• Journal of Forest and Environmental Science
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• v.35 no.2
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• pp.102-114
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• 2019

The issue of sustainable forest management (SFM) continues to emerge as part of the REDD+ mechanism mitigation efforts. Especially for some developing countries, such as Indonesia, forest management is required to provide benefits to the welfare of local communities in addition to forest conservation efforts. This study aims to identify the economic, social, and environmental impacts of community-based forest management (CBFM) implementation activities, which is one of the implementations of SFM at field level. The primary objectives were to find out the impacts of CBFM activities based on local people's perceptions and to identify what factors need to be considered to increase local people's satisfaction on CBFM activities. The data from 6 sub-villages was derived through surveys with local people involved in CBFM activities, interviews with a key informant, and supported by secondary data. The results of the study state that CBFM activities have increased the local people's income as well as their welfare, strengthening the local institution, and help to resolve conflicts in the study area. CBFM has also been successful in protecting forests by rehabilitating unproductive lands and increase forest cover area. By using binary logistic regression analysis, it found that income, business development opportunities, access to forests, conflict resolution, institutional strengthening, and forest rehabilitation variable significantly affected the local people's satisfaction of CBFM activities.

• Journal of Ecology and Environment
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• v.42 no.4
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• pp.198-204
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• 2018

Background: The effective use of habitats is essential for the successful adaptation of a species to the local environment. Although habitats exhibit a hierarchical structure, including macro-, meso-, and microhabitats, the relationships among habitats of differing hierarchy have not been well studied. In this study, we studied the quantitative measures of microhabitat use of Gekko japonicus from three field populations in Japan: one at Tsushima Island, one at Nishi Park, Fukuoka, and one at Ohori Park, Fukuoka. We investigated whether land cover type, a higher hierarchical habitat component, was associated with quantitative microhabitat use, a lower hierarchical component, in these populations. Results: The substrate temperature where we located geckos (SubT) and the distance from the ground to the gecko (Height) were significantly different among the three populations. In particular, SubT on Tsushima Island was lower than it was in the other two populations. Irradiance at gecko location and Height were significantly different among the land cover types. In particular, Height in evergreen needleleaf forest was significantly lower than that in deciduous broadleaf forest. Furthermore, significant interactions between population and land cover type were observed for the SubT and Height variables. Conclusions: The quantitative measures of microhabitat use of G. japonicus varied with population and land cover type, which exhibited significant interaction effects on microhabitat use variables. These results suggest that higher hierarchical habitat components can affect the quantitative measures of lower hierarchical microhabitat use in nocturnal geckos.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.279-288
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• 2019

We evaluated the land cover classification performance of SegNet, which features semantic segmentation of aerial imagery. We selected four semantic classes, i.e., urban, farmland, forest, and water areas, and created 2,000 datasets using aerial images and land cover maps. The datasets were divided at a 8:2 ratio into training (1,600) and validation datasets (400); we evaluated validation accuracy after tuning the hyperparameters. SegNet performance was optimal at a batch size of five with 100,000 iterations. When 200 test datasets were subjected to semantic segmentation using the trained SegNet model, the accuracies were farmland 87.89%, forest 87.18%, water 83.66%, and urban regions 82.67%; the overall accuracy was 85.48%. Thus, deep learning-based semantic segmentation can be used to classify land cover.

• Journal of Forest and Environmental Science
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• v.31 no.4
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• pp.288-296
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• 2015

In many very high density cities in Asia in which there is limited area to expand, growth is forced upward as well as outward. Densely packed detached houses and low-rise buildings are replaced by lower density high-rises, leaving open spaces between high-rise buildings. Through this process, areas that formerly did not have much green space gain valuable green spaces, and new ecological corridors and patches are created. In this study, the demographic and housing-type changes of Wonju City were delineated using land use maps, aerial images, census data, and other administrative data. Green area changes were calculated using land cover data derived from multi-year Landsat TM satellite imagery. The values were then compared against demographic and housing-type changes for each administrative unit. The overall results showed a decrease of forested area in the city and an increase of developed area. Urban sprawl was clearly visible in many of the suburban areas. However, as expected, we also detected areas in which greenness did not decrease when the population greatly increased. These areas were characterized by residential building complexes of ten or more stories. If an equal number of housing units had been built as detached houses, these areas would not have kept as much green space. Our research result showed that high-density and high-rise residential structures can offer an alternative means to protect or create urban green spaces in high-density urban environments.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.6
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• pp.120-129
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• 2007

Rapid urbanization has transformed the spatial pattern of urban land use or cover. This paper concentrates that changed characteristics of landscape structure in the Tancheon Watershed, from 1995 to 2003 were investigated using land cover map. We used FRAGSTATS software to calculate landscape indices to characterize the landscape structure. We found that built up area has been increased rapidly during the study period, while cultivated area and forest area have been decreased rapidly in the same period. From 1995 to 2003, built up area was increased from 19.73% to 39.62% and cultivated area and forest area was decreased 17.60% to 5.97% and 58.31% to 49.41%. Number of patches, mean euclidean nearest-neighbor distance, contagion index, Shannon's diversity index increased considerably from 1995 to 2003, also suggesting the landscape in the study area became more fragmented and heterogeneous. but because of continuously fragmentation, landscape became homogeneity. The study demonstrates that landscape metrics can be a useful indicator in landscape monitoring and landscape assessment.

