Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies

Abstract

ABSTRACT:Aquatic macrophytes (AM) can serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various AM were investigated to determine whether species could be discriminated by remote sensing. In this study the spectral readings of different AM communities identified were done using the ASD Fieldspec® Hand Held spectro-radiometer in the wavelength range of 325 -1075nm. The collected specific reflectance spectra were applied to space borne multi-spectral remote sensing data from Worldview-2, acquired on 26 th March 2011. The dimensionality reduction of the spectro-radiometric data was done using the technique principal components analysis (PCA). Out of the different PCA axes generated, 93.472 % variance of the spectra was explained by the first axis. The spectral derivative analysis was done to identify the wavelength where the greatest difference in reflectance is shown. The identified wavelengths are 510, 690, 720, 756, 806, 885, 907 and 923 nm. The output of PCA and derivative analysis were applied to Worldview-2 satellite data for spectral subsetting. The unsupervised classification was used to effectively classify the AM species using the different spectral subsets. The accuracy assessment of the results of the unsupervised classification and their comparison were done. The overall accuracy of the result of unsupervised classification using the band combinations Red-Edge, Green, Coastal blue & Red-edge, Yellow, Blue is 100%. The band combinations NIR-1, Green, Coastal blue & NIR-1, Yellow, Blue yielded an accuracy of 82.35%. The existing vegetation indices and new hyper-spectral indices for the different type of AM communities were computed. Overall, results of this study suggest that high spectral and spatial resolution images provide useful information for natural resource managers especially with regard to the location identification and distribution mapping of macrophyte species and their communities.

Spatio-Temporal Water Hyacinth Monitoring in the Lower Mondego (Portugal) Using Remote Sensing Data

“…Nevertheless, some studies presented different correlation coefficients over time, depending on the vegetative development [35]. Concerning water hyacinth, John and Kavya [21] also found out that NDVI showed similar values to the ones obtained in this study. However, considering the greater spatial resolution that can be provided by UAV-based data, vegetation-filtering operations can be applied to the thousands of pixels that can fit in a single Sentinel-2 10 m 2 cell [36,37].…”

“…With temporal data it was possible to estimate the water hyacinth coverage in each period and its increase. Commercial platforms as WorldView-2 (0.46 m and 1.84 m of spatial resolution for panchromatic and multispectral data, respectively) were also used [21,22]. John and Kavya [21] used WorldView-2 to map aquatic macrophyte communities, including water hyacinth, in the Vembanad estuary (India), using unsupervised classification based on an iterative self-organizing data analysis technique (ISODATA) algorithm.…”

“…Commercial platforms as WorldView-2 (0.46 m and 1.84 m of spatial resolution for panchromatic and multispectral data, respectively) were also used [21,22]. John and Kavya [21] used WorldView-2 to map aquatic macrophyte communities, including water hyacinth, in the Vembanad estuary (India), using unsupervised classification based on an iterative self-organizing data analysis technique (ISODATA) algorithm. Damtie, Mengistu and Meshesha [23] monitored Lake Tana (Ethiopia), using four images, one for each season, from Sentinel-2 (10 m spatial resolution), observing the effect of this invasive plant on water loss by evapotranspiration increase and mapping its presence using a maximum likelihood classifier.…”