• Korean Journal of Remote Sensing
• /
• v.31 no.2
• /
• pp.149-160
• /
• 2015

A comparative study was conducted for alternative consecutive procedures of detection of cloud-contaminated pixels and gap-filling and smoothing of time-series data to produce high-quality gapless satellite vegetation index (i.e. Normalized Difference Vegetation Index, NDVI). Performances of five alternative methods for detecting cloud contaminations were tested with ground-observed cloudiness data. The data gap was filled with a simple linear interpolation and then, it was applied two alternative smoothing methods (i.e. Savitzky-Golay and Wavelet transform). Moderate resolution imaging spectroradiometer (MODIS) data were used in this study. Among the alternative cloud detection methods, a criterion of MODIS Band 3 reflectance over 10% showed best accuracy with an agreement rate of 85%, which was followed by criteria of MODIS Quality assessment (82%) and Band 3 reflectance over 20% (81%), respectively. In smoothing process, the Savitzky-Golay filter was better performed to retain original NDVI patterns than the wavelet transform. This study demonstrated an operational framework of gapdetection, filling, and smoothing to produce high-quality satellite vegetation index.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1128-1130
• /
• 2003

This study investigated interannual variations in Northeastern Asian vegetation activity inferred from NOAA/AVHRR data during 1984 to 1993. Firstly, original NOAA/AVHRR data was radiometrically and atmospherically corrected in order to produce a consistent and calibrated time series NDVI by eliminating the effect of atmospheric effects and sensor degradation. Next, the NDVI data was analyzed to detect terrestrial ecosystem responses to climate change. A larger increase in growing season NDVI magnitude was observed in Northeastern Asia. Especially, vegetation activity is increasing in north part of Northeastern Asia. However, satellite drift and eruption effect have affect on interannual NDVI variations and it has affected the result in some degree. To improve accuracy of the result, it is necessary to correct these effects.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1400-1402
• /
• 2003

The objective of this paper is to determine the suitable spectral bands for monitoring growth status change during a long period. The long-term ground-level reflectance spectra as well as LAI and biomass were obtained in xiaotangshan area, Beijing, 2001. The narrow-band NDVI type spectral indices by all possible two bands were calculated their correlation coefficients R$^2$ with biomass and LAI. The best NDVIs must have higher R$^2$ with both biomass and LAI. The reasonable band centers and band widths were determined by a systematically increasing bandwidth centered over a wavelength. In addition, the first 19 bands of MODIS were simulated and investigated. Each developed spectral indices was then validated by the biomass and LAI time series using the generalized vector angle. It turned out that six new NDVI type indices within 750-1400nm were developed. NDVI(811_10,957_10) and NDVI(962_10,802_10) performed best. No satisfactory conventional NDVI formed by red and NIR bands were found effective. MODIS_NDVI(band19, band17) and MODIS_NDVI(band19, band2) were much better than MODIS_NDVI(band2,band1) for growth monitoring.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.20 no.6
• /
• pp.151-160
• /
• 2017

This study was conducted by using a Nikon Coolpix S800c camera equipped with a NIR filter to measure the NDVI(Normalized Difference Vegetation Index). It was used for the measurement of the three trees of Pinus densiflora, Taxus cuspidata, Zelkova serrata and Acer palmatum in Kangwon National University. The NDVI value of the surface of the building was compared and analyzed. The average value of NDVI in August and September was high in all species. The NDVI distribution of Taxus cuspidata was higher than the other trees. The NDVI distribution of Pinus densiflora and Taxus cuspidata did not show any significant seasonal differences, but The NDVI distribution of Zelkova serrata and Acer palmatum were relatively low in May and June, which are leafless periods. Previous studies related to NDVI value were generally analyzed using satellite imagery. However, it was scarce related to study the NDVI value of each tree or study the changing process of NDVI by time series. Previous studies have used NDVI values on the ground but this study used NDVI values in the ground section. Future studies will be necessary to measure the NDVI value at different times for various species and also to make efforts to generalize the measurement method. In addition, research related to various fields such as the relationship between NDVI and carbon stocks and the relationship with LAI needs to be conducted.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.3
• /
• pp.1-18
• /
• 2018

The purpose of this study is to analyze sensitivity of forest ecosystem to climate change using spatial analysis methods focused on 6 national parks. To analyze, we constructed MODIS NDVI and temperature of Korea Meteorologic Administration based on 1km spatial resolution and 16 days. And we conducted time-series and correlation analysis using MODIS NDVI and temperature. A most sensitive region to climate change is Jirisa National Park(r=0.434) and Seoraksan National Park(r=0.415), there is the highest mean correlation coefficient. The sensitivity of forest ecosystem varied according to habitat characteristics and forest types in national park. In Abies koreana of Hallsan Nation Park, temperature has raised, but NDVI has decreased. these results will be based data of climate change adaption policy for protecting forest ecosystem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.2
• /
• pp.239-246
• /
• 2010

