• 한국환경복원기술학회지
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• 제18권1호
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• pp.127-133
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• 2015

Interests in ecosystem services have increased and a number of attempts to perform a quantitative valuation on them have been undertaken. To classify the ecosystem types landcover classification maps are generally used. However, some forest types on landcover classification maps have a number of errors. The purpose of this study is to verify the forest types on the landcover map by using a variety of field survey data and to suggest an improved method for forest type classifications. Forest types are compared by overlaying the landcover classification map with the 4th forest type map, and then they are verified by using National Forest Inventory, 3rd National Ecosystem Survey and field survey data. Misclassifications of forest types are found on the forest on the forest type map and farm and other grassland on the landcover map. Some errors of forest types occur at Daegu, Busan and Ulsan metropolitan cities and Gangwon province. The results of accuracy in comprehensive classification show that deciduous forest is 76.1%; coniferous forest is 54.0%; and mixed forest is 22.2%. In order to increase the classification accuracy of forest types a number of remote sensing images during various time periods should be used and the survey period of NFI and the National Forest Inventory and National Ecosystem Survey should be consistent. Also, examining areas with wide forest patch should be prioritized during the field survey in order to decrease any errors.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.760-762
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• 2003

This study confirmed the usefulness of short-wavelength infrared (SWIR) in the discrimination and classification of evergreen forest types. A forested area near Hisayama and Sasaguri in Fukuoka Prefecture, Japan, served as the study area. Warm-temperate forest vegetation dominates the study site vegetation. Coniferous plantation forest, natural broad-leaved forest, and bamboo forest were analyzed using LANDSAT5/TM and SPOT4/HRVIR remote sensing data. Samples were extracted for the three forest types, and reflectance factors were compared for each band. Kappa coefficients of various band combinations were also compared by classification accuracy. For the LANDSAT5/TM data observed in April, October, and November, Bands 5 and 7 showed significant differences between bamboo, broad-leaved, and coniferous forests. The same significant difference was not recognized in the visible or near-infrared regions. Classification accuracy, determined by supervised classification, indicated distinct improvements in band combinations with SWIR, as compared to those without SWIR. Similar results were found for both LANDSAT5/TM and SPOT4/HRVIR data. This study identified obvious advantages in using SWIR data in forest-type discrimination and classification.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.216-224
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• 2021

Aim of this research was to classify forest types at Wando in Jeonnam Province and develop warm temperate forest management system with application of Remote Sensing and GIS. Another emphasis was given to the analysis of satellite images to compare forest type changes over 10 year periods from 2009 to 2019. We have accomplished this study by using ArcGIS Pro and ENVI. For this research, Landsat satellite images were obtained by means of terrestrial, airborne and satellite imagery. Based on the field survey data, all land uses and forest types were divided into 5 forest classes; Evergreen broad-leaved forest, Evergreen Coniferous forest, Deciduous broad-leaved forest, Mixed fores, and others. Supervised classification was carried out with a random forest classifier based on manually collected training polygons in ROI. Accuracy assessment of the different forest types and land-cover classifications was calculated based on the reference polygons. Comparison of forest changes over 10 year periods resulted in different vegetation biomass volumes, producing the loss of deciduous forests in 2019 probably due to the expansion of residential areas and rapid deforestation.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.250-255
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• 2002

This paper describes the study methods of identifying forest vegetation types, based on this study, forest vegetation classification method based on vegetation index is proposed. According to reflectance data of vegetation canopy and soil line equation NIR=1.506R+0.0076 in Jingyuetan, Changchun, China, many vegetation index are calculated and analyzed. The relationships between vegetation index and vegetation types are that PVI identifies broadleaf forest and conifer forest the most easily, the next is TSAVI and MSAVI, but their calculation is complex. RVI values of different conifer trees vary obviously, so RVI can classify conifer trees. In a word, combination of PVI and RVI is evaluated to classify different vegetation types.

• 한국산림과학회지
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• 제105권2호
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• pp.157-166
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• 2016

덕유산 백암봉 일대의 천연림을 대상으로 산림형을 구분하고, 각 산림형별 생태적 특성을 파악하여 천이경향을 제시하였다. 사분각법을 이용하여 225개의 표본점에서 식생자료를 수집하였으며, 다양한 다변량 통계분석(Cluster분석, 지표종분석, 다중판별분석 등)을 실시하여 산림형을 구분하였다. 그 결과, 연구대상지는 5개의 산림형으로 분류되었고, 상층의 우점비율 및 입지환경에 따라 능선부에서는 신갈나무림, 계곡부에서는 들메나무-물푸레나무-층층나무림과 들메나무림, 사면하부에서는 졸참나무-소나무-신갈나무림, 소나무림 등이 분포하여 입지조건에 따라 수종구성 차이가 뚜렷한 것으로 나타났다. 산림유형별 생태적, 환경적 특성을 근거로 천이경향을 추정한 결과, 현재의 산림형은 신갈나무림, 들메나무림, 중생혼합림, 참나무-서어나무림 등으로 천이가 진행될 것으로 예상되었다.

