• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.52-63
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• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.735-747
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• 2020

Recent advanced UAV (Unmanned Aerial Vehicle) technology supply new opportunities for estimating crop condition using high resolution imagery. We analyzed the diurnal change of reflectance and NDVI (Normalized Difference Vegetation Index) in UAV imagery for crop monitoring in clear day condition. Multi-spectral images were obtained from a 5-band multi-spectral camera mounted on rotary wing UAV. Reflectance were derived by the direct method using down-welling irradiance measurement. Reflectance using UAV imagery on calibration tarp, concrete and crop experimental sites did not show stable by time and daily reproducible values. But the CV (Coefficient of Variation) of diurnal NDVI on crop experimental sites was less than 5%. As a result of comparing NDVI at the similar time for two day, the daily mean average ratio of error showed a difference of 0.62 to 3.97%. Therefore, it is considered that NDVI using UAV imagery can be used for time series crop monitoring.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1437-1449
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• 2023

The purpose of this study is to analyze whether pervious and impervious areas in urban areas affect tree growth. In order to determine the differences in the growth of six species of trees planted simultaneously, the effects of pervious and impervious surfaces on tree growth were analyzed using the Normalized Difference Vegetation Index (NDVI) produced using Sentinel-2 and sub-divided land cover map from the Ministry of Environment. For this purpose, the Geospatial eXplainable Artificial Intelligence(GeoXAI) concept was applied. As a result of the analysis, the explanatory power of the model was found to be the best when considering the area of land cover included in the 10m range for Pinus densiflora, the 20 m range for Zelkova Serrata, Metasequoia glyptostroboides, and Ginkgo biloba, the 30 m range for Platanus occidentalis, and the 40 m range for Yoshino cherry trees. In addition, the wider the pervious area, the more active the growth of trees,showing a positive correlation, and the wider the impervious area, such as nearby artificial ground, showed a negative correlation with tree growth. This shows that surrounding pervious and impervious areas affect the growth of trees and that the scope of influence varies depending on the tree species.

• Journal of Environmental Impact Assessment
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• v.27 no.3
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• pp.279-290
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• 2018

The importance of monitoring and assessing the status of urban forests in the aspect of urban forest management is emerging as urban forest edges increase due to urbanization and human impacts. The purpose of this study was to investigate the status of vegetation condition of urban forest edge that is affected by different land cover types using $NDVI_{max}$ images derived from FSDAF (Flexible Spatio-temporal DAta Fusion). Among 4 land cover types,roads had the greatest effect on the forest edge, especially up to 30m, and it was found to affect up to 90m in Seoul urban forest. It was also found that $NDVI_{max}$ increased with distance away from the forest edge. The results of this study are expected to be useful for assessing the effects of land cover types and land cover change on forest edges in terms of urban forest monitoring and urban forest management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.107-120
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• 2010

The effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water was assessed using the SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. The fundamental input data (elevation, meteorological data, land use, soil, vegetation) was collected to calibrate and validate of the SLURP model for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang and Gosam) located in Anseongcheon watershed. Then, the CCCma CGCM2 data by SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (intergovernmental panel on climate change) was used to assess the future potential climate change. The future weather data for the year, m ms, m5ms and 2amms was downscaled by Change Factor method through bias-correction using 3m years (1977-2006) weather data of 3 meteorological stations of the watershed. In addition, the future land uses were predicted by modified CA (cellular automata)-Markov technique using the time series land use data fromFactosat images. Also the future vegetation cover information was predicted and considered by the linear regression between monthly NDVI (normalized difference vegetation index) from NOAA AVHRR images and monthly mean temperature using eight years (1998-2006) data.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.449-451
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• 2007

In the commercially managed timber lands, the information such as height, age, stand density, canopy closure and leaf area index need to be collected periodically. Stand volume is the most fundamental information in the valuation of timber, however, stand age information is the primary element of forest inventory and these two are highly correlated. Conventional method of collecting stand age information by field surveys such as ring count method is accurate; however, it is expensive, labor-intensive and time consuming. In the present study it was aimed to collect stand age information using modem techniques in a commercially managed timberland situated in Tennessee, USA. The Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) of three different periods, Shuttle Radar Topography Mission (SRTM), National elevation dataset (NED) and field inventory data were used. Normalized difference vegetation index (NDVI) and Tasselled Cap (TC) transformation techniques were applied on the TM and ETM+ data. The regression analysis was carried out to identify the correlation between stand age and NDVI, TC. In the present study about 2,469 datasets were analyzed. The $R^{2}$ value for stand age estimation was 0.713. The NDVI, TC2 and TC3 were found to produce accurate timber age information.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.29-39
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• 2020

