• Korean Journal of Remote Sensing
• /
• v.19 no.6
• /
• pp.479-490
• /
• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.406-412
• /
• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.409-418
• /
• 2021

As the amount of construction for aerial photography increases, the need for automation of quality inspection is emerging. In this study, an experiment was performed to classify or detect clouds in aerial photos using deep learning techniques. Also, classification and detection were performed by including satellite images in the learning data. As algorithms used in the experiment, GoogLeNet, VGG16, Faster R-CNN and YOLOv3 were applied and the results were compared. In addition, considering the practical limitations of securing erroneous images including clouds in aerial images, we also analyzed whether additional learning of satellite images affects classification and detection accuracy in comparison a training dataset that only contains aerial images. As results, the GoogLeNet and YOLOv3 algorithms showed relatively superior accuracy in cloud classification and detection of aerial images, respectively. GoogLeNet showed producer's accuracy of 83.8% for cloud and YOLOv3 showed producer's accuracy of 84.0% for cloud. And, the addition of satellite image learning data showed that it can be applied as an alternative when there is a lack of aerial image data.

• Ecology and Resilient Infrastructure
• /
• v.8 no.2
• /
• pp.97-111
• /
• 2021

This study was conducted to develop a habitat type classification system and its map based on the ecological characteristics of species, spatial type, vegetation, topography, and geological conditions preferred by species. To evaluate the relationships between species and their habitats in Korean national parks, we prepared a classification standard table for systematic classification of habitat types. This classification system divides habitats into 6 low-level and 59 mid-level ecological classes based on habitat structure. The mid-level system divided forest ecosystems into 20 subtypes, stream and wetland ecosystems into 8 types, coastal ecosystems into 7 types, arable land into 6 types, development land into 9 types, and 1 type of marine ecosystem. A habitat classification map was drawn utilizing square images, detailed vegetation maps, and forest stand maps, based on the above habitat classification system, and it covered 1,461 plots spanning 21 national parks. The habitat classification system and survey protocol, which consider domestic habitat conditions, should be further developed and applied to habitat assessment, to enhance the utility of this study.

• Journal of Environmental Impact Assessment
• /
• v.22 no.2
• /
• pp.147-156
• /
• 2013

Myanmar(Burma) has been preserved valuable environmental resources because of its political isolation. But recently, Myanmar has moved a capital city(Naypyidaw) at central forest area and it has been urbanized radically since 2005. In this paper, we built multi-temporal land cover map from Landsat images of 1970s to 2012 with ENVI 4.5 software. For a broad approach, administrative district Yamethin which includes Naypyidaw is classified into 3 classes and with only Naypyidaw region is classified with 4-5 classes to analyse specific changes. And with forest cover extracted by Object Oriented Classification, we evaluated forest fragmentation before and after the development using Patch Analyst(FRAGSTATs 3.3) at Yamethin area. For Yamethin area, there were significant forest cover change, 51% in 1999 to 48% in 2012, and for Naypyidaw area, 67% in 1999 to 57% in 2012 respectively. Also landscape indices resulted from Patch Analyst concluded that the total edge, edge density and mean shaped index of forest patches increased and total core area is decreased. It is attributed from land cover change with urbanization and agricultural land expansion.

• Journal of Forest and Environmental Science
• /
• v.25 no.1
• /
• pp.49-55
• /
• 2009

The present study analyses the effect of land use on the vegetation of some desert ecosystems in Abu Dhabi, United Arab Emirates (UAE). Three sites were selected to represent different types of land use, inside Umm Al-Banadeq forest, outside the forest and along Abu Dhabi-Al Ain Trucks Road. In total, fifty-two stands were examined; including a matrix of 14 species ${\times}$ 52 stands. Based on species cover data, stands were classified using TWINSPAN and ordinated using DCA. Four vegetation groups were generated at level three of classification. Zygophyllum mandavillei was dominant in most vegetation groups; Heliotropium bacciferum dominated vegetation groups inhabited the forest. Species richness, species turnover, relative evenness and relative concentration of dominance of forest vegetation groups were 2.8, 5.7, 0.7, and 2.0, respectively. The differences were attributed to both natural variability and forestry-induced changes, including change in land use, drainage and ploughing and shading by trees. Vegetation group inhabited Abu Dhabi-Al Ain Trucks Road, that were dominated by Haloxylon salicornicum and Zygophyllum mandavillei have high total cover (8.8 m per $m^{-1}$). Most community and vegetation attributes were significantly higher inside the forest than outside. Human interventions and environmental factors affected species diversity and abundance of these communities.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.7 no.1
• /
• pp.116-127
• /
• 2004

