• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.161-170
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• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1483-1490
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• 2023

We analyzed the potential for joint utilization of Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery Normalized Difference Vegetation Index (NDVI) in crop assessment, considering the aging of MODerate resolution Imaging Spectroradiometer (MODIS) satellites. Over 11 years from 2012 to 2022, we examined the characteristics of NDVI changes in corn and soybean cultivation areas in Illinois, USA. VIIRS and MODIS satellite imagery NDVI exhibited a high correlation coefficient of over 0.98. However, during periods of rapid crop growth or decline, VIIRS NDVI showed values approximately 0.12 to 0.14 higher than MODIS. Estimating crop anomaly classes based on NDVI, we observed similar trends in corn and soybean crop anomaly classes in 2018 and 2019. However, in 2022, there appeared to be a significant divergence in crop anomaly classes, suggesting the need for further investigation. The correlation coefficients between MODIS and VIIRS satellite imagery NDVI and corn and soybean yields were consistently high, exceeding 0.8, indicating the potential for quantity estimation using both MODIS and VIIRS satellite imagery. Specifically, for VIIRS NDVI, excluding the increasing trend in crop quantity estimation for soybeans enhanced the correlation, and compared to MODIS, it showed a consistently high correlation with quantity from approximately 16 days earlier, indicating the potential for early estimation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.493-504
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• 2010

This study proposes a classification method based on an automated training extraction procedure that may be used with very high resolution (VHR) images of non-accessible areas. The proposed method overcomes the problem of scale difference between VHR images and geographic information system (GIS) data through filtering and use of a Landsat image. In order to automate maximum likelihood classification (MLC), GIS data were used as an input to the MLC of a Landsat image, and a binary edge and a normalized difference vegetation index (NDVI) were used to increase the purity of the training samples. We identified the thresholds of an NDVI and binary edge appropriate to obtain pure samples of each class. The proposed method was then applied to QuickBird and SPOT-5 images. In order to validate the method, visual interpretation and quantitative assessment of the results were compared with products of a manual method. The results showed that the proposed method could classify VHR images and efficiently update GIS data.

• Korean Journal of Agricultural Science
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• v.37 no.1
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• pp.81-86
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• 2010

The urban thermal environment can be an important index to detect heat island phenomena and manage it to improve urban life quality. Cheongju is a typical plain-city that main part has been formed and developed in lowland. The Mushim stream crosses the city from south to north. We reviewed the use of thermal remote sensing in stream around areas and the thermal environments, focusing primarily on the Urban Heat Island(UHI) effect. The purpose of this study is to determine the relationship between the stream nearby urban area and the stream cooling effect of UHI. The objectives are to determine the usefulness of KOMPSAT-2 bands MS3 and MS4 for vegetation cover mapping, and the usefulness of LANDSAT TM band 6 in identifying thermal environmental characteristics and UHI. Land Surface Temperatures (LST) are retrieved by single-channel algorithm to study the UHI from the 6th band (thermal infrared band) of LANDSAT TM images and thermal radiance thermometer based on remote sensing method and the LST distribution maps are accomplished according to the retrieval results. There is also comparison of satellite-derived and in situ measured temperature. The results indicated that the LST of urban center is higher than that of suburban area, the temperature of mountain and water are the lowest area, so it is clearly proved that there are obvious UHI effects by stream. The surface temperature distribution of Mushim stream is detected $2^{\circ}C$ lower than urban area.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.653-664
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• 2010

