RISS 학술연구정보서비스

검색

인기 검색어

    다국어 입력

    http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

    변환된 중국어를 복사하여 사용하시면 됩니다.

    예시)
    • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
    • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
    닫기

    국가 산림자원조사 자료를 이용한 산림통계 추정방법에 관한 연구 = Method of Forest Statistics Estimation Using National Forest Inventory Data

    한글로보기

    https://www.riss.kr/link?id=T11801156

    • 0

      상세조회
    • 0

      다운로드
    서지정보 열기
    • 내보내기
    • 내책장담기
    • 공유하기
    • 오류접수
    인용문이 복사되었습니다.

    부가정보

    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Since 2006, a new Korean National Forest Inventory(NFI), which is its 5th cycle, has been implemented to provide forest information for sustainable forest management and to support for reporting various international organizations such as FAO, Montreal Process, OECD and UNFCCC. The NFI system has changed to a systematic cluster sampling and has been carried out to collect data about 20% of a total sample size over the entire country per year. However, there have been few studies on the estimation of forest statistics so far. Therefore, this study was conducted to develop calculation algorithm of forest categories of interest and then to evaluate the applicability of the forest statistics based on the NFI-field data.
    First, forest categories were classified into three levels according to the purpose of interest; local(county), regional(province), and national levels. And then, forest categories of interest for each level were analyzed based on the statistical yearbook of forestry and the international organizations. Forest categories derived from the NFI were selected; 9 indicators at the local level, and 17 indicators of 5 categories at the regional level, and 33 indicators 13 categories at the national level. Forest factors selected for each level are as follows;
    a. Local level : forest area and growing stocks by forest strata
    b. Regional level : forest area and volume in mortality and cutting, forest damage, forest biomass, carbon stock, and biodiversity
    c. National level : forest area and growing stock by forest strata, forest biomass and carbon stock including dead wood, litter, and soil, forest damage, biodiversity.

    A total of 8,926 field plots, which were collected for 3 years from 2006 to 2008 over the entire country, was used in this study. Since the NFI has been implemented annually, a moving average method was applied to allow for weighting per annual data. There are several forest strata such as by forest condition such as forest cover type, age class, forest origin, forest damage, dominant tree species, land class, ownership. Forest areas for each administrative unit are reported in a forest basic statistic by Korea Forest Service, while forest strata for forest conditions have to be stratified after the collection of field data. For this reason, the field plots were post-stratified by various forest strata by forest conditions.
    At the local level, forest statistics for each category were estimated in Daejeon and Chungcheongnam-do. However, since the NFI has designed to provide forest statistics at the national level, the number of available field plots within small-areas were very few which means that the field plots could not provide reliable information and not estimate some forest statistics such as forest areas and growing stocks for two- and multi-stage stratification, for example, forest cover type, age class, land class, and ownership. In order to provide reliable information for small-areas from the NFI, there are needs for more researches on applying of calibration methods and integrating of field data with satellite data. The same tendency was found in small provinces such as Gwangju and Daegu for carbon stock in soil. Forest growing stock, biomass and carbon stock are highly dependent upon forest land area by province. Since forest damaged areas by insect are related to its geographical location, however, the damaged areas in Gyeongsangnam-do and Ulsan are broadly distributed.
    Forest statistics by forest indicators at the national level were estimated. A total growing stock and growing stock per hectare were approximately 781 million m3 and 122.6 m3/ha, respectively. The carbon stocks in morality and liter were about 15% of the carbon stock in live trees (422 million TC) and the carbon stock in soil accounted for 330 million TC. International organizations define clear several forest terminologies of forest categoreissuch as commercial and economical tree species, forest cover types, etc. However, in Korea, there are no clear definitions in those yet. In order to report international organizations, there are needs for clear definitions at a field observation level.
    The NFI is designed to provide forest statistics at the national level, but it has to support estimation of forest statistics at the local level as well. This study was conducted to develop calculation algorithm for forest statistics demanded and to evaluate the applicability for estimating forest statistics using the NFI-field data. Although there are several problems, it is concluded that this study could provide a baseline to estimate forest statistics for each purpose.
    번역하기

    Since 2006, a new Korean National Forest Inventory(NFI), which is its 5th cycle, has been implemented to provide forest information for sustainable forest management and to support for reporting various international organizations such as FAO, Montrea...

    Since 2006, a new Korean National Forest Inventory(NFI), which is its 5th cycle, has been implemented to provide forest information for sustainable forest management and to support for reporting various international organizations such as FAO, Montreal Process, OECD and UNFCCC. The NFI system has changed to a systematic cluster sampling and has been carried out to collect data about 20% of a total sample size over the entire country per year. However, there have been few studies on the estimation of forest statistics so far. Therefore, this study was conducted to develop calculation algorithm of forest categories of interest and then to evaluate the applicability of the forest statistics based on the NFI-field data.
    First, forest categories were classified into three levels according to the purpose of interest; local(county), regional(province), and national levels. And then, forest categories of interest for each level were analyzed based on the statistical yearbook of forestry and the international organizations. Forest categories derived from the NFI were selected; 9 indicators at the local level, and 17 indicators of 5 categories at the regional level, and 33 indicators 13 categories at the national level. Forest factors selected for each level are as follows;
    a. Local level : forest area and growing stocks by forest strata
    b. Regional level : forest area and volume in mortality and cutting, forest damage, forest biomass, carbon stock, and biodiversity
    c. National level : forest area and growing stock by forest strata, forest biomass and carbon stock including dead wood, litter, and soil, forest damage, biodiversity.

