Knowing the inherent optical properties (IOPs) of water bodies is useful for many water environment studies and applications. To derive the IOPs from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was modified and validated for the highly turbid Poyang Lake in China. In order to supplement and expand the dynamic variation range of the measured water optical properties, a Hydrolight simulated dataset was generated to develop a regional QAA (QAA710) for this area. The QAA710 model was then validated with simulated data, simulated data with random noise, and in situ data. The results show that the effects of random noise (within ±20%) of remote-sensing reflectance on the derived total absorption coefficients (at) and the particulate backscattering coefficients (bbp) by the QAA710 model are insignificant (a band-averaged mean relative error of 4.1% and 12.0%, respectively). The validation of in situat shows a 28.6% mean relative difference. The model process, modeling data, and validation data introduce uncertainties into the derived results. These analyses demonstrate that the QAA710 model, based on the characterization of local environments, performs well in retrieving Poyang Lake’s IOPs.
Honghu Lake, one of the seven largest fresh-water lakes in China, is well known for its ecological and economic importance, as well as its rapid changes in recent years. This study investigates the potential of using remote sensing to map and monitor aquatic vegetation changes in Honghu Lake on a large scale. Landsat TM/ETM+ images dated July 27, 2000, July 9, 2002, and July 17, 2008, and CBERS image dated August 12, 2005, are employed to map the aquatic vegetation distribution in the lake. A hybrid classification method, combining the power of the decision tree classifier, naïve Bayes classifier, and supporting vector machine classifier is used to distinguish different wetland types. A novel polar coordinate map method is proposed to map the changes of aquatic vegetation on a large scale. The map demonstrates vegetation patch size changes and percentage changes in the whole lake directions during four periods. Validation using in situ surveys and historical ancillary data suggests that this approach could map the distribution and monitor the changes of aquatic vegetation on a large scale efficiently.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.