• Proceedings of the KSRS Conference
• /
• 2009.03a
• /
• pp.82-86
• /
• 2009

원격탐사 근적외선(NIR)과 Red 밴드의 반사도로부터 계산되는 정규식생지수(NDVI)는 구름에 오염된 곳에서는 실제보다 낮은 값으로 계산된다. 식생지수에서 구름오염 문제를 극복하는 기존의 대표적인 방법에는 보름 정도 장기간 식생지수 값 중에서 최대인 값을 취하는 MVC(Maximum Value Composite) 방법이 있다. 하지만 MVC 방법으로는 식생지수의 단기간 변동을 파악할 수 없으며, 장기간 계속 구름으로 오염된 곳은 잘못된 식생지수 값으로 계산되는 문제점이 있다. 가시광 RGB 자료로부터 snapshot 영상자료의 구름을 마스크(mask)하는 새로운 방법인 CIM(Color Index Manipulation) 알고리즘을 개발하였다. 이 알고리즘을 사용하면 snapshot 영상자료에서 구름에 오염된 곳은 제외하고 오염되지 않은 곳에 대한 식생지수를 계산할 수 있다. RGB 자료에 대한 정규색상지수 NCI (Normalized Color Index) 3개 성분을 $120^{\circ}$ 간격으로 벌어진 3개 축상의 좌표로 나타낸 후 이들 3개 값의 벡터합(vector sum) 정보를 이용하여 구름을 식별하는 CIM 방법으로 위성영상에서 두꺼운 구름과 않은 구름을 구분하여 식별할 수 있다. 이 구름식별 기법을 MODIS snapshot 위성영상 자료에 적용하여 한반도의 일별(daily) 식생지수 자료를 계산하였다. 그리고 수년간의 일별 식생지수 자료로부터 한반도 식생지수의 계절적 변동을 조사하였다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.119-122
• /
• 2005

정규식생지수는 일반적으로 식생의 활력도를 나타나는 지표로서 널리 사용되고 있다. 최근에는 정규식생지수가 특정지역의 강우량과 온도의 계절 및 경년변화와 어떤 상관관계를 가지며 기후변화는 식생지수에 어떠한 영향을 미치는지 등에 관한 연구가 활발히 수행되고 있다. 본 연구에서는 1981년부터 2001년까지의 NOAA/AVHRR 영상으로부터 계산된 남한지역 정규식생지수의 주성분 분석을 통해 자료의 공간변화패턴을 분석하고 경험적 직교함수를 이용하여 시간적 변화 양상을 파악하였다. 분석결과 정규식생지수의 공간변화도는 첫 주성분에 의하여 약 $60\%$ 정도 설명되어지며 첫 주성분은 남한지역의 지형 자료 패턴을 따르고 두 번째 주성분은 전체 변화도의 약 $17\%$를 나타내며 강한 남북기울기를 보여주는 것은 계절변화와 상관한 위도변화에 따른 정규식생지수의 변화를 나타낸다. 그리고 소양강댐 및 안동댐 유역의 정규식생지수, 강우량 및 유입량 상관관계 분석 결과 정규식생지수의 계절변화와 경년변화는 강우량의 변화에 그리 민감하지 않은 것으로 나타났다.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2007.06a
• /
• pp.169-174
• /
• 2007

식생지수 시계열 자료를 이용한 식생 및 토지피복 모니터링을 수행하기 위해서는 구름으로 인한 누락 및 왜곡된 식생지수 문제를 먼저 해결해야만 한다. 특히 한반도와 같이 여름철 집중 호우기에 대부분의 영상에 구름이 존재하는 경우 이들 구름화소를 제거하거나 복원하지 않을 경우, 분석 결과에 상당한 왜곡이 발생하거나 특정 시기의 영상자료를 분석에 반영할 수 없는 경우가 발생하게 된다. HANTS 알고리즘은 이 같은 구름 화소 문제를 해결하기 위한 알고리즘으로 연중 식생지수의 변화는 비교적 단순한 반복적 주기함수의 형태를 가지므로 소수의 cos 함수를 이용한 푸리에 근사식으로 전체 연중 식생지수를 표현할 수 있다는 가정에서 출발한다. 이 때 구름화소로 인한 원식생지수와의 차이가 특정 임계값을 초과하였을 경우 해당 관측치를 근사과정에서 제외함으로써 구름의 영향을 받지 않은 식생지수 시계열 자료만을 이용하게 된다. 이 과정을 수행하기 위해서는 몇몇 제어변수의 설정이 필요한데, 본 연구에서는 한반도와 같이 특정 시기에 장기간 구름이 분포하는 상황에서 최적의 식생지수 복원을 위한 HANTS 알고리즘의 제어변수를 선정하고 재구축된 식생지수를 평가하였다. 이를 위한 실험으로 2002년 대전 지역의 MODIS Terra 식생지수 시계열 영상을 대상으로 HANTS 알고리즘을 주요 식생피복별로 적용해 보았다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.329-329
• /
• 2023

