Abstract
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.
Highlights
Aquatic vegetation plays an important role in the regulation of lake ecosystems, but in recent years, lake water quality has continuously deteriorated in semi-arid areas
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In order to further explore the transferability of the concave–convex decision function, we studied the effect of transparency, submerged aquatic vegetation (SAV) depth, and coverage on the spectral curves of SAV
Summary
Aquatic vegetation plays an important role in the regulation of lake ecosystems, but in recent years, lake water quality has continuously deteriorated in semi-arid areas. The declining water quality is marked with severe eutrophication, frequent algal blooms, shrinking areas with aquatic vegetation, and even extinction of some vegetation [1]. To better provide early warnings of potential algal bloom outbreaks and accomplish dynamic monitoring of aquatic vegetation, rapid, large-scale, and regular monitoring of aquatic vegetation via remote sensing is an indispensable tool [2,3]. In the early years of remote sensing technology, aerial images were utilized to monitor aquatic vegetation [4,5]. Many extraction methods for aquatic vegetation classification have been developed, such as decision tree classification [14], supervised classification [15], and unsupervised classification [16]
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