• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.103-112
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• 2014

The objective of this study is to suggest the possibility on land cover classification using hyperspectal imagery on area which includes lands and waters. After atmospheric correction as a preprocessing work was conducted on hyperspectral imagery acquired by airborne hyperspectral sensor CASI-1500, the effect of atmospheric correction to a few land cover class in before and after atmospheric correction was compared and analyzed. As the result of accuracy of land cover classification by highspectral imagery using reference data as airphoto and digital topographic map, maximum likelihood method represented overall accuracy as 67.0% and minimum distance method showed overall accuracy as 52.4%. Also product accuracy of land cover classification on road, dry field and green house, but that on river, forest, grassland showed low because the area of those was composed of complex object. Therefore, the study needs to select optimal band to classify specific object and to construct spectral library considering spectral characteristics of specific object.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.1
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• pp.27-37
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• 2002

The classification accuracy of land cover has been considered as one of the major issues to estimate pollution loads generated from diffuse landuse patterns in a watershed. This research aimed to assess the effects of the acquisition methods and sampling size of training reference data on the classification accuracy of land cover using an imagery acquired by optical sensor(OPS) on JERS-1. Two kinds of data acquisition methods were considered to prepare training data. The first was to assign a certain land cover type to a specific pixel based on the researchers subjective discriminating capacity about current land use and the second was attributed to an aerial photograph incorporated with digital maps with GIS. Three different sizes of samples, 0.3%, 0.5%, and 1.0% of all pixels, were applied to examine the consistency of the classified land cover with the training data of corresponding pixels. Maximum likelihood scheme was applied to classify the land use patterns of JERS-1 imagery. Classification run applying an aerial photograph achieved 18 % higher consistency with the training data than the run applying the researchers subjective discriminating capacity. Regarding the sample size, it was proposed that the size of training area should be selected at least over 1% of all of the pixels in the study area in order to obtain the accuracy with 95% for JERS-1 satellite imagery on a typical small-to-medium-size urbanized area.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• Spatial Information Research
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• v.20 no.5
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• pp.25-35
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• 2012

This study compared the influence of water quality according to the data sources of spatial information. Firstly, land cover map was constructed through image classification of Daecheong-dam basin and the accuracy of image classification from satellite image showed high as 88.76% in comparison with the large-scaled land cover map in Ministry of Environment, to calculate Event Mean Concentration (EMC) by land cover that impact on the evaluation of nonpoint source pollutant loads. Also curve number and direct runoff were calculated by spatial overlay with soil map and land cover map from image classification. And Seokcheon and Daecheong-Dam basin showed high in the analysis of curve number and direct runoff. Samgacheon-Joint and Sokcheon-Downstream basin showed high in the nonpoint source pollutant loads of BOD from direct runoff and EMC. And Samgacheon-Joint and Bonghwangcheon- Downstream basin showed high in the nonpoint source pollutant loads of TN and TP. Nonpoint source pollutant loads from image classification were compared with those by the land cover map from Ministry of Environment to present the effectivity of nonpoint source pollutant loads from satellite image. And Daecheong-Dam Upstream basin showed high as 10.64%, 11.70% and 20.00% respectively in the errors of nonpoint source pollutant loads of BOD, TN, and TP. Therefore, it is desirable that spatial information including with paddy and dry field is applied to the evaluation of nonpoint source pollutant loads in order to simulate water quality of basin effectively.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.171-183
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• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.59-65
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• 1998

A thematic land cover map of Pusan port area was developed using Landsat satellite TM(Thematic Mapper) image. Two types of digital data which are road and sea water layer are extracted from existing paper map were overlayed over the developed land cover map. SPIN-2(KNR-1000) image was utilized to make a facility map of JaSungDae port. SPIN-2 image, which has a cell resolution of 1.56 m showed higer accuracy than TM image, which has a cell resolution of 30 m for facility mapping. Overall, the techniques of digital mapping using satellite image are very useful, effective and efficient.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.201-206
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• 1998

In this paper, we have considered the method for clustering land cover types over the East Asia from AVHRR data. The feature vectors such that maximum NDVI, amplitude of NDVI, mean NDVI, and NDVI threshold are extracted from the 10-day composite by maximum value composite(MVC) for reducing the effect of cloud contaninations. To find the land cover clusters given by the feature vectors, we are adapted the self-organizing feature map(SOFM) clustering which is the mapping of an input vector space of n-dimensions into a one - or two-dimensional grid of output layer. The approach is to find first the clusters by the first layer SOFM and then merge several clusters of the first layer to a large cluster by the second layer SOFM. In experiments, we were used the 8-km AVHRR data for two years(1992-1993) over the East Asia.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.445-449
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• 2006

Diffuse pollution has been considering as a major source of the quality deterioration of water resources. The establishment of riparian vegetation strips or buffers along those areas of water bodies is used to reduce the threat of diffuse pollution. Remote sensing offers a means by which critical areas could be identified, so that subsequent action toward the establishment of riparian zones can be taken. On the behalf of KOMPSAT-2 satellite imagery as a high resolution spatial data, Landsat TM satellite data are used to aquire the land cover for the riparian buffers studied. This investigation aims to assess the riparian buffers established on the upper Geum river as a pollution mitigation. Through comparing the delineation of riparian buffer zones developed with the existing zones established by the government, we can find the critical distortion points of the existing riparian buffer zone.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.190-200
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• 2022

In this study, the island's land-use and land-cover change (LULCC) is analyzed in South Korea using remotely sensed land cover data(Globeland 30) acquired from 2000 to 2020 to meet the requirement of providing practical information for forest management. Analysis of LULCC between the 2000 and 2020 images revealed that changes to agricultural land were the most common type of change (7.6% of pixels), followed by changes to the forest (5.7%). The islands forests maintain 157,246 ha (42.2% of the total island area). Land cover types that changed to the forest from grasslands were 262 islands, while reverse cases have occurred on 421 islands. These 683 islands have a possibility of transition and disturbance. The artificial land class was newly calculated in 22 islands. The forests, which account for 42.2% of the 22 island area, turned into grassland, and 27.8% of agricultural land and grassland turned into forests. The development of artificial land often affects developed areas and surrounding areas, resulting in deforestation, management of agriculture, and landscaping. This study can provide insights concerning the fundamental data for assessing ecological functions and constructing forest management plans in islands ecosystems.