가뭄은 수개월에서 수년에 걸쳐 평년보다 낮은 강수량을 특징으로 하는 극심한 기후 현상으로 크게 기상학적 가뭄과 식생 가뭄 또는 농업 가뭄, 수문학적 가뭄, 사회경제적 가뭄으로 구분할 수 있다. 본 연구에 사용된 기상학적 가뭄지수는 표준강수지수 (Standardized Precipitation Index), 증발수요가뭄지수 (Evaporative Demand Drought Index), 표준강수증발산지수 (Standardized Precipitation Evapotranspiration Index), Copula 기반 결합가뭄지수 (Copula-based Joint Drought Index)이다. 식생지수는 0부터 1까지 0.05 간격으로 가중치를 적용하여 21개의 식생건강지수(Vegetation Health Index)를 사용하였다. VHI는 널리 사용되고 있는 원격탐사자료 기반의 가뭄지수이며, 이는 식생상태지수 (Vegetation Condition Index)와 열상태지수 (Thermal condition index)의 선형 결합으로 이루어진다. 기상학적 가뭄지수와 식생지수 사이의 상호의존도 및 민감도를 분석하기 위해 상관성 분석을 수행하였으며, 이를 토지피복 유형 (시가화 건조지역, 농업지역, 초지, 산림지역)에 따른 분석도 수행하고자 하였다.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.3
• /
• pp.63-71
• /
• 2013

This study calculated vegetation indexes such as SR, NDVI, SAVI, and LAI to figure out correlations regarding vegetation by using high resolution KOMPSAT-2 images and LANDSAT images based on the forest biomass distribution map that utilized field survey data, satellite images and LiDAR data and then analyzed correlations between their values and forest biomass. The analysis results reveal that the vegetation indexes of high resolution KOMPSAT-2 images had higher correlations than those of LANDSAT images and that NDVI recorded high correlations among the vegetation indexes. In addition, the study analyzed the characteristics of hyperspectral images by using the COMIS of STSAT-3 and Hyperion images of a similar sensor, EO-1, and further the usability of biomass estimation in hyperspectral images by comparing vegetation index, which had relatively high correlations with biomass, with the vegetation indexes of LANDSAT with the same GSD conditions.

• Journal of Wetlands Research
• /
• v.8 no.1
• /
• pp.49-58
• /
• 2006

Drought is an natural phenomenon which effects greatly on our society. It has various time scale and it is difficult to define the beginning and the end. So we can't aware it quickly and the damage of drought become severe. To cope with these problems, it needs to construct drought monitoring system. And it is required that the definition of drought which is objective and can be applied widely and proper drought index for drought monitoring. Meteorology and hydrology have developed drought index for drought monitoring. There are many attempt to interpret the drought using NDVI(Normalized Difference Vegetation Index) or LST(Land Surface Temperature) in remote sensing. In this study, drought index and precipitation is used to find drought severity of last ten years in South Korea. NDVI and VCI is applied to perceive the state of drought. Finally, the possibility of drought monitoring and evaluating drought depth is estimated by analyzing the correlation between vegetation Index and drought index.

• Journal of the Korean earth science society
• /
• v.35 no.1
• /
• pp.54-68
• /
• 2014

In order to analyze the land surface properties in Seoul and its surrounding metropolitan area, several indices and land surface temperature were calculated by the Landsat satellites (e.g., Landsat 5, Landsat 7, and Landsat 8). The Landsat data came from only in the fall season with Landsat 5 on October 21, 1985, Landsat 7 on September 29, 2003, and Landsat 8 on September 16, 2013. The land surface properties used are the indices that represented Soil Adjusted Vegetation Index (SAVI), Modified Normalized Difference Wetness Index (MNDWI), Normalized Difference Wetness Index (NDWI), Tasseled cap Brightness, Tasseled cap Greenness, Tasseled cap Wetness Index, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) and the land surface temperature of the area in and around Seoul. Most indices distinguish very well between urban, rural, mountain, building, river and road. In particular, most of the urbanization is represented in the new city (e.g., Ilsan) around Seoul. According to NDVI, NDBI and land surface temperature, urban expansion is displayed in the surrounding area of Seoul. The land surface temperature and surface elevation have a strong relationship with the distribution and structure of the vegetation/built-up indices such as NDVI and NDBI. While the NDVI is positively correlated with the land surface temperature and is also negatively correlated with the surface elevation, the NDBI have just the opposite correlations, respectively. The NDVI and NDBI index is closely associated with the characteristics of the metropolitan area. Landsat 8 and Landsat 5 have very strong correlations (more than -0.6) but Landsat 7 has a weak one (lower than -0.5).

