研究目的
To propose and validate a new floating vegetation index (FVI) for detecting vegetation floating over water surfaces using hyperspectral remote sensing data, addressing limitations of existing indices like FAI and MCI.
研究成果
The FVI index effectively detects floating vegetation such as Sargassum and algae in hyperspectral data, overcoming issues with existing indices that use red channels affected by particle scattering. It shows promise for future use with hyperspectral satellite sensors to improve global detection and estimation of primary production in aquatic environments.
研究不足
The FVI index cannot be calculated with current satellite sensors (e.g., MODIS, VIIRS, OLCI) due to lack of necessary narrow channels near 1.0 and 1.07 μm. It has only been tested on a few AVIRIS datasets and not quantitatively validated in large case studies. It may not distinguish floating vegetation from other materials like plastic debris or whitecaps without additional spectral analysis. Errors in atmospheric corrections under high aerosol conditions could affect FVI accuracy.
1:Experimental Design and Method Selection:
The study involved analyzing hyperspectral imaging data to observe a reflectance peak near
2:07 μm in floating vegetation spectra. A new index (FVI) was formulated using three near-IR channels (0 μm, 07 μm, 24 μm) to form a linear baseline, with the reflectance above this baseline at 07 μm defined as FVI. Sample Selection and Data Sources:
Hyperspectral data were acquired using the AVIRIS instrument over the Gulf of Mexico (for Sargassum) and salt ponds in the San Francisco Bay (for algae). Specific datasets from August 2009, May 2010, and June 2008 were used.
3:List of Experimental Equipment and Materials:
The primary instrument was the AVIRIS (Airborne Visible Infrared Imaging Spectrometer), an airborne imaging spectrometer with 224 spectral channels from
4:365 μm to 5 μm. Atmospheric correction was performed using the ATREM (atmosphere removal algorithm) code. Experimental Procedures and Operational Workflow:
Data were processed by applying atmospheric corrections to derive surface reflectances. FVI was calculated using Equations (1) and (2) provided in the paper. Threshold values (e.g., FVI >
5:001) were applied to classify floating vegetation pixels, and land/water masks were created using 25-μm reflectance images. Data Analysis Methods:
Reflectance spectra were analyzed to identify peaks and validate the FVI. Comparisons were made with existing indices (FAI, MCI), and results were visualized through RGB and mask images.
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