• 한국산림과학회지
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• 제104권3호
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• pp.368-374
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• 2015

본 연구는 지리산 일대 천연림에서 점표본법인 사분법에 의해서 수집한 식생자료를 바탕으로 cluster 분석법을 이용하여 산림형을 분류하였다. 연구 대상림은 신갈나무림형, 들메나무-거제수나무림형, 중생혼합림형, 구상나무림형, 서어나무림형, 졸참나무림형, 소나무림형, 굴참나무림형 등 8개의 산림형으로 분류되었다. 분류된 8개의 산림형들의 천이 진행 정도를 비교 평가하기 위하여 각 산림형별로 천이지수를 산출하였다. 연구 결과, 서어나무림형의 천이지수가 219.7로 산출되어 가장 높았고, 미미한 차이의 천이지수 218.3이 산출된 중생혼합림이 그 뒤를 이었으며, 소나무림형의 천이지수가 가장 낮았다. 산림형들의 천이지수와 종다양성지수와의 비례적인 관계는 찾기 어려웠다. 가정적으로, 천이지수가 높은 산림형은 극상림에 보다 가까이 근접한 것으로 사료된다. 그러나 추정된 천이지수는 천이 단계를 가늠하는 절대적인 기준으로 삼을 수는 없지만, 산림형들 간에 천이 계열 상의 위치를 비교 평가할 수 있는 참고자료의 역할을 할 수 있을 것으로 사료된다.

• 한국산림과학회지
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• 제102권3호
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• pp.407-414
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• 2013

본 연구는 다변량 통계 분석법을 이용하여 지리산 서부 천연림을 대상으로 산림 피복형을 분류하기 위해 실시하였다. 점표본법에 의한 식생자료를 바탕으로, 수종-표본점 곡선, 계층적 군집분석, 지표종분석, 다중판별분석 등의 다변량 통계 분석법을 이용하여 식생자료를 분석하였다. 수종-표본점 곡선에서는 산림 피복형 분류에서 전혀 영향력이 없는 수종들을 예외값으로 제거하였다. 예외값을 제외한 산림식생정보를 바탕으로 계층적 군집분석을 이용하여 연구대상지를 2~10개의 클러스터로 분류하였으며, 지표종분석을 통해 연구대상지의 적정 클러스터 수는 7개인 것으로 파악되었다. 이를 통계적으로 검증하기 위해 다중판별분석을 실시하였고, 91.3%가 정확하게 분류되어, 연구대상지 산림 피복형의 개수는 7개가 적당한 것으로 나타났다. 각 클러스터 상층의 우점수종 비율에 따라 신갈나무순림, 중생혼합림, 신갈나무-졸참나무림, 구상나무-신갈나무림, 들메나무림, 졸참나무림, 서어나무림으로 산림 피복형을 명명하였다.

• 한국환경복원기술학회지
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• 제4권2호
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• pp.36-44
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• 2001

The main purpose of this study was to classify forest vegetation effectively using Landsat Thematic Mapper data(June, 1994) in mountainous region. The research area was the Mt. Wolak Experimental Forest of Chungbuk National University, near Chungju and Jecheon city, Chungcheongbuk-do. To classify forest vegetation effectively, Normalized Difference Vegetation Index(NDVI) was used to reduce topographic effects. This NDVI was modified and transformed to the value of 0 to 255, and then the modified values were combined with other Landsat Thematic Mapper bands. To classify forest and land cover types, unsupervised classification method was used. The results of this study are summarized as follows. 1. Combinations of band "3, 5, NDVI" in Landsat Thematic Mapper data showed a good separation with high accuracy. The expected classification accuracy was 95.1% in Landsat Thematic Mapper data. 2. The Land Cover types were classified into six groups : coniferous forest, deciduous forest, mixed forest, paddy and grass, non-forest, and other undetectable areas. As these classified results were compared with the reconnaissance survey and aerial black and white infrared photographs, the overall classification accuracy was 76.5% in Landsat Thematic Mapper data. 3. The portion of non-forest in Mt. Wolak area was 1.9%. The percentages of coniferous, deciduous and mixed forests were 30.9%, 35.7% and 26.4%, respectively. 4. As these classified results were compared with other reference data, the percentages of coniferous, deciduous and mixed forests increased, but the portion of non-forest was exceedingly diminished. These differences are thought to be from the different research method and the different season of received Landsat Thematic Mapper data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.

• 한국지역지리학회지
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• 제20권2호
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• pp.206-216
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• 2014

본 연구는 Landsat 위성영상과 수치임상도 등을 이용하여 충청남도의 임상을 분석하고 이를 바탕으로 산림 탄소저장량을 추정하였다. 임상분석은 NDVI 방법과 Tasseled Cap, ISODATA, 감독분류 등을 사용하였으며, 분류된 결과를 기초로 임상통계를 활용하여 충청남도의 산림 탄소저장량을 추정하였다. 그 결과, 위성영상을 이용한 임상분석에서는 감독분류를 통한 임상분석이 가장 높은 전체정확도를 보였으며, 충청남도 전체 임상에서 차지하는 비율은 침엽수(49.3%), 활엽수(28.0%), 혼효림(22.7%)로 나타났다. 수정된 수치임상도를 통해 추정된 산림 탄소저장량과 다른 추정 방법들을 비교분석한 결과에서는 Tasseled Cap과 무감독분류를 이용한 방법이 가장 유사한 산림 탄소저장량을 추정하였지만, 단순히 수치임상도만을 이용한 경우 가장 많은 차이가 나타났다. 향후 위성영상 및 수치임상도를 통합하여 탄소저장량을 추정한다면 국가단위 산림 탄소저장량 추정에 있어서 보다 정확한 결과를 도출할 수 있을 것으로 기대된다.