• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1095-1106
• /
• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.16 no.2
• /
• pp.88-100
• /
• 2013

Urban green space is one of most important aspects of urban infrastructure for improving the quality of life of city dwellers as it reduces the heat island effect and is used for recreation and relaxation. However, no systematic management of urban green space has been introduced in Korea as past practices focused on efficient development. A way to calculate the amount of green space needed to complement an urban area must be developed to preserve urban green space and to determine 'regulations determining the total amount of greenery'. In recent years, various studies have quantified urban green space and infrastructure using remotely sensed data. However, it is difficult to detect a myriad small green spaces in a city effectively when considering the spatial resolution of the data used in existing research. In this paper, we quantified small urban green spaces using CASI-1500 hyperspectral imagery. We calculated MCARI, a vegetation index for hyperspectral imagery, to evaluate the greenness of small green spaces. In addition, we applied image-classification methods, including the ISODATA algorithm and Spectral Angle Mapper, to detect small green spaces using supervised and unsupervised classifications. This could be used to categorize land-cover into four classes: unclassified, impervious, suspected green, and vegetation green.

• Journal of Environmental Science International
• /
• v.30 no.2
• /
• pp.173-184
• /
• 2021

Ecological disturbance plants distributed throughout the country are causing a lot of damage to us directly or indirectly in terms of ecology, economy and health. These plants are not easy to manage and remove because they have a strong fertility, and it is very difficult to express them quantitatively. In this study, drone hyperspectral sensor data and Field spectroradiometer were acquired around the experimental area. In order to secure the quality accuracy of the drone hyperspectral image, GPS survey was performed, and a location accuracy of about 17cm was secured. Spectroscopic libraries were constructed for 7 kinds of plants in the experimental area using a Field spectroradiometer, and drone hyperspectral sensors were acquired in August and October, respectively. Spectral data for each plant were calculated from the acquired hyperspectral data, and spectral angles of 0.08 to 0.36 were derived. In most cases, good values of less than 0.5 were obtained, and Ambrosia trifida and Lactuca scariola, which are common in the experimental area, were extracted. As a result, it was found that about 29.6% of Ambrosia trifida and 31.5% of Lactuca scariola spread in October than in August. In the future, it is expected that better results can be obtained for the detection of ecosystem distribution plants if standardized indicators are calculated by constructing a precise spectral angle standard library based on more data.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.1
• /
• pp.35-45
• /
• 2018

Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.3
• /
• pp.203-209
• /
• 2021

With the recent development of satellite sensor technology, high-spatial-resolution imagery of various spectral wavelength bands have become possible. Worldview-3 satellite sensor provides panchromatic images with high-spatial-resolution and VNIR (Visible Near InfraRed) and SWIR (ShortWave InfraRed) bands with low-spatial-resolution, so it can be used in various fields such as defense, environment, and surveying. In this study, mineral detection was performed using Worldview-3 satellite imagery. In order to effectively utilize the VNIR and SWIR bands of the Worldview-3 satellite image, the sharpening technique was applied to the spatial resolution of the panchromatic image. To confirm the utility of SWIR bands for mineral detection, mineral detection using only VNIR bands was performed and comparatively evaluated. As the mineral detection technique, SAM (Spectral Angle Mapper), a representative similarity technique, was applied, and the pixels detected as minerals were selected by applying an empirical threshold to the analysis result. Quantitative evaluation was performed using reference data on the results of similarity analysis to evaluate the accuracy of mineral detection. As a result of the accuracy evaluation, the detection rate and false detection rate of mineral detecting using SWIR bands were calculated to be 0.882 and 0.011, respectively, and the results using only VNIR bands were 0.891 and 0.037, respectively. It was found that the detection rate when the SWIR bands were additionally used was lower than that when only the VNIR bands were used. However, it was found that the false detection rate was significantly reduced, and through this, it was possible to confirm the applicability of SWIR bands in mineral detection.