• Membrane and Water Treatment
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• v.10 no.1
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• pp.29-38
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• 2019

Microbial community and composition in stormwater runoff from mixed land use land cover (LULC) catchment with ongoing land development was diverse across the hydrological stage due different environmental parameters (hydrometeorological and physicochemical) and source of runoff. However, limited studies have been made for bacterial composition in this catchment. Therefore, this study aims to: (1) quantify the concentration of fecal indicator bacteria (FIB), stormwater quality and bacterial composition and structure according to hydrological stage; and (2) determine their correlation to environmental parameters. The 454 pyrosequencing was used to determine the bacterial community and composition; while Pearson's correlation was used to determine the correlation among parameters-FIB, stormwater quality, bacterial composition and structure-to environmental parameters. Results demonstrated that the initial and peak runoff has the highest concentration of FIB, stormwater quality and bacterial composition and structure. Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes were dominant bacteria identified in this catchment. Furthermore, the 20 most abundant genera were correlated with runoff duration, average rainfall intensity, runoff volume, runoff flow, temperature, pH, organic matter, nutrients, TSS and turbidity. An increase of FIB and stormwater quality concentration, diversity and richness of bacterial composition and structure in this study was possibly due to leakage from septic tanks, cesspools and latrines; feces of domestic and wild animals; and runoff from forest, destroyed septic system in land development site and urban LULC. Overall, this study will provide an evidence of hydrological stage impacts on the runoff microbiome environment and public health perspective.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.426-428
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• 2003

Optical and microwave remote sensing data have been widely used in land cover and land use classification. Thanks to the spectral absorption characteristics of ground object in visible and near infrared region, optical data enables to extract different land cover types according to their material composition like water body, vegetation cover or bare land. On the other hand, microwave sensor receives backscatter radiance which contains information on surface roughness, object density and their 3-D structure that are very important complementary information to interpret land use and land cover. Separate use of these data have brought many successful results in practice. However, the accuracy of the land use / land cover established by this methodology still has some problems. One of the way to improve accuracy of the land use / land cover classification is just combination of both optical and microwave data in analysis. In this paper for the research, the author used LANDSAT TM scene 127/45 acquired on October 21, 1992, JERS-1 SAR scene 119/265 acquired on October 27, 1992 and aerial photographs taken on October 21, 1992. The study area has been selected in Hanoi City and surrounding area, Vietnam. This is a flat agricultural area with various land use types as water rice, secondary crops like maize, cassava, vegetables cultivation as cucumber, tomato etc. mixed with human settlement and some manufacture facilities as brick and ceramic factories. The use of only optical or microwave data could result in misclassification among some land use features as settlement and vegetables cultivation using frame stages. By combination of multitemporal JERS-1 SAR and TM data these errors have been eliminated so that accuracy of the final land use / land cover map has been improved. The paper describes a methodology for data combination and presents results achieved by the proposed approach.

• 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.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.122-127
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• 1998

This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.

• Journal of Korean Society of Rural Planning
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• v.15 no.2
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• pp.19-26
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• 2009

The purpose of this study is to build Island biogeography in the basic concept of landscape ecology in South Korea by draw relationship between the species side of quantitative habitats and forest area surveyed in the national database based on investigation of the 2nd natural environment. In addition, try to present criterion of habitats character category after understanding habitats character of emergence area side of quality habitats based on the type of formatting. Species and forest area relationship analyzed using correlation analysis and simple regression analysis. Also habitat character limited composition ratio of neighboring land cover and analyzed using hierarchical cluster analysis to classify type of habitat. As a result, we found that forest area is correlated with number of species, forests which is bigger than 100ha are more important of increase in species' population. And according to land cover composition ratio, bird's classified types of forest inner species, forest edge species, forest outer species and mammal's classified types of forest inner species, forest general species, forest edge species. We suggest that study of species-forest area relationship and emergence habitat character be used as some management plans of species' conservation, protection and restoration.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.6
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• pp.123-130
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• 2009

Fragmentation due to land developments causes disturbances and changes of composition in forest vegetation. The purpose of the study was to develop the impact assessment model for quantitative distance or degree of disturbance by land developments. This study conducted a survey about structure and composition of forest vegetation to determine degree of impact from land developments. The results of field survey, there was a difference in structure and composition of forest vegetation such as tree canopy, herbaceous cover, and number of vine and alien species the distances from edge to interior area such as 0m, 10m, 20m, 40m, and over 60m. To assess the disturbance of forest vegetation, the factors selected were the rate of vine's cover and appearance of alien species. The impact assessment model about vine species explained by a distance, forest patch size, type of forest fragmentation, and type of vegetation ($R^2$=0.44, p<0.001). The other model about alien species explained by a distance, type of forest fragmentation, type of vegetation, and width of road (85.9%, p<0.005). The models applied to Samsong housing development in Goyang-si, Gyunggi-do. The vines and alien species in the study area have had a substantial impact on forest vegetation from edge to 20 or 40m. The impact assessment models were high reliability for estimating impacts to land developments. The impact of forest vegetation by development activities could be minimized thorough the adoption of the models introduced at the stage of EIA.

