• Journal of Korea Water Resources Association
• /
• v.49 no.4
• /
• pp.305-314
• /
• 2016

In this study, a quantitative assessment was carried out in order to identify the agricultural drought in time and space using the Terra MODIS remote sensing data for the agricultural drought. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were selected by MOD13A3 image which shows the changes in vegetation conditions. The land cover classification was made to show only vegetation excluding water and urbanized areas in order to collect the land information efficiently by Type1 of MCD12Q1 images. NDVI and EVI index calculated using land cover classification indicates the strong seasonal tendency. Therefore, standardized Vegetation Stress Index Anomaly (VSIA) of EVI were used to estimated the medium-scale regions in Korea during the extreme drought year 2001. In addition, the agricultural drought damages were investigated in the country's past, and it was calculated based on the Standardized Precipitation Index (SPI) using the data of the ground stations. The VSIA were compared with SPI based on historical drought in Korea and application for drought assessment was made by temporal and spatial correlation analysis to diagnose the properties of agricultural droughts in Korea.

• Journal of Cadastre & Land InformatiX
• /
• v.48 no.1
• /
• pp.71-91
• /
• 2018

In recent years, application of UAV(Unmanned Aerial Vehicle) to seed sowing and pest control has been actively carried out in the field of agriculture. In this study, UAS(Unmanned Aerial System) is constructed by combining image sensor of various wavelength band and SfM((Structure from Motion) based image analysis technique in UAV. Utilization of UAS based vegetation survey was investigated and the applicability of precision farming was examined. For this purposes, a UAS consisting of a combination of a VIS_RGB(Visible Red, Green, and Blue) image sensor, a modified BG_NIR(Blue Green_Near Infrared Red) image sensor, and a TIR(Thermal Infrared Red) sensor with a wide bandwidth of $7.5{\mu}m$ to $13.5{\mu}m$ was constructed for a low cost UAV. In addition, a total of ten vegetation indices were selected to investigate the chlorophyll, nitrogen and water contents of plants with visible, near infrared, and infrared wavelength's image sensors. The images of each wavelength band for the test area were analyzed and the correlation between the distribution of vegetation index and the vegetation index were compared with status of the previously surveyed vegetation and ground cover. The ability to perform vegetation state detection using images obtained by mounting multiple image sensors on low cost UAV was investigated. As the utility of UAS equipped with VIS_RGB, BG_NIR and TIR image sensors on the low cost UAV has proven to be more economical and efficient than previous vegetation survey methods that depend on satellites and aerial images, is expected to be used in areas such as precision agriculture, water and forest research.

• Aerospace Engineering and Technology
• /
• v.13 no.2
• /
• pp.80-86
• /
• 2014

In this study, we the estimated surface normalized difference vegetation index by using the KOrea Multi-Purpose SATellite-3 (KOMPSAT-3) multi-spectral images for comparative analysis. The estimated NDVI from KOMPSAT-3 is used as for comparison with the high resolution GeoEye products. The geometry conditions for atmospheric effects are selected from meta files of KOMPSAT-3 bundle data. The used geometry conditions are consist of solar zenith angle, solar azimuth angle, viewing zenith angle, viewing azimuth angle, and date. And, Atmospheric effects such as attenuation, scattering and absorption were physically simulated from water vapor, ozone and aerosol information. Generally, although ground measurements are important for accurate information, in this study, MODIS atmospheric products are used as atmospheric constituents. The surface reflectance from radiative transfer model is utilized for estimating vegetation index. The present study, to reduce atmospheric and geometry conditions between KOMPSAT-3 and GeoEye having difference observation characteristics, data acquisition time is carefully determined for reliable vegetation spectral characteristics.

• Journal of Ecology and Environment
• /
• v.36 no.4
• /
• pp.235-243
• /
• 2013

In Korea, where the plain land is greatly deficient as a mountainous nation, most of riparian zones were transformed into agricultural fields and urban areas. Excessive use of the land, which is close to river, makes the rivers enduring severe pollution stresses. Disappearance of riparian buffer, which plays a function of filter in the riverside, appears as a main factor aggravating water pollution of rivers. In this respect, it is imperative to restore the lost riparian vegetation. This study found out restoration models of riparian vegetation from the Bongseonsa stream, which has remnant riparian vegetation patches as a conservation reserve. Feasible reference information applicable for restoration of riparian vegetation was shown in the species level in the order of herb, shrub, and tree and sub-tree zones as far away from the waterway. Those information could contribute to restoring integrate and healthy rivers and streams beyond simple landscaping differently from the other restoration projects when they will be applied to the restoration project to be carried out in the future. In addition, the spatial range of river and stream, background that riparian zone disappeared in Korea, and application plan of the obtained reference information were discussed.

• Journal of Korea Water Resources Association
• /
• v.36 no.5
• /
• pp.839-849
• /
• 2003

Drought is one of the major environmental disasters. Weather data, particularity rainfall, are currently the primary source of information widely used for drought monitoring. However, weather data are often from a very sparse meteorological network, incomplete and/or not always available in good time to enable relatively accurate and timely drought detection. Data from remote sensing platforms can be used to complements weather data in drought. Therefore, data obtained from the Advanced Very High Resolution Radiometer(AVHRR) sensor on board the NOAA polar-orbiting satellites have been studied as a tool for drought monitoring. The normalized difference vegetation index(NDVI)-based vegetation condition index(VCI) were used in this study These indices showed their excellent ability to detect vegetation stress due to drought. The results clearly show that temporal and spatial characteristics of drought in Korea can be detected and mapped by the VCI index.

