• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.19-25
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• 2013

Recently, the high-resolution satellite images is used the land cover and status data for the natural resources or environment management very helpful. The SVM algorithm of image processing has been used in various field. However, classification accuracy by SVM algorithm can be changed by various kernel functions and parameters. In this paper, the typical kernel function of the SVM algorithm was applied to the KOMPSAT-2 image and than the result of land cover performed the accuracy analysis using the checkpoint. Also, we carried out the analysis for selected the SVM kernel function from the land cover of the target region. As a result, the polynomial kernel function is demonstrated about the highest overall accuracy of classification. And that we know that the polynomial kernel and RBF kernel function is the best kernel function about each classification category accuracy.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.27-37
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• 2021

Conversion of rice paddy field to upland has been accelerated as the central government incentivizes more profitable upland crop cultivation. The objective of this study was to investigate the current status and conversion trend from paddy to upland for the reservoir irrigation districts. Total 605 of reservoir irrigation districts whose beneficiary area is greater than 200 ha were selected for paddy-to-upland conversion analysis using the land cover maps provided by the EGIS of the Ministry of Environment. The land cover data of 2019 was used to analyze up-to-date upland conversion status and its correlation with city proximity, while land cover change between 2007 and 2019 was used for paddy-to-upland conversion trend analysis. Overall 14.8% of the entire study reservoir irrigation area was converted to upland cultivation including greenhouse and orchard areas. Approximately the portion of paddy area was reduced by 17.8% on average, while upland area was increased by 4.9% over the 12 years from 2007 to 2019. This conversion from paddy to upland cultivation was more pronounced in the Gyoenggi and Gyeongsang regions compared to other the Jeolla and Chungcheong provinces. The increase of upland area was also more notable in proximity of the major city. This study findings may assist to identify some hot reservoir districts of the rapid conversion to upland cultivation and thus plan to transition toward upland irrigation system.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1084-1086
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• 2003

The aim of this study is to assess the integrated use of different features extracted from spaceborne interferometric synthetic aperture radar (InSAR) data and optical data for land cover classification. Special attention is given to the discriminatory characteristics of the features derived from the multisource data sets. For the evaluation of the features , the statistical maximum likelihood decision rule and neural network classification are used and the results are compared. The performance of each method was evaluated by measuring the overall accuracy. In all cases, the performance of the first method was better than the performance of the latter one. Overall, the research indicated that multisource data sets containing different information about backscattering and reflecting properties of the selected classes of objects can significantly improve the classification of land cover types.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.135-148
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• 2015

This research is carried out for the land cover change detection in the Nakdong River basin before and after the 4 major rivers restoration project using the LiDAR DEM(Digital Elevation Model) and the multi-temporal Landsat imagery. Firstly the river basin polygon is generated by using the levee boundaries extracted from the LiDAR DEM, and the four river basin imagery are generated from the multi-temporal Landsat-5 TM(Thematic Mapper) and Landsat-8 OLI(Operational Land Imager) imagery by using the generated river basin polygon. Then the main land covers such as river, grass and bare soil are separately generated from the generated river basin imagery by using the image classification method, and the ratio of each land cover in the entire area is calculated. The calculated land cover changes show that the areas of grass and bare soil in the entire area have been significantly changed because of the seasonal change, while the area of the river has been significantly increased because of the increase of the water storage. This paper contributes to proposing an efficient methodology for the land cover change detection in the Nakdong River basin using the LiDAR DEM and the multi-temporal satellite imagery taken before and after the 4 major rivers restoration project.

