• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.3
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• pp.29-38
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• 2008

This study intends to analyze the value of school forests, one type of urban forest. For this purpose, four attributes of school forests were investigated, considering ecological, educational, social and economic values using a conjoint model as the stated preference. Based on literature reviews, the levels of the four attributes were selected, and a questionnaire survey was given to 279 urban residents divided into 2 groups: those impacted by school forests and those not. The study results suggest that the most important attribute of school forests is economic value, and next is ecological, social and educational value according to the part-worth model. The fitness level of the model is 0.900(total group) which is very significant. As for the economic value, free and 1,000 won are more critical factors than the other 2 levels, 5,000 won and 10,000 won and air pollution purification and making the school landscape are more critical factors than small habitats and microclimate factors. In addition, regarding the social value related to residents' leisure activities，the utility of nature observation is higher than walking and exercising. Finally, for educational value, understanding nature's importance is more critical than the emotions and learning of students. The estimated WTP per household/month is 3,580 won, the group related to school forestsis 3,650 won and the non-related group is 3,540 won. Based on these results, the estimated total economic value of all households per year is 6,820 hundred million won. The group related to school forests is 6,970 hundred million won and the non-related group is 6,750 hundred million won.

• 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 Korean Society of Rural Planning
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• v.11 no.2 s.27
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• pp.59-69
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• 2005

Because of increasing numbers of cars many highways are being constructed lively, and the noise of passing cars has influenced surrounding areas. In consideration of this, some alternatives and researches for soundproof facilities are proceeding, but aesthetic approach hasn't been considered. Therefore, this research is focused on soundproof effects for each types, effectual simulation methods, visual assessment and estimation between the landscape before simulation and the landscape after. Soundproof facilities are divided largely by the soundproof barrier, the soundproof mounding, the soundproof grove. The soundproof grove has three main function. First, leaves and branches absorbs sound vibrations. Second, leaves absorbs sound, and branches obstruct sounds. Third, by means of sounds of shaking leaves, forest can offset noises. This research was proceeded by means of classification of soundproof grove types and investigation of visual simulation methods. We made visual simulation for each types, and estimated the landscape for each types.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Korean Journal of Environment and Ecology
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• v.24 no.2
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• pp.178-185
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• 2010

The purpose of this study is to verify the effectiveness of the biotope model in applying and developing Korean urban forests. We found that there are 17 biotope assessment indicators, including forest layer structure, site conditions, ratio of broad-leaved trees, species richness, etc. In terms of correlation analysis between indicators, the stand ages and the period of space formation have the highest relativity(coefficient 0.684). On the other hand, indicators that have negative relativity are layer structure and risk, with a coefficient of -0.412. Ten models were developed for the multiple regression analysis. 10 variables(site conditions(X2), ratio of broad-leaved trees(X3) and so forth except layer structure(X1), species richness(X4)) were found to have a 95% significance level The results from comparing the regression model and adding-up estimation matrix, the most accurate one was Model 3, which has a 91.7% out of the 10 models. However more monitoring will be needed to improve the accuracy of models for the Korean urban forests in future.

• Journal of Environmental Impact Assessment
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• v.19 no.2
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• pp.117-125
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• 2010

Korea's researchers have recently studied the prediction of forest change, but they have not considered landuse/cover change compared to distribution of forest vegetation. The purpose of our study is to predict forest vegetation based on landuse/cover change on the Korean Peninsula in the 2090's. The methods of this study were Multi-layer perceptrom neural network for Landuse/cover (water, urban, barren, wetland, grass, forest, agriculture) change and Multinomial Logit Model for distribution prediction for forest vegetation (Pinus densiflora, Quercus Spp., Alpine Plants, Evergreen Broad-Leaved Plants). The classification accuracy of landuse/cover change on the Korean Peninsula was 71.3%. Urban areas expanded with large cities as the central, but forest and agriculture area contracted by 6%. The distribution model of forest vegetation has 63.6% prediction accuracy. Pinus densiflora and evergreen broad-leaved plants increased but Quercus Spp. and alpine plants decreased from the model. Finally, the results of forest vegetation based on landuse/cover change increased Pinus densiflora to 38.9% and evergreen broad-leaved plants to 70% when it is compared to the current climate. But Quercus Spp. decreased 10.2% and alpine plants disappeared almost completely for most of the Korean Peninsula. These results were difficult to make a distinction between the increase of Pinus densiflora and the decrease of Quercus Spp. because of they both inhabit a similar environment on the Korean Peninsula.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.29-37
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• 2017

