• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.162-165
• /
• 2004

The authors' previous study found an interannual covariability between actual evapotranspiration (ET) and the Normalized Difference Vegetation Index (NDVI) over northern Asia. This result suggested that vegetation controls interannual variation in ET. In this prior study, NDVI data from the Pathfinder AVHRR Land (PAL) dataset were analyzed. However, studies of NDVI interannual change are subject to uncertainty, because NDVI data often contain errors associated with sensor- and atmosphere-related effects. This study is aimed toward reducing this uncertainty by employing NDVI dataset, from the Global Inventory Monitoring and Modeling Studies (GIMMS) group, in addition to PAL. The analysis was carried out for the northern Asia region from 1982 to 2000. 19-year interannual change in PAL-NDVI and GIMMS-NDVI were both compared with interannual change in model-assimilated ET. Although the correlation coefficient between GIMMS-NDVI and ET is slightly less than for PAL-NDVI and ET, for both NDVI datasets the annual maximum correlation with ET occurs in June, which is near the central period of the growing season. A significant positive correlation between GIMMS-NDVI and ET was observed over most of the vegetated land area in June as well as PAL-NDVI and ET. These results reinforce the authors' prior research that indicates the control of interannual change in ET is dominated by interannual change in vegetation activity.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.224-227
• /
• 2008

Land cover and its changes, affecting multiple aspects of the environmental system such as energy balance, biogeochemical cycles, hydrological cycles and the climate system, are regarded as critical elements in global change studies. Especially in arid and semiarid regions, the observation of ecosystem that is sensitive to climate change can improve an understanding of the relationships between climate and ecosystem dynamics. The purpose of this research is analyzing the ecosystem surrounding the Gobi desert in North Asia quantitatively as well as qualitatively more concretely. We used Normalized Difference Vegetation Index (NDVI) derived from SPOT-VEGETATION (VGT) sensor during 1999${\sim}$2007. Ecosystem monitoring of this area is necessary because it is a hot spot in global environment change. This study will allow predicting areas, which are prone to the rapid environmental change. Eight classes were classified and compare with MODerate resolution Imaging Spectrometer (MODIS) global land cover. The time-series analysis was carried out for these 8 classes. Class-1 and -2 have least amplitude variation with low NDVI as barren areas, while other vegetated classes increase in May and decrease in October (maximum value occurs in July and August). Although the several classes have the similar features of NDVI time-series, we detected a slight difference of inter-annual variation among these classes.

• Korean Journal of Remote Sensing
• /
• v.22 no.3
• /
• pp.199-209
• /
• 2006

The Global Land Cover 2000 (GLC 200) project, as a most recent issue, is to provide for the year 2000 a harmonized land cover database over the whole globe. The classifications were performed according to continental or regional scales by corresponding organization using the data of VEGETATION sensor onboard the SPOT4 Satellite. Even if the global land cover classification for Asia provided by Chiba University showed a good accuracy in whole Asian area, some problems were detected in Korean region. Therefore, the construction of new land cover database over Korea is strongly required using more recent data set. The present study focuses on the development of a new upgraded land cover map at 1 km resolution over Korea considering the widely used K-means clustering, which is one of unsupervised classification technique using distance function for land surface pattern classification, and the principal components transformation. It is based on data sets from the Earth observing system SPOT4/VEGETATION. Newly classified land cover was compared with GLC 2000 for Korean peninsula to access how well classification performed using confusion matrix.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.4
• /
• pp.212-217
• /
• 2020

We studied on the design of a sprinkler control system that communicates with the administrator's mobile through a wireless communication network and a sprinkler unit that sprays water on the vegetation area. This sprinkler control system consists of a communication module that receives an operation signal for the operation of the sprinkler unit from the administrator's mobile, and a control module that controls the sprinkler unit according to the operation signal received through the communication module. It is also designed to control sprinkler units by measuring temperature, humidity, light intensities, vibration and field images in the vegetation area in real time through sensors and camera for each of them and comparing them with established limit criteria. The sprinkler allows the administrator to control the sprinkler more easily because the administrator operates the sprinkler through the mobile from a distance, and emergency situations occur and can respond quickly.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.76-78
• /
• 2005

