• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.23-30
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• 2017

NDVI (Normalized Difference Vegetation Index) plays an important role in surface land cover classification and LST (Land Surface Temperature Extraction). Its characteristics do not full carry the information of the surface cover typically in urban areas even though it is widely used in analyses in urban areas as well as in vegetation. However, abnormal NDVI values are frequently found in urban areas. We, therefore, examined NDVI values on whether NDVI is appropriate for LST and whether there are considerations in NDVI analysis typically in urban areas because NDVI is strongly related to the surface emissivity calculation. For the study, we observed the influence of the surface settings (i.e., geometric shape and color) on NDVI values in urban area and transition features between three land cover types, vegetation, urban materials, and water. Interestingly, there were many abnormal NDVI values systematically derived by the surface settings and they might influence on NDVI and eventually LST. Also, there were distinguishable transitions based on the mixture of three surface materials. A transition scenario was described that there are three transition types of mixture (urban material-vegetation, urban material-water, and vegetation-water) based on the relationship of NDVI and LST even though they are widely distributed.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.1
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• pp.19-24
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• 2010

The purpose of this study is to estimate monthly Morton evapotranspiration (ET) using normalized difference vegetation index (NDVI) from MODIS satellite images. Morton ET for land surface conditions was evaluated by using daily meteorological data, and the monthly averaged Morton ETs for each land cover were compared with the monthly NDVIs of three years (2000-2002) at Chungjudam Watershed. There was a high correlation between monthly NDVI and Morton ET for the watershed with average coefficient of determination, 0.80. By comparing the MODIS NDVI ET with SLURP Morton ET, the SLURP ET was smaller than the MODIS NDVI ET. This was estimated from the consideration of soil moisture condition for the ET occurrence in the SLURP model, the limited information from the monthly NDVI values, and the errors from the derived regression equations.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1231-1244
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• 2022

The frequency of exposure of field crops to stress situations is increasing due to abnormal weather conditions. In South Korea, large-scale diseases in representative paddy rice cultivation area were happened. There are limits to field investigation on the crop damage due to large-scale. Satellite-based remote sensing techniques are useful for monitoring crops in cities and counties, but the sensitivity of vegetation index measured from satellite under abnormal growth of crop should be evaluated. The goal is to evaluate satellite-based normalized difference vegetation index (NDVI) retrieved from different spatial scales using drone imagery. In this study, Sentinel-2 and Landsat-8 satellites were used and they have spatial resolution of 10 and 30 m. Drone-based NDVI, which was resampled to the scale of satellite data, had correlation of 0.867-0.940 with Sentinel-2 NDVI and of 0.813-0.934 with Landsat-8 NDVI. When the effects of bias were minimized, Sentinel-2 NDVI had a normalized root mean square error of 0.2 to 2.8% less than that of the drone NDVI compared to Landsat-8 NDVI. In addition, Sentinel-2 NDVI had the constant error values regardless of diseases damage. On the other hand, Landsat-8 NDVI had different error values depending on degree of diseases. Considering the large error at the boundary of agricultural field, high spatial resolution data is more effective in monitoring crops.

• Journal of Environmental Impact Assessment
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• v.19 no.3
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• pp.297-305
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• 2010

This study has intended to analyze the nature of the errors that occur as a result of shadows during the process of NDVI calculation using high-resolution satellite images of Cheongju City, in order to calibrate such errors, and to verify the results. This study has calibrated the shadow errors by utilizing the relationship between the Greenness above Bare Soil (GRABS) calculated through Tasseled-Cap transformation and the original NDVI. To verify the accuracy of the results, this study has compared the shadow area extracted by the difference between before and after calibration of NDVI, with the original shadow area. The NDVI value converged on the value of -1.0, representing water, because shadow areas could not accept the reflection value from each band. However, after performing Tasseled-Cap transformation, the NDVI of shadow areas that had converged on -1.0 prior to calibration had increased to a level similar to the NDVI of neighboring areas. In addition, the average NDVI in general had increased from -0.08 to -0.01. Finally, the shadow area drawn out was almost matched to the original one, meaning that the NDVI calibration method employed turned out to be highly accurate in extracting shadow areas.

