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基于大数据的森林生态系统服务功能评估进展

宋庆丰,牛香,王兵**   

  1. (中国林业科学研究院森林生态环境与保护研究所、国家林业局森林生态环境重点实验室,  北京 100091)
  • 出版日期:2015-10-10 发布日期:2015-10-10

Review on forest ecosystem services assessment based on big data.

SONG Qing-feng, NIU Xiang, WANG Bing**   

  1. (Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment, State Forestry
    Administration, Beijing 100091, China)
  • Online:2015-10-10 Published:2015-10-10

摘要: 与传统的数据相比,大数据的感知、获取、处理和表示都面临着巨大的挑战。森林生态系统作为陆地生态系统的主体,其所产生的服务功能在全球生态系统中发挥着极为重要的作用。森林生态系统服务功能评估在经历了小数据和表象大数据的评估阶段后,已经进入了大数据评估阶段。基于森林生态站长期监测数据开展的森林生态系统服务功能评估,能够在大数据中获取所需要的详细信息,开展多尺度镶嵌评估工作。同时,还可以避免小数据样本选择所带来的随机性误差,使得评估结果更趋于可靠,进而为森林资源的保护与可持续发展提供数据支撑。

关键词: 补偿性生长, 刈割, 块茎, 克隆储存, 克隆植物, 潜在入侵性

Abstract: Compared with traditional data, perception, acquisition, processing and expression of big data has been faced with enormous challenges. As forest ecosystem is the main body of terrestrial ecosystem, forest ecosystem services play an extremely important role in the global ecosystem. Forest ecosystem services assessment has entered the period of big data after the period of small data and surface big data. Forest ecosystem services assessment based on the longterm monitoring data from forest ecological stations could obtain more detailed information from the big data, and thus the multiscale evaluation could be carried out. At the same time, the random error brought by selection of small sample data could be avoided to make the evaluation results more reliable. So longterm monitoring data from forest ecological stations provide supports for forest resources protection and sustainable development.

Key words: clonal plant, tuber., clonal storage, mowing, potential invasive ability, compensatory growth