The objectives of this study are to build up the concept of Environment-Oriented Land Suitability Assessment(EOLSA) and to develop the EOLSA mapping system by applying the EOLSA criteria to the case study area. In order to draw out the EOLSA critera, this study adopted the Delphi method including the experts' awareness survey to urban planners as well as environmental researchers in May and June 2001. As a result, the concept of EPLSA was defined as a process of land use planning to scientifically assess the physical and environmental value of land and to classify conservation aptitude into several grades for the sustainable management of environmental resources. With an outcome of applying the EOLSA criteria with five degrees to the Seoul Metropolitan Area (SMA), Grade I, indicating the highest conservation value, accounted for 57.76% of the SMA. Then, Grade II reached up to 15.06%, Grade III 3.12%, Grade IV 15.92%, and Grade V, the lowest conservation value, 7.99% respectively. And also, the case analysis showed that the share of Grade I area was the highest in Gapyong county and Yangpyong county, Pochon county, Yeonchon county, Yongin city in the order and the lowest in Kwangmyong city, Osan city, Kunpo city, Kuri city, and Buchon city.

• Korean Journal of Remote Sensing
• /
• v.35 no.1
• /
• pp.83-92
• /
• 2019

Land cover (LC) is an important factor in socioeconomic and environmental studies. According to various studies, a number of LC maps, including global land cover (GLC) datasets, are made using polar orbit satellite data. Due to the insufficiencies of reference datasets in Northeast Asia, several LC maps display discrepancies in that region. In this paper, we performed a feasibility assessment of LC mapping using Geostationary Ocean Color Imager (GOCI) data over Northeast Asia. To produce the LC map, the GOCI normalized difference vegetation index (NDVI) was used as an input dataset and a level-2 LC map of South Korea was used as a reference dataset to evaluate the LC map. In this paper, 7 LC types(urban, croplands, forest, grasslands, wetlands, barren, and water) were defined to reflect Northeast Asian LC. The LC map was produced via principal component analysis (PCA) with K-means clustering, and a sensitivity analysis was performed. The overall accuracy was calculated to be 77.94%. Furthermore, to assess the accuracy of the LC map not only in South Korea but also in Northeast Asia, 6 GLC datasets (IGBP, UMD, GLC2000, GlobCover2009, MCD12Q1, GlobeLand30) were used as comparison datasets. The accuracy scores for the 6 GLC datasets were calculated to be 59.41%, 56.82%, 60.97%, 51.71%, 70.24%, and 72.80%, respectively. Therefore, the first attempt to produce the LC map using geostationary satellite data is considered to be acceptable.

• Journal of Environmental Science International
• /
• v.19 no.3
• /
• pp.281-293
• /
• 2010

The present study explores time and spatial thermal environment for Daegu, which is a city built on a basin area, according to varying land cover conditions of the earth's surface by analyzing data derived from meteorological observation and satellite images. The study has classified land use by utilizing MODIS satellite images and analyzed land surface temperature. Also, by using data acquired from automatic weather system, the study has evaluated the effects of atmospheric heating caused by city pavements by analyzing the sensible heat flux between the city's land surface and the atmosphere. The results are as follows. 1) Classification of land use in the Daegu area shows 46.64% of urban and built-up area, 1.39% of watersides, 35.19% of forest, 11.43% of crops, and 5.37% grasslands. 2) During the weekdays throughout the year, the land surface temperature was high for Dalseogu, Bukgu, and Seogu regions where industrial complexes could be found. Comparatively, lower temperature could be observed in the woodlands. 3) While the land surface temperature displayed the effects of pushing air upwards during the weekdays in urban areas, the reverse was true for forest regions. During the night, the temperature did not exert any significant influence on air movement.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.2
• /
• pp.163-174
• /
• 2021

Heatwaves are one of the most common phenomena originating from changes in the urban thermal environment. They are caused mainly by the evapotranspiration decrease of surface impermeable areas from increases in temperature and reflected heat, leading to a dry urban environment that can deteriorate aspects of everyday life. This study aimed to calculate daily maximum ground surface temperature affecting heatwaves, to quantify the effects of urban thermal environment control through water cycle restoration while validating its feasibility. The maximum surface temperature regression equation according to the impermeable area ratios of urban land cover types was derived. The estimated values from daily maximum ground surface temperature regression equation were compared with actual measured values to validate the calculation method's feasibility. The land cover classification and derivation of specific parameters were conducted by classifying land cover into buildings, roads, rivers, and lands. Detailed parameters were classified by the river area ratio, land impermeable area ratio, and green area ratio of each land-cover type, with the exception of the rivers, to derive the maximum surface temperature regression equation of each land cover type. The regression equation feasibility assessment showed that the estimated maximum surface temperature values were within the level of significance. The maximum surface temperature decreased by 0.0450℃ when the green area ratio increased by 1% and increased by 0.0321℃ when the impermeable area ratio increased by 1%. It was determined that the surface reduction effect through increases in the green area ratio was 29% higher than the increasing effect of surface temperature due to the impermeable land ratio.