• 한국측량학회지
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• 제33권6호
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• pp.547-555
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• 2015

The vegetation index is a crucial parameter in many biophysical studies of vegetation, and is also a valuable content in ecological processes researching. The OVIs (Optical Vegetation Index) that of using multispectral and hyperspectral data have been widely investigated in the literature, while the RVI (Radar Vegetation Index) that of considering volume scattering measurement has been paid relatively little attention. Also, there was only some efforts have been put to fuse the OVI with the RVI as an integrated vegetation index. To address this issue, this paper presents a novel FVI (Fusion Vegetation Index) that uses multispectral and full-PolSAR (Polarimetric Synthetic Aperture Radar) data. By fusing a NDVI (Normalized Difference Vegetation Index) of RapidEye and an RVI of C-band Radarsat-2, we demonstrated that the proposed FVI has higher separability in different vegetation types than only with OVI and RVI. Also, the experimental results show that the proposed index not only has information on the vegetation greenness of the NDVI, but also has information on the canopy structure of the RVI. Based on this preliminary result, since the vegetation monitoring is more detailed, it could be possible in various application fields; this synergistic FVI will be further developed in the future.

• 한국측량학회지
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• 제32권6호
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• pp.607-615
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• 2014

RVI (Radar Vegetation Index) has shown some promise in the vegetation fields, but its relationship with MVI (Multispectral Vegetation Index) is not known in the context of various land covers. Presented herein is a comparative analysis of the MVI values derived from the LANDSAT-8 and RVI values originating from the RADARSAT-2 quad-polarimetric SAR (Synthetic Aperture Radar) data. Among the various multispectral vegetation indices, NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were used for comparison with RVI. Four land covers (urban, forest, water, and paddy field) were compared, and the patterns were investigated. The experiment results demonstrated that the RVI patterns of the four land covers are very similar to those of NDVI and SAVI. Thus, during bad weather conditions and at night, the RVI data could serve as an alternative to the MVI data in various application fields.

• 한국지리정보학회지
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• 제22권4호
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• pp.86-101
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• 2019

원격탐사 기술은 플랫폼 개발, 탐사면적 및 탐사기능 등 양적 및 질적 향상의 관점에서 지속적으로 발전되어왔으며 비용절감 및 현장자료보완의 방법으로 유용하게 활용되고 있다. 최근에는 농업분야에서의 활용사례와 관련연구가 증가하는 추세에 있으며 농경지의 상태를 탐지하고 정량화하여 농경지 및 농업환경에 대한 관리방안 수립 및 정책지원이 가능하기 때문에 농작물 생육이상 판별, 시계열 정보에 의한 작황 추정 등 다양한 분야에서 연구되고 있다. 본 연구에서는 다중분광센서를 장착한 UAV를 이용하여 간척지 농경지에 대한 식생지수를 분석하고자 하였다. 한편, UAV를 이용하여 취득한 다중분광영상 자료로부터 산정된 식생지수의 정확도를 평가하기 위해서 현지 조사를 실시하였다. 현지조사에 의한 식생지수와 UAV 다중분광영상으로부터 산정된 식생지수간의 상관성을 평가함으로써 가장 적절한 식생지수를 도출하였으며 대상지역 전체에 대한 식생지수 분석에 활용하고자 하였다.

• 한국환경복원기술학회지
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• 제22권1호
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• pp.61-72
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• 2019

