• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1997-2002
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• 2007

This study selected soil erosion source area of Dechung basin by soil erosion estimation model and field survey for effective soil conservation planning and management. First, unit soil erosion amount of Dechung basin is analyzed using RUSLE (Revised Universal Soil Loss Equation) model based on DEM (Digital Elevation Model), soil map, landcover map and rainfall data. Soil erosion model is difficult to analyze the tracing route of soil particle and to consider the characteristics of bank condition and the types of crop, multidirectional field survey is necessary to choice the soil erosion source area. As the result of analysis of modeling value and field survey, Mujunamde-, Wondang-, Geumpyong stream are selected in the soil erosion source area of Dechung basin. Especially, these areas show steep slope in river boundary and cultivation condition of crop is also weakness to soil erosion in the field survey.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Korean Journal of Environmental Biology
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• v.34 no.1
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• pp.1-7
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• 2016

As the deforestation in North Korea gets severed, the interest for REDD+ is also increasing. This study analyzed historical land cover changes of the study area which is 10,000 ha in Hwanghaebukdo of North Korea for assessing change in landcover and carbon storage. The result showed that the forest area had decreased from 7,035 ha to 4,293 ha which is approximately 39% of total forest area between 1989 and 2013. The deforestation caused that forest carbon storage had decreased approximately $284,399tCO_2$. Set the baseline and analysed the potential reduction amount of carbon emission, it was estimated that REDD+ project could store approximately $364,704tCO_2$ for next 30 years. This study still has limitations such as lacking in direct field survey and the data of stand volume of each tree species which was replaced with the data of stand volume in South Korea. But, study can be applied for future REDD+ projects in North Korea.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1351-1361
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• 2019

In this study, the annual surface soil erosion amount of before (2007 year) and after (2015 year) the river improvement projects were calculated using RUSLE (Revised Universal Soil Loss Equation) in the Geumgang basin (Daecheong-Dam to Geumgang Estuary-Bank). After the results were classified into five classes, the results were compared and analyzed with the results of the change in the land cover. In order to generate each factor of RUSLE, various spatial information data, such as land cover maps for 2007 and 2015 years, national basic spatial information, soil map, and average annual precipitation data were utilized. The results of the analysis are as follows: 1) annual surface soil erosion in the study area increased the area of class 1 in 2015 years compared to 2007, 2) the area of class 2, 3 and 5 decreased, 3) the area of class 4 increased. It is believed that the average annual amount of surface soil erosion decreased in most areas due to the reduction of annual average precipitation, the formation of ecological parks, the expansion of artificial facilities, and the reduction of illegal farmland.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.553-563
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• 2011

According to the improvement of computer's performance, the development of Geographic Information System (GIS), and the activation of offering information, a distributed model for analyzing runoff has been studied a lot in recently years. The distribution model is a theoretical and physical model computing runoff as making target basin subdivided parted. In the distributed model developed by this study, the volume of runoff at the surface flow is calculated on the basis of the parameter determined by landcover data and a two-dimensional diffusion wave equation. Most of existing runoff models compute velocity and discharge of flow by applying Manning-Strickler's mean velocity equation and Manning's roughness coefficient. Manning's roughness coefficient is not matched with dimension and ambiguous at computation; Nevertheless, it is widely used in because of its convenience for use. In order to improve those problems, this study developed the runoff model by applying not only Manning-Strickler's equation but also Chezy's mean velocity equation. Furthermore, this study introduced a power law of exponential friction factor expressed by the function of roughness height. The distributed model developed in this study is applied to 6 events of fan-shape basin, oblong shape test basin and Anseongcheon basin as real field conditions. As a result the model is found to be excellent in comparison with the exiting runoff models using for practical engineering application.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.496-500
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• 2009

소하천은 하천 네트워크의 최상류에 넓게 소재하는 하천이다. 본 연구의 목적은 올바른 소하천 수생태계 관리방안을 도출하기 위하여 소하천의 건강성을 분석하고 현행 소하천 관리제도의 개선점을 모색하는 것이었다. 본 연구에서는 다음과 같은 분석방법을 이용하였다. 첫째, 2007년 한강수계 소하천 28개 지점에서의 부착조류($DAI_{PO}$, TDI), 저서성 대형무척추동물(KSI), 어류(IBI), 서식환경, 수변환경 등 6개 항목에 대한 수생태 건강성 조사결과를 토대로 공간적 분포와 수질현황을 조사하여 소하천에 대한 종합적인 건강성 평가를 실시하였다. 둘째, 분석대상지역을 각각 소하천 구간스케일(28개 지점)과 유역스케일(팔당호, 안성천 유역)로 선정, 하천차수도(1:25,000)를 활용하여 해당 구간과 유역의 소하천도를 작성하였다. 셋째, 작성된 소하천도 는ArcGIS(ver. 9.3)에서 30, 60, 90, 120, 150-m Buffering을 하였다. 다음으로 소하천 구간은 중분류(23개 항목, $2000{\sim}2006$ 또는 2007년) 토지피복도를, 팔당호 및 안성천 유역의 소하천은 대분류(8개 항목, $1975{\sim}2000$년) 토지피복도를 적용하여 분류항목별 면적변화비율을 산정하였다. 끝으로 소하천 정비에 대한 제도적 문제점을 분석하여 소하천 관리의 개선점을 연구하였다. 그 연구 결과, 첫째 연구대상 소하천(28개)의 건강성은 도심 소하천에서 가장 낮게 조사되었고, 일부 소하천은 비록 상류에 위치함에도 불구하고 부착조류의 유기물, 영양염류 평가가 낮게 평가되었다. 둘째, 소하천 구간 스케일의 수변토지피복변화 분석결과 소하천 수생태 건강성은 거시적으로 산림, 도시화, 밭 등의 피복변화에 민감한 것으로 나타났으며 도시화 피복변화의 영향은 수변 30m에서 60m보다 3배 정도 큰 것으로 나타났다. 유역 스케일 분석에서는 상대적으로 도시화가 많이 진행된 안성천 유역의 소하천이 팔당호 유역보다 낮은 건강성일 것으로 예측되었다. 결론적으로 적절한 소하천 수변관리는 지역 하천의 건강성을 온전히 회복시키고 개선 유지하기 위한 중요한 수단들 중에 하나이며 수변토지피복의 변화율은 (소)하천 건강성 또는 유역관리의 지표로 활용될 수가 있는 것으로 조사되었다. 이와 더불어 본 연구를 통해 소하천 복원 및 관리는 소하천 특성을 고려한 장기적인 계획과 관리대상의 우선순위를 바탕으로 점진적인 대안마련이 필요할 것으로 사료된다.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.43-49
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• 2011

In the face of growing concern about global warming, increasing attention has been focused on the reduction of carbon dioxide emissions. One method to mitigating the release of carbon dioxide is Carbon Capture and Storage (CCS). CCS includes separation of carbon dioxide from industrial emission in plants, transport to a storage site, and long-term isolation in underground. It is necessary to conduct analyses on optimal site selection, surface monitoring, and additional effects by the construction of CCS facility in Gyeongsang basin, Korea. For the optimal site selection, necessary data; geological map, landcover map, digital elevation model, and slope map, were prepared, and a weighted overlay analysis was performed. Then, surface monitoring was performed using high resolution satellite image. As a result, the candidate region was selected inside Gyeongnam for carbon storage. Finally, the related regulations about CCS facility were collected and analyzed for legal question of selected site.