基于Sentinel-2卫星遥感的河口潮滩植被指数构建研究

doi:10.3969/j.issn.1000-5641.2025.02.014

摘要/Abstract

摘要：

河口海岸区域因为受到潮汐动态淹没的影响, 位于潮滩前沿和潮沟边缘的低覆盖度植被信息难以被常用的陆地植被指数准确提取. 以长江河口潮滩湿地为研究区域, 在常用陆地植被指数比较与评价的基础上, 结合湿度指数构建了适用于河口潮滩区域的植被指数ETFVI (Estuarine Tidal Flat Vegetation Index), 对其提取的植被分布信息和反演的覆盖度结果进行了精度验证. 结果表明: ETFVI与常用的8种植被指数相比, 由于考虑了下垫面湿度变化对植被指数的影响, 提取植被分布信息的准确率和F₁值相较第2名分别提高了10.38%和6.9%; ETFVI对植被覆盖度的反演结果与其他8种植被指数相比, 平均相对误差最低. 研究结果可为河口潮滩区域植被覆盖时空变化监测提供新的思路和方法.

关键词: 植被指数, 植被覆盖度, 长江口, 潮滩湿地, 湿度指数

Abstract:

Owing to the tidal dynamic inundation, the vegetation information of low cover saltmarsh located at the front of tidal flat and edge of tidal ditch in the estuarine and coastal area is difficult to accurately extract using common terrestrial vegetation indices. This study considered the tidal flat wetland in the Yangtze River Estuary as research area. Based on the comparison and evaluation of commonly used terrestrial vegetation indices, combined with the humidity index, a vegetation index ETFVI (Estuarine Tidal Flat Vegetation Index) suitable for the intended region was developed. The accuracy of the extracted vegetation distribution information and the inverted fractional vegetation cover results was verified. The results suggested that, compared with the commonly used eight vegetation indices, ETFVI improved the extraction accuracy and F₁ score of vegetation distribution information by 10.38% and 6.9%, respectively, compared to the second place considering the impact of humidity changes of underlying surface on vegetation indices. The fractional vegetation cover inversion results based on ETFVI are consistent with the actual situation, with the lowest mean relative error among various vegetation indices. The study results can provide new ideas and methods for monitoring spatiotemporal changes of vegetation cover in estuarine tidal wetlands.

Key words: vegetation index, fractional vegetation cover, the Yangtze River Estuary, tidal wetlands, moisture index

中图分类号:

TP79

叶可, 田波, 王玉纯. 基于Sentinel-2卫星遥感的河口潮滩植被指数构建研究[J]. 华东师范大学学报（自然科学版）, 2025, 2025(2): 141-153.

Ke YE, Bo TIAN, Yuchun WANG. Development of estuarine tidal flat vegetation index based on Sentinel-2 satellite remote sensing[J]. J* E* C* N* U* N* S*, 2025, 2025(2): 141-153.

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植被指数	描述	公式	参考文献
ARVI	使用蓝波段来减少大气影响, 适用于大气气溶胶含量高的区域	$ \dfrac{N-\left[R-\gamma(B-R)\right]}{N+\left[R-\gamma(B-R)\right]},\ \gamma=1 $	[22]
EVI	使用蓝波段校正大气和土壤影响, 优化了植被高覆盖度区域出现的NDVI饱和现象	$ \dfrac{2.5\left(N-R\right)}{N+C_1·R-C_2·B+L}, $ $ {C}_{1}=6, {C}_{2}=7.5, L=1 $	[23]
GDVI	在低覆盖度植被区域具有高敏感性的特点和更大的动态范围	$ \dfrac{N^n-R^n}{N^n+R^n},\ n=2 $	[10]
GRNDVI	与LAI相关性高, 绿波段的加入使得其对LAI的变化反应敏感	$ \dfrac{N-\left(G+R\right)}{N+\left(G+R\right)} $	[24]
MSAVI	较好消除了土壤背景的影响	$ \dfrac{2N+1-\sqrt{\left(2N+1\right)^2-8\left(N-R\right)}}{2} $	[25]
NDVI	应用最为广泛的植被指数, 在高覆盖度植被区域易饱和, 敏感性降低	$ \dfrac{N-R}{N+R} $	[26]
VARI	相较于NDVI, 在中高覆盖度植被区域对覆盖度变化的敏感性增加	$ \dfrac{G-R}{G+R-B} $	[27]
WDRVI	相较于NDVI, 在中高LAI值下, 对LAI变化的敏感性增加	$ \dfrac{\alpha·N-R}{\alpha·N+R},\ \alpha=0.2 $	[28]