• Journal of Ecology and Environment
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• v.38 no.2
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• pp.185-193
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• 2015

A dramatic decline in the abundance of the scaled quail (Callipepla squamata) has been observed across most of its geographic range. In order to evaluate the influence of land cover patterns and their changes on scaled quail abundance, we examined landscape patterns and their changes from the 1970s to the1990s in two large ecoregions with contrasting population trends: (1) the Rolling Plains ecoregion with a significantly decreased scaled quail population and (2) the South Texas Plains ecoregion with a relatively stable scaled quail population. The National Land Cover Database (NLCD) and the U.S. Geological Survey's (USGS) Land Use/Land Cover data were used to quantify landscape patterns and their changes based on 80 randomly located $20{\times}20km^2$ windows in each of the ecoregions. We found that landscapes in the Rolling Plains and the South Texas Plains were considerably different in composition and spatial characteristics related to scaled quail habitats. The landscapes in the South Texas Plains had significantly more shrubland and less grassland-herbaceous rangeland; and except for shrublands, they were more fragmented, with greater interspersion among land cover classes. Correlation analysis between the landscape metrics and the quail-abundance-survey data showed that shrublands appeared to be more important for scaled quail in the South Texas Plains, while grassland-herbaceous rangelands and pasture-croplands were essential to scaled quail habitats in the Rolling Plains. The decrease in the amount of grassland-herbaceous rangeland and spatial aggregation of pasture-croplands has likely contributed to the population decline of scaled quails in the Rolling Plains ecoregion.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.1
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• pp.21-31
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• 2002

This study applied each Neural Networks theory and Fuzzy Set theory to improve accuracy in remotely sensed images. Remotely sensed data have been used to map land cover. The accuracy is dependent on a range of factors related to the data set and methods used. Thus, the accuracy of maps derived from conventional supervised image classification techniques is a function of factors related to the training, allocation, and testing stages of the classification. Conventional image classification techniques assume that all the pixels within the image are pure. That is, that they represent an area of homogeneous cover of a single land-cover class. But, this assumption is often untenable with pixels of mixed land-cover composition abundant in an image. Mixed pixels are a major problem in land-cover mapping applications. For each pixel, the strengths of class membership derived in the classification may be related to its land-cover composition. Fuzzy classification techniques are the concept of a pixel having a degree of membership to all classes is fundamental to fuzzy-sets-based techniques. A major problem with the fuzzy-sets and probabilistic methods is that they are slow and computational demanding. For analyzing large data sets and rapid processing, alterative techniques are required. One particularly attractive approach is the use of artificial neural networks. These are non-parametric techniques which have been shown to generally be capable of classifying data as or more accurately than conventional classifiers. An artificial neural networks, once trained, may classify data extremely rapidly as the classification process may be reduced to the solution of a large number of extremely simple calculations which may be performed in parallel.

• Spatial Information Research
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• v.16 no.1
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• pp.19-32
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• 2008

Usually in South Korea, land cover type and topographic undulation are frequently changed even in a narrow area. However, most of researches using aerial LIDAR(LIght Detection And Ranging) data in abroad had been acquired in the study areas to be changed infrequently. This research was performed to explore reconstruction methodologies of 3D surface models considering the distribution of land cover type and topographic undulation. Composed of variously undulatory forests, rocky river beds and man-made land cover such as streets, trees, buildings, parking lots and so on, an area was selected for the research. First of all, the area was divided into three zones based on land cover type and topographic undulation using its aerial ortho-photo. Then, aerial LIDAR data was clipped by each zone and different 3D modeling processes were applied to each clipped data before integration of each models and reconstruction of overall model. These kinds of processes might be effectively applied to landscape management, forest inventory and digital map composition. Besides, they would be useful to resolve less- or over-extracted problems caused by simple rectangle zoning when an usual data processing of aerial LIDAR.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.472-479
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• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.