• 한국환경생태학회지
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• 제22권4호
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• pp.396-408
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• 2008

국립산림과학원에서 발간된 임상도에 따르면 파주시 광탄면에 위치한 본 연구대상지는 활엽수 Ⅳ영급으로 분류되었으나 식생조사 결과 영급판정이 어려운 활엽수 천연림으로 구성되어 정밀 임상도 작성의 필요성이 제기되었다. 이에 정밀 현존식생 조사와 식생구조 및 연륜 분석 자료를 기초로 한 정밀 임상도 작성방안을 제안하고자 하였다. 정밀 현존식생유형 분석결과 22개 유형이었으며 자연림은 신갈나무림, 굴참나무림 등 11개 유형, 인공림은 밤나무림 등 6개 유형으로 구분되었다. 영급 판정을 위해 정밀 현존식생도를 바탕으로 방형구 42개와 89개 표본목의 목편을 채취하여 수령을 측정한 결과, 저지대에 입지한 인공림, 참나무류 소경목 지역은 II영급(29.8%), 나머지 지역은 III영급(57.6%)으로 토지이용이 가능하였고 Ⅳ영급 이상(8.8%)은 경계부의 급경사 능선부에 분포하여 토지이용은 불가능하였다. 이상과 같이 소규모 개발 예정지가 활엽수 자연림으로 판정된 경우 보전과 이용 판정에 있어 정밀 현존식생 조사, 식물군집구조 및 표본목 분석을 통한 정밀 임상도를 작성하여야 할 것이다. 정밀 임상도에는 식생의 자연성, 희소성, 다양성 등을 종합적으로 판단할 수 있는 자료가 포함되어 소규모 부지의 개발적정성 판단에 유용할 것으로 사료된다.

• 한국수자원학회논문집
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• 제55권11호
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• pp.855-863
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• 2022

본 연구에서는 서울시 기온 지상관측 자료의 지도화를 위해 Artificial Neural Network (ANN)을 사용하였다. 지도화를 위한 보조자료로는 MODerate resolution Imaging Spectroradiometer (MODIS) 자료를 사용하였다. ANN 모델 설계를 위해 입력자료와 출력자료 간의 산점도 및 통계분석을 수행하였으며, 기온과의 상관성이 비교적 높게 나타나는 입력자료인 지표면온도, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI)와 시간(위성관측시각, Day of year), 위치(위도, 경도), 데이터 품질(운량)과 관련된 데이터 종류를 분류 및 조합하여 학습을 진행하였다. 기온자료와 상관성이 높은 데이터만으로 학습을 진행하였을 때 상관계수(r)와 Root Mean Squared Error (RMSE)의 평균값이 0.9667, 2.708℃로 우수한 성능을 보였다. 학습에 사용된 데이터의 종류가 추가될수록 더 우수한 학습 결과를 보였으며, 모든 데이터가 활용될 때에는 r과 RMSE의 평균값이 0.9840, 1.883℃로 가장 우수한 성능을 보였다. ANN 모델으로 생성한 서울시 기온 지도에서는 픽셀별 지형적 특성에 적절하게 기온이 산정된 것으로 판단되며, 추후 연구지역 확대 및 위성자료의 다양화를 통해 시단위 및 전국단위 기온 분포 분석 연구가 가능할 것이다.

• 대한원격탐사학회지
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• 제23권1호
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• pp.21-32
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• 2007

Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential to reduce the uncertainty in mapping fuels and offers the best approach for improving our abilities. Especially, Hyperspectral sensor have a great potential for mapping vegetation properties because of their high spectral resolution. The objective of this paper is to evaluate the potential of mapping fuel properties using Hyperion hyperspectral remote sensing data acquired in April, 2002. Fuel properties are divided into four broad categories: 1) fuel moisture, 2) fuel green live biomass, 3) fuel condition and 4) fuel types. Fuel moisture and fuel green biomass were assessed using canopy moisture, derived from the expression of liquid water in the reflectance spectrum of plants. Fuel condition was assessed using endmember fractions from spectral mixture analysis (SMA). Fuel types were classified by fuel models based on the results of SMA. Although Hyperion imagery included a lot of sensor noise and poor performance in liquid water band, the overall results showed that Hyperion imagery have good potential for wildfire fuel mapping.

