatly. Based on the investigation data of 167 forest typical sampling plots in Guangdong and Guangxi provinces of South China collected from publications, and by using the equations of the relationships between the stand age and the biomass and NPP, the forest biomass and NPP under natural growth in the two Provinces were estimated in 2010. The spatial patterns of the forest biomass and NPP were derived by the spatial interpolation and geostatistics analysis of ArcGIS, with the spatial patterns of the two and of the forest biomass components explored. There existed great differences in the biomass and NPP among different forest types. The spatial pattern of forest biomass decreased from north to south. The NPP was lower in the central area, and the highest in the west and south, where tropical rainforests distributed. Our results would provide a new way to study the forest biomass and NPP on regional scale.

Key words: high-efficient degrading bacteria, rapid and high-throughput screening, petroleum  degradation., enzyme