• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.581-590
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• 2014

With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.75-85
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• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Journal of Soil and Groundwater Environment
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• v.20 no.7
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• pp.53-60
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• 2015

This study suggested the new site classification system according to land use, type of contamination and contaminants. Because the present site classification system can not cover all the areas, we changed the concept of land use to more detail one and enlarged the concept of other areas to cover all the areas not defined as certain land use. In case of the present industrial area, it was merged as other areas to avoid the confusion with oil and toxic material storage tank farm area. Accident area was separated from other areas and defined as only accident area caused by the mobile storage facility. In addition to classify the sites according to the basic land use, we classify the sites again in lower level according to the type of contamination and contaminants. With this classification system, we proposed different soil sampling strategy with the consideration of the origin of contamination and the interactions between soil and contaminants. We removed the surface soil sample (0~15 cm depth) around above storage tank because it was not a effective sample to assess whether that area contaminated or not. We also proposed to take the deeper soil samples at minimum three sampling points to confirm the depth of contamination in exploratory soil survey. We also proposed to remove the one point of 15 m depth sampling because it is not effective to confirm contaminated soil depth and needs the exhausted labor and cost. Instead of doing this, we added the continuous sampling to uncontaminated subsoil. Soil sampling points and depth in detailed soil survey is determined based on the results of exploratory soil survey. Therefore, effectiveness and reinforcements of exploratory soil survey would play an important role in improving the reliability of detailed soil survey.

• Proceedings of the KSRS Conference
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• v.2
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• pp.555-558
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• 2006

In recent years, urban development has expanded rapidly in Nawabshah City of Pakistan. A major effect associated with this population trend is transformation of the landscape from natural cover types to increasingly impervious urban land. The core objective of this study are to provide time-series information to define and measure the urban land cover changes of Nawabshah, Pakistan between the years 1992 and 2002, and to examine related urbanization impacts on air quality of the study area. Two multi-temporal Landsat images acquired in 1992 and 2002 together with standard topographical maps to measure land cover changes were used in this study. The image processing and data manipulation were conducted using algorithms supplied with the ERDAS Imagine software. An unsupervised classification approach, which uses a minimum spectral distance to assign pixels to clusters, was used with the overall accuracy ranging from 84 percent to 92 percent. Land cover statistics demonstrate that during the study period (1992-2002) extensive transformation of barren and vegetated lands into urban land have taken place in Nawabshah City. Results revealed that land cover changes due to urbanization has not only contaminated the air quality of the study area but also raised the health concerns for the local residents.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.153-158
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• 1993

The direction of land use for the subject district (Zuksan-myun, Anseong-gun, Kyeonggi-do) were analyzed as the basic model of new rural land management system connected to production and living. General land use planning was presented by land suitability classification which was applied to geographical information system(software ARC/INFO). The course of analysis of land suitability using the geographical information system were generalized and the results of analysis for paddy and upland fields and settlement were presented as 5 criteria of the suitability rank. It was found out that the analysis of land suitability is able to use as primary data of rural land use planning.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.145-153
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• 2002

Reforming land is an important foundation for benefit of society as well as national development. A correct investigation and information acquirement as to land must go first to establish land management and plan. Land investigation by remote sensing is one of the most reasonable methods that doesn't need lots of time and manpower. In this study, Image classification on land use from Landsat data was carried out, which were respectively in 1980, 1985, 1990, 1995 and 2000, covering southern Han river and then land use changes were detected. In addition, an available information was reported, which could be used in the control of southern Han river. As a result, there is an obvious change in land use, especially the increase of water and decrease of forest and agriculture. Those are caused by the industrialization and the construction of dam.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.329-337
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• 2017

In this study, land-use changes from 1990 to 2010 in Jeju Island by different approaches were produced and compared to suggest a more efficient approach. In a sample-based method, land-use changes were analyzed with different sampling intensities of 2 km and 4 km grids, which were distributed by the fifth National Forest Inventory (NFI5), and their uncertainty was assessed. When comparing the uncertainty for different sampling intensities, the one with the grid of 2 km provided more precise information; ranged from 6.6 to 31.3% of the relative standard error for remaining land-use categories for 20 years. On the other hand, land-cover maps by a wall-to-wall approach were produced by using time-series Landsat imageries. Forest land increased from 34,194 ha to 44,154 ha for 20 years, where about 69% of total forest land were remained as forest land and 19% and 8% within forest lands were converted to grassland and cropland, respectively. In the case of grassland, only about 40% of which were remained as grassland and most of the area were converted to forest land and cropland. When comparing land-cover area by land-use categories with land-use statistics, forest areas were underestimated while areas of cropland and grassland were overestimated. In order to analyze land use change, it is necessary to establish a clear and consistent definition on the six land use classification.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.51-64
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• 2015

We improved the MODerate resolution Imaging Spectroradiometer (MODIS) land cover map over the Asia-Oceania region through the reclassification of the misclassified pixels. The misclassified pixels are defined where the number of land cover types are greater than 3 from the 12 years of MODIS land cover map. The ratio of misclassified pixels in this region amounts to 17.53%. The MODIS Normalized Difference Vegetation Index (NDVI) time series over the correctly classified pixels showed that continuous variation with time without noises. However, there are so many unreasonable fluctuations in the NDVI time series for the misclassified pixels. To improve the quality of input data for the reclassification, we corrected the MODIS NDVI using Correction based on Spatial and Temporal Continuity (CSaTC) developed by Cho and Suh (2013). Iterative Self-Organizing Data Analysis (ISODATA) was used for the clustering of NDVI data over the misclassified pixels and land cover types was determined based on the seasonal variation pattern of NDVI. The final land cover map was generated through the merging of correctly classified MODIS land cover map and reclassified land cover map. The validation results using the 138 ground truth data showed that the overall accuracy of classification is improved from 68% of original MODIS land cover map to 74% of reclassified land cover map.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.83-100
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• 1995

The land-use has changed rapidly during the last two decades in accordance with urbanization in the Seoul Metropolitan Region. As a result of these changes, the local climate has undergone changes as well. This study intends to define the land-use changes, and then to show how they have brought in significant changes in the local climates. Land-use changes in the study area so repidly that up-to date maps and documents are not available at present. Therefore, Landsat data for land-use classification and NOAA AVHRR thermal data for the temperature fields were analyzed. Additionary, to visualize the effect of the land-use on the local climate, computer-enhanced brightness temperatures, Green Belt and city boundaries were overlaid on land-use patterns obtained from satellite images using GIS techniques. The results of analysis demonstrate that Green Space in the Seoul Metropolitan Region decreased from 94% to 62% while urban land-use increased ten times, from 4% to 39% for the period of 1972-1992. The resulting disappearance of biomass caused by land-use changes may have implications for the local-and micro-climate. The results show that the local climate of the study area became drier and warmer. This study also suggests a need for further studies of man's effects on local climate to minimize adverse influences and hazardous pollution and efficacious ways for urban planning.