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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Use of Vegetation Index “Fingerprints” From Hyperion Data to Characterize Vegetation States Within Land Cover/Land Use Types in an Australian Tropical Savanna
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Use of Vegetation Index “Fingerprints” From Hyperion Data to Characterize Vegetation States Within Land Cover/Land Use Types in an Australian Tropical Savanna

机译：利用Hyperion数据中的植被指数“指纹”来表征澳大利亚热带稀树草原的土地覆盖/土地利用类型内的植被状态

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Suites of spectral indices may be derived from hyperspectral sensors such as Hyperion on EO-1. Spectral indices linked to vegetation and landscape function that are scalable to multi-spectral global sensors, could provide “fingerprints” for vegetation states in tropical savannas. In this study, Hyperion images were acquired on three occasions throughout the dry season over each of two consecutive years in the tropical savanna near Darwin, Northern Territory, Australia $(12^{circ}25^{prime}{rm N},130^{circ}50^{prime}{rm E})$ during 2005 and 2006. This paper examines the changes in fractional cover of photosynthetic and non-photosynthetic vegetation and bare soil and key diagnostic narrow band vegetation indices for major land cover/land use (LCLU) types over two contrasting post-monsoon seasons. The fractional cover proportions and vegetation indices responded strongly to the additional month of full monsoon rains in 2006 versus 2005. There were differences in vegetation indices sensitive to pigments, canopy water and cellulose between LU and LC classes, but within class variation was very high for large sized sample areas. When fine scale variation in vegetation indices and fractional cover were examined as “fingerprints” for small, more uniform areas of specific LC, distinct differences were evident. Vegetation indices and derived vegetation properties can be used to characterize vegetation states at the scale of natural and management-induced variation. The vegetation indices and fractional cover methods used here can be translated and scaled-up to current and new global sensors to improve description of vegetation structure and function in savannas.
机译：光谱指数套件可以从高光谱传感器（例如EO-1上的Hyperion）获得。与植被和景观功能相关的光谱指数可扩展到多光谱全球传感器，可以为热带稀树草原的植被状态提供“指纹”。在这项研究中，在澳大利亚北部地区达尔文附近的热带稀树草原中，连续两年每年两次在整个干旱季节采集Hyperion图像。 $（ 12 ^ {circ} 25 ^ {prime} {rm N}，130 ^ {circ} 50 ^ {prime} {rm E}）$ 在2005年和2006年期间。在两个相对的季风后季节，主要土地覆盖/土地利用（LCLU）类型的光合和非光合植被和裸露土壤的分数覆盖率以及关键的诊断性窄带植被指数。相对于2006年和2005年的季风降雨，月份的覆盖率和植被指数有很强的响应。LU和LC类别之间对色素，冠层水和纤维素敏感的植被指数存在差异，但对于大型样本区域。当将植被指数和覆盖率的小尺度变化作为“指纹”检查特定LC的较小且更均匀的区域时，明显的差异是显而易见的。植被指数和导出的植被特性可用于以自然和管理诱发的变化为尺度表征植被状态。此处使用的植被指数和部分覆盖方法可以转换为当前和新的全局传感器，并按比例放大，以改善对热带稀树草原植被结构和功能的描述。

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