• Journal of Ecology and Environment
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• 제29권4호
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• pp.343-352
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• 2006

This study investigated the relationship between land cover and the water quality variables in the rivers, which are located in the Yamaguchi prefecture of West Japan. The study area included 12 catchments covering $5,809\;Km^2$. pH, dissolved oxygen, suspended solid, E. coli, total nitrogen and total phosphorus were considered as river water quality variables. Satellite data was applied to generate land cover map. For linking alterations in land cover (at whole catchment and buffer zone levels) and the river water quality variables, multiple regression modeling was applied. The results indicated that non-spatial attribute (%) of land cover types (at whole catchment level) consistently explained high amounts of variation in biological oxygen demand (72%), suspended solid (72%) and total nitrogen (87%). At buffer zone-scale, multiple regression models that were developed to represent the linkage between the alterations of land cover and the river water quality variables could also explain high level of total variations in suspended solid (86%) and total nitrogen (91%).

• 대한토목학회논문집
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• 제40권5호
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• pp.535-546
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• 2020

환경부에서는 위성영상과 항공영상을 이용하여 토지피복지도를 1998년부터 제작하여 배포하고 있으나, 권역별 제작 주기가 달라 활용성이 저하된다. 이에, 본 연구에서는 항공정사영상과 Landsat 8 위성영상을 이용하여, 토지피복지도를 자동으로 생성하기 위한 연구를 수행하였다. 토지피복지도를 자동적으로 제작하기 위하여 딥러닝 기반 세그먼테이션 방법의 하나인 Residual U-Net을 활용하였다. 토지피복지도의 제작 시기와 가장 근접한 시기의 항공 및 위성영상을 신경망을 통하여 학습하고, 학습결과를 3가지 실험군으로 나누어 토지피복지도와 비교하여 정확도 평가를 수행하였다. 첫 번째 군으로 대분류 7개 전체를 활용한 결과의 경우, 선행연구에서 대분류 4개에만 적용된 결과보다도 향상된 86.6 %의 분류 정확도를 나타내었다. 중분류를 일부 포함한 2개의 실험군의 경우에는 71 %의 정확도를 나타내었다. 본 연구 결과를 바탕으로 신경망을 활용한 대분류 항목에 대한 자동 분류 가능성을 제시하였으며, 중분류 및 세분류에 대한 기초연구로 활용이 가능할 것으로 판단된다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.378-381
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• 2008

The regulating and relaxing targets in the Land Use Regulation and Total Maximum Daily Loads are influenced by Land cover information. For the providing more accurate land information, this study attempted to generate an impervious surface map using KOMPSAT-2 image which a Korea manufactured high resolution satellite image. The classification progress of this study carried out by tasseled-cap spectral enhancement through each class extraction technique neither existing classification method. KOMPSAT-2 image of this study is enhanced by Soil Brightness Index(SBI), Green vegetation Index(GVI), None-Such wetness Index(NWI). Then ranges of extracted each index in enhanced image are determined. And then, Confidence Interval of classes was determined through the calculating Non-exceedance Probability. Spectral distributions of each class are changed according to changing of Control coefficient(${\alpha}$) at the calculated Non-exceedance Probability. Previously, Land cover classification map was generated based on established ranges of classes, and then, pervious and impervious surface was reclassified. Finally, impervious ratio of reclassified impervious surface map was calculated with blocks in the study area.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.34-38
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• 2001

The purpose of this study is to estimate temporal soil loss change according to long-term land cover changes using GIS and RS. Revised USLE(Universal Soil Loss Equation) factors were made by using point rainfall data, DEM(Digital Elevation Model), soil map and land cover map. Past two decades land cover changes were traced by using Landsat MSS and TM data. Soil loss in 2000 increased $6.3\;kg/m^{2}/yr$ compared with that in 1983. This was mainly caused by the increased upland area.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.330-334
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• 2001

1. The purpose of this study is to evaluate the hydrologic impact due to temporal land cover changes of Gyueongan-cheon watershed. 2. WMS(Watershed Modeling System) HEC-1 was adopted and the required data such as DEM(Digital Elevation Model), stream network, soil map were prepared, and land cover map was made by using Landsat TM data. 3. Due to the increase of urban area and paddy field, the runoff ratio increased 5.8% during the past decade.

