A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data
A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data
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DOI：
10.3390/rs10081279
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Qi Chen;R. Yu;Yanling Hao;L. Wu;Wenxing Zhang;Qi Zhang-;Xunan Bu
中科院分区：
文献类型：
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作者：
Qi Chen;R. Yu;Yanling Hao;L. Wu;Wenxing Zhang;Qi Zhang-;Xunan Bu
由于水体的强吸收削弱了浅水湖泊水下植被反射的高近红外光谱特征，传统的植被指数难以从卫星图像中准确识别和提取水体和水下植被。以中国半干旱区乌兰苏海浅水湖为研究对象，利用2015年7月和8月获得的高汾1号（GF-1）分辨率为16米的多光谱卫星影像，提出了一种新的凹凸决策函数，用于水下植被（SAV）检测和水体识别。同时，利用决策树方法对挺水植被、“黄台水华”和SAV进行了分类。通过野外采样调查验证，7月和8月分类精度分别为92.17%和91.79%，说明GF-1数据具有4 d短重访周期和高空间分辨率，能够满足水生植被提取的精度要求。结果表明，凹凸决策函数在区分水体和SAV方面上级传统的分类方法，从而显著提高了SAV的分类精度。该凹凸决策函数适用于透明度为1.5 m时，0.3 m以上SAV覆盖率大于40%，0.1 m以上SAV覆盖率大于40%的沃茨，为其他区域SAV的准确提取提供了新的方法。
It is difficult to accurately identify and extract bodies of water and underwater vegetation from satellite images using conventional vegetation indices, as the strong absorption of water weakens the spectral feature of high near-infrared (NIR) reflected by underwater vegetation in shallow lakes. This study used the shallow Lake Ulansuhai in the semi-arid region of China as a research site, and proposes a new concave–convex decision function to detect submerged aquatic vegetation (SAV) and identify bodies of water using Gao Fen 1 (GF-1) multi-spectral satellite images with a resolution of 16 meters acquired in July and August 2015. At the same time, emergent vegetation, “Huangtai algae bloom”, and SAV were classified simultaneously by a decision tree method. Through investigation and verification by field samples, classification accuracy in July and August was 92.17% and 91.79%, respectively, demonstrating that GF-1 data with four-day short revisit period and high spatial resolution can meet the standards of accuracy required by aquatic vegetation extraction. The results indicated that the concave–convex decision function is superior to traditional classification methods in distinguishing water and SAV, thus significantly improving SAV classification accuracy. The concave–convex decision function can be applied to waters with SAV coverage greater than 40% above 0.3 m and SAV coverage 40% above 0.1 m under 1.5 m transparency, which can provide new methods for the accurate extraction of SAV in other regions.