With the diversification of research using UAV(Unmanned Aerial Vehicle)s, the possibility of remote sensing research for urban green spaces is increasing. UAVs can be used as an investigation method to monitor the successful construction of the park and the planting of vegetation since its creation. This study was carried out to investigate UAVs utilization of urban green space monitoring in Dosol Square. It was photographed three times on May 21, July 13, and September 16, 2018 using DJI Phantom3 pro, Inspire2, and Parrot Sequoia multispectral camera. Orthographic images were overlaid on the planting plan of the site and the construction results were checked, the change of vitality of the plantation area was analyzed by NDVI(Normalized Difference Vegetation Index) and SAVI(Soil Adjusted Vegetation Index). As a result, it was confirmed that the UAVs are very effective for surveying the view of the urban green space after the construction and recording the results, which can be grasped quantitatively by overlaying the planting plan map. UAVs are more likely to be used in terms of monitoring vegetation vitality. It is interpreted that SAVI is better than NDVI in the green space just after composition. Chionanthus retusus and Pinus strobus were analyzed for their low level of vitality, and partially damaged and their vitality was lowered. In addition, there was difficulty in grass planting area and flower garden due to drainage and summer drought problems. In the future, it is expected that orthoimage and multispectral data using UAVs will be useful in the early vegetation monitoring and management field of urban green spaces.

• 대한원격탐사학회지
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• 제40권4호
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

• 대한원격탐사학회지
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• 제19권5호
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• pp.371-380
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• 2003

The primary objective of this study is to develop a suitable methodology to generate forest leaf area index (LAI) map at regional and local scales. To build empirical models, we collected the LAI values at 30 sample plots over the forest within the kyongan watershed area by the field measurements using an optical instrument. Landsat-7 ETM＋ multispectral data obtained at the same growing season with the field LAI measurement were used. Three datasets of remote sensing signal were prepared for analyzing the relationship with the field measured LAI value and they include raw DN, atmospherically corrected reflectance, and topographically corrected reflectance. From the correlation analysis and regression model development, we found that the radiometric correction of topographic effects was very critical step to increase the sensitivity of the multispectral reflectance to LAI. In addition, the empirical model to generate forest LAI map should be separately developed for each of coniferous and deciduous forest.

• 농촌계획
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• 제30권1호
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• pp.57-66
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• 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.

• 한국측량학회지
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• 제35권1호
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• pp.1-10
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• 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.

• 한국초지조사료학회지
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• 제40권4호
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• pp.196-202
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• 2020

본 연구는 동계사료작물의 무인기기반 생육모니터링을 위하여 호밀, 총체보리, IRG를 대상으로 다중분광영상으로 건물수량을 예측하기 위한 최적식생지수를 테스트하였다. 2019년 2월부터 4월까지 나주의 실경작지에서 무인기 다중분광카메라로 분광영상을 수집하여 4종류의 식생지수(Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI)를 산출하고 지상에서 건물수량을 조사하여 식생지수와 건물수량의 상관관계를 조사하였다. 호밀, 총체보리, IRG에 대하여 건물수량과 NDVI의 상관관계(R2)는 0.91~0.92, GNDVI는 0.92~0.94, NGRDI는 0.71~0.85, NDREI는 0.84~0.91로 GNDVI가 가장 효과적이었다.

• 한국수자원학회논문집
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• 제56권11호
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• pp.729-735
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• 2023

다중분광센서인 Moderate resolution Imaging Spectroradiometer (MODIS)는 지표 및 대기 산출물을 통해 다양한 분야에서 활발한 연구가 진행되어 왔다. MODIS는 발사된지 20년이 지났고, 비슷한 특성의 이를 대체할 만한 자료의 필요성이 지속적으로 제기되어 왔다. 본 연구에서는 2011년에 발사된 Suomi National Polar-orbiting Partnership (Suomi NPP) 위성의 Visible Infrared Imaging Radiometer Suite (VIIRS) sensor에서 제공하는 지표 산출물 중 지표면 온도(Land Surface Temperature, LST)와 식생 지수인 정규식생지수(Normalized Differences Vegetation Index, NDVI)를 소개하고, 기존의 MODIS에서 제공되는 자료와의 비교 및 검증을 통해 연구 지역인 남한에서의 지역적인 적용성을 파악하고자 한다. 지표면 온도와 식생 활력은 농업적인 가뭄을 발생시키는 주요한 인자로써, 남한의 극심한 가뭄기간인 2014년과 2015년을 대상으로 가뭄의 시공간적인 변동성을 분석하여, VIIRS 산출물의 활용 가능성을 파악하였다.