• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1298-1300
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• 2003

Based on the Leaf Area Index (LAI) data derived from remote sensing information and eco-climate data, the responses of regional ecosystem variations in seasonal and interannual scales to the East Asian monsoon are studied in this paper. It is found that the vegetation ecosystems of eastern China are remarkably correlated with the East Asian monsoon in seasonal and interannual scales. In the seasonal timescale, the obvious variations of the vegetation ecosystems occur with the development of the East Asian monsoon from the south in the spring to the north in the autumn. In the interannual scale, high LAI appears in the strong East Asian monsoon year, whereas low LAI is related to the weak East Asian monsoon year. These further lead to the characteristic of 'onsoon-driven ecosystem' in the eastern China monsoon region, which can be revealed by LAI.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.120-122
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• 2003

Geographical position and associated climatic influences can be a negative environmental condition that affects sustainable use of land resources, especially pastoral livestock production. Vegetation condition of the country is sensitively changes upon climate changes and human impacts. Within last 60 years data the annual air temperature has increased in 1.66 degrees in average and the total precipitation amount had almost no change. The main goal of this work is to relate climate change within last 20 years with pasture condition, estimated by NOAA/NDVI data set.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.682-686
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• 2003

This paper is concerned with 'reverse difference' of marine sediments at the Camau tongue in the extreme south of Vietnam. We demonstrate the importance of remote sensing in geomorphology and marine geological application, using only visual evaluation and some data-processing techniques. In this paper, about 10,000 km$^2$ of the territorial water in the extreme south of Vietnam is being studied. We show that form and behavior of Mekong and its branch can be determined by visually interpreting remote sensing images and using ERDAS IMAGE 8.5 software. Besides, the 'reverse difference' phenomenon is explained by flows of Mekong river and its branches.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.34-37
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• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.691-693
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• 2005

The prototype of the radiometric calibration algorithm, including the correction of scan mirror's angle, has been developed for the stationary meteorological sensor, firstly in Korea. We use this system on GOES-12 to evaluate two coefficients, slope and intercept. The evaluated coefficients show good agreement with the NESDIS's results for the five-case data. The calculated coefficients have been applied to the conversion from the measured counts to the radiance and the converting methods according to the scanning are investigated to enhance the radiometric accuracy.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.170-173
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• 2004

When the data from the artificial satellite is analyzed, recent years it is perceived to vegetation index using BRF(Bi-directional Reflectance Factor) of the observation target. To make the BRF models, it is important to measure the 3D structure of the observation target actually. In this study, it is proposed to the observation technique by using multi-angle stereo pair image, and shown the observation result in grassland area. Also, our team has been operating the radio controlled helicopter which can fly over the tall forest canopy and it can be equipped the measurement system.

• Proceedings of the KSRS Conference
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• v.1
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• pp.332-335
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• 2006

Red tide(harmful algae) in the Korean Coastal Waters has a given a great damage to the fishery every year. However, the aim of our study understands the influence of meteorological factors (air and water temperature, precipitation, sunshine, solar radiation, winds) relating to the mechanism of red tide occurrence and monitors red tide by satellite remote sensing, and analyzes the potential area for red tide occurrence by GIS. The meteorological factors have directly influenced on red tide formation. Thus, We want to predict and apply to red tide formation from statistical analyses on the relationships between red tide formation and meteorological factors. In future, it should be realized the near real time monitoring for red tide by the development of remote sensing technique and the construction of integrated model by the red tide information management system (the data base of red tide - meteorological informations). Finally our purpose is support to the prediction information for the possible red tide occurrence by coastal meteorological information and contribute to reduce the red tide disaster by the prediction technique for red tide.

• Proceedings of the KSRS Conference
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• v.2
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• pp.884-887
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• 2006

This paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing, and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps, and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub-basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 minutes, considering rainfall duration, peak discharge, and flooding in the Yeongdeok basin.

• Proceedings of the KSRS Conference
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• v.2
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• pp.543-546
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• 2006

NDVI has been very frequently used to estimate several biophysical parameters that are required for ecosystem models. Leaf area index (LAI), canopy closure, and biomass are among those biophysical parameters that are estimated by empirical relationship with NDVI. However, the type of remote sensing signals (raw DN value, at-sensor radiance, atmospherically corrected reflectance) used can vary the calculation of NDVI. In this study, we tried to attempt to compare the influence of NDVI linked with forest LAI for the watershed-scale ecosystem models to estimate evapotranspiration. Landsat ETM+ data were used to obtain various NDVI values over the study area in central Korea. The NDVI-based LAI and the resultant evapotranspiration estimation were greatly varied by the remote sensing signal applied.