• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.597-609
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• 2012

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.2
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• pp.53-63
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• 2016

Monitoring of cyanobacteria bloom in reservoir systems is important for water managers responsible of water supply system. Cyanobacteria affect the taste and smell of water and pose considerable filtration problems at water use places. Harmful cyanobacteria bloom in reservoir have significant economic impacts. We develop a new method for estimating the cyanobacteria bloom using Landsat TM and ETM+ data. Developed model was calibrated and cross-validated with existing in situ measurements from Daecheong Reservoir's Water Quality Monitoring Program and Algae Alarm System. Measurements data of three stations taken from 2004 to 2012 were matched with radiometrically converted reflectance data from the Landsat TM and ETM+ sensor. Stepwise multiple linear regression was used to select wavelengths in the Landsat TM and ETM+ bands 1, 2 and 4 that were most significant for predicting cyanobacteria cell number and bio-volume. Based on statistical analysis, the linear models were that included visible band ratios slightly outperformed single band models. The final monitoring models captured the extents of cyanobacteria blooms throughout the 2004-2012 study period. The results serve as an added broad area monitoring tool for water resource managers and present new insight into the initiation and propagation of cyanobacteria blooms in Daecheong reservoir.

• Journal of Environmental Impact Assessment
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• v.6 no.1
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• pp.55-66
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• 1997

This study summarizes the use of satellite data to detect the change of southwestern coastal wetlands in Korea. The images used for this study were two Landsat Thematic Mapper(TM) images (June 12, 1984 & June 2, 1992). TM images were used to classify such different types of wetlands as aquatic bed, nonaquatic bed and other land use in the region. Then it, was possible to a) determine the status of wetlands using image classification products, and b) detect the changes of various types of wetlands influenced by both human and nature. The results from spatiotemporal analysis showed that approximately 120 lad of coastal wetlands were lost from the year of 1984 to 1992. 71 % of the lost wetlands were converted to the reclaimed land. This loss of wetlands has been causing the profound environmental impacts. It has been successfully proved that satellite data are very effective for spatiatemporal change analysis, especially for that of coastal wetlands.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.287-290
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• 2002

Real-time flood submerged area map provides important scientific basis for the decision-making of flood control and relieving disaster. Taking the Wuhan area as an example, this article gives out a image interpretation method under influence of flood, and describes real-time or quasi-real-time flood submerged area map by using the integration of ERS-2 SAR image and LANDSAT TM image in support of remote sensing images process software ERDAS.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.87-99
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• 2006

The Median Resolution Sensors (MRSs) for land observation such as Landsat-ETM+ and SPOT-HRV are more effective than Ocean Color Sensors (OCSs) for studying of detailed ecological and biogeochemical components of the coastal waters. In this study, we developed suspended sediment algorithm for Landsat TM/ETM+ by considering the spectral response curve of each band. To estimate suspended sediment concentration (SS) from satellite image data, there are two difference types of algorithms, that are derived for enhancing the accuracy of SS from Landsat imagery. Both empirical and remote sensing reflectance model (hereafter referred to as $R_{rs}$ model) are used here. This study tried to compare two algorithm, and verified using in situ SS data. It was found that the empirical SS algorithm using band 2 produced the best result. $R_{rs}$ model-based SS algorithm estimated higher values than empirical SS algorithm. In this study we used $R_{rs}$ model developed by Ahn (2000) focused on the Mediterranean coastal area. That's owing to the difference of oceanic characteristics between Mediterranean and Korean coastal area. In the future we will improve that $R_{rs}$ model for the Korean coastal area, then the result will be advanced.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.441-444
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• 2005

The goal of this study is to investigate the relationship between microphytobenthic biomass and normalized vegetation index obtained from Landsat TM images. Monitoring a seasonal change of microphytobenthic biomass in the sand bar is specifically focused. Since the study area, Hampyeong Bay, was difficult to approach, we failed to obtain ground truths simultaneously on satellite image acquisition. Instead, chlorophyll-a concentration in surface top layer was measured on different dates for microphytobenthic biomass. Although data were acquired on different dates, a correlation between the field and satellite images was calculated for investigating general trends of seasonal change. NDVI and tasseled cap transformed images were also used to review the variation of microphytobenthic biomass by using Landsat TM and ETM+ images. Atmosphere effects were corrected by applying COST model. Seaweeds were also flouring in the same season of microphytobentic blooming. Songseok-ri area was minimally affected by seaweeds from February to May, and selected as a test site. NDVI value was classified into high-, moderate-, and low-grade. It was well developed over fme-grained sediments and rapidly reduced from May to November over sand bar. In this bay, correlation between grain size and microphytobenthic biomass was clearly seen. From the classified NDVI and tasseled cap transformed data, we finally constructed spatial distribution and seasonal variation maps of microphytobenthic biomass.