• 한국지리정보학회지
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• 제8권4호
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• pp.114-122
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• 2005

본 연구의 목적은 오대산 국립공원을 대상으로 수치표고모텔, 용도지구 및 토지피복분류도를 작성하여 경관특성을 파악하고, 가시권분석을 통해 시각적 경관관리방안을 제시하는 것이었다. 분석결과, 용도지구는 자연환경지구의 면적이 감소하고 자연보존지구가 상대적으로 확대되어 보전의 성격이 강화된 반면, 취락지구도 면적이 증가하여 개발 가능성이 증가하였다. 토지피복은 자연환경지구에서 농경지 및 도시지역의 증가가 나타났다. 가시권분석을 통해 가시중복도가 높게 나타난 지역은 대부분 자연보존지구였으며 산림지역이 대부분을 차지하였다. 그러나 일부지역은 자연환경지구 내에 포함되어 지속적인 경관관리가 요구되었다.

• 대한원격탐사학회지
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• 제37권4호
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• pp.763-776
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• 2021

This paper introduces a method of selecting priority update areas for subdivided land cover maps by training orthoimages and serial cadastral maps in a deep learning model. For the experiment, orthoimages and serial cadastral maps were obtained from the National Spatial Data Infrastructure Portal. Based on the VGG-16 model, 51,470 images were trained on 33 subdivided classifications within the experimental area and an accuracy evaluation was conducted. The overall accuracy was 61.42%. In addition, using the differences in the classification prediction probability of the misclassified polygon and the cosine similarity that numerically expresses the similarity of the land category features with the original subdivided land cover class, the cases were classified and the areas in which the boundary setting was incorrect and in which the image itself was determined to have a problem were identified as the priority update polygons that should be checked by operators.

• 대한원격탐사학회지
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• 제22권2호
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• pp.101-110
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• 2006

본 연구에서는 항공 관측으로 얻어진 다중분광영상과 LIDAR (LIght Detection And Ranging) 자료를 이용하여 농업지역의 토지피복 분류 정도를 분석하였다. 다중분광영상은 녹색, 적색, 근적외역의 3분광으로 이루어져 있다. LIDAR 벡터 자료로부터 최초 반사강도 영상과 최초 반사 표고 자료와 최후 반사의 지상 표고 자료의 차이로 산출된 식생 높이 영상이 얻어졌다. 토지피복 분류 방법은 최대우도법을 사용했으며, 다중분광영상의 3밴드 영상 LIDAR의 반사강도 영상, 식생 높이 영상을 이용하였다. 모든 영상을 이용한 토지피복 분류의 전체 정도는 85.6%로 다중분광영상만을 이용한 정도보다 10%이상 향상되었다. 여러 농작물간의 높이의 차이, 수목과 농작물 높이의 차이와 LIDAR 반사강도 차이로 인하여 다중분광영상과 LIDAR 영상을 사용한 토지피복 분류의 정도가 향상되었다.

• 한국측량학회지
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• 제35권1호
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• pp.11-22
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• 2017

In recent years, coastal dune erosion has accelerated as various structures have been developed around the coastal dunes. A land cover map should be developed to identify the characteristics of sand dunes and to monitor the condition of sand dunes. The Korean Ministry of Environment's land cover maps suffer from problems, such as limited classes, target areas, and durations. Thus, this study conducted experiments using RGB and multispectral images based on UAV (Unmanned Aerial Vehicle) over an approximately one-year cycle to create a land cover map of coastal dunes. RF (Random Forest) classifier was used for the analysis in accordance with the experimental region's characteristics. The pixel- and object-based classification results obtained by using RGB and multispectral cameras were evaluated, respectively. The study results showed that object-based classification using multispectral images had the highest accuracy. Our results suggest that constant monitoring of coastal dunes can be performed effectively.

• Journal of Forest and Environmental Science
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• 제36권3호
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• pp.187-198
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• 2020

This study extracted deforestation area and degraded forestland area, which are potential REDD+ (Reducing Emissions from Deforestation and Forest Degradation) project candidate areas in Paraguay using Land Cover Map (LCM) and Tree Cover Map (TCM). The REDD+ project objectives scenarios were set three stages: 'afforestation and economic efficiency scenario', 'local capacity reinforcement scenario', and 'Infrastructure-oriented scenario'. And then, we evaluated the project unit suitable area of the REDD+ project. All scenarios selected the evaluation factors for each scenario in addition to the area ratio factors for deforestation area and degraded forestland area and weighted values were extracted by assigning category scores. As a result of the three scenarios comparison analysis, Concepcion state score was the highest. Within Concepcion state, the Belon district had the highest score, making it appropriate as a project unit REDD+ project candidate area in Paraguay, while the San Carlos district had the lowest score. This study can be used as basic data for selecting REDD+ project candidate area in Paraguay, and it is expected to contribute sufficiently to REDD+ project if additional data or information of social, cultural and economic sectors are secured.