Collaborative Research: Greening of the Arctic - Synthesis and Models to Examine the Effects of Climate, Sea-ice, and Terrain on Circumpolar Vegetation Change

合作研究：北极绿化——检验气候、海冰和地形对极地植被变化影响的综合和模型

• 批准号: 0531166
• 负责人: Howard Epstein
• 金额: $ 40.66万
• 依托单位: University of Virginia Main Campus
• 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: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0531166&HistoricalAwards=false
• 关键词: Collaborative; Research; Greening; Arctic; Synthesis

AbstractChanges to the vegetation of the Arctic are intimately linked to changes to the sea-ice cover, land surface temperatures, and a host of terrain variables. The overarching goals of this research are to (1) explore the sea-ice/terrain/vegetation linkages by synthesizing a group of recently available long-term circumpolar databases, (2) examine how the vegetation of the circumpolar Arctic is responding to global climate change, and (3) use this information in combination with models to help predict future response of arctic vegetation. The project will address directly the question of how the terrestrial vegetation of the Arctic has responded to climate change to date and how it will respond in the future as portions of the Arctic Ocean become seasonally ice free as indicated by current understanding of trends in sea ice. An average 17% increase in greenness, as measured by the normalized difference vegetation index (NDVI), occurred in northern Alaska from 1981 to 2001, concomitant with strong ice retreat in the Beaufort Sea and rising land-surface temperatures. The trend in NDVI is consistent with observations regarding shrub cover, modeling, and experimental evidence linking temperature increases to biomass increases. The group will synthesize 23+ years of data from Earth orbiting satellites in combination with detailed circumpolar maps of climate, vegetation, terrain, and substrate variables to determine how the ocean and land have interacted during the years of record, and then use this information to improve existing models of Arctic vegetation change. They will use primarily a time series of surface temperature, sea-ice, and NDVI data from the Advanced Very High Resolution Radiometers (AVHRR) aboard the NOAA satellites. A host of other remote sensing data will complement these to extend the length of the record and to look at finer scale changes. The primary region of study is the circumpolar Arctic as defined by the presence of tundra vegetation and an Arctic climate. A larger area defined by the arctic watershed will be studied in less detail. They will create a circumpolar integrated geographic information system for this region. The NDVI patterns will be analyzed spatially and temporally with respect to mapped variables including land-surface temperatures (LSTs), sea-ice patterns, the age of the terrain, substrate, topography, elevation, and regional floras. They will use vegetation change models to determine if the greening detected thus far can be used to project future patterns of vegetation change in the Arctic. A spatial analysis using BIOME4, a global model of vegetation change, will predict changes in the patterns of arctic and boreal plant communities, and ArcVeg, a model of arctic vegetation dynamics, will link the changes detected using remote sensing to ground-based measurements of plant species composition and structure to examine the rates at which changes can be expected.

北极植被的抽象变化与海冰覆盖，地表温度和许多地形变量的变化密切相关。这项研究的总体目标是（1）通过合成一组最近可用的长期极度圆形数据库来探索海冰/地形/植被连接，（2）检查极层北极的植被如何响应全球气候变化，以及（3）与模型结合使用此信息来帮助预测未来artcic植被的未来响应。该项目将直接解决一个问题，即北极的陆地植被如何应对迄今为止的气候变化，以及随着北极海洋的一部分在季节性的冰上无季节的反应，这是对海冰趋势的当前理解所表明的。从1981年至2001年，阿拉斯加北部发生了通过标准化差异指数（NDVI）衡量的平均绿色，绿色度的平均增长，与Beaufort Sea的强大冰撤退和地面表面温度升高。 NDVI的趋势与有关灌木覆盖，建模和实验证据的观察结果一致，将温度升高与生物量增加有关。该小组将通过详细的气候，植被，地形和底物变量的详细的极度圆形图合成23年以上的数据，以确定海洋和土地在记录年度的相互作用，然后使用此信息来改善现有的北极植被变化模型。他们将主要使用NOAA卫星上的高级非常高分辨率辐射仪（AVHRR）的表面温度，海冰和NDVI数据的时间序列。许多其他遥感数据将补充这些数据以扩展记录的长度并查看更细微的比例更改。研究的主要区域是由苔原植被的存在和北极气候所定义的近极北极。北极分水岭定义的较大区域将被详细研究。他们将为该区域创建一个极度集成的地理信息系统。 NDVI模式将相对于映射的变量在空间和时间上进行分析，包括土地表面温度（LST），海冰模式，地形，基板，地形，高度和区域植物群的年龄。他们将使用植被变化模型来确定到目前为止检测到的绿色是否可以用于投射北极植被变化的未来模式。使用Biome4（一种全球植被变化模型）进行空间分析，将预测北极和北方植物群落模式的变化，而Arcveg（北极植被动力学模型）将使用遥控感检测到的变化与基于地面的植物物种组成和结构的基于地面测量的变化联系，以检查预期变化的速率。