초록

• COMIS 영상을 이용한 산림 및 녹지 바이오매스 산정 기법
- COMIS 영상을 이용한 산림 및 녹지 지역 분류
- 산림 및 녹지별 수종 분류
- 수종별 바이오매스 산정
- 고품질 공간정보와 COMIS 영상을 융합한 바이오매스 산정 기법
• COMIS 영상의 활용성 증대 방안
- 고해상도 영상과 COMIS 영상 융합을 통한 고품질화
- 공간정보 서비스 활용 방안 제시

(출처 : 보고서 요약서 5p)

Abstract ▼

The purpose of this study was to analyze the usability of hyperspectral images by testing the possibilities of forest biomass estimation for the forest and green areas of Compact Imaging Spectrometer COMIS installed in STSAT-3. For that purpose, the study carried out characteristic analysis of hyper

The purpose of this study was to analyze the usability of hyperspectral images by testing the possibilities of forest biomass estimation for the forest and green areas of Compact Imaging Spectrometer COMIS installed in STSAT-3. For that purpose, the study carried out characteristic analysis of hyperspectral images, selected pilot forest areas to estimate biomass, and estimated forest biomass through field survey. Based on those data, the study combined high resolution satellite images, KOMPSAT-2 images, and LiDAR data to extract forest type information, tree height, and diameter of breast and developed a technique of forest biomass estimation for small areas with such information.
The study categorized forests according to types with KOMPSAT-2 satellite images based on forest biomass estimation and extracted tree points with LiDAR data, proposing a forest biomass estimation technique for small areas whose mean tree extraction accuracy was 90% or higher.
By using the optimal estimation technique for forest biomass proposed in the study, the investigator analyzed problems with estimating and applying forest biomass for wide areas and suggested improvement measures. In addition, parameters were obtained with field survey data to secure parameter accuracy in a non-linear regression equation for the diameter of breast based on tree height.
Digital Map Sheet 1 of 1/5,000 for the whole area of Hoingseong-gun-cheong was selected for the experiment area for the optimal estimation technique of forest biomass for small areas. The forest biomass estimation results show that stem volume, forest biomass, and carbon dioxide absorption recorded an accuracy of 88.3% by the areas according to the field survey data. A forest biomass distribution map was made with the forest biomass estimated for Digital Map Sheet 1 of 1/5,000. The study calculated vegetation indices 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 indices 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 NDVI and SAVI, which had relatively high correlations with biomass, with the vegetation indices of LANDSAT with the same GSD conditions.

(출처 : SUMMARY 10p)

목차 Contents

• 표지 ... 1
• 제 출 문 ... 3
• 보고서 요약서 ... 5
• 요 약 문 ... 7
• S U M M A R Y ... 10
• C O N T E N T S ... 12
• 목차 ... 13
• 제 1 장 연구개발과제의 개요 ... 15
• 제 1 절 연구의 필요성 및 목적 ... 15
• 제 2 절 연구 내용 및 방법 ... 16
• 제 3 절 연구의 추진일정 및 조직 ... 17
• 제 2 장 국내외 기술개발 현황 ... 19
• 제 1 절 국내외 연구 사례 ... 19
• 제 2 절 원격탐사 위성시스템 ... 27
• 제 3 절 영상처리기술 ... 40
• 제 4 절 산림 바이오매스 산정 기법 ... 48
• 제 3 장 연구개발수행 내용 및 결과 ... 55
• 제 1 절 기존 산림 바이오매스 산정 ... 55
• 제 2 절 식생지수와 바이오매스 ... 70
• 제 3 절 COMIS 유사센서 분석 ... 78
• 제 4 장 결 론 ... 90
• 제 5 장 목표달성도 및 관련분야에의 기여도 ... 92
• 제 1 절 연구 목표달성도 ... 92
• 제 2 절 관련분야에의 기여도 ... 93
• 제 6 장 참 고 문 헌 ... 94
• 끝페이지 ... 100