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.111-119
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• 2001

This study analyzes time variability of the normalized difference vegetation index (NDVI), the leaf area index (LAI) and surface temperature (Ts) estimated from AVHRR data collected from across the Korean peninsula from 1981 to 1994. In the present study, LAI defined as vegetation density, as a function of NDVI applied for the vegetation types and Ts defined by the split-window formulation of Becker and Li (1990) with emissivity of a function of NDVI, are used. Results of the inter-annual, intra-annual and intra-seasonal variabilities in Korea show: (1) Inter-annual variability of NDVI is generally larger in the southem and eastern parts of the peninsula than in the western part. This large variability results from the significant mean variation. (2) Inter-annual variability of Ts is larger in the areas of smaller NDVI. This result shows that the NDVI play a small role in emissivity. (3) Inter-annual variability of LAI is larger in the regions of higher elevation and urban areas. Changes in LAI are unlikely to be associated with NDVI changes. (4) Changes in NDVI and Ts are likely dominant in July and are relatively small in spring and fall. (5) Urban effect would be obvious on the time-varying properties of NDVI and Ts in Seoul and the northern part of Taejon, where NDVI decreases and Ts increases with a significant magnitude.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.649-654
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• 2005

This study proposes a particular approach to assess information about NDVI(Normalized Difference Vegetation Index) and LAI(Leaf Area Index) from the spectroradiometer and NOAA/AVHRR satellite data. AVHRR data were collected in twelves months over a one year period in 2004. We calculated 10-day composite NDVI using daily composite AVHRR surface reflectance products(1km spatial resolution). The 10-day composite NDVI have a great effect on the plant growth conditions. Considerably, NDVI was increased by developing muscle fiber tissue from April to May. Then the NDVI increased until the August and then decreased until February. The highest month was at August and the lower month was at December. The difference NDVI analysis using December and another months data was conducted, the results were provided information on the variation of vegetation coverage. The result suggest that a relationship established between the LAI and NDVI in 2004.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.25-38
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• 2016

With the increasing need for high temporal resolution satellite imagery for monitoring land surfaces, this study evaluated the temporal resolution of the NDVI composites from Geostationary Ocean Color Imager (GOCI) data. The GOCI is the first geostationary satellite sensor designed to provide continuous images over a $2,500{\times}2,500km^2$ area of the northeast Asian region with relatively high spatial resolution of 500 m. We used total 2,944 hourly images of the GOCI level 1B radiance data obtained during the one-year period from April 2011 to March 2012. A daily NDVI composite was produced by maximum value compositing of eight hourly images captured during day-time. Further NDVI composites were created with different compositing periods ranging from two to five days. The cloud coverage of each composite was estimated by the cloud detection method developed in study and then compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud product and 16-day NDVI composite. The GOCI NDVI composites showed much higher temporal resolution with less cloud coverage than the MODIS NDVI products. The average of cloud coverage for the five-day GOCI composites during the one year was only 2.5%, which is a significant improvement compared to the 8.9%~19.3% cloud coverage in the MODIS 16-day NDVI composites.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.509-520
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• 2012

The objective of this study was to estimate rice yield in Korea using satellite and meteorological data such as sunshine hours or solar radiation, and rainfall. Terra and Aqua MODIS (The MOderate Resolution Imaging Spectroradiometer) products; MOD13 and MYD13 for NDVI and EVI, MOD15 and MYD15 for LAI, respectively from a NASA web site were used. Relations of NDVI, EVI, and LAI obtained in July and August from 2000 to 2011 with rice yield were investigated to find informative days for rice yield estimation. Weather data of rainfall and sunshine hours (climate data 1) or solar radiation (climate data 2) were selected to correlate rice yield. Aqua NDVI at DOY 233 was chosen to represent maximum vegetative growth of rice canopy. Sunshine hours and solar radiation during rice ripening stage were selected to represent climate condition. Multiple regression based on MODIS NDVI and sunshine hours or solar radiation were conducted to estimate rice yields in Korea. The results showed rice yield of $494.6kg\;10a^{-1}$ and $509.7kg\;10a^{-1}$ in 2011, respectively and the difference from statistics were $1.1kg\;10a^{-1}$ and $14.1kg\;10a^{-1}$, respectively. Rice yield distributions from 2002 to 2011 were presented to show spatial variability in the country.

• 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 Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.83-89
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• 2012

Natural Vegetation cover, which is very important earth resource, has been significantly altered by humans in some manner. Since this has currently resulted in a significant effect on global climate, various studies on vegetation environment including forest have been performed and the results are utilized in policy decision making. Remotely sensed data can detect, identify and map vegetation cover change based on the analysis of spectral characteristics and thus are vigorously utilized for monitoring vegetation resources. Among various vegetation indices extracted from spectral reponses of remotely sensed data, NDVI is the most popular index which provides a measure of how much photosynthetically active vegetation is present in the scene. In this study, for change detection in vegetation cover, a Multi-layer Perceptron Network (MLPN) as a nonparametric approach has been designed and applied to MODIS/Aqua vegetation indices 16-day L3 global 250m SIN Grid(v005) (MYD13Q1) data. The feature vector for change detection is constructed with the direct NDVI diffenrence at a pixel as well as the differences in some subset of NDVI series data. The research covered 5 years (2006-20110) over Korean peninsular.