Title of article :
Quantitative estimation of intertidal sediment characteristics using remote sensing and GIS
Author/Authors :
Choi، نويسنده , , Jong-Kuk and Ryu، نويسنده , , Joo Hyung and Lee، نويسنده , , Yoon-Kyung and Yoo، نويسنده , , Hong-Rhyong and Woo، نويسنده , , Han Jun and Kim، نويسنده , , Chang Hwan، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2010
Abstract :
High spatial resolution satellite data (IKONOS) combined with in situ data was used to quantitatively estimate the spatial distribution of tidal flat characteristics for the Hwangdo tidal flat, Cheonsu Bay, Korea. The classification result was accurate in terms of a comparison with the in situ data, and the overall accuracy was 90.7%, which confirmed the validity of the classification. GIS analysis based on a probabilistic model was applied to a quantitative estimation of the relationship between each surface sediment facies and the spectral reflectance. Mud flat facies showed a high positive correlation (R2 = 0.91), and sand flat facies showed a high negative correlation (R2 = 1.00), which was a good reflection of the sedimentary characteristics of Hwangdo tidal flat. Relationships between each sediment facies and DEM also showed good agreement with the topographic characteristics in the study area. The study revealed that intertidal surface sediment classification using high resolution remote sensing imagery and in situ data successfully shows spectral and topographic characteristics of the study area. It was noted that spectral reflectance was affected by a combination of environmental factors, including grain size, topography, and remnant surface water. It is possible to determine the type of tidal flat through quantitative estimates of the spatial distribution of surface sediments according to their spectral reflectance.
Keywords :
IKONOS , intertidal DEM , Hwangdo tidal flat , GIS-based frequency ratio model , surface sedimentary facies
Journal title :
Estuarine, Coastal and Shelf Science
Journal title :
Estuarine, Coastal and Shelf Science