Use of Hyperspectral Imagery to Assess Cryptic Color Matching in Sargassum Associated Crabs
نویسندگان
چکیده
Mats of the pelagic macroalgae Sargassum represent a complex environment for the study of marine camouflage at the air-sea interface. Endemic organisms have convergently evolved similar colors and patterns, but quantitative assessments of camouflage strategies are lacking. Here, spectral camouflage of two crab species (Portunus sayi and Planes minutus) was assessed using hyperspectral imagery (HSI). Crabs matched Sargassum reflectance across blue and green wavelengths (400-550 nm) and diverged at longer wavelengths. Maximum discrepancy was observed in the far-red (i.e., 675 nm) where Chlorophyll a absorption occurred in Sargassum and not the crabs. In a quantum catch color model, both crabs showed effective color matching against blue/green sensitive dichromat fish, but were still discernible to tetrachromat bird predators that have visual sensitivity to far red wavelengths. The two species showed opposing trends in background matching with relation to body size. Variation in model parameters revealed that discrimination of crab and background was impacted by distance from the predator, and the ratio of cone cell types for bird predators. This is one of the first studies to detail background color matching in this unique, challenging ecosystem at the air-sea interface.
منابع مشابه
Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery
Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...
متن کاملCrop Land Change Monitoring Based on Deep Learning Algorithm Using Multi-temporal Hyperspectral Images
Change detection is done with the purpose of analyzing two or more images of a region that has been obtained at different times which is Generally one of the most important applications of satellite imagery is urban development, environmental inspection, agricultural monitoring, hazard assessment, and natural disaster. The purpose of using deep learning algorithms, in particular, convolutional ...
متن کاملA New Dictionary Construction Method in Sparse Representation Techniques for Target Detection in Hyperspectral Imagery
Hyperspectral data in Remote Sensing which have been gathered with efficient spectral resolution (about 10 nanometer) contain a plethora of spectral bands (roughly 200 bands). Since precious information about the spectral features of target materials can be extracted from these data, they have been used exclusively in hyperspectral target detection. One of the problem associated with the detect...
متن کاملContinued Development of the Look-up-table (LUT) Methodology For Interpretation of Remotely Sensed Ocean Color Data and Fusion of Hyperspectral Imagery with LIDAR Bathymetry
The overall goal of this work is to refine, validate, and transition a spectrum-matching and look-uptable (LUT) technique for rapidly inverting remotely sensed hyperspectral reflectances to extract environmental information such as water-column optical properties, bathymetry, and bottom classification. The work also seeks to combine hyperspectral imagery and LIDAR bathymetry to improve the capa...
متن کاملAnalysis of Hyperspectral Imagery for Oil Spill Detection Using SAM Unmixing Algorithm Techniques
Oil spill is one of major marine environmental challenges. The main impacts of this phenomenon are preventing light transmission into the deep water and oxygen absorption, which can disturb the photosynthesis process of water plants. In this research, we utilize SpecTIR airborne sensor data to extract and classify oils spill for the Gulf of Mexico Deepwater Horizon (DWH) happened in 2010. For t...
متن کامل