• 대한원격탐사학회지
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• 제40권2호
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• pp.179-190
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• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

• International Journal of Advanced Culture Technology
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• 제11권2호
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• pp.381-388
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• 2023

Small-scale environmental impact assessments have limitations in terms of survey duration and evaluation resources, which can hinder the assessment and analysis of the current situation. In this study, we propose the use of drone technology during the environmental impact assessment process to supplement these limitations in the current situation analysis. Drone photography can provide rapid and accurate high-resolution images, allowing for the collection of various information about the target area. This information can include different types of data such as terrain, vegetation, landscape, and real-time 3D spatial information, which can be collected and processed using GIS software to understand and analyze the environmental conditions. In this study, we confirmed that terrain and vegetation analysis and prediction of the target area using drone photography and GIS analysis software is possible, providing useful information for environmental impact assessments.

• 대한원격탐사학회지
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• 제22권3호
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• 한국환경생태학회:학술대회논문집
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• 한국환경생태학회 2008년도 추계 학술대회
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• pp.38-40
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• 2008

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.679-681
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• 2003

The objectives of this research are as follows. First, to investigate methods for a national-scale land cover map based on multi-temporal classification of MODIS data and multi-spectral classification of Landsat TM data. Second, to investigate methods to p roduce ecological zone maps of Korea based on vegetation, climate, and topographic characteristics. The results of this research can be summarized as follows. First, NDVI and EVI of MODIS can be used to ecological mapping of the country by using monthly phenological characteris tics. Second, it was found that EVI is better than NDVI in terms of atmospheric correction and vegetation mapping of dense forests of the country. Third, several ecological zones of the country can be identified from the VI maps, but exact labeling requires much field works, and sufficient field data and macro-environmental data of the country. Finally, relationship between land cover types and natural environmental factors such as temperature, precipitation, elevation, and slope could be identified.

• 대한원격탐사학회지
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• 제24권6호
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• pp.621-629
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• 2008

인도의 Tamil Nadu 지역을 대상지역으로 선택하여 Landsat ETM+ 영상과 현장 조사 자료(기저면적, 개체 수, 종의 수)를 취득하였다. 취득된 자료를 통하여 (1) 영상의 분류, (2) 식생지수 영상의 추출(NDVI, Tasseled Cap 토양명도, 녹색식생, 토양습도), (3) 가장 상관관계가 높은 결과를 보인 NDVI와 기저면적(Basal area)을 이용한 식생다양성 분포 예측 지도 제작이 이루어 졌다. 기저면적과 NDVI가 가장 높은 상관관계를 가지며 대상지역 영상분류 결과 69%정도의 정확도를 보였다.

• 환경영향평가
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• 제8권3호
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• pp.77-90
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• 1999

The degree of green naturality (DGN) has played a key role for maintaining the environmental quality from inappropriate developments, although the quality and effectiveness of the mapping of DGN has been under debate. In this study, spatial distribution of degree of green naturality was initially estimated from forest stand maps that were produced from the aerial photo interpretation and extensive field survey. Once the boundary of initial classes of DGN were defined, it were overlaid with normalized difference vegetation index (NDVI) data that were derived from the recently obtained Landsat Thematic Mapper data. NDVI was calculated for each pixel from the radiometrically corrected satellite image. There were no significant differences in mean values of vegetation index among the initial DGN classes. However, the satellite derived vegetation index was very effective to delineate the developed and damaged forest lands and to adjust the initial value of DGN according to the distribution of NDVI within each class.