• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.9-17
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• 2014

The Vegetation cover is a significant factor to comprehend characteristics of the ground surface for meterological and hydrological models, which measure energy in the atmosphere or predict the runoff of ground surface. Deardorff introduced vegetation cover fraction to quantitatively comprehend the vegetation cover in 1978. After Deardorff, most of previous researches were conducted on low-resolution or high-resolution images, but only few researches on Landsat that are in medium-resolution images. Therefore, this study aims to investigate a way of calculating the vegetation cover fraction by using NDVI of Landsat images, which were hardly handled previously. For accurate vegetation cover fraction, we compared the evaluated parameters from this study with past vegetation cover fraction parameters that have been calculated for using NDVI of Landsat OLI images. The result of research was shown that NDVI is quite correlated with the vegetation fraction cover in the previous researches. In fact, RMSE of vegetation cover fraction values that obtained through the suggested parameters on this study showed the highest accuracy of 7.3% among all the cases.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.537-547
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• 2010

The importance of vegetation in studies of global climate and biogeochemical cycles is well recognized. Especially. the FPAR (fraction of photosynthetically active radiation) is one of the important parameters in ecosystem productivity and carbon budget models. Therefore, accurate estimates of vegetation parameters are increasingly important in environmental impact assessment studies. In this study, optical FPAR using the Terra MODIS (MODerate resolution Imaging Spectroradiometer), SPOT VEGETATION and ECOCLIMAP data reproduced on the Korean peninsula. We applied the empirical method which is usually estimated as a linear or nonlinear function of vegetation indices. As results, we estimated the accurate expression which is 0.9039 of $R^2$ in cropland and 0.7901 of $R^2$ in forest. Finally, this study could be demonstrated to calibrate that produced FPAR while the overall pattern and random noise through the comparative analysis of FPAR on the reference data. Optimal use of input parameter on the Korean peninsula should be helping the accuracy of output as well as the improved quality of research.

• Journal of Korean Society on Water Environment
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• v.28 no.4
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• pp.582-594
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• 2012

A simple ecohydorlogic model that simulates hydrologic components and vegetation dynamics simultaneously based on equations of soil water dynamics and vegetation's growth and mortality is discussed. In order to simulate ungauged watersheds, the proposed model is calibrated with indirected estimated observation data set; 1) empirically estimated annual vaporization, 2) monthly surface runoff estimated by NRCS-CN method, and 3) vegetation fraction estimated by SPOT/VEGETATION NDVI. In order to check whether the model is performed well with indirectly estimated data or not, four upper dam watersheds (Andong, Habcheon, Namgang, Milyang) in Nakdong River watershed are selected, and the model is verified.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.401-404
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• 2008

The vegetation in the surrounding area of river is a primary factor to increase water level during flood. The influence of vegetation on the river flow in a bank has been investigated by using a hydraulic experiment. For a hydraulic experiment square-shaped piers are used as a model of unsubmerged rigid vegetation in a open channel. For fully developed uniform flows, the water elevation of the experiment was measured as varying the interval of piers and the porosity which presents the fraction of water flowing area in the cross-sectional area. The Manning's roughness coefficient, which implicates energy losses due to the vegetation, was obtained by using the experimental data. As a result, the energy losses were varied when the distance of piers and the porosity of area were changed, and the Manning's coefficient increased nonlinearly when a water elevation increased.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.67-73
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• 2015

This study evaluated the effects of fibers on the void ratio, compressive strength and repeated freezing and thawing resistance of porous vegetation concrete with binder type (non-sintering inorganic binder and blast furnace slag cement) and natural jute fiber volume fraction (0.0 %, 0.1 % and 0.2 %). The natural jute fiber volume fraction affected the void ratio, compressive strength and repeated freezing and thawing resistance. Added of natural jute fiber resulted in improved properties of the void ratio, compressive strength and freezing and thawing resistance. Also, the both compressive strength and freezing and thawing resistance increased with natural jute fiber volume fraction up to 0.1 % and then decreased with fiber volume fraction at 0.2 %.

• Journal of Biosystems Engineering
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• v.41 no.2
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• pp.126-137
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• 2016

Purpose: The overall objective of this study was to evaluate the vegetation fraction of soybeans, grown under different cropping conditions using an unmanned aerial vehicle (UAV) equipped with a red, green, and blue (RGB) camera. Methods: Test plots were prepared based on different cropping treatments, i.e., soybean single-cropping, with and without herbicide application and soybean and barley-cover cropping, with and without herbicide application. The UAV flights were manually controlled using a remote flight controller on the ground, with 2.4 GHz radio frequency communication. For image pre-processing, the acquired images were pre-treated and georeferenced using a fisheye distortion removal function, and ground control points were collected using Google Maps. Tarpaulin panels of different colors were used to calibrate the multi-temporal images by converting the RGB digital number values into the RGB reflectance spectrum, utilizing a linear regression method. Excess Green (ExG) vegetation indices for each of the test plots were compared with the M-statistic method in order to quantitatively evaluate the greenness of soybean fields under different cropping systems. Results: The reflectance calibration methods used in the study showed high coefficients of determination, ranging from 0.8 to 0.9, indicating the feasibility of a linear regression fitting method for monitoring multi-temporal RGB images of soybean fields. As expected, the ExG vegetation indices changed according to different soybean growth stages, showing clear differences among the test plots with different cropping treatments in the early season of < 60 days after sowing (DAS). With the M-statistic method, the test plots under different treatments could be discriminated in the early seasons of <41 DAS, showing a value of M > 1. Conclusion: Therefore, multi-temporal images obtained with an UAV and a RGB camera could be applied for quantifying overall vegetation fractions and crop growth status, and this information could contribute to determine proper treatments for the vegetation fraction.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.175-182
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• 2006