• Korean Journal of Soil Science and Fertilizer
• /
• v.36 no.2
• /
• pp.92-103
• /
• 2003

Research and technological advances in the field of remote sensing have greatly enhanced the ability to detect and quantify physical and biological stresses that affect the productivity of agricultural crops. Reflectance in specific visible and near-infrared regions of the electromagnetic spectrum have proved useful in detection of nutrient deficiencies. Especially crop canopy sensors as a ground remote sensing measure the amount of light reflected from nearby surfaces such as leaf tissue or soil and is in contrast to aircraft or satellite platforms that generate photographs or various types of digital images. Multi-spectral vegetation indices derived from crop canopy reflectance in relatively wide wave band can be used to monitor the growth response of plants in relation to environmental factors. The normalized difference vegetation index (NDVI), where NDVI = (NIR-Red)/(NIR+Red), was originally proposed as a means of estimating green biomass. The basis of this relationship is the strong absorption (low reflectance) of red light by chlorophyll and low absorption (high reflectance and transmittance) in the near infrared (NIR) by green leaves. Thereafter many researchers have proposed the other indices for assessing crop vegetation due to confounding soil background effects in the measurement. The green normalized difference vegetation index (GNDVI), where the green band is substituted for the red band in the NDVI equation, was proved to be more useful for assessing canopy variation in green crop biomass related to nitrogen fertility in soils. Consequently ground remote sensing as a non destructive real-time assessment of nitrogen status in plant was thought to be useful tool for site specific crop nitrogen management providing both spatial and temporal information.

• Korean Journal of Remote Sensing
• /
• v.27 no.3
• /
• pp.329-338
• /
• 2011

The time-series normalized difference vegetation index (NDVI) product has proven to be a powerful tool to investigate the phenological information because it can monitor the change of the forests with very high time-resolution, This study described the application of the DFT analysis over the 9 year MODIS data for the identification of the two types of vegetation cover, Pinus densiflora(Pd) and Querqus mongolica(Qm) which are dominant species of evergreen and broadleaved deciduous forest, respectively, The total number of samples was 5148 reference cycles which consist of 2160 Pd and 2988 Qm. They were extracted from the pixel-based MODIS scenes over the 9 years from 2000 to 2008 of South Korea. The DFT analysis was mainly focused on the 0th and $1^{st}$ harmonic components, each of which represents the mean value and the variation amplitude of the NDVI over the years, respectively. The $0^{th}$ harmonic values of the vegetation Pd and Qm averaged over the 9 years were 0.74 and 0.65, respectively. This implies that Pd has a higher NDVI than Qm. Similarly obtained $1^{st}$ harmonic values of Pd and Qm were 0.19 and 0.27, respectively. This can be intuitively understood considering that the seasonal variation of Qm is much larger than Pd. This distinctive difference of the $1^{st}$ harmonic value has been used to identify evergreen and deciduous forests. Overall agreement between the Fourier analysis-based map and the actal vegetation map has been estimated to be as high as 75%. This study found that the DFT analysis can be a concise and repeatable method to separate and trace the changes of evergreen and deciduous forest using the annual NDVI cycles.

• Spatial Information Research
• /
• v.22 no.5
• /
• pp.53-63
• /
• 2014

As recent interest in ecological resources increases, an effort in three-dimensional visualization of the ecological resources has increased for the restoration and preservation of the natural environment as well as the evaluation of the landscape. However, in the case of forest resources, information extraction has been active, but the effort in trying to apply that information into an effective visualization has not happened. In other words, the effort for effective visualization is lacking when it comes to the visualization of forest resources, and numerous cases are ether non-realistic or the simulation required for analysis is inappropriate. Therefore, this paper extracts information through the use of airborne LiDAR data, aerial photograph, and forest type maps to create a vegetation layer, and then uses Flora3D forest modeling tools and ArcGlobe to accurately visualize the vegetation layer into the three dimension. An effective application for restoration and preservation of ecological resources as well as analysis on the urban landscape can be considered as a result of intuitively and realistically enabling the user's awareness of forest information within the Geographic Information System.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.26 no.6
• /
• pp.85-96
• /
• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_3
• /
• pp.1197-1208
• /
• 2019

Wildfire is a major natural disaster affecting socioeconomics and ecology. Remote sensing data have been widely used to estimate the wildfire danger with an advantage of higher spatial resolution. Among the several wildfire related indices using remote sensing data, Temperature Vegetation Dryness Index (TVDI) assesses wildfire danger based on both Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Although TVDI has physical advantages by considering both weather and vegetation condition, previous studies have shown TVDI does not performed well compare to other wildfire related indices over the Korean Peninsula. In this study we have attempted multiple modification to improve TVDI performance over the study region. In-situ measured air temperature was employed to increase accuracy, regression line was generated using monthly data to include seasonal effect, and TVDI was calculated at each province level to consider vegetation type and local climate. The modified TVDI calculation method was evaluated in wildfire cases and showed significant improvement in wildfire danger estimation.