    A total of 8,926 field plots, which were collected for 3 years from 2006 to 2008 over the entire country, was used in this study. Since the NFI has been implemented annually, a moving average method was applied to allow for weighting per annual data. There are several forest strata such as by forest condition such as forest cover type, age class, forest origin, forest damage, dominant tree species, land class, ownership. Forest areas for each administrative unit are reported in a forest basic statistic by Korea Forest Service, while forest strata for forest conditions have to be stratified after the collection of field data. For this reason, the field plots were post-stratified by various forest strata by forest conditions.
    At the local level, forest statistics for each category were estimated in Daejeon and Chungcheongnam-do. However, since the NFI has designed to provide forest statistics at the national level, the number of available field plots within small-areas were very few which means that the field plots could not provide reliable information and not estimate some forest statistics such as forest areas and growing stocks for two- and multi-stage stratification, for example, forest cover type, age class, land class, and ownership. In order to provide reliable information for small-areas from the NFI, there are needs for more researches on applying of calibration methods and integrating of field data with satellite data. The same tendency was found in small provinces such as Gwangju and Daegu for carbon stock in soil. Forest growing stock, biomass and carbon stock are highly dependent upon forest land area by province. Since forest damaged areas by insect are related to its geographical location, however, the damaged areas in Gyeongsangnam-do and Ulsan are broadly distributed.
    Forest statistics by forest indicators at the national level were estimated. A total growing stock and growing stock per hectare were approximately 781 million m3 and 122.6 m3/ha, respectively. The carbon stocks in morality and liter were about 15% of the carbon stock in live trees (422 million TC) and the carbon stock in soil accounted for 330 million TC. International organizations define clear several forest terminologies of forest categoreissuch as commercial and economical tree species, forest cover types, etc. However, in Korea, there are no clear definitions in those yet. In order to report international organizations, there are needs for clear definitions at a field observation level.
    The NFI is designed to provide forest statistics at the national level, but it has to support estimation of forest statistics at the local level as well. This study was conducted to develop calculation algorithm for forest statistics demanded and to evaluate the applicability for estimating forest statistics using the NFI-field data. Although there are several problems, it is concluded that this study could provide a baseline to estimate forest statistics for each purpose.

    더보기

    목차 (Table of Contents)

    • I. 서 론...........................................1
    • 1. 연구 배경 및 필요성........................1
    • 2. 연구목적.......................................3
    • II. 연 구 사..........................................4
    • I. 서 론...........................................1
    • 1. 연구 배경 및 필요성........................1
    • 2. 연구목적.......................................3
    • II. 연 구 사..........................................4
    • 1. 산림자원조사.................................4
    • 2. 산림통계 추정방법..........................7
    • III. 재료 및 방법....................................9
    • 1. 표본점 자료....................................9
    • 1) 자료수집 방법...............................9
    • 2) 조사자료의 정리...........................12
    • 2. 산림통계 산출 알고리즘의 작성.........13
    • 1) 산림통계 산출 범위의 결정.............13
    • 2) 산림통계 산출 항목의 결정.............14
    • 3) 알고리즘 작성 방법.......................19
    • 3. 산림통계의 산출방법.......................23
    • 1) 연구 대상지.................................23
    • 2) 면적의 결정.................................24
    • 3) 시군구 적용 산림통계....................26
    • 4) 기본계획구 적용 산림통계..............27
    • 5) 국제기구 요구 산림통계.................29
    • IV. 결과 및 고찰...................................30
    • 1. 표본점 자료에 의한 산림통계 산출 가능성 분석..........30
    • 1) 시군구 산림통계...........................30
    • 2) 기본계획구 산림통계.....................31
    • 3) 국제기구 요구 산림통계.................33
    • 2. 산림통계 산출 알고리즘의 작성.........38
    • 1) 표본점 자료에 의한 임황정보의 결정......38
    • 2) 시군구 산림통계 산출 알고리즘.............41
    • 3) 기본계획구 산림통계 알고리즘.............49
    • 4) 국제기구 요구 산림통계 알고리즘.............59
    • 3. 산림통계의 산출.......................................77
    • 1) 시군구 산림통계의 산출...........................77
    • 2) 기본계획구 산림통계의 산출....................101
    • 3) 국제기구 요구 산림통계의 산출.............112
    • V. 결 론....................................................119
    • VI. 인용문헌.................................................122
    • Abstract.....................................................129
    • 부 록 Ⅰ.....................................................132
    • 부 록 Ⅱ.....................................................135
    • 부 록 Ⅲ 139
    더보기

    분석정보

    View

    상세정보조회

    0

    Usage

    원문다운로드

    0

    대출신청

    0

    복사신청

    0

    EDDS신청

    0

    동일 주제 내 활용도 TOP

    더보기

    주제

    연도별 연구동향

    연도별 활용동향

    연관논문

    연구자 네트워크맵

    공동연구자 (7)

    유사연구자 (20) 활용도상위20명

    이 자료와 함께 이용한 RISS 자료

    나만을 위한 추천자료

    해외이동버튼