• Ecology and Resilient Infrastructure
• /
• v.4 no.2
• /
• pp.115-121
• /
• 2017

This study estimated potential natural vegetation in Gayasan National Park through the occurrence probability distribution by using geographic information system (GIS). in Gayasan National Park. Correlation and factor analysis were analyzed to estimate probability distribution. The presence of the Gaya National Park Vegetation survey results showed that 128 communities were distributed. The analyzed relationship between actual vegetation and distribution factors such as elevation, aspect, slope, topographic index, annual mean temperature, warmth index and potential evapotranspiration in Gayasan national park. The probability distribution of potential natural vegetation communities at least 0.3 odds were the advent of Pinus densiflora communities with the highest 55.80%, Quercus mongolica community is 44.05%, 0.09% is Quercus acutissima communities, Quercus variabilis communities are found to be 0.06%. If you want to limit the factors that affect the distribution of vegetation by factors presented in this study, the potential natural vegetation of the Gaya National Park was expected to appear in Quercus mongolica community (43.1%) and Pinus densiflora communities (56.9%).

• Spatial Information Research
• /
• v.17 no.1
• /
• pp.117-129
• /
• 2009

South Korea has been undergoing spring drought periodically and diverse researches using vegetation index have been carried out to monitor spring droughts. The strength of the vegetation index-based drought monitoring is that the monitoring method enables efficient spatio-temporal grasp of changes in drought events. According to the development of low resolution satellite images such as MODIS, which are characterized by outstanding temporal resolution, the use of the method is expected to increase. Drought analysis using vegetation index considered only meteorological factor as a cause that affects vitality of vegetation. But many indirect and direct factors affect vegetation stress, So many uncertainties are involved in such method of analysis. To secure objectivity of drought analysis that uses vegetation index it is therefore necessary to compare the method with most representative drought analysis tools that are used for drought management. In this study, PDSI and SPI which a meteorological drought index that quantifies drought and that is used as a basic index for drought monitoring and MODIS NDVI are compared to propose correlation among them and to show usefulness of drought assessment that uses vegetation index. This study shows changing patterns of NDVI and SPI 6-month are similar and correlation between NDVI and SPI was highest in inland vegetation cover.

• Korean Journal of Remote Sensing
• /
• v.30 no.5
• /
• pp.665-675
• /
• 2014

In this paper spectral characteristics and spectral patterns of pine wilt disease at different development stage were analyzed in Geoje-do where the disease has already spread. Ground-based hyperspectral imaging containing hundreds of wavelength band is feasible with continuous screening and monitoring of disease symptoms during pathogenesis. The research is based on an hyperspectral imaging of trees from infection phase to witherer phase using a ground based hyperspectral camera within the area of pine wilt disease outbreaks in Geojedo for the analysis of pine wilt disease. Hyperspectral imaging through hundreds of wavelength band is feasible with a ground based hyperspectral camera. In this research, we carried out wavelength band change analysis on trees from infection phase to witherer phase using ground based hyperspectral camera and comparative analysis with major vegetation indices such as Normalized Difference Vegetation Index (NDVI), Red Edge Normalized Difference Vegetation Index (reNDVI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index 2 (ARI2). As a result, NDVI and reNDVI were analyzed to be effective for infection tree detection. The 688 nm section, in which withered trees and healthy trees reflected the most distinctions, was applied to reNDVI to judge the applicability of the section. According to the analysis result, the vegetation index applied including 688 nm showed the biggest change range by infection progress.

• Korean Journal of Environmental Biology
• /
• v.41 no.3
• /
• pp.283-290
• /
• 2023

The Republic of Korea reclaimed land to increase its food self-sufficiency rate, but the yield was reduced due to abnormal climate. In this study, it was hypothesized that rapid and continuous monitoring technology could help improve yield. Using the vegetation index (VI) analysis, the drought stress index was calculated and the drought stress for corn grown in Hwaong, Saemangeum, and Yeongsan River reclaimed tidal land was predicted according to drying treatment. The vegetation index of corn did not decrease during the last 20 days of irrigation when soil moisture rapidly decreased, but decreased rapidly during the 20 days after irrigation. The reduction rate of the vegetation index according to the drying treatment was in the order of Saemangeum>Yeongsan River>Hwaong reclaimed tidal land, and normalized difference vegetation index(NDVI) decreased by approximately 50% in all reclaimed tidal lands, confirming that drought stress occurred due to the decrease in moisture content of the leaves. In addition, structure pigment chlorophyll index (SIPI) and photochemical reflectance index (PRI), which are calculated based on changes in light use efficiency and carotenoids, were reduced; drought stress caused a decrease in light use efficiency and an increase in carotenoid content. Therefore, vegetation index analysis was confirmed to be effective in evaluating and predicting drought stress in corn growing on reclaimed tidal land corn.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.3
• /
• pp.182-196
• /
• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.

