• 한국산학기술학회논문지
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• 제12권9호
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• pp.4192-4198
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• 2011

지상라이다는 구조물과 자연사면의 거동이나 변화를 모니터링 할 수 있는 고정밀 측위기술이지만 측위자료를 대상으로 한 분류작업(지표면과 식생 또는 구조물과 식생)은 주관적인 수작업에 의존하게 된다. 그 결과 다양한 지형지물이 혼재해 있는 지표특성으로 인해 자료분류의 신뢰도는 떨어지며, 작업시간은 길어지는 문제가 있다. 이러한 문제를 해결하기 위해 지표면(식생 등)의 변화탐지 모니터링을 위한 주요한 지표로 사용되는 NDVI(Normalized Difference Vegetation Index)를 이용하여 피복을 분류하고 그 결과를 지상라이다 자료와 융합하여 항목별로 분류하는 기법을 개발하였다. 개발기법을 적용한 결과, NDVI 자료는 항목 간 경계지점에서 0.003%의 오(誤) 분류가 있었으나 약 94%의 융합 정확도를 나타내었고 기존의 수작업에 비해 자료처리 시간이 짧아지며 정확도가 높아져 다양한 분야에 활용도가 높아질 것으로 판단된다.

• 한국측량학회지
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• 제35권5호
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• pp.357-364
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• 2017

The purpose of the study is to develop and validate landslide susceptibility map of Bhotang village development committee, Nepal using FR (Frequency Ration) and SI (Statistical Index) methods. For the purpose, firstly, a landslide inventory map was constructed based on mainly high resolution satellite images available in Google Earth Pro, and rest fieldwork as verification. Secondly, ten conditioning factors of landslide occurrence, namely: altitude, slope, aspect, mean topographic wetness index, landcover, normalized difference vegetation index, dominant soil, distance to river, distance to lineaments and rainfall, were derived and used for the development of landslide susceptibility map in GIS (Geographic Information System) environment. The landslide inventory of total 116 landslides was divided randomly such that 70% were used for training and remaining 30% for validating result by receiver operating characteristics curve analysis. The area under the curve were found to be greater than 0.7 indicating an acceptable susceptibility maps obtained using FR and SI methods in GIS for hilly region of Nepal.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.108-108
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• 2022

In this study, multispectral images of wheat according to soil water state were collected, compared, and analyzed to measure the physiological response of crops to environmental stress at the individual level. CMS-V multi-spectral camera(Silios Technologies) was used for image acquisition. The camera lens consists of eight spectral bands between 550nm and 830nm. Light Reflective information collected in each band sensor and stored in digital values, and it is converted into a reflectance for calculating the vegetation index and used. According to the camera manual, the NDVI(Normalized Difference vegetation index) value was calculated using 628 nm and 752 nm bands. Image measurement was conducted under natural light conditions, and reflectance standards(Labsphere) were captured with plants for reflectance calculation. The wheat variety used Gosomil, and the wheat grown in the field was transplanted into a pot after heading date and measured. Three treatments were performed so that the soil volumetric water content of the pot was 13~17%, 20~23%, and 25%, and the growth response of wheat according to each treatment was compared using the NDVI value. In the first measurement after port transplantation, the difference in NDVI value according to treatment was not significant, but in the subsequent measurement, the NDVI value of the treatment with a water content of 13 to 17% was lowest and was the highest at 20 to 23%. The NDVI values decreased compared to the first measurement in all treatment, and the decrease was the largest at 13-17% water content and the smallest at 20-23%. Although the difference in NDVI values could be confirmed, it would be difficult to directly relate it to the water stress of plants, and further research on the response of crops to environmental stress and the analysis of multi-spectral image will be needed.

• 대한원격탐사학회지
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• 제25권5호
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• pp.399-409
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• 2009