植被指数	线性模型		对数模型		指数模型
植被指数	R²	$ {E}_{\mathrm{R}\mathrm{M}\mathrm{S}} $	R²	$ {E}_{\mathrm{R}\mathrm{M}\mathrm{S}} $	R²	$ {E}_{\mathrm{R}\mathrm{M}\mathrm{S}} $
GRNDVI	0.7233	0.0797	0.6290	0.0923	0.7192	0.0803
NDVI	0.7229	0.0707	0.6300	0.0817	0.7167	0.0715
WDRVI	0.6812	0.1021	0.5536	0.1208	0.7001	0.0990
GDVI	0.6962	0.0749	0.6625	0.0790	0.6596	0.0793
MSAVI	0.6105	0.0951	0.5158	0.1060	0.6170	0.0943
ARVI	0.5722	0.0608	0.4970	0.0660	0.5718	0.0609
EVI	0.5328	0.0674	0.4686	0.0719	0.5295	0.0676
VARI	0.3938	0.0735	0.2766	0.0802	0.4290	0.0713

组合方式	$ {V}_{\mathrm{N}\mathrm{E}} $	R²	拟合方程
GRNDVI_NDWI	0.1169	0.7283	$ y=0.065\;9 + 0.734\;6x $
GRNDVI_CIWI	0.1217	0.7258	$ y=-0.239\;6 + 0.885\;7x $
GRNDVI	0.1224	0.7233	$ y=-0.178\;6 + 0.650\;8x $
NDVI	0.1226	0.7229	$ y=0.165\;8 + 0.576\;9x $
NDVI_CIWI	0.1232	0.7209	$ y=0.295\;2 + 0.708\;3x $
NDVI_NDWI	0.1235	0.7198	$ y=0.261\;6 + 0.619\;3x $

植被指数	植被区域		光滩区域		水体区域
植被指数	均值	标准差	均值	标准差	均值	标准差
ETFVI	0.7409	0.0722	0.5719	0.0401	0.2854	0.0638
ARVI	0.7658	0.0778	0.6124	0.0655	0.3246	0.0733
EVI	0.5648	0.0617	0.4563	0.0414	0.3225	0.0285
GDVI	0.8359	0.1081	0.5957	0.1056	0.1380	0.0863
GRNDVI	0.6106	0.0920	0.4332	0.0683	0.1816	0.0535
MSAVI	0.4538	0.0768	0.3376	0.0541	0.2063	0.0226
NDVI	0.7057	0.0797	0.5489	0.0634	0.2715	0.0618
VARI	0.2761	0.0444	0.2360	0.0340	0.2491	0.0280
WDRVI	0.3383	0.0808	0.2116	0.0605	0.0700	0.0248

$ {I}_{\mathrm{V}} $	准确率	精确率	召回率	F₁值	$ {I}_{\mathrm{V}} $	准确率	精确率	召回率	F₁值
ETFVI	0.8357	0.8399	0.9456	0.8896	EVI	0.6905	0.7547	0.8265	0.7890
GRNDVI	0.7571	0.8057	0.8605	0.8322	WDRVI	0.6714	0.7955	0.7143	0.7527
GDVI	0.7476	0.7994	0.8537	0.8257	MSAVI	0.6429	0.7432	0.7483	0.7457
NDVI	0.7310	0.7768	0.8639	0.8180	VARI	0.5405	0.6906	0.6224	0.6547
ARVI	0.7024	0.7600	0.8401	0.7980