The land surface reflectance is a key parameter influencing the climate near the surface. Therefore, it must be determined with sufficient accuracy for climate change research. In particular, the characteristics of the bidirectional reflectance distribution function (BRDF) when using earth observation system (EOS) are important for normalizing the reflected solar radiation from the earth's surface. Also, wide swath satellites like SPOT/VGT (VEGETATION) permit sufficient angular sampling, but high resolution satellites are impossible to obtain sufficient angular sampling over a pixel during short period because of their narrow swath scanning. This gives a difficulty to BRDF model based reflectance normalization of high resolution satellites. The principal objective of the study is to add BRDF modeling of high resolution satellites and to supply insufficient angular sampling through identifying BRDF components from SPOT/VGT. This study is performed as the preliminary data for apply to high-resolution satellite. The study provides surface parameters by eliminating BRD effect when calculated biophysical index of plant by BRDF model. We use semi-empirical BRDF model to identify the BRD components. This study uses SPOT/VGT satellite data acquired in the S1 (daily) data. Modeled reflectance values show a good agreement with measured reflectance values from SPOT satellite. This study analyzes BRD effect components by using the NDVI(Normalized Difference Vegetation Index) and the angle components such as solar zenith angle, satellite zenith angle and relative azimuth angle. Geometric scattering kernel mainly depends on the azimuth angle variation and volumetric scattering kernel is less dependent on the azimuth angle variation. Also, forest from land cover shows the wider distribution of value than cropland, overall tendency is similar. Forest shows relatively larger value of geometric term ($K_1{\cdot}f_1$) than cropland, When performed comparison between cropland and forest. Angle and NDVI value are closely related.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.384-390
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• 2015

Remote sensing can be used to provide information about the monitoring of crop situation. This study was conducted to estimate the amount of nitrogen present in paddy fields by measuring the amount of nitrogen in hairy vetch using an UAV (Unmaned Aerial Vehicle). NDVIs (Normalized Difference Vegetation Index) were calculated using UAV images obtained from paddy fields in Seocheon on May $14^{th}$ 2015. There was strong relationship between UAV NDVI and the amount of nitrogen in hairy vetch ($R^2=0.79$). Spatial distribution maps of green manure nitrogen were generated on each paddy field using the nitrogen-vegetation index relations to help farmers determine the amount of N fertilizers added to their rice fields after the application of green manure such as hairy vetch.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.385-394
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• 2015

Pine wilt disease has greatly damaged pine forests not only in East Asia including South Korea and China, but also in European region. The damage caused by pine wood nematode (Bursaphelenchus xylophilus) is expressed in bundles within stands and rapidly spreading, however, present field survey methods have limitations to detecting damaged trees at regional level. This study extracted the damaged trees by pine wilt disease using time series hyperspectral aerial photographs, and analyzed their distribution characteristics. Hyperspectral aerial photographs of 1 meter spatial resolution were obtained in June, September, and October. Damaged trees by pine wilt disease were extracted using Normalized Difference Vegetation Index (NDVI) and Vegetation Index green (VIgreen) of the September photograph. Among extracted damaged trees, dead trees with leaves and without leaves were classified, and the spectral reflectance values from the photographs obtained in June, September, and October were compared to extract new outbreaks in September and October. Based on the time series dispersion of extracted damaged trees, nearest neighbor analysis was conducted to analyze distribution characteristics of the damaged trees within the region where hyperspectral aerial photographs were acquired. As a result, 2,262 damaged trees were extracted in the study area, and 604 dead trees (dead trees in last year) with leaves in relation to the damaged time and 300 and 101 newly damaged trees in September and October were classified. The result of nearest neighbor analysis using the data shows that aggregated distribution was the dominant pattern both previous and current year in the study area. Also, 80% of the damaged trees in current year were found within 60 m of dead trees in previous year.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.505-510
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• 2014

Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.295-303
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• 2010

The variability of vegetation is strongly related to the variability of hydrometeorological factors such as precipitation, temperature, runoff and so on. Analysis of the variability of vegetation will aid to understand the regional impact of climate change. Thus we analyzed the spatial-temporal variability of NOAA(National Oceanic and Atmospheric Administration)/AVHRR(Advanced Very High Resolution Radiometer) NDVI(Normalized Difference Vegetation Index). In the results from Mann-Kendall test, there is no significant linear trend of annual NDVI from 1982 to 2006 in the most area except the downward trend on the significance level 90% in the Guem-river basin area. In addition, using EOF(Empirical Orthogonal Function) analysis, the variability of NDVI in the region of higher latitude and altitude is higher than that in the other region since the spatial variability of NDVI follows the latitudinal gradient. Also we could get higher NDVI in June, July, August and September. We had the highest NDVI in Han-river basin area and the lowest in Je-Ju island.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.