• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.473-481
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• 2007

Joint Forest Management(JFM) is a concept of developing partnerships between fringe forest user groups and the Forest Department(FD) on the basis of mutual trust and jointly defined roles and responsibilities with regard to forest protection and development. In India, JFM was started during 1992 and it was implemented in many states. However success rate of JFM activity was not promising. Though there are many factors attributed to the failures, one of the main factors is the JFM site. This paper deals with the significant ground works to be done before planning for JFM using recent technologies such as remote sensing(RS) and Geographic Information System(GIS). Also it deals with the advantages of weighted overlay analysis in selecting suitable sites for JFM taking into consideration the various criteria. As a result of weighted overlay analysis, there were four types of suitability classes viz., less, moderate, highly and un-suitable. The moderately suitable class occupied maximum area(13209.64 ha) than less and highly suitable classes. If JFM is implemented on the suitability area, then the failure could be avoided in the future.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.253-259
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat + ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by National Geographic Information based on aerial photograph and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The classification result by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• Journal of Plant Biotechnology
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• v.43 no.1
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• pp.21-29
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• 2016

Remote Sensing (RS) is a technique to obtain necessary information in a non-contact and non-destructive method by using various sensors on the surface, water or atmospheric phenomena. These techniques combine elements such as sensors, and platform and information communication technology (ICT) for mounting the sensor. ICT has contributed significantly to the success of smart agriculture through quantification and measurement of environmental factors and information such as weather, crop and soil management to distribution and consumption stage, as well as the production stage by the cloud computer. Remote sensing techniques, including non-destructive non-contact bioimaging (remote imaging) is required to measure the plant function. In addition, bioimaging study in plant science is performed at the gene, cellular and individual plant level. Recently, bioimaging technology is considered the latest phenomics that identifies the relationship between the genotype and environment for distinguishing phenotypes. In this review, trends in remote sensing in plants, plants diagnostics and response to environment and status of plants phonemics research were presented.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.93-95
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• 2003

Vector-borne diseases have been the most important worldwide health problem for many years and still represent a constant and serious risk to a large part of the world’s population. GIS and RS is used to evaluate and model the relationships between environmental factors/indicators and the incidences of viral diseases. The aim of the study is to identify the risk factors in Dengue Haemorrhagic Fever DHF) from the highest prevalence area and lowest prevalence area in Sukhothai province, Thailand using statistical, spatial and GIS Modeling. Results obtained in the study of the Dengue show that it is now possible to identify and localize precisely environmental indicators and factors of viral diseases.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.70-74
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• 1999

Remote sensing (RS) data show the surfaces of the earth only but cannot provide the shape data of building sides. The proposed method recovers a 3D shape of building sides from stereo images. Its result shows a higher possibility for recovering a large shaped object by overcoming the difficulties of traditional stereo matching techniques. The urban area will be visualized more realistically than the current model based on graphic and vector data.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.6 no.5
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• pp.1-8
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• 2003

산림의 절편화 및 마식은 단순한 녹지면직의 축소만이 아니라 아생동물의 이동통로 단절로 인한 서식시 축소 및 니아가서는 소멸을 초래한다. 이를 해결하고자 최근에는 생태통로를 조성하는 작업이 수행되고 있으며 이는 목표종의 신정 및 적절한 위치 선정을 필요로 한다. 본 연구에서는 대전광역시와 그 주변을 대상으로 지난 50년 동안 인간 활동에 의해 절편화된 녹지를 파악하고자 원격탐사(RS) 자료를 이용하였다. Landsat MSS(30m)나 TM(79m)과 같은 중저해상도 위성영상으로는 절편화를 야기하는 산림 절편화 및 마식 파악이 어려워 본연구에서는 발행한 수치지도 및 수치토지이용도를 이용하여 1954년, 2000년 각각의 삼림분포도를 작성하였다. 현지 확인 및 수정과정을 거쳐 최종적으로 생성된 자료를 근거로 산림 절편화 및 마식의 정도를 정량적으로 기술 및 평가하였고 녹지(patch)의 크기와 현장 조사한 야생동물서 식도를 바탕으로 생태통로의 위치를 도출하였다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1221-1223
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• 2003

With speediness of economy, the structure of land use has taken lots of change. How can we quickly and exactly obtain detailed land use/cover change information, and then we know land resource amount, quality, distributing and change direction. More and more high resolution satellite systems are under development. So we can make good use of RS data, existed GIS data and GPS data to extract change information and update map. In this paper a fully automated approach for detecting land use/cover change using remote sensing data with object-oriented classification based on GIS data, GPS data is presented (referring to Fig.1). At same time, I realize integrating raster with vector methods of updating the basic land use/land cover map based on 3S technology and this is becoming one of the most important developing direction in 3S application fields; land-use and cover change fields over the world. It has been successful applied in two tasks of The Ministry of Land and Resources P.R.C and taken some of benefit.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1146-1148
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• 2003

This study purposes to present strategic forest planning that can support the identification of forest resources and the maximum uses of forest functions through FMIS combining GIS, RS and LP. Forest information such as compartment structure and forest road was entered into GIS to construct FMIS. Remotely sensed data were used to land cover classification, and then geographical attributes were identified. New grouping unit, so called 'arvest unit' was introduced as the forest management unit. Strategic forest planning based on the 'arvest unit' was proposed that considered together the economic, environmental aspect of forest.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.307-318
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• 2012

To understand forest structures, the Geoscience Laser Altimeter System (GLAS) instrument have been employed to measure and monitor forest canopy with feasibility of acquiring three dimensional canopy structure information. This study tried to examine the potential of GLAS dataset in measuring forest canopy structures, particularly maximum canopy height estimation. To estimate maximum canopy height using feasible GLAS dataset, we simply used difference between signal start and ground peak derived from Gaussian decomposition method. After estimation procedure, maximum canopy height was derived from airborne Light Detection and Ranging (LiDAR) data and it was applied to evaluate the accuracy of that of GLAS estimation. In addition, several influences, such as topographical and biophysical factors, were analyzed and discussed to explain error sources of direct maximum canopy height estimation using GLAS data. In the result of estimation using direct method, a root mean square error (RMSE) was estimated at 8.15 m. The estimation tended to be overestimated when comparing to derivations of airborne LiDAR. According to the result of error occurrences analysis, we need to consider these error sources, particularly terrain slope within GLAS footprint, and to apply statistical regression approach based on various parameters from a Gaussian decomposition for accurate and reliable maximum canopy height estimation.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.3
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• pp.39-45
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• 2010

Quantification of carbon absorption and understanding the human induced land use changes (LUC) forms one of the major study with respect to global climatic changes. An attempt study has been made to quantify the carbon absorption by LUC through remote sensing technology. The Landsat imagery four time periods was classified with the hybrid classification method in order to quantify carbon absorption by LUC. Thereafter, for estimating the amount of carbon absorption, the stand biomass of forest was estimated with the total weight, which was the sum of individual tree weight. Individual tree volumes could be estimated with the crown width extracted from digital forest cover type map. In particular, the carbon conversion index and the ratio of the $CO_2$ molecular weight to the C atomic weight, reported in the IPCC guideline, was used to convert the stand biomass into the amount of carbon absorption. Total carbon absorption has been modeled by taking areal estimates of LUC of four time periods and carbon factors for land use type and standing biomass. Results of this study, through LUC suggests that over a period of construction, 7.10 % of forest and 9.43 % of barren were converted into urban. In the conversion process, there has been a loss of 6.66 t/ha/y (7.94 %) of carbon absorption from the study area.