• Journal of Environmental Impact Assessment
• /
• v.25 no.6
• /
• pp.384-401
• /
• 2016

The vegetation assessment indicator has been developed recently as a biological part of the integrated assessment system for river environment to improve the efficiency of river restoration projects. This study carried out to test the vegetation assessment indicator and to reset its grade criteria on experimental streams. We classified and mapped vegetation communities at the level of physiognomic-floristic composition by each assessment unit. A total of 204 sampling quadrats were set up on the 68 assessment units at 5 experimental streams. By analyzing the vegetation data collected, we examined the appropriate numbers of sampling quadrats, the criteria of vegetation index score, classification of vegetation community, and grade criteria for vegetation assessment. The developed vegetation assessment indicator composed with the vegetation complexity index (VCI), the vegetation diversity index (VDI), and the vegetation naturalness index (VNI) was proved to reflect the current conditions of the streams sufficiently. The contribution of vegetation naturalness index to grading by vegetation assessment indicator was larger, but three indexes were closely correlated to each other. Also there was more clearer discrimination of grading with the application of adjusted criteria of vegetation assessment indicator and the standardized classification of vegetation community, but the stream segment type did not influence the vegetation assessment grade significantly.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.23 no.2
• /
• pp.21-35
• /
• 2020

Vegetation information is a very important factor used in various fields such as urban planning, landscaping, water resources, and the environment. Vegetation varies according to canopy density or chlorophyll content, but vegetation vitality is not considered when classifying vegetation areas in previous studies. In this study, in order to satisfy various applied studies, a study was conducted to set a threshold value of vegetation index considering vegetation vitality. First, an eBee fixed-wing drone was equipped with a multi-spectral camera to construct optical and near-infrared orthomosaic images. Then, GIS calculation was performed for each orthomosaic image to calculate the NDVI, GNDVI, SAVI, and MSAVI vegetation index. In addition, the vegetation position of the target site was investigated through VRS survey, and the accuracy of each vegetation index was evaluated using vegetation vitality. As a result, the scenario in which the vegetation vitality point was selected as the vegetation area was higher in the classification accuracy of the vegetation index than the scenario in which the vegetation vitality point was slightly insufficient. In addition, the Kappa coefficient for each vegetation index calculated by overlapping with each site survey point was used to select the best threshold value of vegetation index for classifying vegetation by scenario. Therefore, the evaluation of vegetation index accuracy considering the vegetation vitality suggested in this study is expected to provide useful information for decision-making support in various business fields such as city planning in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.2
• /
• pp.135-144
• /
• 2022

Since vegetation provides humans with various ecological spaces and is also very important in terms of water resources and climatic environment, many vegetation monitoring studies using vegetation indexes based on near infrared sensors have been conducted. Therefore, if the near infrared sensor is not provided, the vegetation monitoring study has a practical problem. In this study, to improve this problem, the NDVI (Normalized Difference Vegetation Index) was used as a reference to evaluate the accuracy of the vegetation index based on the optical sensor. First, the Kappa coefficient was calculated by overlapping the vegetation survey point surveyed in the field with the NDVI. As a result, the vegetation area with a threshold value of 0.6 or higher, which has the highest Kappa coefficient of 0.930, was evaluated based on optical sensor based vegetation index accuracy. It could be selected as standard data. As a result of selecting NDVI as reference data and comparing with vegetation index based on optical sensor, the Kappa coefficients at the threshold values of 0.04, 0.08, and 0.30 or higher were the highest, 0.713, 0.713, and 0.828, respectively. In particular, in the case of the RGBVI (Red Green Red Vegetation Index), the Kappa coefficient was high at 0.828. Therefore, it was found that the vegetation monitoring study using the optical sensor is possible even in environments where the near infrared sensor is not available.