When the data from the artificial satellite is analyzed, recent years it is perceived to vegetation index using BRF(Bidirectional Reflectance Factor) of the observation target. To make the BRF models, it is important to measure the 3D structure of the observation target actually. In this study, it is proposed to the observation technique by using laser scanning data. Also, our team has been operating the radio controlled helicopter which can fly over the tall forest canopy and it can be equipped the measurement system.

• 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.

• Journal of the Korean earth science society
• /
• v.27 no.6
• /
• pp.677-686
• /
• 2006

Surface evapotranspiration is one of the most important factors to determine the surface energy budget, and its estimation is strongly related with the accuracy of weather forecasting. Surface evapotranspiration over Daegu Metropolitan was estimated using high resolution LANDSAT TM data. The estimation of surface evapotranspiration is based on the relationship between surface radiative temperature and vegetation index provided by a TM sensor. The distribution of NDVI (Normalized Difference of Vegetation Index) corresponds well with that of land-used in Deagu Metropolitan. The temperature of several part of downtown in Deagu metropolitan is lower in comparison with the averaged radiative temperature. This is caused by the high evapotranspiration from dense vegetation like DooRyu Park in Deagu Metropolitan. But, weak evapotranspiration availability is distinguished over the central part of downtown and the difference of evapotranspiration availability on industrial complexes and residential area is also clear.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.947-958
• /
• 2021

This study was conducted to provide basic data for crop monitoring by comparing and analyzing changes in reflectance and vegetation index by sensor of multi-spectral sensors mounted on unmanned aerial vehicles. For four types of unmanned aerial vehicle-mounted multispectral sensors, such as RedEdge-MX, S110 NIR, Sequioa, and P4M, on September 14 and September 15, 2020, aerial images were taken, once in the morning and in the afternoon, a total of 4 times, and reflectance and vegetation index were calculated and compared. In the case of reflectance, the time-series coefficient of variation of all sensors showed an average value of about 10% or more, indicating that there is a limit to its use. The coefficient of variation of the vegetation index by sensor for the crop test group showed an average value of 1.2 to 3.6% in the crop experimental sites with high vitality due to thick vegetation, showing variability within 5%. However, this was a higher value than the coefficient of variation on a clear day, and it is estimated that the weather conditions such as clouds were different in the morning and afternoon during the experiment period. It is thought that it is necessary to establish and implement a UAV flight plan. As a result of comparing the NDVI between the multi-spectral sensors of the unmanned aerial vehicle, in this experiment, it is thought that the RedEdeg-MX sensor can be used together without special correction of the NDVI value even if several sensors of the same type are used in a stable light environment. RedEdge-MX, P4M, and Sequioa sensors showed a linear relationship with each other, but supplementary experiments are needed to evaluate joint utilization through off-set correction between vegetation indices.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2019.08a
• /
• pp.296-297
• /
• 2019

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.1
• /
• pp.1-10
• /
• 2017

An UAV (Unmanned Aerial Vehicle) is a flight system that is designed to conduct missions without a pilot. Compared to traditional airborne-based photogrammetry, UAV-based photogrammetry is inexpensive and can obtain high-spatial resolution data quickly. In this study, we aimed to classify the land cover using high-spatial resolution images obtained using a UAV. An RGB camera was used to obtain high-spatial resolution orthoimage. For accurate classification, multispectral image about same areas were obtained using a multispectral sensor. A DSM (Digital Surface Model) and a modified NDVI (Normalized Difference Vegetation Index) were generated using images obtained using the RGB camera and multispectral sensor. Pixel-based classification was performed for twelve classes by using the RF (Random Forest) method. The classification accuracy was evaluated based on the error matrix, and it was confirmed that the proposed method effectively classified the area compared to supervised classification using only the RGB image.