1. School of Geography and Environment, Liaocheng University, Liaocheng 252059, China
2. Liaocheng Center of Data and Application of National High Resolution Earth Observation System, Liaocheng 252000, China
3. Dongpinghu Wetlands Research Institute of Liaocheng University, Liaocheng 252059, China
摘要： 光谱特征是地物的固有属性，分析地物光谱不仅有助于提高地物识别精度，也是定量遥感研究的基础。然而受限于尺度效应，近地空间采集的光谱与遥感像元尺度的光谱往往差异较大。因此，在遥感像元尺度上揭示湿地典型景观地类的光谱特征，将有助于大尺度湿地遥感分类和植被参数反演精度的提高。以华北平原典型的草型湖泊湿地南阳湖为对象，基于EO-1 Hyperion星载成像高光谱数据，提取荷田、芦苇地、林地、水田、旱地、建筑用地、河道和湖泊鱼塘等8类湿地景观的反射率，并进行光谱一阶导数变换，同时计算多种高光谱植被指数，定量分析景观尺度上湿地地物的光谱特征。结果表明：（1）8种湿地景观地物反射率光谱差异明显，其中5种不同植被景观也存在差异。荷田反射率在全波段明显高于其他景观地类，荷田的绿波段反射峰和红波段吸收谷最明显。芦苇地与水田在可见光和红边区域具有相似的反射光谱特征，水田与旱地反射光谱曲线不同，且水田的绿峰明显高于旱地。（2）8种景观在蓝边、黄边及红边处的一阶导数光谱差异明显，尤以红边处最显著。荷田的红边斜率最大且红边位置明显蓝移（712 nm），说明其叶绿素含量高，健康状况最好。林地的红边斜率次之，但红边位置明显红移（722 nm）。（3）林地具有最大的植被指数，水体和建筑用地植被指数均较低，其他景观地类居中。芦苇地、水田、旱地和荷田在大多数与归一化植被指数（NDVI）相关的指数中差异不明显，仅在增强型植被指数（EVI）和红边叶绿素指数（Chlorophyll Index RedEdge 710）中存在较明显差异，说明这两个指数能够更有效地指示湿地植被类型之间绿度和覆盖度的差异。该研究对于草型湖泊湿地景观地物高精度分类及其植被参数的遥感反演具有借鉴意义。
关键词：南阳湖；Hyperion；高光谱遥感；光谱分析；湿地景观
Abstract：Spectral characteristics are the inherent attributes of ground objects. Analyzing spectrum is help to improve the accuracy of ground objects recognition and a basis of quantitative remote sensing. However, limited by scale effect, the spectrum acquired in near-earth space is often quite different from that of remote sensing pixels. Therefore, revealing the spectral characteristics of typical wetland landscapes on the scale of remote sensing pixels is useful to improve the accuracy of large-scale wetland remote sensing classification and inversion of vegetation parameters. Based on the satellite-borne EO-1 Hyperion data, the reflectance of lotus field, reed land, woodland, paddy, highland, construction land, river，lake and pond were extracted from Lake Nanyang, one of the grass lake wetlands in North China Plain.The spectral characteristics of the pixel-scale ground objects were quantitatively analyzed by using the first derivative of the spectrum and calculating a variety of hyperspectral vegetation indexes. The results showed that: (1) The reflectance spectrumof eight wetland landscapes were significantly different, andthere were also differences in the 5 vegetation types. The reflectance of the lotus field was significantly higher than that of other landscapes in the whole wave-band range. It sreflective peak in the green band and absorptive valley in the red band was the most obvious. The reflectance spectrum of the reed field and paddy were similar in visible light and red edge region. The reflectivity curves of paddy and upland farms were different, and the green paddy’s reflective peak was higher than that of upland. (2) The first derivative spectrum of eight landscapes were obviously different at the blue, yellow, and red edge regions, especially at the red edge.The red edge slope of the lotus field was the largest, and the red edge position was obviously blue shift (712 nm), indicates that it has high chlorophyll content and the best health condition. The red-edge slope of woodland was the second, but its red edge position was an obvious red-shift (722 nm). (3) Woodland hadthe highest vegetation index, the vegetation index of water bodies and construction mode rate landscapes land was low, and other. There was no significant difference in the mean values of indexes related to normalized difference vegetation index (NDVI) among reed land, paddy, upland and lotus fields, but only in the Enhanced Vegetation Index (EVI) and Chlorophyll Index RedEdge 710. It suggested that EVI and Chlorophyll Index RedEdge 710 index can more effectively indicate the difference of greenness and coverage between wetland vegetation types. The research has great significance for the high-precision classification wetland of and inversion of vegetation parameters.
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