NDVI(Normal Difference Vegetation Index)는 식생의 광합성량과 직접적인 상호관련성이 있기 때문에 생태학적인 목적으로 이러한 지수들의 수많은 응용 가능성이 있다. 본 연구에서는 시계열 MODIS(Moderate Resolution Imaging Spectroradiometer) NDVI 자료를 이용하여 9년 동안 (2000~2008)의 북한 북부고원지대 아고산 고산 식생의 생물계절 특징을 모니터링 하였다. 5월 초순의 급격한 성장 이후 6월부터 8월까지 NDVI값은 0.8이상 지속적으로 유지되었고 최대 식생 생산량과 시기는 각 각 0.86, 7월 28일 이었다. 식생 성장의 시작과 쇠퇴는 평균적으로 각각 5월 9일, 9월 30일이었고, NDVI 값은 각각 0.51,0.54이었다. 식생 성장 기간은 2003년에 128일로 가장 짧았고 2000년과 2005년은 176일로 가장 길었다. 그리고, 동일한 수직적 산림 식생 환경을 가지고 있는 남한의 설악산과 지리산의 생물계절 특성과 비교하였다. 남북한의 아고산 고산 식생대는 약 30일 이상의 식생 생육 기간의 차이가 있었고 남한지역이 MODIS 자료의 합성 주기인 16일만큼 개서시기가 빨랐다. 본 연구는 북한 고원지대 산림 식생의 시계열 및 생물계절적인 변화를 정량적으로 분석한 연구로서 그 의의가 클 뿐만 아니라 북한 지역의 산림 식생 라이브러리 작성 및 통일을 대비한 북한 자연환경계획 수립에 기초 베이스맵으로 활용될 수 있다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.533-536
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• 2007

The LiDAR data structure has the potential for modeling in three dimensions because the LiDAR data can represent voxels with z value under certain defined conditions. Therefore, it is possible to classify the physical damaged degree of vegetation by forest fire as using the LiDAR data because the physical loss of canopy height and width by forest fire can be relative to an amount of points reached to the ground through the canopy of damaged forest. On the other hand, biological damage of vegetation by forest fire can be explained using the NDVI (Normalized Difference Vegetation Index) which show vegetation vitality. In this study, we graded the damaged degree of vegetation by forest fire in Yangyang-Gun of South Korea using the LiDAR data for physical grading and digital aerial photograph including Red, Green, Blue and Near Infra-Red bands for biological grading. The LiDAR data was classified into 2 classes, of which one was Serious Physical Damaged (SPD) and the other was Light Physical Damaged (LPD) area. The NDVI was also classified into 2 classes which are Serious Biological Damaged (SBD) and Light Biological Damaged (LBD) area respectively. With each 2 classes ofthe LiDAR data and NDVI, the damaged area by forest fire was graded into 4 degrees like damaged class 1,2,3 and 4 grade. As a result of this study, 1 graded area was the broadest and next was the 3 grade. With this result, we could know that the burned area by forest fire in Yangyang-Gun was damaged rather biologically because the NDVI in 1 and 3 grade appeared low value whereas the LiDAR data in 1 and 3 grade included light physical damage like the LPD.

• 한국측량학회지
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• 제34권4호
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• pp.431-441
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• 2016

Since the use of UAV (Unmanned Aerial Vehicle) is convenient for the acquisition of data on broad or inaccessible regions, it is nowadays used to establish spatial information for various fields, such as the environment, ecosystem, forest, or for military purposes. In this study, the process of estimating FVC (Fractional Vegetation Cover), based on multi-spectral UAV, to overcome the limitations of conventional methods is suggested. Hence, we propose that the FVC map is generated by using multi-spectral imaging. First, two types of result classifications were obtained based on RF (Random Forest) using RGB images and NDVI (Normalized Difference Vegetation Index) with RGB images. Then, the result map was reclassified into vegetation and non-vegetation. Finally, an FVC map-based RF were generated by using pixel calculation and FVC map-based GI (Gutman and Ignatov) model were indirectly made by fixed parameters. The method of adding NDVI shows a relatively higher accuracy compared to that of adding only RGB, and in particular, the GI model shows a lower RMSE (Root Mean Square Error) with 0.182 than RF. In this regard, the availability of the GI model which uses only the values of NDVI is higher than that of RF whose accuracy varies according to the results of classification. Our results showed that the GI mode ensures the quality of the FVC if the NDVI maintained at a uniform level. This can be easily achieved by using a UAV, which can provide vegetation data to improve the estimation of FVC.

• 대한원격탐사학회지
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• 제36권5_2호
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• pp.975-988
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• 2020