It has gradually become important to estimate a forest stand volume utilizing LiDAR data. Recently, various statistical models including a linear regression model has been introduced to estimate a forest stand volume using LiDAR data. One of limitations of the current approaches is in that the accuracy of observed forest stand volume data, which is used as a response variable, is questionable unstable. To overcome this limitation, we consider a spatial structure for a forest stand volume. In this research, we propose a hierarchical model for applying a spatial structure to a forest stand volume. The proposed model is applied to the LiDAR data and the forest stand volume for Bonghwa, Gyeongsangbuk-do.

• Spatial Information Research
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• v.17 no.3
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• pp.251-259
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• 2009

As a result of the current trend towards promoting conservation of the ecosystem, there have been various studies conducted to determine ways to establish an ecological network. The development of analytical methods and an environmental database of GIS has made the creation of this network more efficient. This study focuses on the development of an urban spatial decision support system based on 'Landscape Ecology Theory'. The spatial decision support system suggested in this study consists of four stages. First, landscape patch for the core areas, which are major structures of the ecological network, was determined using the GIS overlay method. Second, a forest habitat was investigated to determine connectivity assessment. Using the gravity model, connectivity assessment at the habitat forest was conducted to select the needed connecting area. Third, the most suitable corridor routes for the eco-network were presented using the least-cost path analysis. Finally, a brief investigation was conducted to determine the conflict areas between the study result and landuse. The results of this study can be applied to urban green network planning. Moreover, the method developed in this study can be utilized to control urban sprawl, promote biodiversity.

• Journal of Forest and Environmental Science
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• v.35 no.4
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• pp.240-247
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• 2019

This study quantified carbon reduction services through direct harvesting of Ilex rotunda and Machilus thunbergii, which are the typical urban landscape tree species in southern Korea. A total of 20 open-grown tree specimens (10 specimens for each species) were selected reflecting various sizes of stem diameter at breast height of 1.2 m (DBH) at a regular interval. The study measured biomass for each part of the tree specimens including roots to compute total carbon storage per tree. Annual carbon uptake per tree was also calculated analyzing the DBH growth rate of stem disk specimens. Quantitative models were developed using DBH as an independent variable to easily estimate storage and annual uptake of carbon by tree growth for each species. All the models had a high goodness-of-fit with R²=0.95-0.99. The difference in carbon reduction services between DBH sizes increased with increasing DBH. The storage and annual uptake of carbon from a tree with DBH of 10 cm were 13.5 kg and 2.4 kg/yr for I. rotunda, and 19.1 kg and 3.6 kg/yr for M. thunbergii, respectively. The tree of this size stored the amount of carbon equivalent to that emitted from a gasoline use of approximately 24 L for I. rotunda and 34 L for M. thunbergii, respectively. The study provides actual measurement data to quantify carbon reduction services of urban open-grown landscape trees for the warm-temperate species that have been little known until now.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.151-152
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• 2001

A common problem of the model simulations of the land surface - atmosphere interaction is to choose the appropriate spatial scale and resolution at which the simulations are to be performed. The accuracy of energy and water exchange predictions between the land surface and the atmosphere in regional and global scale atmospheric models is mainly influenced by: model simplifications applied to describe the spatial heterogeneity of land surface properties within individual grid cells; ignoring the variability of sub-grid properties (e.g. relief, vegetation, soils), and; lacks of necessary input meteorological and biophysical data.(omitted)