Satellite image has capability of getting a broad data rapidly. It is possible that acquisition of change information about topography, land, ecosystem and urbanization etc. from multi-temporal satellite Images. In this study, the time-series change of vegetation has detected using four period Landsat Imageries. Also, NDVI was used to recognize the vitality of vegetation. Time series change of vegetation about study area was able to detect effectively by the results of classification and NDVI. It is expected that this study should be utilized as the decision making related to the effective management and plan establishment.

• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.1
• /
• pp.113-121
• /
• 2015

Remote sensing technology using a multi-spectral satellite imagery can be utilized for the analysis of urban heat island effect in large area. However, weather condition of Korea mostly has a lot of clouds and it makes periodical observation using time-series of satellite images difficult. For this reason, we proposed the analysis of urban heat island effect using time-series of Landsat TM images and ATC model. To analyze vegetation condition and urbanization, NDVI and NDBI were calculated from Landsat images. In addition, land surface temperature was calculated from thermal infrared images to estimate the parameters of ATC model. Furthermore, the parameters of ATC model were compared based on the land cover map created by Korean Ministry of Environment to analyze urban heat island effect relating to the pattern of land use and land cover. As a result of a correlation analysis between calculated spectral indices and parameters of ATC model, MAST had high correlation with NDVI and NDBI (-0.76 and 0.69, respectively) and YAST also had correlation with NDVI and NDBI (-0.53 and 0.42, respectively). By comparing the parameters of ATC model based on land cover map, urban area had higher MAST and YAST than agricultural land and grassland. In particular, residential areas, industrial areas, commercial areas and transportation facilities showed higher MAST than cultural facilities and public facilities. Moreover, residential areas, industrial areas and commercial areas had higher YAST than the other urban areas.

• Korean Journal of Remote Sensing
• /
• v.31 no.2
• /
• pp.51-64
• /
• 2015

We improved the MODerate resolution Imaging Spectroradiometer (MODIS) land cover map over the Asia-Oceania region through the reclassification of the misclassified pixels. The misclassified pixels are defined where the number of land cover types are greater than 3 from the 12 years of MODIS land cover map. The ratio of misclassified pixels in this region amounts to 17.53%. The MODIS Normalized Difference Vegetation Index (NDVI) time series over the correctly classified pixels showed that continuous variation with time without noises. However, there are so many unreasonable fluctuations in the NDVI time series for the misclassified pixels. To improve the quality of input data for the reclassification, we corrected the MODIS NDVI using Correction based on Spatial and Temporal Continuity (CSaTC) developed by Cho and Suh (2013). Iterative Self-Organizing Data Analysis (ISODATA) was used for the clustering of NDVI data over the misclassified pixels and land cover types was determined based on the seasonal variation pattern of NDVI. The final land cover map was generated through the merging of correctly classified MODIS land cover map and reclassified land cover map. The validation results using the 138 ground truth data showed that the overall accuracy of classification is improved from 68% of original MODIS land cover map to 74% of reclassified land cover map.

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

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.6
• /
• pp.101-115
• /
• 2021

The purpose of this study is to evaluate the compatibility of the vegetation index between the two satellites and the applicability of agricultural monitoring by comparing and verifying NDVI (Normalized Difference Vegetation Index) based on Sentinel-2 and Terra MODIS (Moderate Resolution Imaging Spectroradiometer). Terra MODIS NDVI utilized 16-day MOD13Q1 data with 250 m spatial resolution, and Sentinel-2 NDVI utilized 10-day Level-2A BOA (Bottom Of Atmosphere) data with 10 m spatial resolution. To compare both NDVI, Sentinel-2 NDVIs were reproduced at 16-day intervals using the MVC (Maximum Value Composite) technique. As a result of time series NDVIs based on two satellites for 2019 and compare by land cover, the average R² (Coefficient of determination) and RMSE (Root Mean Square Error) of the entire land cover were 0.86 and 0.11, which indicates that Sentinel-2 NDVI and MODIS NDVI had a high correlation. MODIS NDVI is overestimated than Sentinel-2 NDVI for all land cover due to coarse spatial resolution. The high-resolution Sentinel-2 NDVI was found to reflect the characteristics of each land cover better than the MODIS NDVI because it has a higher discrimination ability for subdivided land cover and land cover with a small area range.