Because of the phenological variation of vegetation growth in temperate region, it is often difficult to accurately assess the surface conditions of agricultural croplands, grasslands, and disturbed forests by multi-spectral remote sensor data. In particular, the spectral similarity between soil and dry vegetation has been a primary problem to correctly appraise the surface conditions during the non-growing seasons in temperature region. This study analyzes the spectral characteristics of the mixture of dry vegetation and soil. The reflectance spectra were obtained from laboratory spectroradiometer measurement (GER-2600) and from EO-1 Hyperion image data. The reflectance spectra of several samples having different level of dry vegetation fractions show similar pattern from both lab measurement and hyperspectral image. Red-edge near 700nm and shortwave IR near 2,200nm are more sensitive to the fraction of dry vegetation. The use of hyperspectral data would allow us for better separation between bare soils and other surfaces covered by dry vegetation during the leaf-off season.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.147-149
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• 2003

The wetness, a function of precipitation and temperature etc, and the warmth, a function of temperature, are the dominant factor for global vegetation distribution. This paper employs the normalized difference vegetation index (NDVI), warmth index (WAI), and wetness index (WEI), and focuses on an essential climate-vegetation relationship at global scale. The NDVI was acquired from ‘Twenty-year global 4-minute AVHRR NDVI dataset.’ The WEI is defined as the fraction of the precipitation to the potential evaporation. The WAI was calculated by accumulating the monthly mean temperature of the portion exceeded 5$^{\circ}C$ throughout the year. Meteorological data for the WEI and WAI calculation were obtained from the ISLSCP CD-ROM. All analyses were conducted for 1 ${\times}$ 1 degree grid box on the terrestrial area of the Earth, and on annual value basis averaged in 1987 and 1988. The result of analyses demonstrated that there are two regimes in their relations, that is, a regime in which NDVIs vary depending on the WEI, and a regime in which NDVIs vary depending on the WAI. These two regimes appeared to correspond to the wetness dominant and warmth dominant vegetation, respectively. The geographical distributions of two regimes were mapped. Most of the world vegetation is categorized into wetness dominant, while warmth dominant vegetation is seen in the high-latitude area mainly to the north of 60$^{\circ}$N in the Northern Hemisphere and high-altitude areas.

• Journal of the Korean association of regional geographers
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• v.12 no.4
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• pp.496-507
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• 2006

A hybrid approach integrating both high-resolution and hyperspectral data sets was used to map vegetation cover of a coastal lowland area in the Hawaii Volcanoes National Park. Three common grass species (broomsedge, natal redtop, and pili) and other non-grass species, primarily shrubs, were focused in the study. A 3-step, hybrid approach, combining an unsupervised and a supervised classification schemes, was applied to the vegetation mapping. First, the IKONOS 1-m high-resolution data were classified to create a binary image (vegetated vs. non--vegetated) and converted to 20-meter resolution percent cover vegetation data to match AVIRIS data pixels. Second, the minimum noise fraction (MNF) transformation was used to extract a coherent dimensionality from the original AVIRIS data. Since the grasses and shubs were sparsely distributed and most image pixels were intermingled with lava surfaces, the reflectance component of lava was filtered out with a binary fractional cover analysis assuming that tile total reflectance of a pixel was a linear combination of the reflectance spectra of vegetation and the lava surface. Finally, a supervised approach was used to classify the plant species based on tile maximum likelihood algorithm.

• Journal of Environmental Science International
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• v.9 no.2
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• pp.101-108
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• 2000

Through numerical experiment using simplified OSU-1D PBL(Oregon State University One-Dimensional Planetary Boundary Layer) model and field measurement, we studied the impacts of vegetation canopy on heat island that was one of the characteristics of local climaate in urban area. it was found that if the fraction of vegetation was extended by 10 percent, the maximum air temperature and the maximum ground temperature can come down about 0.9${\circ}C$, 2.3${\circ}C$, respectively. Even though the field measurement was done under a little unstable atmospheric condition, the canopy air temperature was lower in the daytime, and higher at night than the air and ground temperature. This result suggests that the extention of vegetation canopy can bring about more pleasant local climate by causing the oasis, the shade and the blanket effect.