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

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          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 km 2 in 2001 to 364.1 km 2 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.

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          Most cited references50

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          Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements.

          R Pope, E Fry (1997)
          Definitive data on the absorption spectrum of pure water from 380 to 700 nm have been obtained with an integrating cavity technique. The results are in good agreement with those recently obtained by our group with a completely independent photothermal technique. As before, we find that the absorption in the blue is significantly lower than had previously been generally believed and that the absorption minimum is at a significantly shorter wavelength, i.e., 0.0044 ? 0.0006 m(-1) at 418 nm. Several spectroscopic features have been identified in the visible spectrum to our knowledge for the first time.
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            Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization.

            In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R2 = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.
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              Mapping marine environments with IKONOS imagery: enhanced spatial resolution can deliver greater thematic accuracy

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                June 2008
                25 June 2008
                : 8
                : 6
                : 3988-4005
                Affiliations
                State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, PO Box 210008, People's Republic of China
                Author notes
                [* ]Author to whom correspondence should be addressed: E-mails: rhma@ 123456niglas.ac.cn ; mrhua2002@ 123456163.com ; Fax: +86 25 57714759
                Article
                sensors-08-03988
                10.3390/s8063988
                3924936
                19779307-3d84-4811-a38a-e1e2898fe872
                © 2008 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 06 May 2008
                : 19 June 2008
                Categories
                Article

                Biomedical engineering
                aquatic vegetation,taihu lake,decision tree,biomass,remote sensing
                Biomedical engineering
                aquatic vegetation, taihu lake, decision tree, biomass, remote sensing

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