Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research
Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research
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DOI：
10.1016/j.jag.2021.102529
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Genyun Sun;Z. Jiao;A. Zhang;Feng Li;Hang Fu;Zheng Li
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文献类型：
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作者：
Genyun Sun;Z. Jiao;A. Zhang;Feng Li;Hang Fu;Zheng Li
准确监测城市植被的数量和质量有助于区域绿化工作，并提高对植被对环境影响的认识。然而，建筑物阴影和合成材料等因素会极大地阻碍植被估计。此外，植被指数（维斯）在高生物量条件下迅速饱和，使植被质量评估复杂化。为了解决这些问题，我们提出了一个新的植被指数，即高光谱图像为基础的植被指数（HSVI）。HSVI的构建分为三个步骤：波段选择、饱和波段重构和索引结构重定义。首先，我们选择了四个有代表性的波段，并构建了一个增强的植被指数（EVI），以消除复杂的城市表面因素的干扰。其次，我们重建容易饱和的波段（760 nm），通过指数函数，形成一个优化的增强植被指数（OEVI）。最后，通过将红色边缘（689 nm）和绿色（520 nm）波段之和的分母添加到OEVI中来重新定义索引结构，以进一步增强植被的光谱信息并消除维斯典型的饱和问题。利用上海戏剧学院、大珠山和休斯顿大学3个不同地貌特征的数据集，将HSVI与城市生态学研究中广泛采用的6个维斯指标进行了比较，差异植被指数（DVI）、归一化差异植被指数（NDVI）、简单比值（SR）、优化土壤调节植被指数（OSVAI）、修正的转换植被指数（MTVI 2）和宽动态范围植被指数（WDRVI）。结果表明，HSVI的植被提取精度在90%以上，明显高于其他维斯植被提取方法。此外，HSVI还解决了维斯饱和问题。因此，HSVI显示出很高的潜在有用性的城市生态研究应用。
Accurately monitoring the quantity and quality of urban vegetation contributes to regional greening efforts and improves the understanding of vegetation's impact on the environment. However, factors such as building shadows and synthetic materials can greatly obstruct vegetation estimates. Additionally, vegetation indices (VIs) saturate quickly in high biomass conditions, complicating vegetation quality assessments. To address these issues, we propose a new vegetation index, namely the hyperspectral image-based vegetation index (HSVI). HSVI is built in three steps: band selection, saturable band reconstruction, and index structure redefinition. Firstly, we select four representative bands and construct an enhanced vegetation index (EVI) to eliminate the interference of complex urban surface factors. Secondly, we reconstruct the easily saturable band (760 nm) through an exponential function to form an optimized enhanced vegetation index (OEVI). Finally, the index structure is redefined by adding the denominator of the sum of the red edge (689 nm) and green (520 nm) bands to OEVI to further enhance the spectral information of vegetation and eliminate the saturation problem typical of VIs. Three datasets with different geomorphological features (Shanghai Theatre Academy, Dazhu Mountain, and the University of Houston) are used to compare the performance of HSVI with six VIs widely adopted in urban ecological research, i.e., the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the simple ratio (SR), the optimized soil-adjusted vegetation index (OSVAI), the modified transformed vegetation index (MTVI2) and the wide-dynamic-range vegetation index (WDRVI). The results show that the vegetation extraction accuracy of HSVI is more than 90%, which is significantly higher than those of the other VIs. Additionally, HSVI also solves the VIs saturation issue. Therefore, HSVI shows high potential usefulness for urban ecological research applications.