• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.47-57
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• 2005

In this research, the main theme is the system integration of driving simulator and unmanned vehicle. The total system is composed of the mater system and the slave system. The master system has a cockpit system and the driving simulator. The slave system means an unmanned vehicle, which is composed of the actuator system the sensory system and the vision system. The communication system is composed of RS-232C serial communication system which combines the master system with the slave system. To integrate both systems, the signal classification and system characteristics considered DSP(Digital Signal Processing) filter is designed with signal sampling and measurement theory. In addition, to simulate the motion of tele-operated unmanned vehicle on the driving simulator, the classical washout algorithm is applied to this filter, because the unmanned vehicle does not have a limited working space, while the driving simulator has a narrow working space and it is difficult to cover all the motion of the unmanned vehicle. Because the classical washout algorithm has a defect of fixed high pass later, fuzzy logic is applied to reimburse it through an adaptive filter and scale factor for realistic motion generation on the driving simulator.

• Journal of the Korean Geographical Society
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• v.38 no.4
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• pp.630-639
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• 2003

This study aims to analysis the distribution and change of cropland and forest, the Onseong, Saebyeol, and Eundeok counties on the lower reach of Duman(Tumen) river, northeast Korea, using 1992 year Landsat TM data, 2000 year Landsat ETM data, and digital terrain elevation data(DTED). Land cover and land use of the study areas are classified into cropland, forest, village, and water body, using the supervised classification method including 1:50,000 DTED analysis, image band composition, and principal component analysis(PCA). Results of quantitative analysis present that each growth rate of cropland of Onseong and Eundeok are 22.8% and 14.7% corresponding to decreasing rates of forest, 8% and 13.6% during 8 years from 1992 to 2000. In Onseong, Saebyeol, and Eundeok, each values of mean elevations and slope gradients increased to 192m, 95m, and 91m from 157m, 85m, and 78m, and to 6.6$^{\circ}$, 3.0$^{\circ}$, and 4.4$^{\circ}$ from 5.2$^{\circ}$, 2.5$^{\circ}$, and 3.0$^{\circ}$. Especially, in case of newly developed cropland, the values of mean elevation and mean gradient have 225m, 122m, and 127m, and 9.4$^{\circ}$, 5.1$^{\circ}$, and 8.0$^{\circ}$, in above three regions. These new croplands were developing along to deeper valleys and toward lower hill and mountain slope up to knickpoint zone of gradient change. Deforested lands for cropland have formed irregular pattern of patch-type, and become sources for the sheet erosion, rilling and gulleying in mountain slope and sedimentation in local river channel. Though there were no field checking, analysis using landsat images and GIS mapping can help understand actual environmental problems relating to cropland development of mountain slope in North Korea.

• Korean Journal of Environment and Ecology
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• v.35 no.4
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• pp.377-386
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• 2021

This study was conducted to provide basic data for habitat management and preservation of Jeongmaek. A total of 18 priority research areas were selected with consideration to terrain and habitat environment, and 54 fixed plots were selected for three types of habits: development, valley, and forest road and ridge. The survey was conducted in each season (May, August, and October), excluding the winter season, from 2016 to 2018. The distribution analysis of birds observed in each habitat type using a self-organizing map (SOM) classified them into a total of four groups (MRPP, A=0.12, and p <0.005). The comparative analysis of the number of species, the number of individuals, and the species diversity index for each SOM group showed that they were all the highest in group III (Kruskal-Wallis, the number species: x² = 13.436, P <0.005; the number of individuals: x² = 8.229, P <0.05; the species diversity index: x² = 17.115, P <0.005). Moreover, the analysis by applying the land cover map to the random forest model to examine the index species of each group and identify the characteristics of the habitat environment showed a difference in the ratio of the habitat environment and the indicator species among the four groups. The index species analysis identified a total of 18 bird species as the indicator species in three groups except for group II. When applying the random forest model and indicator species analysis to the results of classification into four groups using the SOM, the composition of the indicator species by the group showed a correlation with the habitat characteristics of each group. Moreover, the distribution patterns and densities of observed species were clearly distinguished according to the dominant habitat for each group. The results of the analysis that applied the SOM, indicator species, and random forest model together can derive useful results for the characterization of bird habitats according to the habitat environment.