• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.98-106
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• 2016

Remote sensing is an efficient technology for observing and monitoring the land surfaces inaccessible to humans. This research proposes a methodology for improving the accuracy of the land cover classification using the Landsat-8 operational land imager(OLI) image. The proposed methodology consists of the following steps. First, the normalized difference vegetation index(NDVI) and normalized difference water index(NDWI) images are generated from the given Landsat-8 OLI image. Then, a new image is generated by adding both NDVI and NDWI images to the original Landsat-8 OLI image using the layer-stacking method. Finally, the maximum likelihood classification(MLC), and support vector machine(SVM) methods are separately applied to the original Landsat-8 OLI image and new image to identify the five classes namely water, forest, cropland, bare soil, and artificial structure. The comparison of the results shows that the utilization of the layer-stacking method improves the accuracy of the land cover classification by 8% for the MLC method and by 1.6% for the SVM method. This research proposes a methodology for improving the accuracy of the land cover classification by using the layer-stacking method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.261-268
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• 2004

Analysis on forest through satellite image data can be obtained from normalized burn ration (NBR) and normalized difference vegetation index (NDVI) from descriptive information of reflection on the earth's surface recorded each waveband. This study focuses on the efficiency of NBR through comparative analysis after obtaining NBR and NDVI of images form 1 you, 2 years and just after the forest fire and the time of forest-preserved of the area before the forest fire in Sacheon myeon, Cangneung City where the forest fro broke out. As a result, it shows dynamic changes with greater range that differences between NBR images rather than differences between NDVI images, which means it would be better to use NBR image for the analysis of the degrees of damages from forest fire or the status of vegetation restoration and also NBR image more distinctly shows both than NDVI image in forest fro damage area.

• Spatial Information Research
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• v.23 no.6
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• pp.31-41
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• 2015

This research is primarily intended to explore a novel way to monitor desiccation of inland wetland by combining MNDWI (Modified Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index). The changes for vegetation and water condition on Upo Wetland located at southeastern Korea were investigated by MNDWI and NDVI derived from 2002, 2010 and 2015 Landsat data. The integrated use of MNDWI and NDVI made it possible to identify area-wide vegetation cover changes and to assess water storage changes on multi-annual time scales simultaneously. Comparing MNDWI with NDVI reveals the quantitative evidences for anthropogenic and environmental influences (such as road, building, water) causing an accelerated wetland desiccation. In fact, our monitoring approach raises critical issues regarding the hydrological cycle and its inter-annual changes for inland wetland under threat of drying up and highlights the important role of MNDWI and NDVI integration for any urgent or long-term treatment plan. This research presents scientific and objective evidences to support integrated approach of NDVI and MNDWI in exploring drying up trends of wetlands.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.3
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• pp.115-123
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• 2008

Evapotranspiration mapping using both meteorological ground-based measurements and satellite-derived information has been widely studied during the last few decades and various methods have been developed for this purpose. It is significant and necessary to estimate regional evapotranspiration (ET) distribution in the hydrology and water resource research. The study focused on analyzing the surface ET of Chungbuk region using Landsat 7 ETM imagery. For this process, we estimated the regional daily evapotranspiration on May 8, 2000. The estimation of surface evapotranspiration is based on the relationship between Temperature Vegetation Dryness Index (TVDI) and Morton's actual ET. TVDI is the relational expression between Normalized Difference of Vegetation Index (NDVI) and Land Surface Temperature (LST). The distribution of NDVI corresponds well with that of land-use/land cover in Chungbuk. The LST of several part of city in Chungbuk region is higher in comparison with the averaged LST. And TVDI corresponds too well with that of land cover/land use in Chungbuk region. The low evapotranspiration availability is distinguished over the large city like Cheongju-si, Chungju-si and the difference of evapotranspiration availability on forest and paddy is high.

• Water for future
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• v.28 no.1
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• pp.71-80
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• 1995

The purpose of this study is to estimate evapotranspiration and its spatial distribution using NOAA-AVHRR data. Evapotranspiration phenomena are exceedingly complex. But, factors which control evapotranspiration can be considered that these are reflected by conditions of the vegetation. To evaluate the vegetation condition as a fixed quantity, the NDVI(Normalized Difference Vegetation Index) calculated from NOAA data is utilized. In this study, land cover classification of the Korean peninsula using property of NDVI is performed. Also, from the relationship between evapotranspiration and NDVI histograms, evapotranspiration and its distribution of the Han River basin are estimated.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.4
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• pp.37-42
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• 2009

This study extracted Near Infrared (NIR) band using Image Processing Technology (IPT), and calculated Normalized Difference Vegetation Index (NDVI). Aerial photography from Daum portal in combination with high resolution satellite image was employed to improve vegetation sensitivity by extracting NIR band and calculating NDVI with comparison to QuickBird result. The extracted NIR band and NDVI through IPT presented similar distribution pattern. In addition, a regression analysis by land cover character showed high correlation paddy and forest Therefore, this approach could be acceptable to acquire vegetation environment information.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.2
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• pp.103-108
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• 2023

