• Journal of the Korean Society of Environmental Restoration Technology
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• v.4 no.2
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• pp.36-44
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• 2001

The main purpose of this study was to classify forest vegetation effectively using Landsat Thematic Mapper data(June, 1994) in mountainous region. The research area was the Mt. Wolak Experimental Forest of Chungbuk National University, near Chungju and Jecheon city, Chungcheongbuk-do. To classify forest vegetation effectively, Normalized Difference Vegetation Index(NDVI) was used to reduce topographic effects. This NDVI was modified and transformed to the value of 0 to 255, and then the modified values were combined with other Landsat Thematic Mapper bands. To classify forest and land cover types, unsupervised classification method was used. The results of this study are summarized as follows. 1. Combinations of band "3, 5, NDVI" in Landsat Thematic Mapper data showed a good separation with high accuracy. The expected classification accuracy was 95.1% in Landsat Thematic Mapper data. 2. The Land Cover types were classified into six groups : coniferous forest, deciduous forest, mixed forest, paddy and grass, non-forest, and other undetectable areas. As these classified results were compared with the reconnaissance survey and aerial black and white infrared photographs, the overall classification accuracy was 76.5% in Landsat Thematic Mapper data. 3. The portion of non-forest in Mt. Wolak area was 1.9%. The percentages of coniferous, deciduous and mixed forests were 30.9%, 35.7% and 26.4%, respectively. 4. As these classified results were compared with other reference data, the percentages of coniferous, deciduous and mixed forests increased, but the portion of non-forest was exceedingly diminished. These differences are thought to be from the different research method and the different season of received Landsat Thematic Mapper data.

• Proceedings of the Korean Society of Environment and Ecology Conference
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• 2008.04a
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• pp.116-118
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• 2008

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.439-442
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• 1999

It is important to analyze the seasonal change profiles of land cover type in large scale for establishing preservation strategy and environmental monitoring. Because the NOAA-AVHRR data sets provide global data with high temporal resolution, it is suitable for the land cover classification of the large area. The objectives of this study were to classify land cover of Korea, to investigate the phenological profiles of land cover. The NOAA-AVHRR data from Jan. 1998 to Dec. 1998 were received by Korea Ocean Research & Development Institute(KORDI) and were used for this study. The NDVI data were produced from this data. And monthly maximum value composite data were made for reducing cloud effect and temporal classification. And the data were classified using the method of supervised classification. To label the land cover classes, they were classified again using generalized vegetation map and Landsat-TM classified image. And the profiles of each class was analyzed according to each month. Results of this study can be summarized as follows. First, it was verified that the use of vegetation map and TM classified map was available to obtain the temporal class labeling with NOAA-AVHRR. Second, phenological characteristics of plant communities of Korea using NOAA-AVHRR was identified. Third, NDVI of North Korea is lower on Summer than that of South Korea. And finally, Forest cover is higher than another cover types. Broadleaf forest is highest on may. Outline of covertype profiles was investigated.

• The Korean Journal of Ecology
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• v.18 no.2
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• pp.219-230
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• 1995

Although many studies of nutrient cycling in forest ecosystems have reported that clearcutting creates increased organic matter decomposition and nitrogen (N) mineralization in soils, little is known about the change of these factors following various levels of canopy removal. A series of experimental plots with four levels of canopy cover, i.e., clearcut, 25％, 75％, and uncut, was established in northern red oak (Quercus rubra L.) stands in northern Lover Michigan, U.S.A. I examined decomposition of cellulose filter papers and N mineralization using an in situ soil incubation technique in the top 15cm of mineral soil during the second growing season (1992, May-October) following stand manipulation. Mass loss from cellulose filter papers was more rapid in the canopy removal treatments than in the uncut treatment. similarly, net N mineralization was significantly greater in the canopy removal treatments than in the uncut treatment. There was no significant difference in net N mineralization rates among the three levels of canopy removal. Net N mineralization for the growing season was 58 kg/ha for the clearcut, 54 kg/ha for the 25％ canopy cover, 51 kg/ha for the 75％ canopy cover, and 22 kg/ha for the uncut treatment. These results indicated that even only small amounts of canopy removal (leaving 75％ canopy cover) let to substantial increases of cellulose decomposition and the amount of available soil nitrogen.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.580-583
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• 2007

