• KSCE Journal of Civil and Environmental Engineering Research
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• 제30권3B호
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.2023-2027
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• 2008

교통과 산업의 발달로 농촌인구의 감소가 빠르게 진행되고, 도시로 유입된 인구의 거주 공간 확보를 위해 도시주변의 피복 변화가 빠르게 진행되고 있다. 도시 지표의 대부분이 아스팔트나 콘크리트 등으로 피복되어 있어 도심의 기온이 교외와 비교하여 높게 형성되는 도시열섬현상(urban heat island, UHI)이 두드러지고 있다. 따라서 도시열섬현상 해소 대책으로 다양한 방법들이 분야 별로 제안되고 있다. 본 연구에서는 청주시 소재(미호천과 무심천을 대상으로) 하천으로 부터 떨어진 거리에 따른 열환경에 대하여 토지 피복에 따른 공간적 특성 분석을 수행함으로서 하천이 도심지 열 환경에 미치는 영향에 대하여 조사하였다. 하천을 중심으로 $0{\sim}1000m$ 까지 200m 간격의 버퍼를 생성하고 Landsat 영상에 의한 NDVI와 온도분포도를 이용하여 청주시 하천의 근접성에 따른 NDVI 및 온도 분포 분석 결과, NDVI는 하천을 기준으로 거리가 멀어질수록 점차적으로 증가하는 경향을 보였고 온도는 감소하는 것으로 나타났다. 또한 Quick-bird 영상에 의한 토지피복도와 NDVI, 온도 데이터를 중첩분석한 결과 NDVI는 산림-경작지-초지-나지-시가지 및 건조지 순으로 나타났고 평균온도는 NDVI의 역순으로 나타났다. 특히, 시가지를 비롯하여 공업지, 상업지 등 건조지역과 나지는 평균 $24^{\circ}C$ 이상으로 인구밀집지역은 높은 온도분포를 나타내는 것을 알 수 있었다.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.325-328
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• 2007

We discuss the Normalized Difference Vegetation Index (NDVI) of the forest canopy and floor separately based on airborne spectral reflectance measurements and simultaneous airborne land surface images acquired around Yakutsk, Siberia in 2000. The aerial land surface images were visually classified into four forest types: no-green canopy and snow floor (Type-1), green canopy and snow floor (Type-2), no-green canopy and no-snow floor (Type-3), and green canopy and no-snow floor (Type-4). The mean NDVI was calculated for these four types. Although Type-2 had green canopy, the NDVI was rather small (0.17) because of high reflection from the snow cover on the floor. Type-3, which had no green canopy, indicated considerably large NDVI (0.45) due to the greenness of the floor. Type-4 had the largest NDVI (0.75) because of the greenness of both the canopy and floor. These results reveal that the NDVI depends considerably on forest floor greenness and snow cover in addition to canopy greenness.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.357-359
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• 2003

There is various problem in grasping change of vegetation by NDVI, PVI, etc. It is very difficult especially to remove various noise ingredients in the received satellite data. Until now, it is difficult to compensate for shadow effect when NDVI is used in vegetation analysis. The essence of this study is to describe data simulation and then applied the result to the NOAA AVHRR data. When a pixel contains shadow more than 60% then this pixe1 is extracted for shadow effects on NDVI.

• Journal of Korean Society of Forest Science
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• 제101권1호
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• pp.121-129
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• 2012

The edge area with burn severity is known as significant factor that has great effects on the ecosystem recovery. However, there is little study on the edge area and its effects in the South Korea. Thus, this study aimed to analyze immediate responses of vegetation following forest fires due to combined effect of burn severity and edge-interior effect. Burn Severity (BS), or ${\Delta}NBR$ values were computed using satellite images of pre and post-forest fire in Samcheock areas. The burn forest was classified 231 $1-km^2$ girds and these grids were further reclassified into 4 groups by BS type (low BS and high BS areas) and forest areas (edge areas and interior areas). These four groups of grids including low BS-interior (group A), low BS-edge (group B), high BS-interior (group C) and high BS-edge (group D). Post-fire vegetation responses measured with (${\Delta}NDVI$) among four groups were then compared and tested by T-test. The results indicated that group C (${\Delta}NDVI$=0.047) and D (${\Delta}NDVI$ = 0.059) showed considerably greater vegetation regeneration than those of low BS areas including group A (${\Delta}NDVI$ = -0.039) and group B (${\Delta}NDVI$ = -0.036). It was also observed that edges areas showed greater vegetation regeneration than interior areas when BS is the same. Group B (${\Delta}NDVI$ = -0.036) showed greater (${\Delta}NDVI$) values than group A (${\Delta}NDVI$ = -0.039) in low BS condition. Similar relationship is observed between group C and group D in high BS condition. Thus adequate restoration practices for burned areas might need to pay close attention to interior areas with low BS to minimize the secondary damages and to rehabilitate the burned forests.