• Journal of Korean Society on Water Environment
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• v.27 no.2
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• pp.159-166
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• 2011

The use of land cover was sharply changed during 1975~2007 in the Kyungan watershed $(561.12 km^2)$. The changes occurred over an area of more than $227.65 km^2$ during the overall period at changing rates of 1.04% per year for water area, 1.79% per year for residential area, 2.99% per year for bare area, 3.03% per year for wetland area, 3.04% per year for grass area, 0.87% per year for forest and 2.32% per year for agriculture area. Water, residential, bare and wetland areas increased, while grass, forest and agriculture areas decreased during the last 32 years. BOD concentrations of representative sites for each sub-watershed continuously increased until the early 2000s as residential area increased with the highest discharged load, but decreased after the mid 2000s except upper Kyungan watershed. Such decline appears to be associated with the planning of Total Maximum Daily Load management for Gwangju city and expansion of waste water treatment plant. It is necessary to control land use/cover changes of the upper watershed and to prepare appropriate watershed management system for improvement in river environment including water quality, stream flow and bio-diversity.

• Journal of Korean Society of Forest Science
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• v.103 no.3
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• pp.408-421
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• 2014

This study was carried out to investigate flora and vegetation in cutting slope along a construction age sequence (2007, 2009, 2010, 2011, 2012) of forest roads in Yongchiri, Younghyunmyon, Sacheonshi, Geyongsangnamdo. Mean slopes of the cutting and banking slopes of forest roads constructed were ranged from $42^{\circ}$ to $54^{\circ}$. Soil texture in the cutting and banking sides of forest roads constructed in 2012 was loam, while sandy loam in the cutting and banking slopes of forest roads constructed between 2007 and 2011. Vegetation cover percentage was higher in the banking slopes (66%) than the cutting slopes (49%) of forest roads. Total flora were higher in the banking slopes (50 species) than the cutting slopes (46 species) of forest roads. Species diversity was generally higher in the banking slopes than in the cutting slopes in all forest roads. In addition, the species diversity index was the highest in the cutting slopes (1.4015) of forest roads constructed in 2011, while the highest in the banking slopes (1.5603) of forest roads constructed in 2012. The results indicate that evenness index in the cutting and banking slopes of recent construction roads was high compared with old construction roads because of the distribution of simple plant species.

• Journal of Korean Society of Forest Science
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• v.83 no.4
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• pp.441-449
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• 1994

The two reflective channels(red and near infrared spectrum) of advanced very high resolution radiometer(AVHRR) data were used to classify primary vegetation cover types in the Korean Peninsula. From the NOAA-11 satellite data archive of 1991, 27 daytime scenes of relatively minimum cloud coverage were obtained. After the initial radiometric calibration, normalized difference vegetation index(NDVI) was calculated for each of the 27 data sets. Four or five daily NDVI data were then overlaid for each of the six months starting from February to November and the maximum value of NDVI was retained for every pixel location to make a monthly composite. The six bands of monthly NDVI composite were nearly cloud free and used for the computer classification of vegetation cover. Based on the temporal signatures of different vegetation cover types, which were generated by an unsupervised block clustering algorithm, every pixel was classified into one of the six cover type categories. The classification result was evaluated by both qualitative interpretation and quantitative comparison with existing forest statistics. Considering frequent data acquisition, low data cost and volume, and large area coverage, it is believed that AVHRR data are effective for vegetation cover type mapping at regional scale.

• Spatial Information Research
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• v.20 no.4
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• pp.47-55
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• 2012

Normalized Difference Vegetation Index (NDVI) has been used to measure and monitor plant growth, vegetation cover, and biomass from multispectral satellite data. It is also a valuable index in forest applications, providing forest resource information. In this research, an approach for monitoring forest change using MODIS NDVI time series data is explored. NDVI difference-based approaches for a specific point in time have possible accuracy problems and are lacking in monitoring long-term forest cover change. It means that a multi-time NDVI pattern change needs to be considered. In this study, an efficient methodology to consider long-term NDVI pattern is suggested using a harmonic model. The suggested method reconstructs MODIS NDVI time series data through application of the harmonic model, which corrects missing and erroneous data. Then NDVI pattern is analyzed based on estimated values of the harmonic model. The suggested method was applied to 49 NDVI time series data from Aug. 21, 2009 to Sep. 6, 2011 and its usefulness was shown through an experiment.

• 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%.