• Journal of Korean Society of Rural Planning
• /
• v.30 no.1
• /
• pp.57-66
• /
• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2010.04a
• /
• pp.239-243
• /
• 2010

This paper shows how to establish vegetation index analysis for reducing soil erosion in mountain watershed. Soil erosion results from a combination of rainfall, soil, topography, and vegetation, so we need much time and costs when analyse it. We comparatively analysed the factors of topography, soil, and vegetation with variable resources, then established GIS DB. The possibility of practical use of this DB was also analysed. The soil and vegetation information of the sediment runoff section, and the NDVI vegetation index from KOMPSAT-2 imagery were referenced for this conducting research.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.364-364
• /
• 2019

최근 전세계적으로 기후변화로 인한 국내외 가뭄에 대한 피해 및 발생 빈도가 점차 증가하고 있으며, 우리나라의 경우 2000년대 이후 가뭄 주기가 점점 짧아져 2013년 이후 매년 가뭄이 발생하고 있다. 북한은 자연재해에 취약한 국가이며 특히 가뭄으로 인한 식량난 문제가 대두되고 있지만, 북한의 제한적인 정보로 인해 북한 지역에서의 가뭄의 발생과 피해 정보는 한정적이고, 활용할 수 있는 자료의 경우 외국 기관의 정보에 의존하는 실정이다. 향후 농업부문에 대한 대북한 지원과 통일 후를 대비한 농업정책의 수립을 위하여 북한의 가뭄에 대하여 독자적으로 신속한 정보를 취득, 분석할 수 있는 능력을 확보하는 것이 필요하다. 위성영상을 이용한 원격탐사 기술은 접근이 용이하지 못한 지역의 주기적인 관측이 가능하며, 동일한 정확도로 기상자료의 획득이 가능하다. Vegetation Drought Response Index (VegDRI)는 위성영상기반의 가뭄지수인 정규식생지수(Nomalized Difference Vegetation Index, NDVI), 기상학적 가뭄지수를 활용한 기후적 요소, 토지피복 및 생태지역 등의 생물물리학적 요소를 활용한 가뭄지표이다. 본 연구에서는 MODerate resolution Imaging Spectroradiometer (MODIS) 위성의 MOD13Q1 영상자료의 NDVI (2001~2018년)를 이용하였으며, 북한의 기상자료를 이용한 표준강수지수 (Standardized Precipitation Index, SPI)와 파머가뭄심도지수 (Palmer Drought Severity Index, PDSI), 그리고 북한 지역의 토지피복 및 생태지역 등의 요소들을 활용한 VegDRI를 통하여 북한의 가뭄 시기에 따른 시도별 가뭄 특성에 대하여 분석하고자 한다.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1475-1477
• /
• 2003

The satellite remote sensing data is good in order to grasp the wide natural environment. The purpose of study is that it examines spectral reflection characteristic and vegetation index by the utilization of the plant energy ( chlorophyll ) for examining the reliability of satellite data and grasps the transition of the natural environment using the result. According to result of analysis, there were NDVI and mutual relationship on chlorophyll, and luminance compensation of NDVI was effective for all area. In vegetation transition, there were no luminance compensation and relation, and there was a decrease of vegetation in area in south and north. The reason was a result by the artificial and natural effect. This analysis is an effective method in order to confirm the change of specific vegetation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.459-459
• /
• 2022

물수지의 불균형으로 발생되는 가뭄은 장기간에 걸쳐 넓은 규모로 발생되는 자연재해로서, 농업 및 산업에 직접 피해와 다양한 상품에 대한 공급 부족으로 인한 가격 상승 등의 간접 피해를 야기하는 재해이다. 이러한 가뭄을 정량적으로 평가하기 위하여 기상 요인(강수, 기온), 농업 요인(식생), 수문 요인(증발산, 토양수분) 등과 같은 설명 변수를 기초로 하는 많은 가뭄지수들이 개발되어 왔다. 대표적인 가뭄지수에는 Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Vegetation condition index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Scaled Drought Condition Index (SDCI), Integrated Crop Drought Index (ICDI) 등이 있다. 본 연구는 최근 개발된 통합작물가뭄지수(ICDI)를 통해 미국 옥수수의 약 90%를 생산하는 농업지역인 미국 콘벨트의 가뭄 특성을 분석하고자 한다. ICDI는 기상 요인(강우량 및 지표면 온도), 수문학적 요인(잠재 증발산 및 토양수분), 식생 요인(강화식생지수(Enhanced Vegetation Index, EVI))의 조합을 통해 지표면의 건조·습윤 상태 및 식생의 건강 상태를 설명하는 가뭄지수이다. 2004년부터 2019년까지 주요 콘벨트 지역인 일리노이, 인디애나, 아이오와를 대상으로 가뭄분석을 실시하였으며, 옥수수 수확량 아노말리와의 상관성을 분석하였다.