• Korean Journal of Remote Sensing
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• 제28권5호
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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 Soil Science and Fertilizer
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• 제42권2호
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• pp.70-78
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• 2009

Remote sensing technique in agriculture can be used to identify chlorophyll content, biomass, and yield caused from N stress level. This study was conducted to evaluate biomass, N stress levels, and yield of tobacco (Nicotiana tabacum L.) under sand culture in a plastic film house using ground-based remote sensors. Nitrogen rates applied were 40, 60, 80, 100, 120, and 140 percent of N concentration in the Hoagland's nutrient solution. Sensor readings for reflectance indices were taken at 30, 35, 40, 45, 50 and 60 days after transplanting(DAT). Reflectance indices measured at 40th DAT were highly correlated with dry weight(DW) of tobacco leaves and N uptake by leaves. Especially, green normalized difference vegetation index(gNDVI) from spectroradiometer and aNDVI from Crop Circle passive sensor were able to explain 85% and 84% of DW variability and 85% and 92% of N uptake variability, respectively. All the reflectance indices measured at each sampling date during the growing season were significantly correlated with tobacco yield. Especially the gNDVI derived from spectroradiometer readings at the 40th DAT explained 72% of yield variability. N rates of tobacco were distinguished by sufficiency index calculated using the ratio of reflectance indices of stress to optimum plot of N treatment. Consequently results indicate that the reflectance indices by ground-based remote sensor can be used to predict tobacco yield and recommend the optimum application rate of N fertilizer for top dressing of tobacco.

• Korean Journal of Remote Sensing
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• 제38권6_1호
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• pp.1343-1356
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• 2022

Clouds or shadows are the most problematic when monitoring crops using optical satellite images. To reduce this effect, a composite algorithm was used to select the maximum Normalized Difference Vegetation Index (NDVI) for a certain period. This Maximum NDVI Composite (MNC) method reduces the influence of clouds, but since only the maximum NDVI value is used for a certain period, it is difficult to show the phenomenon immediately when the NDVI decreases. As a way to maintain the spectral information of crop as much as possible while minimizing the influence of clouds, a Score-Based Composite (SBC) algorithm was proposed, which is a method of selecting the most suitable pixels by defining various environmental factors and assigning scores to them when compositing. In this study, the Sentinel-2A/B Level 2A reflectance image and cloud, shadow, Aerosol Optical Thickness(AOT), obtainging date, sensor zenith angle provided as additional information were used for the SBC algorithm. As a result of applying the SBC algorithm with a 15-day and a monthly period for Dangjin rice fields and Taebaek highland cabbage fields in 2021, the 15-day period composited data showed faster detailed changes in NDVI than the monthly composited results, except for the rainy season affected by clouds. In certain images, a spatially heterogeneous part is seen due to partial date-by-date differences in the composited NDVI image, which is considered to be due to the inaccuracy of the cloud and shadow information used. In the future, we plan to improve the accuracy of input information and perform quantitative comparison with MNC-based composite algorithm.

• Atmosphere
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• 제20권4호
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• pp.415-426
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• 2010

A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.

• Journal of the Korean Society of Environmental Restoration Technology
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• 제9권6호
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• pp.1-12
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• 2006

Remote sensing cannot provide a direct measurement of vegetation index (VI) but it can provide a reasonably good estimate of vegetation index, defined as the ratio of satellite bands. The monitoring of vegetation in nearby urban regions is made difficult by the low spatial resolution and temporal resolution image captures. In this study, enhancing spatial resolution method is adapted as to improve a low spatial resolution. Recent studies have successfully estimated normalized difference vegetation index (NDVI) using improved resolution method such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra satellite. Image enhancing spatial resolution is an important tool in remote sensing, as many Earth observation satellites provide both high-resolution and low-resolution multi-spectral images. Examples of enhancement of a MODIS multi-spectral image and a MODIS NDVI image of Cheongju using a Landsat TM high-resolution multi-spectral image are presented. The results are compared with that of the IHS technique is presented for enhancing spatial resolution of multi-spectral bands using a higher resolution data set. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectroradiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.