Forest fire is a critical disaster that causes massive destruction of forest ecosystem and economic loss. Hence, accurate estimation of the burned area is important for evaluation of the degree of damage and for preparing baseline data for recovery. Since most of the area size damaged by wildfires in Korea is less than 1 ha, it is necessary to use satellite or drone images with a resolution of less than 10m for detecting the damage area. This paper aims to detect wildfire-damaged area from a Kompsat-3 image using the indices such as NDVI (normalized difference vegetation index) and FBI (fire burn index) and to examine the classification characteristics according to the methods such as Otsu thresholding and ISODATA(iterative self-organizing data analysis technique). To mitigate the salt-and-pepper phenomenon of the pixel-based classification, a gaussian filter was applied to the images of NDVI and FBI. Otsu thresholding and ISODATA could distinguish the burned forest from normal forest appropriately, and the salt-and-pepper phenomenon at the boundaries of burned forest was reduced by the gaussian filter. The result from ISODATA with gaussian filter using NDVI was closest to the official record of damage area (56.9 ha) published by the Korea Forest Service. Unlike Otsu thresholding for binary classification,since the ISODATA categorizes the images into multiple classes such as(1)severely burned area, (2) moderately burned area, (3) mixture of burned and unburned areas, and (4) unburned area, the characteristics of the boundaries consisting of burned and normal forests can be better expressed. It is expected that our approach can be utilized for the high-resolution images obtained from other satellites and drones.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.31-40
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• 2020

The rapid development of technologies in unmanned aerial vehicles (UAVs) has led to their use in various areas. UAVs are mainly used for commercial purposes, but their utilization is increasingly important in other areas because their operation cost is less than satellites and aerial imaging. The utilization of UAVs in the environment/ecology area is relatively new. Therefore, identifying the trends of UAV-related spatial information is significant in basic research for UAV utilization. This study quantitatively identified domestic and international research trends related to UAV utilization and analyzed research areas. An attempt was also made to identify upcoming UAV-related topics in the environment/ecology research field using text mining to analyze the bibliographic information of global research literature. Domestic UAV-related studies were classified into seven clusters where basic research on "UAV technology/industry trends" was abundant, and studies on data collection and analysis through UAV remote sensing technology have increased since 2015. Eight clusters were identified for international studies where the most active research area international was "remote sensing technology/data analysis". In addition, Canopy, Classification, Forest, Leaf Area Index, Normalized Difference Vegetation Index, Temperature, Tree, and Atmosphere appeared as the main keywords related to environment and ecology. The appearance frequencies and association strengths were high because the advancement in UAV optical sensor technology and the rapid development of image processing technology enabled the acquisition of data that could not be obtained from existing spatial information. They are recognized as future research topics as related domestic studies have begun corresponding to international research.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.3
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• pp.217-224
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• 2014

Ground-based remote sensing can be used as one of the non-destructive, fast, and real-time diagnostic tools for predicting yield, biomass, and nitrogen stress during growing season. The objectives of this study were: 1) to assess biomass and nitrogen (N) status of tobacco (Nicotiana tabacum L.) plants under N stress using ground-based remote sensors; and 2) to evaluate the feasibility of spectral reflectance indices for estimating an application rate of N and predicting yield of tobacco. Dry weight (DW), N content, and N uptake at the 40th and 50th day after transplanting (DAT) were positively correlated with chlorophyll content and normalized difference vegetation indexes (NDVIs) from all sensors (P<0.01). Especially, Green NDVI (GNDVI) by spectroradiometer and Crop Circle-passive sensors were highly correlated with DW, N content and N uptake. The yield of tobacco was positively correlated with canopy reflectance indices measured at each growth stage (P<0.01). The regression of GNDVI by spectroradiometer on yield showed positively quadratic curve and explained about 90% for the variability of measured yield. The sufficiency index (SI) calculated from data/maximum value of GNDVI at the $40^{th}$ DAT ranged from 0.72 to 1.0 and showed the same positively quadratic regression with N application rate explaining 84% for the variability of N rate. These results suggest that use of reflectance indices measured with ground-based remote sensors may assist in determining application rate of fertilizer N at the critical season and estimating yield in mid-season.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.3
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• pp.197-204
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• 2017

This study was carried out to apply the UAV(Unmanned Aerial Vehicle) coupled with Multispectral sensor for the algae bloom monitoring in river. The study acquired remote sensing data using UAV on the midstream area of Gum River, one of four major rivers in South Korea. Normalized difference vegetation index (NDVI) is used for monitoring algae change. This study conducted water sampling and analysis in the field for correlating with NDVI values. Among the samples analyzed, the chlorophyll concentration exhibited strong and significant linear relationships with NDVI, and thus NDVI was chosen for algae bloom index to identify emergence aspect of phytoplankton in river. Aerial remote sensing technology can provide more accurate, flexible, cheaper, and faster monitoring methods of detecting and predicting eutrophication and therefore cyanobacteria bloom in water reservoirs compared to currently used technology. As a result, there was high level of correlation in chlorophyll-a and NDVI. It is expected that when this remote water quality and pollution monitoring technology is applied in the field, it would be able to improve capabilities to deal with the river water quality and pollution at the early stage.