Due to the recent impact of global warming, heavy rainfall and droughts have been occurring regardless of the season, affecting the growth of Italian ryegrass (IRG), a winter forage crop. Particularly, delayed sowing due to frequent heavy rainfall or autumn droughts leads to poor growth and reduced winter survival rates. Therefore, techniques to improve yield through additional sowing in spring have been implemented. In this study, the growth of IRG sown in Spring and Autumn was compared and analyzed using vegetation indices during the months of April and May. Spectral data was collected using an Unmanned Aerial Vehicle (UAV) equipped with a hyperspectral sensor, and the following vegetation indices were utilized: Normalized Difference Vegetation Index; NDVI, Normalized Difference Red Edge Index; NDRE (I), Chlorophyll Index, Red Green Ratio Index; RGRI, Enhanced Vegetation Index; EVI and Carotenoid Reflectance Index 1; CRI1. Indices related to chlorophyll concentration exhibited similar trends. RGRI of IRG sown in autumn increased during the experimental period, while IRG sown in spring showed a decreasing trend. The results of RGRI in IRG indicated differences in optical characteristics by sowing seasons compared to the other vegetation indices. Our findings showed that the timing of sowing influences the optical growth characteristics of crops by the results of various vegetation indices presented in this study. Further research, including the development of optimal vegetation indices related to IRG growth, is necessary in the future.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.583-590
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• 2008

This study presents a correlation between timber Age, image bands and vegetation indices for timber age estimation. Basically, this study used Landsat TM images of three difference years (1994, 1994, 1998) and difference between Shuttle Radar Topography Mission (SRTM) and National Elevation Dataset (NED). Bands of 4, 5 and 7, Normalized Difference Vegetation Index (NDVI), Infrared Index (II), Vegetation Condition Index (VCI) and Soil Adjusted Vegetation Index (SA VI) were obtained from Landsat TM images. Tasseled cap - greenness and wetness images were also made by Tasseled cap transformation. Finally, analysis of correlation between timber age, difference between Shuttle Radar Topography Mission (SRTM) and National Elevation Dataset (NED), individual TM bands (4, 5, 7), Normalized Difference Vegetation Index (NDVI), Tasseled cap-Greenness, Wetness, Infrared Index (II), Vegetation Condition Index (VCI) and Soil Adjusted Vegetation Index (SAVI) using regression model. In this study about 1,992 datasets were analyzed. The Tasseled cap - Wetness, Infrared Index (II) and Vegetation Condition Index (VCI) showed close correlation for timber age estimation.

• Journal of Environmental Impact Assessment
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• v.6 no.2
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• pp.181-188
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• 1997

Vegetation is an important factor in EIA(Environmental Impact Assessment) and it is assessed according to DGN(Degree of Green Naturality) in EIS(Environmental Impact Statement) preparation. But DGN has room for improvement of assessing vegetation Status. This study introduced NDVI(Normalized Difference Vegetation Index) for improving status assessment method that subjects to DGN. For the application of NDVI, Landsat TM data of Chunchon on May 2, 1989 and June 1, 1994, and data of Ulsan on November 5, 1984, November 2, 1992 and May 9, 1994 were used. It compared NDVI with DGN according to season and location. The correlation coefficient value for the spring image (1994, 0.7, p=0.01) was proved to be higher than that of autumn (1984, 0.5, p=0.01).

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.1
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• pp.73-83
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• 2016

Forest degradation reduces ecosystem services provided by forest and could lead to change in composition of species. In North Korea, there has been significant forest degradation due to conversion of forest into terrace fields for food production and cut-down of forest for fuel woods. This study analyzed the phenological changes in North Korea, in terms of vegetation and moisture in soil and vegetation, from March to Octorber 2013, using MODIS (MODerate resolution Imaging Spectroradiometer) images and indexes including NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), and NDWI (Normalized Difference Water Index). In addition, marginal farmland was derived using elevation data. Lastly, degraded terrace fields of 16 degree was analyzed using NDVI, NDSI, and NDWI indexes, and marginal farmland characteristics with slope variable. The accuracy value of land cover classification, which shows the difference between the observation and analyzed value, was 84.9% and Kappa value was 0.82. The highest accuracy value was from agricultural (paddy, field) and forest area. Terrace fields were easily identified using slope data form agricultural field. Use of NDVI, NDSI, and NDWI is more effective in distinguishing deforested terrace field from agricultural area. NDVI only shows vegetation difference whereas NDSI classifies soil moisture values and NDWI classifies abandoned agricultural fields based on moisture values. The method used in this study allowed more effective identification of deforested terrace fields, which visually illustrates forest degradation problem in North Korea.