Various matching methods have been introduced by investigators to improve digital elevation model (DEM) accuracy of satellite imagery. This study proposed an area-based matching method according to land cover property using correlation coefficient of pixel brightness value between the two images for DEM generation from IKONOS stereo imagery. For this, matching line (where "matching line" implies straight line that is approximated to complex nonlinear epipolar geometry) is established by exterior orientation parameters to minimize search area. The matching is carried out based on this line. Land cover classes are divided off into water, urban land, forest and agricultural land. Matching size is selected using a correlation-coefficient image in the four areas. The selected sizes are $81{\times}81$ pixels window, $21{\times}21$ pixels window, $119{\times}119$ pixels window and $51{\times}51$ pixels window in the water area, urban land, forest land and agricultural land, respectively. And hence, DEM is generated from IKONOS stereo imagery using the selected matching sizes and land cover map on the four types.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.354-359
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• 2009

Land cover types of Hustai National Park (HNP) in Mongolia, a hotspot area with rare species, were classified and their temporal changes were evaluated using Landsat MSS TM/ETM data between 1994 and 2000. Maximum likelihood classification analysis showed an overall accuracy of 88.0% and 85.0% for the 1994 and 2000 images, respectively. Kappa coefficients associated with the classification were resulted to 0.85 for 1994 and 0.82 for 2000 image. Land cover types revealed significant temporal changes in the classification maps between 1994 and 2000. The area has increased considerably by $166.5km^2$ for mountain steppe. By contrast, agricultural areas and degraded areas affected by human being activity were decreased by $46.1km^2$ and $194.8km^2$ over the six year span, respectively. These areas were replaced by mountain steppe area. Specifically, forest area was noticeably fragmented, accompanied by the decrease of $\sim400$ ha. The forest area revealed a pattern with systematic gain and loss associated with the specific phenomenon called as forest free-south slope. We discussed the potential environmental conditions responsible for the systematic pattern and addressed other biological impacts by outbreaks of forest pests and ungulates.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.61-70
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• 2019

The modified Kling-Gupta efficiency fusion method to merge actual evapotranspiration was proposed and compared with the simple Taylor skill's score method using Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MODIS Global Evapotranspiration Project (MOD16), and the flux tower on three different land cover types over the Korean peninsula and China. In the results of the weights estimated from two actual evapotranspiration merging techniques (i.e., STS and KGF), the weights of reanalysis data (i.e, GLDAS and GLEAM) in cropland and grassland showed similar performance, while the results of weights are different according to the merging techniques in forest. Both two merging techniques showed better results than original dataset in grassland and forest. However, there were no improvement in cropland compared to the other land cover types. The results of the KGF method slightly improved compared to those of the STS in grassland and forest.

• Journal of Korean Society of Rural Planning
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• v.11 no.4 s.29
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• pp.47-57
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• 2005

The goal of this research is to evaluate landscape-ecological characteristics of watersheds in the Nakdong River Basin by using Geogaphic Information System (GIS) and landscape indices for integation of spatio-temporal informations and multivariate statistical techniques for quantitative analysis of forest landscape. Fragmentation index and change matrix techniques using factor analysis and grid overlay method were used to efficiently analyze and manage huge amount of information for ecological-environmental assessment (land-cover and forest landscape patterns). According to the results based on the pattern analysis of land-cover changes using the change detection matrix between 1980s and 1990s, addition on 750km$^2$ became urbanized areas. The altered 442.04km$^2$ was agricultural areas which is relatively easy for shifting of land-use, and 205.1km$^2$ of forests became urbanized areas, and average elevation and slope of the whole altered areas were 75m and 4$^{\circ}$. On the other hand, 120km$^2$ of urban areas were changed into other areas (i.e., agricultural areas and green space), and fortunately, certain amount of naturalness had been recovered. But still those agricultural areas and fallow areas, which were previously urban areas, had high potential of re-development for urbanization due to their local conditions. According to the structural analysis of forest landscape using the landscape indices, the forest fragmentation of watersheds along the main stream of the Nakdong River was more severe than my other watersheds. Furthermore, the Nakdong-sangju and Nakdong-miryang watersheds had unstable forest structures as well as least amount of forest quantity. Thus, these areas need significant amount of forest through a new forest management policy considering local environmental conditions.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.81-99
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• 2015

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.