• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• Journal of Korean Society of Forest Science
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• v.96 no.6
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• pp.730-736
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• 2007

The heterogeneity of forest environment plays an important role in the structure and dynamics of tree population, the composition of forest community, and the maintenance of species diversity. Based upon the research data of the nine hectare permanent plot in the typical mixed broadleaved-Korean pine forest, this study was conducted to analyze the characteristics of spatial pattern of Acer tegmentosum population for seedlings, saplings, and living and dead trees so as to evaluate the effect of micro-topography on spatial pattern of the species. The results noted that A. tegmentosum preferred to gentle slopes. There was no difference in density of seedlings by the variation of aspect, but the density of saplings, and living and dead trees was high on the western and southeastern slopes. Living trees of A. tegmentosum showed the clumped pattern for all scales within 150 m and highest at the scale of 30 m. Dead stems of the species indicated the clumped pattern within 111 m, highest at the scale of 72 m, and random pattern beyond the scale of 111 m (P < 0.01 ). The similarity of occurrence by developmental stages of A. tegmentosum showed that seedlings vs. saplings, saplings vs. living trees, and living trees vs. dead stems had highly positive correlation to each other, respectively (P < 0.01 ), indicating that the occurrence of previous developmental stages was positively correlated to following stages.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.52-67
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• 2020

Recently, the Arctic has been exposed to snow-covered land due to melting permafrost every year, and the Korea Geographic Information Institute(NGII) provides polar spatial information service by establishing spatial information of the polar region. However, there is a lack of spatial information on vegetation sensitive to climate change. This research used a multi-temporal Sentinel-2 image to perform land cover classification of the Ny-Ålesund in Arctic Svalbard. In the pre-processing step, 10 bands and 6 vegetation spectral index were generated from multi-temporal Sentinel-2 images. In image-classification step is consisted of extracting the vegetation area through 8-class land cover classification and performing the vegetation species classification. The image classification algorithm used Random Forest to evaluate the accuracy and calculate feature importance through Out-Of-Bag(OOB). To identify the advantages of multi- temporary Sentinel-2 for vegetation classification, the overall accuracy was compared according to the number of images stacked and vegetation spectral index. Overall accuracy was 77% when using single-time Sentinel-2 images, but improved to 81% when using multi-time Sentinel-2 images. In addition, the overall accuracy improved to about 83% in learning when the vegetation index was used additionally. The most important spectral variables to distinguish between vegetation classes are located in the Red, Green, and short wave infrared-1(SWIR1). This research can be used as a basic study that optimizes input characteristics in performing the classification of vegetation in the polar regions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.313-318
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• 2017

Land surface temperature is essential for monitoring abnormal climate phenomena such as UHI (Urban Heat Islands), and for modeling weather patterns. However, the quality of surface temperature obtained from the optical space imagery is affected by many factors such as, revisit period of the satellite, instance of capture, spatial resolution, and cloud coverage. Landsat 8 imagery, often used to obtain surface temperatures, has a high resolution of 30 meters (100 meters rearranged to 30 meters) and a revisit frequency of 16 days. On the contrary, MODIS imagery can be acquired daily with a spatial resolution of about 1 kilometer. Many past attempts have been made using both Landsat and MODIS imagery to complement each other to produce an imagery of improved temporal and spatial resolution. This paper applied machine learning methods and performed downscaling which can obtain daily based land surface temperature imagery of 30 meters.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.565-572
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• 2001

The spatial genetic structure of Needle fir(Abies holophylla Max.) seedlings on forest gap within a Needle fir forest at Mt. Odae in Korea was analyzed on the basis of ISSR(inter-simple sequence repeats) marker analysis. The gap size was $1,500m^2(50m{\times}30m)$, and we sampled 416 one- or two-year-old seedlings by 2m intervals. Some trees at the upper crown layer except Needle firs and all trees at the middle and lower crown layers were removed, and Needle firs at the upper crown layer showed very weak growth strength or to be withering to death. The results of spatial autocorrelation using 31 polymorphic ISSR markers revealed that it was genetically homogeneous within spatial distance of 15.6m and the randomness of genetic distribution was from 15.6m to 31.2m. The genetic patch size of seedlings in forest gap might be restricted by the density of mother trees, making allow for the average height of adult Needle firs, the seed dispersal area, and the average distance between adults. For the directionality of seedling distribution, we investigated the variography using 'genetic configuration' which was the value of configuration in Multidimensional Scaling by genetic distance. In directional variogram, the increment of spatial distance from East to West direction was inversely proportional to genetic homogeneity. We presumed that this anisotrophy of seedling distribution at this forest gap resulted from the directionality of seed dispersal rather than the difference of fecundity between mother trees or the microhabitat variation, taking the evenness of forest floor condition, a vast seed production and the random distribution of seedlings at the studied site into consideration.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.74-99
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• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• Journal of Ecology and Environment
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• v.47 no.3
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• pp.75-84
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• 2023

Background: Knowledge of the spatial trends of plant invasions in different habitats is essential for a better understanding of the process of these invasions. We examined the variation in invasive alien plant species (IAS) richness and composition at two spatial scales defined by elevation and habitat types (roadside, forest, and cultivated lands) in the Makawanpur district of Nepal. Following an elevation gradient ranging from 500 to 2,400 m asl along a mountain road, plant species cover was recorded within sample plots of size 10 m × 5 m. Systematic random sampling was adopted in every 100 m elevation intervals on three habitat types. Results: Altogether 18 invasive alien plants belonging to eight families were recorded within 60 plots, of which 14 species (representing 80%) were from tropical North and South America. The most common plants by their frequency were Ageratina adenophora, Chromolaena odorata, Bidens pilosa, Lantana camara, and Parthenium hysterophorus. We found a significant relationship between species composition and elevation in the study area. Low-elevation regions had a higher number of alien species as compared to high-elevation regions within different habitat types. Conclusions: The species richness and density of IAS were higher in the road site followed by the cultivated land and forest sites. This pattern occurred throughout the elevation range and habitats. IAS were found mostly in the open land with high sunlight availability. Information from such scientific assessment of invasive alien plants will assist in developing appropriate management plans in the Makawanpur district.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.9-17
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• 2017

Hyperspectral imagery is used in the land cover classification with the principle component analysis and minimum noise fraction to reduce the data dimensionality and noise. Recently, studies on the supervised classification using various features having spectral information and spatial characteristic have been carried out. In this study, principle component bands and normalized difference vegetation index(NDVI) was utilized in the supervised classification for the land cover classification. To utilize additional information not included in the principle component bands by the hyperspectral imagery, we tried to increase the classification accuracy by using the NDVI. In addition, the extended attribute profiles(EAP) generated using the morphological filter was used as the input data. The random forest algorithm, which is one of the representative supervised classification, was used. The classification accuracy according to the application of various features based on EAP was compared. Two areas was selected in the experiments, and the quantitative evaluation was performed by using reference data. The classification accuracy of the proposed algorithm showed the highest classification accuracy of 85.72% and 91.14% compared with existing algorithms. Further research will need to develop a supervised classification algorithm and additional input datasets to improve the accuracy of land cover classification using hyperspectral imagery.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.