변화탐지는 서로 다른 시점에 촬영된 영상에서 일어난 변화를 관측하는 기술로 위성영상을 활용한 원격탐사 분야에서 중요한 기술이다. 변화탐지 기법 중 하나인 무감독 변화탐지 기법은 단시간 내에 변화지역을 추출할 수 있는 장점을 지니지만, 임계값을 통해 변화된 지역을 이진영상으로 나타내기 때문에 토지피복변화를 파악하기 어렵다는 단점이 있다. 본 연구는 이러한 무감독 변화탐지의 단점을 보완하기 위해 공간정보를 기반으로 생성된 격자 포인트를 이용하여 위성영상의 토지피복변화 및 정확도 평가를 수행하였다. 변화탐지 알고리즘은 Spectral Angle Mapper(SAM)를 사용하였으며, 김제자유무역지역 일대를 촬영한 KOMPSAT-3A(K3A) 위성영상을 대상으로 진행하였다. 변화탐지결과는 자동 임계값 추출 알고리즘들 중 Otsu, Kittler, Kapur, Tsai 방법을 사용하여 이진영상으로 나타냈다. 또한, 변화탐지에 사용된 두 시점의 위성영상은 계절에 의한 식생 변화가 존재하기 때문에 확률밀도함수를 통한 Differenced Normalized Difference Vegetation Index(dNDVI)의 임계값으로 계절적 영향을 받는 지역을 제거하였다. 연구 결과, 자동 임계값 추출 알고리즘 중 Otsu와 Kapur의 정확도가 58.16%로 나타났고, dNDVI를 통해 계절적 영향을 제거하였을 때 85.47%로 정확도가 개선된 결과를 보였다. 본 연구결과를 기반으로 생성된 알고리즘은 무감독 변화탐지를 수행할 때 정확도 평가와 토지피복변화를 정량적으로 파악하여 기존의 단점을 보완할 수 있다고 판단된다.

• 대한원격탐사학회지
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• 제39권6_1호
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• pp.1299-1308
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• 2023

기후변화 등으로 인해 전 세계적으로 산불이 점점 잦아지고 대형화되는 추세다. 위성영상 등의 원격탐사를 통한 산불피해 면적 및 피해강도를 산정하는 것은 현장조사에 따른 여러 가지 어려움을 줄일 수 있어 대안 및 보조자료로 활용이 가능하다. 산불피해강도(differenced normalized burn ratio, dNBR)는 산불 전후의 정규탄화지수(normalized burn ratio, NBR) 차이를 통해 산정하며, NBR 수식에 사용되는 영상은 Landsat의 근적외선(near infrared, NIR)과 단적외선(short-wavelength infrared, SWIR) 밴드를 기본으로 한다. 우리나라 위성영상의 경우, SWIR 밴드를 가지고 있지 않기 때문에 산불피해와 관련한 국내 연구들은 해외영상을 사용하거나 우리나라 위성영상을 사용한 경우, 정규식생지수(normalized difference vegetation index, NDVI)를 이용하여 간접적인 방법으로 dNBR을 산출하였다. 따라서 본 연구에서는 Kompsat-3A호(K3A)의 중적외선(mid-wavelength infrared, MWIR) 밴드를 NBR 수식의 SWIR 밴드 대신 대입하여 dNBR을 산정하고, dNBR의 기준이 되는 Landsat을 이용한 dNBR 결과 값과 비교하였다. 그 결과 K3A MWIR을 이용한 dNBR이 Landsat SWIR을 이용한 dNBR에 비해 나타낼 수 있는 값의 범위가 더 넓고 세분화하여 표현이 가능하였다. 따라서 산불피해 지역을 조사하는데 있어 K3A의 활용도가 높을 것이라 사료된다. 뿐만 아니라 본 연구에서는 30m로 열화된 K3A MWIR 밴드를 사용했으나 그보다 높은 해상도의 MWIR 밴드를 사용한다면 본 연구보다 훨씬 더 나은 결과를 얻을 수 있을 것이라 사료된다.

• 한국토양비료학회지
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• 제48권5호
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• pp.556-563
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• 2015

Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi-spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop $Circle^{TM}$) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.

• 한국농공학회논문집
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• 제63권6호
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• pp.101-115
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• 2021

The purpose of this study is to evaluate the compatibility of the vegetation index between the two satellites and the applicability of agricultural monitoring by comparing and verifying NDVI (Normalized Difference Vegetation Index) based on Sentinel-2 and Terra MODIS (Moderate Resolution Imaging Spectroradiometer). Terra MODIS NDVI utilized 16-day MOD13Q1 data with 250 m spatial resolution, and Sentinel-2 NDVI utilized 10-day Level-2A BOA (Bottom Of Atmosphere) data with 10 m spatial resolution. To compare both NDVI, Sentinel-2 NDVIs were reproduced at 16-day intervals using the MVC (Maximum Value Composite) technique. As a result of time series NDVIs based on two satellites for 2019 and compare by land cover, the average R² (Coefficient of determination) and RMSE (Root Mean Square Error) of the entire land cover were 0.86 and 0.11, which indicates that Sentinel-2 NDVI and MODIS NDVI had a high correlation. MODIS NDVI is overestimated than Sentinel-2 NDVI for all land cover due to coarse spatial resolution. The high-resolution Sentinel-2 NDVI was found to reflect the characteristics of each land cover better than the MODIS NDVI because it has a higher discrimination ability for subdivided land cover and land cover with a small area range.