![]() We give examples from medical diagnosis, minefield detection, cluster recovery from noisy data, and spatial density estimation. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We review a general methodology for model-based clustering that provides a principled statistical approach to these issues. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. Our results suggest the conventional NDVI: (1) can be used to depict SAV canopies at water surface (2) is not a good indicator for SAV that is adapted to live underwater or other aquatic plants that are submerged during flooding even at shallow waters (0.3 m) and (3) the index values can significantly improve if information on spectral reflectance attenuation caused by water volume increases is collected simultaneously through ground-truthing and integrated.Ĭluster analysis is the automated search for groups of related observations in a dataset. When corrected for water attenuation using the data obtained from the black panel, the NDVI values significantly increased at all depths that we tested (0.1 - 0.5 m). NDVI values ranged 0.6-0.65 when the SAV canopy was at the water level, then they decreased linearly (slope of 0.013 NDVI/meter) with water depth increases in clear water. The measured upwelling radiance was converted to % reflectance and we integrated the hyperspectral reflectance to match the Red and NIR bands of three satellite sensors: Landsat 7 ETM, SPOT 5 HRG, and ASTER. We used a GER 1500 spectroradiometer to collect spectral data over floating waterhyacinth (Eichhornia crassipes) and also over the tanks that contain SAV and black panel at varying water depths. A 100-gallonoutdoor tank was lined with black pond liners, a black panel or SAV shoots were mounted on the bottom, and filled with water up to 0.5 m. We experimentally tested if NDVI can be used to depict canopies of aquatic plants in shallow waters. However, the NIR absorption by water and light scattering from suspended particles reduces the practical application of such indices in aquatic vegetation studies, especially for the Submerged Aquatic Vegetation (SAV) that grows below water surface. These spectral characteristics were used to develop vegetation indices, including Normalized Difference Vegetation Index (NDVI). Remote sensing of terrestrial vegetation has been successful thanks to the unique spectral characteristics of green vegetation, low reflectance in red and high reflectance in Near-InfraRed (NIR). This error (producers accuracy 67 percent) was found in the "Reef ≤ 5 m" class and was primarily attributed to the diversity of this spectral class, which may lead to a spectral signature based on the dominant cover type in a given pixel. However, error still remained in discriminating small, diverse patch-reef features. It was concluded that the Ikonos data were useful for discriminating sand, coral reef (at two depth intervals), and seagrass features (providing overall accuracies of 89 percent each for the two study areas). An accuracy assessment of the classification results was performed using in situ data collected at 62 points one day prior to the image being acquired. Classification was performed using bands 1, 2, and 3 (blue, green, and red) to maximize the water-penetration capabilities of the sensor. The Single-Image Normalization Using Histogram Adjustment was used for atmospheric corrections on the imagery. ![]() In this study, the Ikonos satellite with a 4- by 4-m spatial resolution in the multispectral bands was used as a tool for subsurface feature identification. While aerial reconnaissance may offer higher spatial resolutions than satellite sensors, it is often limited by the high costs of planning and implementing the missions, image rectification, area that can be covered, and repeat coverage. Previous attempts at subsurface feature discrimination with satellite remote sensing have been limited in accuracy due to the effects of pixel mixing associated with poor spatial resolutions. ![]() Monitoring coral reef, seagrass, and sand features using contemporary remotely sensed data may prove to be a cost-effective and time-efficient tool for reef surveys, change detection, and management.
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