• Journal of Environmental Impact Assessment
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• v.26 no.6
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• pp.385-403
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• 2017

This study showed the national ecosystem classification for the spatial standards of ecosystems-based approaches to the risk assessments and adaptation plan. The characteristics of climate change risk assessment, implement national adaptation plans, and ecosystem/habitat classification status was evaluated. Focusing on the land cover classification widely utilized as spatial data for the assessments of biodiversity and ecosystem services in the UK and other countries in Europe, the applicability of the national land cover classification for climate change risk assessments was reviewed. Considering the ecosystem classification for climate change risk assessment and establishing adaptation measures, it is difficult to apply rough classification method to the land cover system because of lack of information on habitat trend by categorization. The results indicated that forest ecosystems and agro-ecosystem occupied 62.3% and 25.0% of land cover, respectively, of the entire country. Although the area is small compared with the land area, wetland ecosystem (2.9%), marine ecosystem (0.4%), coastal ecosystem (0.6%), and urban ecosystem (6.1%) can be included in the risk assessments. Therefore, it is necessary to subdivide below the medium classification for the forest and agricultural land, as well as Inland wetland, which has a higher proportion of habitat preference of taxa than land area, marine/coastal habitat, and transition areas such as urban and natural ecosystem.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.436-443
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• 2009

There have been rapid increases to the demands for modeling diverse and complex spatial phenomena and utilizing spatial data through the computer across all the aspects of society. As a result, the importance and utilization of remote sensing and GIS's(geographic information systems) have also increased. It can produce digital data of enormous accuracy and value by incorporating remote sensing images into GIS analysis technology and make various thematic maps by classifying and analyzing land cover. Once such a map is made for the target area, it can easily do modeling and constant monitoring based on the map, revise the database with ease, and thus efficiently update geo-spatial information. Under the goal of analyzing changes to land cover along the road by combining the remote sensing and GIS technology, this study classified land cover from the images of two periods, detected changes to the six classes over ten years, and obtained statistics about the study area's quantitative area changes in order to provide basic decision making data for urban planning and development. By analyzing land use along the road, one can set up plans for the area along the road and the downtown to supplement each other.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.497-500
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• 2005

Research and development of remote sensing technique is necessary so that more accurate and extensive information may be obtained. To achieve this goal, the synthesized technique which integrates the high resolution optic and SAR image, and topographical information was examined to investigate the quantitative/qualitative characteristics of the Earth's surface environment. For this purpose, high-precision DEMs of Jeju-Island was generated and data fusion algorithm was developed in order to integrate the multi-spectral optic and polarimetric SAR image. Three dimensional land-cover and two dimensional soil moisture maps were generated conclusively so as to investigate the Earth's surface environments and extract the geophysical parameters.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.310-313
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• 2007

In this study, a new technique for atmospheric transmittance estimated from ALOS PRISM data is developed. It is based on satellite's observing radiances of different view angles and assumes that the cause of difference in radiances is the different view angles. The ALOS PRISM has three independent optical systems for viewing forward and backward and producing a stereoscopic image along the satellite's track. This stereo pair data can be used to estimate the transmittance according to the radiative transfer theory. This derived transmittance will be validated by the AERONET data and compared with the MODTRAN4 simulation results. Results show that the higher the land cover albedo, the better the derived transmittance compared to the AERONET data. Besides, this technique also shows the transmittance retrieval will be underestimated for the low land cover albedo.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.601-611
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• 2011

Land cover changes are occurring for a variety of reasons such as urbanization, infrastructure construction, desertification, drought, flood, and so on. Many researchers have studied the cause and effect of land cover changes, and also the methods for change detection. However, most of the detection methods are based on the dichotomy of "change" and "not change" according a threshold value. In this paper, we present a change detection method with the integration of probability, spatial autocorrelation, and hotspot detection. We used the AMOEBA (A Multidirectional Ecotope-Based Algorithm) and developed the AMOEBA-CH (core hotspot) because the original algorithm tends to produce too many clusters. Our method considers the probability of land cover changes and the spatial interactions between each pixel and its neighboring pixels using a local spatial autocorrelation measure. The core hotspots of land cover changes can be delineated by a contiguity-dominance model of our AMOEBA-CH method. We tested our algorithm in a simulation for land cover changes using NDVI (Normalized Difference Vegetation Index) data in South Korea between 2000 and 2008.