SGER: Large-scale Ecology of a Diverse Tropical Rain Forest Landscape: a Multidisciplinary Proposal to Capitalize on a Unique Research Opportunity

SGER：多样化热带雨林景观的大规模生态学：利用独特研究机会的多学科提案

• 批准号: 0533575
• 负责人: Elizabeth Losos
• 金额: --
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0533575&HistoricalAwards=false
• 关键词: SGER; Large; scale; Ecology; Diverse

This will use Small Foot Print Light Detection and Ranging (SF-LIDAR) to measure canopy structure in three dimensions at high resolution at La Selva Biological Station, Costa Rica. The data collected are at such high resolution that individual tree canopies can be readily identified. The data can be used to estimate stand basal area, aboveground biomass, leaf area index, light transmittance, canopy and individual tree heights, and stem densities. This is a relatively new technology that has, thus far, been used in mainly in temperate forest sites, but rarely in tropical forests. As one of the most intensively studied tropical forests in the world, there are several ongoing projects at La Selva, including two NSF funded projects directed at tree demography and forest succession following agricultural abandonment that will be able to take advantage of access to the SF-LIDAR data. Projects funded by other federal agencies and private organizations are directed at such topics as forest canopy structure and physiology, biodiversity monitoring of selected forest taxa, carbon dynamics, and landscape-scale ecological and demographic studies of rain forest trees. Many of these studies are being conducted by overlapping groups of scientists and are thus, highly integrated and interdisciplinary.

这将在哥斯达黎加的La Selva生物站使用小脚印光探测和测距（SF-LIDAR）以高分辨率测量三维树冠结构。 所收集的数据是在这样的高分辨率，个别树冠可以很容易地识别。 这些数据可以用来估计林分断面积、地上生物量、叶面积指数、透光率、冠层和单株树高以及树干密度。 这是一项相对较新的技术，迄今为止主要用于温带森林，很少用于热带森林。 作为世界上研究最深入的热带森林之一，拉塞尔瓦有几个正在进行的项目，包括两个NSF资助的项目，旨在研究农业废弃后的树木人口统计和森林演替，这些项目将能够利用SF-LIDAR数据。 由其他联邦机构和私人组织资助的项目针对森林冠层结构和生理学、选定森林分类群的生物多样性监测、碳动力学以及热带雨林树木的大规模生态和人口研究等主题。 其中许多研究是由重叠的科学家小组进行的，因此是高度综合和跨学科的。