Seagrass beds provide habitat for invertebrate and fish species, many of which are economically important. In addition, they perform important physical functions such as trapping sediment particulates associated with dissipating wave energy, thus are helpful to maintain clear waters. We, here, generated the map of seagrass distribution using remotely sensed images to which atmospheric corrections and water column corrections had been applied. Then, the seagrass habitat distribution changes were calculated by seagrass habitat map. For this study, we selected Deukryang Bay located on the southern coast of the Korean peninsula. It is surrounded by small villages like Jinmok-ri and Ongam-ri. Zostera marina dominated at the bay, small amounts of Z. caulescens and Halophila nipponica are also distributed in this area. The results showed that image classifications to which the water column correction had been applied produced improved accuracies in all the classification algorithms we had employed. The object-based classification algorithm showed the highest accuracy, but it is effective method for the high spatial resolution remotely sensed images, consequently not suitable for monitoring changes of the long-term base. Thus, we applied the Mahalanobis distance method which had been known to suitable for medium spatial resolution images like Landsat. This study revealed that seagrass beds in the study area showed similar pattern of distribution during recent 20 years.
Photosynthesis available radiation (PAR) that makes primary producers to compose carbon compounds is the energy source of carbon circulation at the ocean. In these days, global scale PAR is efficiently observed from satellite remotesensing with low cost and high resolution. Here, Geostationary Ocean Color Imager (GOCI) which is geostationary orbit sensor is used to estimate daily PAR at smaller scale area for decreasing influence of diurnal variation such as cloud. GOCI daily PAR is estimated using PAR model based on Plane-parallel theory and compared with in-situ data observed during year of 2015 at two stations that has turbid and clear ocean area, respectively. Each band image of GOCI L1B data and solar altitude data are input data for PAR model to estimate daily PAR. Correlation coefficient between GOCI daily PAR and in-situ daily PAR is 0.98 and root-mean-square error (RMSE) is 4.50 Ein/m2 /day. To correct underestimated GOCI daily PAR, correction equation is developed from linear regression between GOCI daily PAR and in-situ daily PAR observed during clear sky condition days. RMSE of GOCI daily PAR which corrected with correction equation is decreased to 3.08 Ein/m2 /day and seasonal bias between GOCI and in-situ daily PAR is decreased, too. Validation is carried out with in-situ daily PAR observed during year of 2016. Correlation coefficient is 0.98 and RMSE is 2.69 Ein/m2 /day. Estimating GOCI daily PAR is expected to make accurate daily PAR by reducing meteorological element and regional error.
The design and performance analysis of a new sensor is introduced which is on board a small unmanned aerial vehicle (UAV) for coastal water remote sensing. The top level requirements of sensor are to have at least 4cm spatial resolution at 500m operating height, and 4° field of view (FOV) and 100 signal-to-noise ratio (SNR) value at 660nm. We determined the design requirements that its entrance pupil diameter is 70mm, and F-ratio is 5.0 as an optical design requirement. The three-mirror system is designed including aspheric primary and secondary mirrors, which optical performance are 1/15 λRMS wavefront error and 0.75 MTF value at 660nm. Considering the manufacturing and assembling phase, we performed the sensitivity, tolerance, and stray-light analysis. From these analysis we confirmed this optical system, which is having 4cm spatial resolution at 500m operating height, will be applied with remote sensing researches.
We examined the relations of the channel distribution with the sedimentary facies in Geunso-bay
tidal flat, Korea. The tidal channel networks were extracted from an aerial photograph. The patterns of the
channel distribution were compared with one another for several sites in terms of the fractal analysis, channel
density. The channels in each sediment facies showed relatively constant meandering patterns, however, the
density and the complexity were distinguishable for each facies. The 2nd fractal dimension which indicates the
branch pattern of the tidal channel were 1.87 in the mud flat, 1.41 in the mixed flat, and about 1.30 in the sand
flat. The channel density in the mud flat was 0.035-0.06 m/m2 which was the highest among the three
sedimentary facies.
Using the differences in fractal dimensions and tidal channel densities in each sedimentary facies, we tried
to adjust the sedimentary facies classification which had been generated from the interpolation of the surveyed
data. For each grain size sampling site, the percentage of sand particles was compared with the channel density.
It was shown that the higher the sand percentage, the lower the tidal channel density except at a few points. The
locations showing the exceptional pattern were mainly inside the tidal channel or adjacent to the inland. We
suggest that those differentiated features of tidal channels among the different sedimentary facies should be
applied to the surface sedimentary facies classification in the tidal flat.
The applicability of remotely sensed data to the detection and monitoring of the seasonal
variation of microphytobenthos distribution in a tidal flat was examined for the Geunso-bay tidal flat in the west
coast of Korean peninsula. The biomass of diatom within the surface sediments was estimated through field
campaigns and the seasonal change in the spectral reflectance of the remotely sensed data was investigated.
Field spectrum data were acquired monthly at the fixed locations for monitoring the microphytobenthos
blooming and comparing with the spectral reflectance of satellite images. Sediments facies was also analyzed
along with the spectral reflectance based on the in situ data, and the spectral characteristics of the area where
microphytobenthos occupied was examined. A medium to low spatial resolution of satellite image was not
suitable for the detection of the surface sediments changes in the study area due to its ambiguity of sediments
facies boundary, but the seasonal changes of benthic distribution could be obviously detected. From this, we
suggest that the study on the distribution of surface sedimentary facies and detailed ecological mapping in a tidal
flat based upon the remote sensing images should consider the seasonal variations of microphytobenthos
distribution which would be included in the spectral characteristics of the satellite images.
Conference Committee Involvement (2)
Active and Passive Remote Sensing of Oceans, Seas, and Lakes
2 December 2024 | Kaohsiung, Taiwan
Remote Sensing of the Open and Coastal Ocean and Inland Waters
24 September 2018 | Honolulu, Hawaii, United States
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