Spatial variation and seasonal dynamics of leaf-area index in the arctic tundra-implications for linking ground observations and satellite images
Spatial variation and seasonal dynamics of leaf-area index in the arctic tundra-implications for linking ground observations and satellite images
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DOI：
10.1088/1748-9326/aa7f85
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发表时间：
2017-09-01
影响因子：
6.7
通讯作者：
Aurela, Mika
中科院分区：
文献类型：
--
作者：
Juutinen, Sari;Virtanen, Tarmo;Aurela, Mika
北极苔原的植被通常由小规模的植物群落镶嵌组成，物种在生长形式、季节性和地球化学性质上存在差异，这种差异的表征对于理解和模拟北极苔原在全球碳循环中的作用以及评估遥感分辨率要求至关重要。我们的目标是量化的叶面积指数（LAI）的季节性发展及其变化的植物群落在北极苔原Tiksi附近，西伯利亚沿海，包括禾本科，矮灌木，苔藓和地衣植被。我们测量了LAI在该领域，并使用两个非常高的空间分辨率的多光谱卫星图像（QuickBird和WorldView-2），在不同的物候期获得，预测植被规模的模式。我们使用的经验关系的植物群落特异性叶面积指数和度日积累（0摄氏度阈值）和量化的叶面积指数和卫星植被指数（归一化差异植被指数）之间的关系。由于现场数据和卫星图像之间的时间差异，叶面积指数近似的图像数据，使用经验模型。LAI很好地解释了NDVI值的变化（R-adj. (2)0.42-0.92）。在植物功能类型中，禾本科植物叶面积指数的季节变化幅度最大，是造成两幅图像间NDVI和相关叶面积指数空间格局变化的主要原因。我们的研究结果说明了如何短的生长季节，快速发展的叶面积指数，每年的气候变化，以及卫星数据的时间应占在北极地区的匹配图像和现场验证数据。
Vegetation in the arctic tundra typically consists of a small-scale mosaic of plant communities, with species differing in growth forms, seasonality, and biogeochemical properties.Characterization of this variation is essential for understanding and modeling the functioning of the arctic tundra in global carbon cycling, as well as for evaluating the resolution requirements for remote sensing. Our objective was to quantify the seasonal development of the leaf-area index (LAI) and its variation among plant communities in the arctic tundra near Tiksi, coastal Siberia, consisting of graminoid, dwarf shrub, moss, and lichen vegetation. We measured the LAI in the field and used two very-high-spatial resolution multispectral satellite images (QuickBird and WorldView-2), acquired at different phenological stages, to predict landscape-scale patterns. We used the empirical relationships between the plant community-specific LAI and degree-day accumulation (0 degrees C threshold) and quantified the relationship between the LAI and satellite NDVI (normalized difference vegetation index). Due to the temporal difference between the field data and satellite images, the LAI was approximated for the imagery dates, using the empirical model. LAI explained variation in the NDVI values well (R-adj.(2) 0.42-0.92). Of the plant functional types, the graminoid LAI showed the largest seasonal amplitudes and was the main cause of the varying spatial patterns of the NDVI and the related LAI between the two images. Our results illustrate how the short growing season, rapid development of the LAI, yearly climatic variation, and timing of the satellite data should be accounted for in matching imagery and field verification data in the Arctic region.