• Journal of Environmental Impact Assessment
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• v.11 no.4
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• pp.247-258
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• 2002

This study aimed at verifying the grain-sized distribution of surface deposits in a tidal flat using multi-spectral Landsat TM. In this study, we employed the grain-sized analysis, PCA and unsupervised classification techniques for analyzing the distribution of deposits. As a result in this study, the unsupervised classification method using PCA image was found to be most useful in classifying tidal flat deposits using satellite data. This method is considerably effective in analyzing not only the aspects of distribution in terms of accumulated deposits and erosion, but also the changes in seaside topography and shoreline. The grain-sized distribution analysis indicates that the mud flat inside the Cheonsu-bay tidal flat is distributed, the mixed flat located in the middle, and the sand flat distributed near the sea. The sand flat is dominant around the southern part of Seomot isle and its beach. On the other hand, the mud and mixed flat is dominant on the western part. Likewise, the western coast of Seomot isle and its beach is significantly affected by waves facing the offshore. However, the eastern side of the bay could be a site for the evolution of tidal flat made of fine materials where it is less affected by ocean waves. These results show that multi-spectral satellite data are effective for the classification of distribution materials and environmental impact assessment and continuous monitoring. In particular, the research on environmental deposits can provide important decision-supporting information for decision-making on seaside development, by analyzing the progress of deposits and environmental changes.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.39-48
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• 2011

Global warming causes the climate change and makes severe damage to ecosystem and civilization Carbon dioxide greatly contributes to global warming, thus many studies have been conducted to estimate the forest biomass carbon stock as an important carbon storage. However, more studies are required for the selection and use of technique and remotely sensed data suitable for the carbon stock estimation in Korea In this study, the aboveground forest biomass carbon stocks of Danyang-Gun in South Korea was estimated using $k$NN($k$-Nearest Neighbor) algorithm and regression model, then the results were compared. The Landsat TM and 5th NFI(National Forest Inventory) data were prepared, and ratio images, which are effective in topographic effect correction and distinction of forest biomass, were also used. Consequently, it was found that $k$NN algorithm was better than regression model to estimate the forest carbon stocks in Danyang-Gun, and there was no significant improvement in terms of accuracy for the use of ratio images.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.11-20
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• 2009

Due to increasing frequency of yellow dust, not to mention high rate of precipitation and cloud formation in summer season of Korea, atmospheric correction of satellite remote sensing is necessary. This research analyzes the effect of atmospheric correction has on imagery classification by comparing DN distribution before and after atmospheric correction. The image used in the research is LANDSAT-5 TM. As for atmospheric correction module, commercial product ATCOR, FLAASH as well as COST model released on the internet, were used. The result of experiment shows that class separability increased in building areas.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1329-1331
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• 2003

Improvement of the classification accuracy is one of the major concerns in the field of remote sensing application research in recent years. Previous research shows that the accuracy of the conventional classification methods based only on the original spectral information were usually unsatisfied and need to be refined by manual edit. This present paper describes a method of combining the image processing, ancillary data (such as digital elevation model) and expert knowledge (especially the knowledge of local professionals) to improve the efficiency and accuracy of the satellite image classification in hilly land. Firstly, the Landsat TM data were geo-referenced. Secondly, the individual bands of the image were intensitynormalized and the normalized difference vegetation index (NDVI) image was also generated. Thirdly, a set of sample pixels (collected from field survey) were utilized to discover their corresponding DN (digital number) ranges in the NDVI image, and to explore the relationships between land use type and its corresponding spectral features . Then, using the knowledge discovered from previous steps as well as knowledge from local professionals, with the support of GIS technology and the ancillary data, a set of conditional statements were applied to perform the TM imagery classification. The results showed that the integration of image processing and spatial analysis functions in GIS improved the overall classification result if compared with the conventional methods.