Detecting Aquatic Vegetation Changes in Taihu Lake, China Using Multi-temporal Satellite Imagery

Abstract

We have measured the water quality and bio-optical parameters of 94 samples from Taihu Lake in situ and/or in the lab between June 10-18, 2007. A transparency-assisted decision tree was developed to more accurately divide the aquatic vegetation zone into a floating vegetation-dominated zone and a submerged vegetation-dominated zone, whose respective present biomass retrieval models were easily developed with an empirical approach because of the quasi-concurrence of ground field investigations with the satellite sensor flight over the lake. The significant quantitative relationships between the vegetation index NDVI (Normalized Difference Vegetation Index) of different images at different times were used to help develop the past biomass retrieval model on the basis of the present developed model. In Taihu Lake, the total covering area of aquatic vegetations decreased from 454.6 km2 in 2001 to 364.1 km2 in 2007. Correspondingly, the total biomass decreased from 489,000 tons in 2001 to 406,000 tons in 2007, suggesting that a great change in the ecological environment has been taking place in Taihu Lake over this period.

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

“…Some previous studies involving separating the signals of aquatic vegetation and algal blooms in Lake Taihu were performed by masking the "phytoplankton-dominated region" based on prior knowledge [28,29]. However, according to our investigation, the "phytoplankton-dominated regions" such as Zhushan Bay, Meiliang Bay, Gonghu Bay and the open lake were also areas of aquatic vegetation, which was verified by other studies [46].…”

“…Based on our investigation, the majority of aquatic vegetation in December and January consisted of Phragmites australis. Phragmites australis is a perennial herbaceous aquatic plant that is primarily present in the southern and western littoral zone and in East Lake Taihu [29]. In February and March, the high-frequency vegetation areas expanded, particularly in Xukou Bay and Meiliang Bay.…”

“…It is difficult or impossible to accurately estimate the historical distribution of aquatic vegetation without using field observations and remote sensing images simultaneously [29]. An empirical estimation model was developed based on field observations and concurrent satellite imagery and was then used on the past satellite imagery.…”

“…However, in waters with both aquatic vegetation and algal blooms, retrieval errors can occur due to their similar optical characteristics; for example, floating algae are often misclassified as emergent or submerged aquatic vegetation [10]. The majority of studies in which algae or aquatic vegetation were detected using remote sensing were based on prior environmental knowledge [28,29] or independent in situ datasets of questionable accuracy [3].…”

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

“…Therefore, in this study, we assumed that SAV signal influences the water leaving reflectance and that the bottom albedo is mainly from the SAV bed in the basin. In addition, as the detection of the submerged plant in the water also depends on the plant canopy depth from the water surface and the optical depth (i.e., water clarity) [27,37], we included water transparency as an important parameter for SAV biomass estimation. The SAV decomposition coefficient (a v ), obtained from the Spectral Decomposition Algorithm was used as an independent variable to estimate the SAV biomass of the classified SAV pixels.…”